diff --git a/README.md b/README.md
index 10618c7..2bb4e61 100644
--- a/README.md
+++ b/README.md
@@ -150,3 +150,4 @@ SUPPORT.md
 
 - Код проекта распространяется по лицензии MIT. См. [LICENSE](./LICENSE).
 - Контент сайта, статьи и учебные материалы - CC BY-NC-SA 4.0. См. [CC-BY-NC-SA-4.0](./CC-BY-NC-SA-4.0).
+
diff --git a/package-lock.json b/package-lock.json
index dd0917d..d16bbd0 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -3424,6 +3424,21 @@
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+      "dev": true,
+      "hasInstallScript": true,
+      "license": "MIT",
+      "optional": true,
+      "os": [
+        "darwin"
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diff --git a/public/search-index.json b/public/search-index.json
index 6d93a54..8bc6b43 100644
--- a/public/search-index.json
+++ b/public/search-index.json
@@ -1 +1 @@
-{"version":1,"generatedAt":"2026-04-04T09:32:21.969Z","docs":[{"id":"ai","href":"/docs/ai","slug":["ai"],"section":"ai","sectionTitle":"AI","title":"AI","description":"Рабочие тетради по искусственному интеллекту в формате MDX.","preview":"AI обзор Раздел объединяет 8 рабочих тетрадей по дисциплине «Искусственный интеллект», перенесенных в MDX формат. Материалы идут от базового Python и научных библиотек к классическим ML методам, нейросетям, эволюционным алгоритмам и кластеризации. Что внутри P","keywords":["notebook","python","ai","ml","данных","деревья","методы","раздел","решений","knn","mdx","numpy","pandas","алгоритмам","базового","библиотек","внутри","генетические"],"topics":["python","ai","knn","pandas","numpy","oop","algorithms","clustering","regression"],"chunks":[{"id":"chunk-0","heading":"AI обзор","text":"Раздел объединяет 8 рабочих тетрадей по дисциплине «Искусственный интеллект», перенесенных в MDX формат. Материалы идут от базового Python и научных библиотек к классическим ML методам, нейросетям, эволюционным алгоритмам и кластеризации.","keywords":["ai","mdx","mdx.","ml","python","алгоритмам","базового","библиотек","дисциплине","идут","интеллект","интеллекту"],"topics":["python","ai","oop","algorithms"]},{"id":"chunk-1","heading":"Что внутри","text":"Python, NumPy и pandas для подготовки данных; метрики расстояния, KNN и базовые техники классификации; регрессия и деревья решений; эволюционные методы, нейросети и кластеризация.","keywords":["ai","knn","mdx.","numpy","pandas","python","базовые","внутри","данных","деревья","интеллекту","искусственному"],"topics":["python","knn","pandas","numpy","oop","clustering","regression"]},{"id":"chunk-2","heading":"Ноутбуки","text":"Notebook 1 — Основа Python типы данных, условия, циклы и вводные примеры. Notebook 2 — NumPy и pandas массивы, таблицы и базовая подготовка данных. Notebook 3 — Метрики и KNN расстояния между объектами и классификация ближайших соседей. Notebook 4 — Регрессия линейные модели, аппроксимация и оценка качества. Notebook 5 — Деревья решений деревья решений и работа с классификаторами","keywords":["notebook","ai","деревья","решений","knn","mdx.","numpy","pandas","python","аппроксимация","базовая","ближайших"],"topics":["python","knn","pandas","numpy","oop","regression"]},{"id":"chunk-3","heading":"Ноутбуки","text":". Notebook 6 — Генетические и эволюционные методы оптимизация, генетические алгоритмы и отжиг. Notebook 7 — Нейронные сети персептрон, MLP и основы обучения сети. Notebook 8 — Кластеризация методы группировки данных без учителя.","keywords":["ai","notebook","генетические","методы","mdx.","mlp","алгоритмы","без","группировки","данных","интеллекту","искусственному"],"topics":["ai","algorithms","clustering"]},{"id":"chunk-4","heading":"Как читать раздел","text":"Начните с Notebook 1 и Notebook 2 , если нужно выровнять базу по Python и обработке данных. Для классического ML переходите к Notebook 3 , Notebook 4 и Notebook 5 . Темы оптимизации и более продвинутых подходов собраны в Notebook 6 , Notebook 7 и Notebook 8 .","keywords":["notebook","ai","mdx.","ml","python","базу","более","выровнять","данных.","если","интеллекту","искусственному"],"topics":["python","ai","oop"]}]},{"id":"ai/notebook-01-python-basics","href":"/docs/ai/notebook-01-python-basics","slug":["ai","notebook-01-python-basics"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 1 — Основа Python","description":"Типы данных, условия, циклы и старт работы с NumPy.","preview":"Основа Python. Библиотеки. Дата 25.02.2023 1.1. Теоретический материал – Типы данных Типы данных Все типы данных в Python относятся к одной из 2 х категорий: изменяемые (mutable) и неизменяемые (immutable). Неизменяемые объекты: исловые данные (int, float), bo","keywords":["print","python","type","задача","10","elif","dev","от","plt.plot","range","данных","apple","df","if","import","rng","trapz","массива"],"topics":["python","ai","knn","numpy"],"chunks":[{"id":"chunk-0","heading":"","text":"Типы данных Все типы данных в Python относятся к одной из 2 х категорий: изменяемые (mutable) и неизменяемые (immutable). Неизменяемые объекты: исловые данные (int, float), bool, None, символьные строки (class 'str'), кортежи (tuple). Изменяемые объекты: списки (list), множества (set), словари (dict).","keywords":["ai","данных","типы","python","изменяемые","неизменяемые","объекты","bool","class","dict","float","immutable"],"topics":["python"]},{"id":"chunk-1","heading":"","text":"python x= 3+5.2 7 y= None z= 'a',5,12.345, (2,'b') df= [['Антонова Антонина',34,'ж'],['Борисов Борис',26,'м']] A={1,'title',2,'content'} print(x,' ',type(x),'\\n',y,' ',type(y),'\\n',df,' ',type(df),'\\n',A,' ',type(A),'\\n')","keywords":["type","ai","df","python","12.345","26","3+5.2","34","content","none","notebook","numpy."],"topics":["python"]},{"id":"chunk-2","heading":"","text":"code 39.4 None [['Антонова Антонина', 34, 'ж'], ['Борисов Борис', 26, 'м']] {'content', 1, 2, 'title'}","keywords":["ai","26","34","39.4","code","content","none","notebook","numpy.","python","title","антонина"],"topics":[]},{"id":"chunk-3","heading":"","text":"python x=5 =2 A={ 1,3,7,8} B={2,4,5,10,'apple'} C=A&B df= 'Антонова Антонина',34,'ж' z='type' D=[1,'title',2,'connect'] print(x,' ',type(x),'\\n',A,' ',type(A),'\\n',B,' ',type(B),'\\n',C,' ',type(C),'\\n',df,' ',type(df),'\\n',z,' ',type(z),'\\n',D,' ',type(D),'\\n')","keywords":["type","ai","df","python","10","34","apple","connect","notebook","numpy.","print","title"],"topics":["python"]},{"id":"chunk-4","heading":"","text":"text True {8, 1, 3, 7} {2, 'apple', 4, 5, 10} set() ('Антонова Антонина', 34, 'ж') type [1, 'title', 2, 'connect']","keywords":["ai","10","34","apple","connect","notebook","numpy.","python","set","text","title","true"],"topics":[]},{"id":"chunk-5","heading":"","text":"В коде часто приходится проверять выполнимость или невыполнимость каких то условий. Синтаксис:","keywords":["ai","notebook","numpy.","python","выполнимость","данных","каких","коде","невыполнимость","основа","приходится","проверять"],"topics":[]},{"id":"chunk-6","heading":"","text":"Обратите внимание, что код, который должен выполняться внутри каждого условия, записывается с отступом в 4 пробела от уровня if, elif и else: в питоне области видимости переменных обозначаются отступами.","keywords":["ai","условия","elif","else","if","notebook","numpy.","python","видимости","внимание","внутри","выполняться"],"topics":["python"]},{"id":"chunk-7","heading":"","text":"То есть, отступы позволяют понять, где начинается код, который должен выполняться при выполнении условия в if, и где заканчивается.","keywords":["ai","условия","if","notebook","numpy.","python","выполнении","выполняться","данных","должен","заканчивается.","код"],"topics":[]},{"id":"chunk-8","heading":"","text":"Задача:Вывести на экран является ли переменная х положительной, отрицательной или равна нулю.","keywords":["ai","notebook","numpy.","python","вывести","данных","задача","ли","нулю.","основа","отрицательной","переменная"],"topics":[]},{"id":"chunk-9","heading":"","text":"python x=125 if x<0: print('x отрицательный') elif x==0: print('x равен 0') else: print('x положительный')","keywords":["ai","print","python","125","elif","else","if","notebook","numpy.","данных","основа","отрицательный"],"topics":["python"]},{"id":"chunk-10","heading":"","text":"Задача: Напишите код. Задается х, напечатать какому из интервалов принадлежит: ( infinity, 5), [ 5, 5] или от (5, +infinity)","keywords":["ai","+infinity","infinity","notebook","numpy.","python","данных","задается","задача","интервалов","какому","код."],"topics":[]},{"id":"chunk-11","heading":"","text":"python x=int(input()) if x < 5: print('x пренадлежит интервалу от бесконечности до 5') elif 5<x<5: print('x пренадлежит интервалу от 5 до 5') else: print('x пренадлежит интервалу от 5 до +бесконечности')","keywords":["ai","print","до","интервалу","от","пренадлежит","python","+бесконечности","elif","else","if","input"],"topics":["python","numpy"]},{"id":"chunk-12","heading":"","text":"Цикл while проверяет истинность условия, и если условие истинно, то выполняет инструкции цикла.","keywords":["ai","условия","notebook","numpy.","python","while","выполняет","данных","если","инструкции","истинно","истинность"],"topics":[]},{"id":"chunk-13","heading":"","text":"Инструкции, которые относятся к циклу while, располагаются на последующих строках и должны иметь отступ от начала ключевого слова while.","keywords":["ai","notebook","numpy.","python","while","while.","данных","должны","иметь","инструкции","ключевого","которые"],"topics":[]},{"id":"chunk-14","heading":"","text":"Цикл for проходит по набору значений, помещает каждое значение в переменную с которой в цикле можно производить различные действия.","keywords":["ai","notebook","numpy.","python","данных","действия.","значение","значений","каждое","которой","можно","набору"],"topics":[]},{"id":"chunk-15","heading":"","text":"После ключевого слова for идет название переменной, в которую будут помещаться значения. После оператора in указывается набор значений и двоеточие. Инструкции, которые относятся к циклу, располагаются на последующих строках и должны иметь отступ. При создании цикла удобно пользоваться функцией range(a, b) , которая создает последовательность чисел от a до b 1 .","keywords":["ai","после","notebook","numpy.","python","range","будут","данных","двоеточие.","до","должны","значений"],"topics":[]},{"id":"chunk-16","heading":"","text":"python models=['KNN','decision tree','lineral models'] for model in models: print (model)","keywords":["ai","models","model","python","decision","knn","lineral","notebook","numpy.","print","tree","данных"],"topics":["python","knn"]},{"id":"chunk-17","heading":"","text":"Задача:Срез. Необходимо получить каждый k й элемент массива a, начиная с элемента с индексом i (включительно) и заканчивая элементом с индексом j (не включительно), c шагом 2. Переставить все элементы в обратном порядке.","keywords":["ai","включительно","индексом","2.","notebook","numpy.","python","данных","задача","заканчивая","каждый","массива"],"topics":[]},{"id":"chunk-18","heading":"","text":"3.3.1 Задание Задача:Выведите числа из примера на while (3.2.1) в обратном порядке.","keywords":["ai","3.2.1","3.3.1","notebook","numpy.","python","while","выведите","данных","задание","задача","обратном"],"topics":[]},{"id":"chunk-19","heading":"","text":"Задача: При решении задач машинного обучения часто в качестве объектов исследования выступает человек. Создайте список значимых характеристик (признаков), идентифицирующих человека. Выведите список на экран.","keywords":["ai","список","notebook","numpy.","python","выведите","выступает","данных","задач","задача","значимых","идентифицирующих"],"topics":[]},{"id":"chunk-20","heading":"","text":"python id=['Фамилия','Имя', 'Отчетсво',Int Возраст,'Город'] for i in id: print(i)","keywords":["ai","id","python","int","notebook","numpy.","print","возраст","город","данных","имя","основа"],"topics":["python"]},{"id":"chunk-21","heading":"","text":"Задача: Выведите числа из примера на for c функцией range() (3.2.4) в обратном порядке.","keywords":["ai","3.2.4","notebook","numpy.","python","range","выведите","данных","задача","обратном","основа","порядке."],"topics":[]},{"id":"chunk-22","heading":"","text":"Задача: Срез. Напишите код, который все элементы массива x с четными индексами переставит в обратном порядке. Т.е. если x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], то код должен сформировать [8, 1, 6, 3, 4, 5, 2, 7, 0, 9].","keywords":["ai","код","notebook","numpy.","python","данных","должен","если","задача","индексами","который","массива"],"topics":[]},{"id":"chunk-23","heading":"","text":"Машинное обучение опирается на фундаментальные понятия и законы математики и информатики. Библиотека математических функций (math) применяется при решении большинства прикладных задач анализа данных. Также, и некоторые другие библиотеки Python содержат математические функции (например, NumPy)","keywords":["ai","python","math","notebook","numpy","numpy.","анализа","библиотека","библиотеки","большинства","данных","данных."],"topics":["python","ai","numpy"]},{"id":"chunk-24","heading":"","text":". Важную роль играет библиотека matplotlib, которая позволяет производить визуализацию расчетов в удобной для восприятия человеком форме: графики, диаграммы, гистограммы.","keywords":["ai","matplotlib","notebook","numpy.","python","библиотека","важную","визуализацию","восприятия","гистограммы.","графики","данных"],"topics":[]},{"id":"chunk-25","heading":"","text":"Библиотеки в python содержат не только функции, позволяющие решать различные задачи, но и некоторые популярные константы, например число Эйлера.","keywords":["ai","python","notebook","numpy.","библиотеки","данных","задачи","константы","например","некоторые","основа","позволяющие"],"topics":["python"]},{"id":"chunk-26","heading":"","text":"Задача: Для функции sin⁡〖(xe^cos⁡〖(x)〗 )〗 на интервале [0;10] с шагом 0,05 построить график функции, закрасить площадь под ним и вычислить значение этой площади. Д ля вычисления площади воспользуйтесь функцией trapz(), вычисляющей интеграл по правилу трапеции. Для ее корректной работы необходимо подключить следующие библиотеки:","keywords":["ai","работы","функции","05","10","cos","notebook","numpy.","python","sin","trapz","xe"],"topics":[]},{"id":"chunk-27","heading":"","text":"````python from scipy.integrate import simps from numpy import trapz x=np.arange(0.0,10,0.1) y=np.abs(np.sin(x np.exp(np.cos(x)))) plt.grid() plt.plot(x,y,c='r') plt.fill between(x,y)","keywords":["ai","import","python","0.0","0.1","10","between","notebook","np.abs","np.arange","np.cos","np.exp"],"topics":["python","numpy"]},{"id":"chunk-28","heading":"","text":"Задача:Дано некоторое распределение оценок в группе за экзамен. Нарисовать круговую и точечную диаграммы распределения.","keywords":["ai","notebook","numpy.","python","группе","данных","дано","диаграммы","за","задача","круговую","нарисовать"],"topics":[]},{"id":"chunk-29","heading":"","text":"data = [3,7,8,4] fig= plt.figure(figsize=(10,7)) plt.pie(data, labels=marks) plt.show() plt.grid() plt.scatter (marks,data) ````","keywords":["ai","data","marks","10","fig","figsize","labels","notebook","numpy.","plt.figure","plt.grid","plt.pie"],"topics":[]},{"id":"chunk-30","heading":"","text":"Задача: Задайте массив случайных значений из интервала (0; 1). Рассчитайте средние и медианные значения для массива, сравните результаты, какие выводы можно сделать о значениях? Постройте точечную диаграмму рассеяния полученного ряда.","keywords":["ai","notebook","numpy.","python","выводы","данных","диаграмму","задайте","задача","значений","значения","значениях"],"topics":[]},{"id":"chunk-31","heading":"","text":"python size = 10 x = range(0, size) random = np.random.random sample(size = size) average = np.average(random) median = np.median(random) print(random, average, median) if average == median: print(\"Средние и медианные значения массива равны\") elif average median: print(\"Среднее значение больше медианного\") else: print(\"Медианное значение больше среднего\") fig, ax = plt.subplots(figsize=(10, 6)) ax.scatter(x, random)","keywords":["random","average","median","print","size","ai","10","python","больше","значение","ax","ax.scatter"],"topics":["python","numpy"]},{"id":"chunk-32","heading":"","text":"plt.show()","keywords":["ai","notebook","numpy.","plt.show","python","данных","основа","работы","старт","типы","условия","циклы"],"topics":[]},{"id":"chunk-33","heading":"","text":"Дана функция image.png Создать массив из 10 значений функции (x, например, изменяется от 1 до 10). Выделить срез первой половины массива и построить графики для основного массива, используя plt.plot(), для среза – точечный (plt.scatter()).","keywords":["ai","10","массива","image.png","notebook","numpy.","plt.plot","plt.scatter","python","выделить","графики","дана"],"topics":[]},{"id":"chunk-34","heading":"","text":"python max=[] midium=[] n=11 fig, ax = plt.subplots() ax.set facecolor('black') for x in range(1, n): max.append(((1+np.e x (0.5)+np.cos(x 2)) 0.5/ abs(1 np.sin(x) 3))+np.log10(abs(x 2))) print(max) plt.plot(max) plt.show() for i in range (5): midium.append(max[i]) print(midium) plt.scatter(midium, midium, c = 'black') plt.show()","keywords":["max","midium","ai","0.5","abs","black","plt.show","print","python","range","+np.cos","+np.log10"],"topics":["python","numpy"]},{"id":"chunk-35","heading":"","text":"Задача: Дана функция cos⁡(xe^((cos⁡(x)+ln⁡(x+1) ) ) ) Построить график на интервале (0, 10) с шагом 1 с заливкой площади и найти эту площадь под ним. Для вычисления площади воспользуйтесь функцией trapz(), вычисляющей интеграл по правилу трапеции. Для ее корректной работы необходимо подключить следующие библиотеки: from scipy.integrate import simps from numpy import trapz","keywords":["ai","cos","import","trapz","площади","работы","+ln","10","notebook","numpy","numpy.","python"],"topics":["numpy"]},{"id":"chunk-36","heading":"","text":"python from scipy.integrate import simps from numpy import trapz y=[] x=np.arange(0.0, 10, 1) for i in range(1, 11): y.append((np.cos(i np.e ((np.cos(i)+np.log10(i+1)))) 2) 0.5) print(max) plt.grid() plt.plot(x, y, c=\"r\") plt.fill between(x, y) area=trapz(y) print (area)","keywords":["ai","area","import","np.cos","print","python","trapz","+np.log10","0.0","0.5","10","11"],"topics":["python","numpy"]},{"id":"chunk-37","heading":"","text":"Задача: Откройте статистические данные по стоимости акций Apple, Microsoft, Google за 12 месяцев 2021 г. Постройте 3 графика на плоскости и оцените их динамику. Подумайте, как можно улучшить визуализацию результатов. Для начальных данных допустимо использовать следующий ресурс: https://finance.yahoo.com","keywords":["ai","данных","12","2021","apple","finance.yahoo.com","google","https","microsoft","notebook","numpy.","python"],"topics":[]},{"id":"chunk-38","heading":"","text":"python Apple = [167.30, 154.73, 170.09, 157.28, 137.13, 147.04, 165.35, 150.7, 140.09, 142.16, 129.62,132.62] Microsoft = [252.75, 295.2, 294.95, 309.42, 284.47, 272.42, 259.58, 278.01, 260.04, 240.74, 228.11, 154.69] Google = [89.70, 135.3, 134.17, 140.7, 117.16, 114.14, 109.08, 115.48, 110.55, 99.3, 90.5, 101.28] rng = numpy.arange(12) fig, ax = plt.subplots(figsize=(10, 5)) plt.plot(1 + rng, Microsoft, c = 'red',","keywords":["ai","microsoft","python","rng","10","101.28","109.08","110.55","114.14","115.48","117.16","12"],"topics":["python","numpy"]},{"id":"chunk-39","heading":"","text":"label = 'Microsoft') plt.plot(1 + rng, Apple, c = 'gray', label = \"Apple\") plt.plot(1 + rng, Google, c = 'blue', label = 'Google') ax.set xlim(xmin=1 + rng[0], xmax=1 + rng[ 1]) ax.legend(loc='upper left') plt.grid() plt.show()","keywords":["rng","ai","label","apple","google","plt.plot","ax.legend","ax.set","blue","gray","left","loc"],"topics":[]},{"id":"chunk-40","heading":"","text":"Задача: Создайте простейший калькулятор, включающий основные действия для двух переменных '+',' ','∙','÷', а также вычисление следующих функций: e^(x+y),sin⁡〖(x+y)〗,cos⁡〖(x+y)〗,x^y.","keywords":["ai","x+y","cos","notebook","numpy.","python","sin","y.","включающий","вычисление","данных","двух"],"topics":[]},{"id":"chunk-41","heading":"","text":"for dev in range(0, 8): if dev == 0: print(x + y) elif dev == 1: print(x y) elif dev == 2: print(x y) elif dev == 3: print(x / y) elif dev == 4: print(math.e (x + y)) elif dev == 5: print(math.sin(x + y)) elif dev == 6: print(math.cos(x + y)) else: print(x y) ````","keywords":["dev","print","elif","ai","else","if","math.cos","math.e","math.sin","notebook","numpy.","python"],"topics":[]},{"id":"chunk-42","heading":"","text":"code 17 7 60 0.4166666666666667 24154952.753575277 0.9613974918795568 0.27516333805159693 244140625","keywords":["ai","0.27516333805159693","0.4166666666666667","0.9613974918795568","17","24154952.753575277","244140625","60","code","notebook","numpy.","python"],"topics":[]}]},{"id":"ai/notebook-02-numpy-pandas","href":"/docs/ai/notebook-02-numpy-pandas","slug":["ai","notebook-02-numpy-pandas"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 2 — NumPy и pandas","description":"Базовые операции с массивами и таблицами, масштабирование данных.","preview":"NumPy и pandas Дата 25.02.2023 1.1. Теоретический материал – Библиотека NumPy NumPy (NumericalPython) это библиотека Python с открытым исходным кодом, которая используется практически во всех областях науки и техники. Это универсальный стандарт для работы с чи","keywords":["0.","1.","print","dataframe","python","данных","url","10","задача","cm","создать","np.nan","numpy","https","можно","...","0.2","code"],"topics":["python","ai","pandas","numpy","oop","testing","algorithms"],"chunks":[{"id":"chunk-0","heading":"","text":"NumPy (NumericalPython) это библиотека Python с открытым исходным кодом, которая используется практически во всех областях науки и техники. Это универсальный стандарт для работы с числовыми данными в Python.","keywords":["ai","numpy","notebook","numericalpython","pandas","python","python.","базовые","библиотека","всех","данными","данных."],"topics":["python","numpy"]},{"id":"chunk-1","heading":"","text":"Если у вас уже есть Python, вы можете установить NumPy с помощью командной строки:","keywords":["ai","numpy","notebook","pandas","python","базовые","вас","вы","данных.","если","командной","массивами"],"topics":["python","numpy"]},{"id":"chunk-2","heading":"","text":"Чтобы начать использовать NumPy необходимо импортировать соответствующую библиотеку:","keywords":["ai","numpy","notebook","pandas","базовые","библиотеку","данных.","импортировать","использовать","массивами","масштабирование","начать"],"topics":["numpy"]},{"id":"chunk-3","heading":"","text":"Основным объектом NumPy является однородный многомерный массив (в numpy называется numpy.ndarray). Это многомерный массив элементов (обычно чисел), одного типа.","keywords":["ai","numpy","массив","многомерный","notebook","numpy.ndarray","pandas","базовые","данных.","массивами","масштабирование","называется"],"topics":["numpy"]},{"id":"chunk-4","heading":"","text":"ndarray.shape размеры массива, его форма. Это кортеж натуральных чисел, показывающий длину массива по каждой оси. Для матрицы из n строк и m столбов, shape будет (n,m). Число элементов кортежа shape равно ndim.","keywords":["ai","shape","массива","ndarray.shape","ndim.","notebook","numpy","pandas","базовые","будет","данных.","длину"],"topics":[]},{"id":"chunk-5","heading":"","text":"ndarray.size количество элементов массива. Очевидно, равно произведению всех элементов атрибута shape.","keywords":["ai","элементов","ndarray.size","notebook","numpy","pandas","shape.","атрибута","базовые","всех","данных.","количество"],"topics":[]},{"id":"chunk-6","heading":"","text":"ndarray.dtype объект, описывающий тип элементов массива. Можно определить dtype, используя стандартные типы данных Python . NumPy здесь предоставляет целый букет возможностей, как встроенных, например: bool , character , int8 , int16 , int32 , int64 , float8 , float16 , float32 , float64 , complex64 , object , так и возможность определить собственные типы данных, в том числе и составные.","keywords":["ai","numpy","данных","определить","типы","bool","character","complex64","dtype","float16","float32","float64"],"topics":["python","numpy"]},{"id":"chunk-7","heading":"","text":"Обычно не нужно использовать этот атрибут, так как обращаться к элементам массива проще всего с помощью индексов.","keywords":["ai","notebook","numpy","pandas","атрибут","базовые","всего","данных.","индексов.","использовать","массива","массивами"],"topics":[]},{"id":"chunk-8","heading":"","text":"Подробнее о массивах в NumPy можно найти в официальной документации https://numpy.org/doc/stable/user/absolute beginners.html","keywords":["ai","numpy","absolute","beginners.html","doc","https","notebook","numpy.org","pandas","stable","user","базовые"],"topics":["ai","numpy"]},{"id":"chunk-9","heading":"","text":"Задача: Создать массив 5x2. Вывести все значения массива, значение элемента с индексом (3,1) и второй столбец. Индексация начинается с нуля.","keywords":["ai","5x2.","notebook","numpy","pandas","базовые","второй","вывести","данных.","задача","значение","значения"],"topics":[]},{"id":"chunk-10","heading":"","text":"python x=np.array([[1,2],[3,4],[5,6],[7,8],[9,10]]) print(x) print(x[3][1]) print(x[1])","keywords":["ai","print","10","notebook","np.array","numpy","pandas","python","базовые","данных.","массивами","масштабирование"],"topics":["python","numpy"]},{"id":"chunk-11","heading":"","text":"Задача: Пример. Выполнить следующее: 1. Создать вектор (одномерный массив) размера 10, заполненный нулями. 2. Создать вектор размера 10, заполненный единицами. 3. Создать вектор размера 10, заполненный заданным числом. 4. Создать вектор со значениями от 10 до 19.","keywords":["10","вектор","создать","ai","заполненный","размера","1.","19.","2.","3.","4.","notebook"],"topics":[]},{"id":"chunk-12","heading":"","text":"python a = np.zeros(10) b = np.ones(10) c = np.full(10, 5) d = np.arange(10, 20) print(a,\"\\n\", b,\"\\n\", c,\"\\n\", d,\"\\n\")","keywords":["10","ai","20","notebook","np.arange","np.full","np.ones","np.zeros","numpy","pandas","print","python"],"topics":["python","numpy"]},{"id":"chunk-13","heading":"","text":"code [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [5 5 5 5 5 5 5 5 5 5] [10 11 12 13 14 15 16 17 18 19]","keywords":["0.","1.","ai","10","11","12","13","14","15","16","17","18"],"topics":[]},{"id":"chunk-14","heading":"","text":"Задача: Создать массив 10x10 со случайными значениями, найти минимум, максимум и среднее значение.","keywords":["ai","10x10","notebook","numpy","pandas","базовые","данных.","задача","значение.","значениями","максимум","массив"],"topics":[]},{"id":"chunk-15","heading":"","text":"python Z = np.random.random((10,10)) Zmin,Zmax,Zmean = Z.min(),Z.max(),Z.mean() print(Zmin,Zmax,Zmean)","keywords":["ai","10","zmax","zmean","zmin","notebook","np.random.random","numpy","pandas","print","python","z.max"],"topics":["python","numpy"]},{"id":"chunk-16","heading":"","text":"code [[ 5 6 7 8 9] [ 0 1 2 3 4] [10 11 12 13 14] [15 16 17 18 19] [20 21 22 23 24]]","keywords":["ai","10","11","12","13","14","15","16","17","18","19","20"],"topics":[]},{"id":"chunk-17","heading":"","text":"Задача: Выяснить результат следующих выражений: 0 np.nan np.nan == np.nan np.inf np.nan np.nan np.nan 0.3 == 3 0.1","keywords":["np.nan","ai","0.1","0.3","notebook","np.inf","numpy","pandas","базовые","выражений","выяснить","данных."],"topics":["numpy"]},{"id":"chunk-18","heading":"","text":"python print(0 np.nan) print(np.nan == np.nan) print(np.inf np.nan) print(np.nan np.nan) print(0.3 == 3 0.1)","keywords":["np.nan","print","ai","0.1","0.3","notebook","np.inf","numpy","pandas","python","базовые","данных."],"topics":["python","numpy"]},{"id":"chunk-19","heading":"","text":"Задача: Создать 8x8 матрицу и заполнить её в шахматном порядке нулями и единицами.","keywords":["ai","8x8","notebook","numpy","pandas","базовые","данных.","единицами.","ее","задача","заполнить","массивами"],"topics":[]},{"id":"chunk-20","heading":"","text":"code [[0. 1. 0. 1. 0. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0.]]","keywords":["0.","1.","ai","code","notebook","numpy","pandas","базовые","данных.","массивами","масштабирование","операции"],"topics":[]},{"id":"chunk-21","heading":"","text":"Задача: Создать 5x5 матрицу со значениями в строках от 0 до 4. Для создания необходимо использовать функцию arrange.","keywords":["ai","4.","5x5","arrange.","notebook","numpy","pandas","базовые","данных.","до","задача","значениями"],"topics":[]},{"id":"chunk-22","heading":"","text":"code [[0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.]]","keywords":["0.","1.","2.","3.","4.","ai","code","notebook","numpy","pandas","базовые","данных."],"topics":[]},{"id":"chunk-23","heading":"","text":"````code [[[0.12103427 0.58612308 0.97128317] [0.85425347 0.05382162 0.24655898] [0.62425414 0.42687911 0.88727021]]","keywords":["ai","0.05382162","0.12103427","0.24655898","0.42687911","0.58612308","0.62425414","0.85425347","0.88727021","0.97128317","code","notebook"],"topics":[]},{"id":"chunk-24","heading":"","text":"[[0.89520018 0.6148935 0.34365111] [0.4170647 0.74841712 0.53984955] [0.44451711 0.42021953 0.81843469]]","keywords":["ai","0.34365111","0.4170647","0.42021953","0.44451711","0.53984955","0.6148935","0.74841712","0.81843469","0.89520018","notebook","numpy"],"topics":[]},{"id":"chunk-25","heading":"","text":"[[0.52503207 0.5691061 0.32632757] [0.63784264 0.08092314 0.28269851] [0.36876011 0.05424346 0.48220954]]] ````","keywords":["ai","0.05424346","0.08092314","0.28269851","0.32632757","0.36876011","0.48220954","0.52503207","0.5691061","0.63784264","notebook","numpy"],"topics":[]},{"id":"chunk-26","heading":"","text":"code [[1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1.]]","keywords":["0.","1.","ai","code","notebook","numpy","pandas","базовые","данных.","массивами","масштабирование","операции"],"topics":[]},{"id":"chunk-27","heading":"","text":"python arr = np.array([2,1,5,3,7,4,6,8]) arr=np.sort(arr) arr=arr[:: 1] print(arr)","keywords":["arr","ai","notebook","np.array","np.sort","numpy","pandas","print","python","базовые","данных.","массивами"],"topics":["python","numpy"]},{"id":"chunk-28","heading":"","text":"python x = random.randint(1,10) y = random.randint(1,10) A = np.zeros((x, y), int) print(A) if x == y: print('Форма матрицы: квадратная матрица') else: print('Форма матрицы: прямоугольная матрица') print(\"Размер: \",x+y) print('Размерность: ', x y)","keywords":["print","ai","10","random.randint","матрица","матрицы","форма","else","if","int","notebook","np.zeros"],"topics":["python","numpy"]},{"id":"chunk-29","heading":"","text":"code [[0 0 0 0 0] [0 0 0 0 0] [0 0 0 0 0] [0 0 0 0 0]] Форма матрицы: прямоугольная матрица Размер: 9 Размерность: 20","keywords":["ai","20","code","notebook","numpy","pandas","базовые","данных.","массивами","масштабирование","матрица","матрицы"],"topics":[]},{"id":"chunk-30","heading":"","text":"Первым шагом в любом начинании в области машинного обучения является введение исходных данных в систему. Исходные данные могут вводиться вручную, содержаться в файле или храниться в интернете в каком либо формате. Кроме того, часто требуется получить данные из нескольких источников.","keywords":["ai","данные","notebook","numpy","pandas","базовые","введение","вводиться","вручную","данных","данных.","интернете"],"topics":[]},{"id":"chunk-31","heading":"","text":"Библиотека pandas – это удобный и быстрый инструмент для работы с данными, обладающий большим функционалом. Если очень кратко, тоpandas – это библиотека, которая предоставляет очень удобные с точки зрения использования инструменты для хранения данных и работе с ними. Библиотека pandas присутствует в стандартной поставке Anaconda. Если же ее там нет, то его можно установить отдельно","keywords":["ai","pandas","библиотека","если","очень","anaconda.","notebook","numpy","базовые","большим","быстрый","данными"],"topics":["pandas"]},{"id":"chunk-32","heading":"","text":". Для этого введите командной строке:","keywords":["ai","notebook","numpy","pandas","базовые","введите","данных.","командной","массивами","масштабирование","операции","строке"],"topics":[]},{"id":"chunk-33","heading":"","text":"Библиотека pandas предоставляет две ключевые структуры данных: Series и DataFrame .","keywords":["ai","pandas","dataframe","notebook","numpy","series","базовые","библиотека","данных","данных.","две","ключевые"],"topics":["pandas","algorithms"]},{"id":"chunk-34","heading":"","text":"Series – это одномерная структура данных, ее можно представить, как таблицу с одной строкой. С Series можно работать как с обычным массивом (обращаться по номеру индекса), и как с ассоциированным массивом, когда можно использовать ключ для доступа к элементам данных.","keywords":["ai","можно","series","данных.","массивом","notebook","numpy","pandas","ассоциированным","базовые","данных","доступа"],"topics":["pandas"]},{"id":"chunk-35","heading":"","text":"DataFrame – это двумерная структура. Идейно она очень похожа на обычную таблицу, что выражается в способе ее создания и работе с ее элементами.","keywords":["ai","ее","dataframe","notebook","numpy","pandas","базовые","выражается","данных.","двумерная","идейно","массивами"],"topics":["pandas"]},{"id":"chunk-36","heading":"","text":"Задача: Создать Series из списка Python, словаря Python, и массива Numpy (установить буквенные метки для последнего).","keywords":["ai","numpy","python","notebook","pandas","series","базовые","буквенные","данных.","задача","массива","массивами"],"topics":["python","pandas","numpy"]},{"id":"chunk-37","heading":"","text":"````python lst = [1,2,3,4,5] d={'a':1,'b':2,'c':3} ndarr = np.array([1,2,3,4,5])","keywords":["ai","lst","ndarr","notebook","np.array","numpy","pandas","python","базовые","данных.","массивами","масштабирование"],"topics":["python","numpy"]},{"id":"chunk-38","heading":"","text":"code 0 1 1 2 2 3 3 4 4 5 dtype: int64 a 1 b 2 c 3 dtype: int64 a 1 b 2 c 3 d 4 e 5 dtype: int64","keywords":["ai","dtype","int64","code","notebook","numpy","pandas","базовые","данных.","массивами","масштабирование","операции"],"topics":[]},{"id":"chunk-39","heading":"","text":"Задача: Дано два Series. Напечатать их первые элементы и все элементы после третьего (во втором фрейме).","keywords":["ai","элементы","notebook","numpy","pandas","series.","базовые","втором","данных.","дано","два","задача"],"topics":["pandas"]},{"id":"chunk-40","heading":"","text":"python s1=pd.Series([1,2,3,4,5],['a','b','c','d','e']) s2=pd.Series([5,4,3,2,1]) print(s1['a']) print(s2[0]) print(s1[3:])","keywords":["ai","print","s1","pd.series","s2","notebook","numpy","pandas","python","базовые","данных.","массивами"],"topics":["python","pandas"]},{"id":"chunk-41","heading":"","text":"python dataframe = pd.DataFrame() dataframe['Имя']=['Джеки Джексон','Стивен Стивенсон'] dataframe['Возраст']=[38,25] dataframe['Водитель']=[True,False] dataframe","keywords":["dataframe","ai","25","38","false","notebook","numpy","pandas","pd.dataframe","python","true","базовые"],"topics":["python","pandas"]},{"id":"chunk-42","heading":"","text":"Задача: Загрузите фрейм данных по ссылке: https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv","keywords":["ai","chrisalbon","datasets","https","master","notebook","numpy","pandas","raw.githubusercontent.com","simulated","titanic.csv","базовые"],"topics":["python"]},{"id":"chunk-43","heading":"","text":"python Создать url адрес url = 'https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv' Загрузить данные dataframe = pd.read csv(url) показать 5 строк dataframe.head(5)","keywords":["ai","url","chrisalbon","csv","dataframe","dataframe.head","datasets","https","master","notebook","numpy","pandas"],"topics":["python","pandas"]},{"id":"chunk-44","heading":"","text":"Одна из самых простых вещей, которые мы можем сделать после загрузки данных, – это взглянуть на первые несколько строк с помощью метода head. На последние строки можно посмотреть с помощью функции tail. Мы также можем взглянуть на количество строк и столбцов: dataframe.shape. Кроме того, используя метод describe, мы можем получить описательную статистику для любых числовых столбцов.","keywords":["ai","можем","мы","взглянуть","помощью","строк","dataframe.shape.","describe","head.","notebook","numpy","pandas"],"topics":["ai","pandas"]},{"id":"chunk-45","heading":"","text":"Более подробно с возможностями работы с фреймами данных можно узнать по ссылке ниже:","keywords":["ai","notebook","numpy","pandas","базовые","более","возможностями","данных","данных.","массивами","масштабирование","можно"],"topics":[]},{"id":"chunk-46","heading":"","text":"url = 'https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv' dataframe = pd.read csv(url)","keywords":["ai","url","chrisalbon","csv","dataframe","datasets","https","master","notebook","numpy","pandas","pd.read"],"topics":["python","pandas"]},{"id":"chunk-47","heading":"","text":"print('\\n',dataframe.head(2)) print() print('\\n',dataframe.tail(3)) print('\\n',dataframe.shape) print('\\n',dataframe.describe()) ````","keywords":["print","ai","dataframe.describe","dataframe.head","dataframe.shape","dataframe.tail","notebook","numpy","pandas","базовые","данных.","массивами"],"topics":["ai","pandas"]},{"id":"chunk-48","heading":"","text":"Для выбора одной или нескольких строк, либо значений, можно использовать методы 1ос или iloc.","keywords":["ai","1ос","iloc.","notebook","numpy","pandas","базовые","выбора","данных.","значений","использовать","либо"],"topics":[]},{"id":"chunk-49","heading":"","text":"url = 'https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv' dataframe = pd.read csv(url)","keywords":["ai","url","chrisalbon","csv","dataframe","datasets","https","master","notebook","numpy","pandas","pd.read"],"topics":["python","pandas"]},{"id":"chunk-50","heading":"","text":"Задача: Требуется отобрать строки фрейма данных на основе некоторого условия. Необходимо сформировать новый фрейм данных из пассажиров первого класса.","keywords":["ai","данных","notebook","numpy","pandas","базовые","данных.","задача","класса.","массивами","масштабирование","некоторого"],"topics":["oop"]},{"id":"chunk-51","heading":"","text":"url = 'https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv' dataframe = pd.read csv(url)","keywords":["ai","url","chrisalbon","csv","dataframe","datasets","https","master","notebook","numpy","pandas","pd.read"],"topics":["python","pandas"]},{"id":"chunk-52","heading":"","text":"Задача: Найдите евклидово расстояние между двумя Series (точками) a и b, не используя встроенную формулу.","keywords":["ai","notebook","numpy","pandas","series","базовые","встроенную","данных.","двумя","евклидово","задача","используя"],"topics":["pandas"]},{"id":"chunk-53","heading":"","text":"Задача: Найдите в Интернете ссылку на любой csv файл и сформируйте из него фрейм данных (например, коллекцию фреймов данных можно найти здесь: https://github.com/akmand/datasets).","keywords":["ai","данных","akmand","csv","datasets","github.com","https","notebook","numpy","pandas","базовые","данных."],"topics":[]},{"id":"chunk-54","heading":"","text":"python url = 'https://raw.githubusercontent.com/akmand/datasets/main/anneal.csv' dataframe = pd.read csv(url) dataframe.head(5)","keywords":["ai","url","akmand","anneal.csv","csv","dataframe","dataframe.head","datasets","https","main","notebook","numpy"],"topics":["python","ai","pandas"]},{"id":"chunk-55","heading":"","text":"Задача: Проделайте с получившемся из предыдущего задания фреймом данных те же действия, что и в примерах 2.2.5 2.2.7.","keywords":["ai","2.2.5","2.2.7.","notebook","numpy","pandas","базовые","данных","данных.","действия","же","задания"],"topics":[]},{"id":"chunk-56","heading":"","text":"url='https://raw.githubusercontent.com/akmand/datasets/main/anneal.csv' dataframe=pd.read csv(url)","keywords":["ai","url","akmand","anneal.csv","csv","dataframe","datasets","https","main","notebook","numpy","pandas"],"topics":["ai","pandas"]},{"id":"chunk-57","heading":"","text":"print(dataframe.head(2)) print(dataframe.tail(3)) print(dataframe.shape) print(dataframe.describe()) print(dataframe.iloc[1:4]) ````","keywords":["print","ai","dataframe.describe","dataframe.head","dataframe.iloc","dataframe.shape","dataframe.tail","notebook","numpy","pandas","базовые","данных."],"topics":["ai","pandas"]},{"id":"chunk-58","heading":"","text":"Количественные данные что то измеряют – будь то размер класса, ежемесячные продажи или оценки учащихся. Естественным способом представления этих величин является численным (например, 150 студентов, $529 392 продаж).","keywords":["ai","то","150","392","529","notebook","numpy","pandas","базовые","будь","величин","данные"],"topics":["oop"]},{"id":"chunk-59","heading":"","text":"Нормализация данных — это общепринятая задача предобработки в машинном обучении. Многие алгоритмы предполагают, что все признаки находятся в единой шкале, как правило, от 0 до 1 или от 1 до 1.","keywords":["ai","до","от","1.","notebook","numpy","pandas","алгоритмы","базовые","данных","данных.","единой"],"topics":["algorithms"]},{"id":"chunk-60","heading":"","text":"Существует множество способов нормализации значений признаков, чтобы масштабировать их к единому диапазону и использовать в различных моделях машинного обучения. В зависимости от используемой функции, их можно разделить на 2 большие группы: линейные и нелинейные. При нелинейной нормализации в расчетных соотношениях используются функции логистической сигмоиды или гиперболического тангенса","keywords":["ai","их","нормализации","функции","notebook","numpy","pandas","базовые","большие","гиперболического","группы","данных."],"topics":[]},{"id":"chunk-61","heading":"","text":". В линейной нормализации изменение переменных осуществляется пропорционально, по линейному закону.","keywords":["ai","notebook","numpy","pandas","базовые","данных.","закону.","изменение","линейной","линейному","массивами","масштабирование"],"topics":[]},{"id":"chunk-62","heading":"","text":"Мин и макс – линейное преобразование данных в диапазоне [0..1], где минимальное и максимальное масштабируемые значения соответствуют 0 и 1 соответственно;","keywords":["ai","0..1","notebook","numpy","pandas","базовые","данных","данных.","диапазоне","значения","линейное","макс"],"topics":[]},{"id":"chunk-63","heading":"","text":"Z масштабирование данных на основе среднего значения и стандартного отклонения: производят деление разницы между переменной и средним значением на стандартное отклонение.","keywords":["ai","масштабирование","notebook","numpy","pandas","базовые","данных","данных.","деление","значением","значения","массивами"],"topics":[]},{"id":"chunk-64","heading":"","text":"При масштабировании данных мы будем использовать одну из популярных библиотек машинного обучения Scikit learn. Библиотека содержит пакет sklearn.preprocessing, который предоставляет широкиевозможности для нормализации данных. Следует отметить, что в целом алгоритмы обучения выигрывают от стандартизации набора данных.","keywords":["ai","данных.","обучения","learn.","notebook","numpy","pandas","scikit","sklearn.preprocessing","алгоритмы","базовые","библиотек"],"topics":["algorithms"]},{"id":"chunk-65","heading":"","text":"Создать признак feature = np.array([[ 500.5],[ 100.1],[0],[100.1],[900.9]]) Создать шкалировщик minmax scale = preprocessing.MinMaxScaler(feature range = (0,1))","keywords":["ai","100.1","feature","создать","500.5","900.9","minmax","notebook","np.array","numpy","pandas","preprocessing.minmaxscaler"],"topics":["numpy"]},{"id":"chunk-66","heading":"","text":"Задача: Преобразуйте признак, чтобы он имел среднее значение 0 и стандартное отклонение 1.","keywords":["ai","1.","notebook","numpy","pandas","базовые","данных.","задача","значение","имел","массивами","масштабирование"],"topics":[]},{"id":"chunk-67","heading":"","text":"````python x=np.array([[ 1000.1],[ 200.2],[500.5],[600.6],[9000.9]]) Создать шкалировщик scaler = preprocessing.StandardScaler() Преобразовать признак standardized = scaler.fit transform(x) Показать признак standardized","keywords":["ai","standardized","признак","1000.1","200.2","500.5","600.6","9000.9","notebook","np.array","numpy","pandas"],"topics":["python","numpy"]},{"id":"chunk-68","heading":"","text":"Мы можем увидеть эффект стандартизации, обратившись к среднему значению и стандартному отклонению результата нашего решения:","keywords":["ai","notebook","numpy","pandas","базовые","данных.","значению","массивами","масштабирование","можем","мы","нашего"],"topics":[]},{"id":"chunk-69","heading":"","text":"print(\"Среднее:\",round(standardized.mean())) print(\"Стандартное отклонение:\", standardized.std()) ````","keywords":["ai","print","notebook","numpy","pandas","round","standardized.mean","standardized.std","базовые","данных.","массивами","масштабирование"],"topics":[]},{"id":"chunk-70","heading":"","text":"````python from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() dfTest = pd.DataFrame({'A':[14.00,90.20,90.95,96.27,91.21], 'B':[103.02,107.26,110.35,114.23,114.68], 'C':['big','small','big','small','small']})","keywords":["ai","small","big","minmaxscaler","103.02","107.26","110.35","114.23","114.68","14.00","90.20","90.95"],"topics":["python","pandas","testing"]},{"id":"chunk-71","heading":"","text":"code A B C 0 0.000000 0.000000 big 1 0.926219 0.363636 small 2 0.935335 0.628645 big 3 1.000000 0.961407 small 4 0.938495 1.000000 small","keywords":["ai","small","0.000000","1.000000","big","0.363636","0.628645","0.926219","0.935335","0.938495","0.961407","code"],"topics":[]},{"id":"chunk-72","heading":"","text":"Необходимо выполнить нормализацию первого числового признака (sepal length cm) с использованием минимаксного преобразования, а второго (sepal width cm) с задействованием z масштабирования.","keywords":["ai","cm","sepal","length","notebook","numpy","pandas","width","базовые","второго","выполнить","данных."],"topics":[]},{"id":"chunk-73","heading":"","text":"````python from sklearn import preprocessing from sklearn.preprocessing import MinMaxScaler","keywords":["ai","import","minmaxscaler","notebook","numpy","pandas","preprocessing","python","sklearn","sklearn.preprocessing","базовые","данных."],"topics":["python"]},{"id":"chunk-74","heading":"","text":"url='https://raw.githubusercontent.com/akmand/datasets/master/iris.csv' dataframe=pd.read csv(url) scaler = MinMaxScaler() dataframe[['sepal length cm']] = scaler.fit transform(dataframe[['sepal length cm']]) dataframe ````","keywords":["dataframe","ai","cm","length","sepal","url","akmand","csv","datasets","https","iris.csv","master"],"topics":["python","pandas"]},{"id":"chunk-75","heading":"","text":"````code sepal length cm sepal width cm petal length cm petal width cm \\ 0 0.222222 3.5 1.4 0.2 1 0.166667 3.0 1.4 0.2 2 0.111111 3.2 1.3 0.2 3 0.083333 3.1 1.5 0.2 4 0.194444 3.6 1.4 0.2 .. ... ... ... ... 145 0.666667 3.0 5.2 2.3 146 0.555556 2.5 5.0 1.9 147 0.611111 3.0 5.2 2.0 148 0.527778 3.4 5.4 2.3 149 0.444444 3.0 5.1 1.8","keywords":["0.2","...","3.0","cm","1.4","ai","2.3","5.2","length","petal","sepal","width"],"topics":[]},{"id":"chunk-76","heading":"","text":"species 0 setosa 1 setosa 2 setosa 3 setosa 4 setosa .. ... 145 virginica 146 virginica 147 virginica 148 virginica 149 virginica","keywords":["setosa","virginica","ai","..","...","145","146","147","148","149","notebook","numpy"],"topics":[]},{"id":"chunk-77","heading":"","text":"````python from sklearn import preprocessing from sklearn.preprocessing import StandardScaler","keywords":["ai","import","notebook","numpy","pandas","preprocessing","python","sklearn","sklearn.preprocessing","standardscaler","базовые","данных."],"topics":["python"]},{"id":"chunk-78","heading":"","text":"url='https://raw.githubusercontent.com/akmand/datasets/master/iris.csv' dataframe=pd.read csv(url) scaler= StandardScaler() dataframe[['sepal width cm']] = scaler.fit transform(dataframe[['sepal width cm']]) dataframe ````","keywords":["dataframe","ai","cm","sepal","url","width","akmand","csv","datasets","https","iris.csv","master"],"topics":["python","pandas"]},{"id":"chunk-79","heading":"","text":"````text sepal length cm sepal width cm petal length cm petal width cm \\ 0 5.1 1.032057 1.4 0.2 1 4.9 0.124958 1.4 0.2 2 4.7 0.337848 1.3 0.2 3 4.6 0.106445 1.5 0.2 4 5.0 1.263460 1.4 0.2 .. ... ... ... ... 145 6.7 0.124958 5.2 2.3 146 6.3 1.281972 5.0 1.9 147 6.5 0.124958 5.2 2.0 148 6.2 0.800654 5.4 2.3 149 5.9 0.124958 5.1 1.8","keywords":["0.2","...","0.124958","cm","1.4","ai","2.3","5.0","5.1","5.2","length","petal"],"topics":[]},{"id":"chunk-80","heading":"","text":"species 0 setosa 1 setosa 2 setosa 3 setosa 4 setosa .. ... 145 virginica 146 virginica 147 virginica 148 virginica 149 virginica","keywords":["setosa","virginica","ai","..","...","145","146","147","148","149","notebook","numpy"],"topics":[]}]},{"id":"ai/notebook-03-metrics-and-knn","href":"/docs/ai/notebook-03-metrics-and-knn","slug":["ai","notebook-03-metrics-and-knn"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 3 — Метрики и KNN","description":"Метрики качества, расстояния и классификация K ближайших соседей.","preview":"1.1. Теоретический материал – Функции Python Перед тем как рассматривать задачи классификации вспомним понятие функции (метода) в Python. Функция в python объект, принимающий аргументы и возвращающий значение. Обычно функция определяется с помощью инструкции d","keywords":["данных","0.","python","между","print","test","расстояния","np.array","1.","2.","расстояние","iris","задача","соседей","функции","функция","ближайших","от"],"topics":["python","ai","knn","pandas","numpy","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"","text":"Перед тем как рассматривать задачи классификации вспомним понятие функции (метода) в Python. Функция в python объект, принимающий аргументы и возвращающий значение. Обычно функция определяется с помощью инструкции def .","keywords":["ai","метрики","функция","def","knn","notebook","python","python.","аргументы","ближайших","возвращающий","вспомним"],"topics":["python","oop"]},{"id":"chunk-1","heading":"","text":"Инструкция return говорит, что нужно вернуть значение. В нашем случае функция возвращает сумму x и y. Теперь мы ее можем вызвать:","keywords":["ai","метрики","knn","notebook","return","y.","ближайших","вернуть","возвращает","вызвать","говорит","ее"],"topics":[]},{"id":"chunk-2","heading":"","text":"Функция может быть любой сложности и возвращать любые объекты (списки, кортежи, и даже функции):","keywords":["ai","метрики","knn","notebook","ближайших","быть","возвращать","даже","качества","классификация","кортежи","любой"],"topics":[]},{"id":"chunk-3","heading":"","text":"Функция может и не заканчиваться инструкцией return, при этом функция вернет значение None:","keywords":["ai","метрики","функция","knn","none","notebook","return","ближайших","вернет","заканчиваться","значение","инструкцией"],"topics":[]},{"id":"chunk-4","heading":"","text":"Функция может принимать произвольное количество аргументов или не принимать их вовсе. Также распространены функции с произвольным числом аргументов, функции с позиционными и именованными аргументами, обязательными и необязательными.","keywords":["ai","аргументов","метрики","принимать","функции","knn","notebook","аргументами","ближайших","вовсе.","именованными","их"],"topics":[]},{"id":"chunk-5","heading":"","text":"Как видно из примера, args это кортеж из всех переданных аргументов функции, и с переменной можно работать также, как и с кортежем.","keywords":["ai","метрики","args","knn","notebook","аргументов","ближайших","видно","всех","качества","классификация","кортеж"],"topics":[]},{"id":"chunk-6","heading":"","text":"Функция может принимать и произвольное число именованных аргументов, тогда перед именем ставится :","keywords":["ai","метрики","knn","notebook","аргументов","ближайших","именем","именованных","качества","классификация","может","перед"],"topics":[]},{"id":"chunk-7","heading":"","text":"Задача: Напишите функцию sum range(start, end), которая суммирует все целые числа от значения «start» до величины «end» включительно. Если пользователь задаст первое число большее чем второе, просто поменяйте их местами.","keywords":["ai","end","start","метрики","knn","notebook","range","sum","ближайших","большее","величины","включительно."],"topics":[]},{"id":"chunk-8","heading":"","text":"````python def sum range(start,end): if start end: end,start = start,end return sum(range(start,end+1))","keywords":["start","end","ai","range","sum","метрики","def","end+1","if","knn","notebook","python"],"topics":["python"]},{"id":"chunk-9","heading":"","text":"python def fact(num): if num ==0: return 1 По договоренности факторивл 0 равен 1 else: return num fact(num 1) возвращает результат произведения num и результата возвращенного функцией fact(num 1)","keywords":["num","ai","fact","return","метрики","def","else","if","knn","notebook","python","ближайших"],"topics":["python"]},{"id":"chunk-10","heading":"","text":"Сходство или различие между объектами классификации устанавливается в зависимости от выбранного метрического расстояния между ними. Если каждый объект описывается n свойствами (признаками), то он может быть представлен как точка в n мерном пространстве, и сходство с другими объектами будет определяться как соответствующее расстояние. При классификации используются различные меры расстояния между объектами.","keywords":["ai","между","расстояния","классификации","метрики","объектами","сходство","knn","notebook","ближайших","будет","быть"],"topics":["oop"]},{"id":"chunk-11","heading":"","text":"Это, пожалуй, наиболее часто используемая мера расстояния. Она является геометрическим расстоянием в многомерном пространстве и вычисляется следующим образом:","keywords":["ai","метрики","knn","notebook","ближайших","вычисляется","геометрическим","используемая","качества","классификация","мера","многомерном"],"topics":[]},{"id":"chunk-12","heading":"","text":"P – расстояние между объектами A и B; Ai – значение i свойства объекта A; Bi – значение i свойства объекта B.","keywords":["ai","значение","метрики","объекта","свойства","b.","bi","knn","notebook","ближайших","качества","классификация"],"topics":["ai"]},{"id":"chunk-13","heading":"","text":"Данная мера расстояния используется в тех случаях, когда требуется придать больше значение более отдаленным друг от друга объектам. Это расстояние вычисляется следующим образом:","keywords":["ai","метрики","расстояния","knn","notebook","ближайших","более","больше","вычисляется","данная","друг","друга"],"topics":[]},{"id":"chunk-14","heading":"","text":"Применяется в тех случаях, когда каждому i свойству удается приписать некоторый «вес» wi, пропорционально степени важности признака в задаче классификации:","keywords":["ai","метрики","knn","notebook","wi","ближайших","важности","вес","задаче","каждому","качества","классификации"],"topics":["oop"]},{"id":"chunk-15","heading":"","text":"Также называется манхэттенским, сити блок расстоянием или расстоянием городских кварталов. Это расстояние является разностью по координатам. Хеммингово расстояние вычисляется по формуле:","keywords":["ai","метрики","расстояние","расстоянием","knn","notebook","ближайших","блок","вычисляется","городских","качества","кварталов."],"topics":[]},{"id":"chunk-16","heading":"","text":"Принимает значение наибольшего модуля разности между значениями соответствующих свойств (признаков) объектов:","keywords":["ai","метрики","knn","notebook","ближайших","значение","значениями","качества","классификация","между","модуля","наибольшего"],"topics":[]},{"id":"chunk-17","heading":"","text":"Выбор меры расстояния и весов для классифицирующих свойств – очень важный этап, так как от этих процедур зависят состав и количество формируемых классов, а также степень сходства объектов внутри классов.","keywords":["ai","метрики","расстояния","knn","notebook","ближайших","важный","весов","внутри","выбор","зависят","качества"],"topics":["oop"]},{"id":"chunk-18","heading":"","text":"Задача: Напишите функции в Python, которая вычисляет Евклидово расстояние между двумя массивами NumPy.","keywords":["ai","метрики","knn","notebook","numpy.","python","ближайших","вычисляет","двумя","евклидово","задача","качества"],"topics":["python","numpy"]},{"id":"chunk-19","heading":"","text":"````python def euclidean distance(v1,v2): return sum((x y) 2 for x,y,in zip(v1,v2)) 0.5","keywords":["ai","v1","v2","метрики","0.5","def","distance","euclidean","knn","notebook","python","return"],"topics":["python"]},{"id":"chunk-20","heading":"","text":"Задача: Напишите 4 функции в Python, которые рассчитывают квадрат Евклидова расстояния, взвешенное евклидово расстояние, Хеммингово расстояние и расстояние Чебышева между двумя массивами NumPy.","keywords":["ai","расстояние","метрики","расстояния","knn","notebook","numpy.","python","ближайших","взвешенное","двумя","евклидова"],"topics":["python","numpy"]},{"id":"chunk-21","heading":"","text":"def weighted euclidean distance(v1,v2,w): return sum((x y) 2 s for x,y,s in zip(v1,v2,w)) 0.5","keywords":["ai","v1","v2","метрики","0.5","def","distance","euclidean","knn","notebook","return","sum"],"topics":[]},{"id":"chunk-22","heading":"","text":"x= np.array([0,0,0]) y= np.array([3,3,3]) w= np.array([0,0,1]) print(sqr euclidean distance(x,y)) print(weighted euclidean distance(x,y,w)) print(manhattan distance(x,y)) print(chebyshev distnce(x,y)) ````","keywords":["print","ai","distance","np.array","euclidean","метрики","chebyshev","distnce","knn","manhattan","notebook","sqr"],"topics":["numpy"]},{"id":"chunk-23","heading":"","text":"Задача: В Python есть встроенные функции для вычисления расстояний между векторами. Мы будем использовать NumPy для расчета расстояния для двух точек, поскольку ранее рассмотренные структуры данных могут быть переведены в Numpy массив с помошью специальных функциий. Например, для серий это будет выглядеть следующим образом: seriesName.to numpy().","keywords":["ai","numpy","метрики","расстояния","knn","notebook","python","seriesname.to","ближайших","будем","будет","быть"],"topics":["python","pandas","numpy","algorithms"]},{"id":"chunk-24","heading":"","text":"Для удобства визуализации и анализа результатов в дальнейших расчетах будм использовать 2 точки в 3 х мерном пространстве:","keywords":["ai","метрики","knn","notebook","анализа","ближайших","будм","визуализации","дальнейших","использовать","качества","классификация"],"topics":[]},{"id":"chunk-25","heading":"","text":"Задача: Рассчитать расстояния между двумя точками с использованием методов определения расстояний, представленных выше.","keywords":["ai","метрики","расстояния","knn","notebook","ближайших","выше.","двумя","задача","использованием","качества","классификация"],"topics":[]},{"id":"chunk-26","heading":"","text":"python x= np.array([0,0,0]) y= np.array([3,3,3]) w= np.array([0,0,1]) Расстояние Евклида print(np.linalg.norm(x y)) Квадрат Евклидова расстояния print(np.linalg.norm(x y) 2) Раcстояние Чебышева print(np.linalg.norm(x y, ord = np.inf)) Расстояние Хемминга print(np.linalg.norm(x y, ord = 1))","keywords":["np.linalg.norm","print","ai","np.array","ord","метрики","расстояние","расстояния","knn","notebook","np.inf","python"],"topics":["python","numpy"]},{"id":"chunk-27","heading":"","text":"Задача: Задайте 4 точки в трехмерном пространстве, рассчитайте между ними расстояния по описанным в примере выше метрикам. Отобразите точки в трехмерном пространстве.","keywords":["ai","метрики","расстояния","точки","трехмерном","knn","notebook","ближайших","выше","задайте","задача","качества"],"topics":[]},{"id":"chunk-28","heading":"","text":"t1 = np.array([1,1,1]) t2 = np.array([5,3,1]) t3 = np.array([4,5,6]) t4 = np.array([2,2,8])","keywords":["np.array","ai","метрики","knn","notebook","t1","t2","t3","t4","ближайших","качества","классификация"],"topics":["numpy"]},{"id":"chunk-29","heading":"","text":"ax.scatter(t1[0], t1[1], t1[2]) ax.scatter(t2[0], t2[1], t2[2]) ax.scatter(t3[0], t3[1], t3[2]) ax.scatter(t4[0], t4[1], t4[2])","keywords":["ax.scatter","ai","t1","t2","t3","t4","метрики","knn","notebook","ближайших","качества","классификация"],"topics":[]},{"id":"chunk-30","heading":"","text":"print(f'Вектор AB = {np.linalg.norm(t1 t2)}') print(f'Вектор BC = {np.linalg.norm(t2 t3)}') print(f'Вектор CD = {np.linalg.norm(t3 t4)}') print(f'Вектор DA = {np.linalg.norm(t4 t1)}') ````","keywords":["np.linalg.norm","print","вектор","ai","t1","t2","t3","t4","метрики","ab","bc","cd"],"topics":["numpy"]},{"id":"chunk-31","heading":"","text":"Задача: Создать 5x5 матрицу со значениями в строках от 0 до 4. Для создания необходимо использовать функцию arange.","keywords":["ai","метрики","4.","5x5","arange.","knn","notebook","ближайших","до","задача","значениями","использовать"],"topics":[]},{"id":"chunk-32","heading":"","text":"code [[0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.]]","keywords":["0.","1.","2.","3.","4.","ai","метрики","code","knn","notebook","ближайших","качества"],"topics":[]},{"id":"chunk-33","heading":"","text":"Решение задачи классификации методом k ближайших соседей Метод k ближайших соседей используется для решения задачи классификации. Он относит объекты к классу, которому принадлежит большинство из k его ближайших соседей в многомерном пространстве признаков. Это один из простейших алгоритмов обучения классификационных моделей","keywords":["ближайших","ai","соседей","задачи","метрики","knn","notebook","алгоритмов","большинство","его","используется","качества"],"topics":["knn","oop","algorithms"]},{"id":"chunk-34","heading":"","text":". Число k – это количество соседних объектов в пространстве признаков, которые сравниваются с классифицируемым объектом. Иными словами, если k=10, то каждый объект сравнивается с 10 ю соседями. В процессе обучения алгоритм просто запоминает все векторы признаков и соответствующие им метки классов. При работе с реальными данными, т.е","keywords":["ai","10","метрики","признаков","knn","notebook","алгоритм","ближайших","векторы","данными","если","запоминает"],"topics":["oop","algorithms"]},{"id":"chunk-35","heading":"","text":". наблюдениями, метки класса которых неизвестны, вычисляется расстояние между вектором нового наблюдения и ранее запомненными. Затем выбирается k ближайших к нему векторов, и новый объект относится к классу, которому принадлежит большинство из них. Приведем алгоритм метода. 1. Выберите значение K соседей (скажем, k = 5) 2","keywords":["ai","ближайших","метрики","1.","knn","notebook","алгоритм","большинство","векторов","вектором","выберите","выбирается"],"topics":["oop","algorithms"]},{"id":"chunk-36","heading":"","text":". Найдите ближайшую точку данных K (5) для нашей новой точки данных на основе евклидова расстояния (которое мы обсудим позже) 3. Среди этих K точек данных подсчитайте точки данных в каждой категории. 4. Назначьте новую точку данных категории, которая имеет наибольшее количество соседей с новой точкой данных","keywords":["данных","ai","метрики","новой","расстояния","точки","точку","3.","4.","knn","notebook","ближайших"],"topics":[]},{"id":"chunk-37","heading":"","text":"Модуль библиотеки sklearn sklearn.neighbors предоставляет функциональные возможности для контролируемого обучения на основе соседей. Обучение на основе контролируемых соседей бывает двух видов: классификация данных с дискретными метками и регрессия для данных с непрерывными метками. В данном разделе рассмотрим несколько примеров с использованием названного метода.","keywords":["ai","данных","классификация","метрики","основе","соседей.","knn","notebook","sklearn","sklearn.neighbors","библиотеки","ближайших"],"topics":["oop","regression"]},{"id":"chunk-38","heading":"","text":"Задача: В примере показано создание 2d массива со значениями x и y. Список target содержит возможные выходные классы (часто называемые метками). Далее происходит обучене классификатора k ближайших соседей по исходным данным. Далее производится прогноз принадлежности к классам для двух точек данных","keywords":["ai","ближайших","далее","метрики","2d","knn","notebook","target","y.","возможные","выходные","данным."],"topics":["knn","oop"]},{"id":"chunk-39","heading":"","text":"данные X = np.array([[ 1, 1],[ 2, 1],[ 3, 2],[1,1],[2,1],[3,2]]) target = [0,0,0,1,1,1]","keywords":["ai","метрики","knn","notebook","np.array","target","ближайших","данные","качества","классификация","расстояния","соседей."],"topics":["numpy"]},{"id":"chunk-40","heading":"","text":"обучаем модель k ближайших соседей к данным K=3 model = KNeighborsClassifier(n neighbors = K) model.fit(X,target) print(model)","keywords":["ai","model","ближайших","метрики","kneighborsclassifier","knn","model.fit","neighbors","notebook","print","target","данным"],"topics":["knn"]},{"id":"chunk-41","heading":"","text":"code KNeighborsClassifier(n neighbors=3) ( 2, 2) is class [0] 91,3) is class [1]","keywords":["ai","class","метрики","91","code","kneighborsclassifier","knn","neighbors","notebook","ближайших","качества","классификация"],"topics":[]},{"id":"chunk-42","heading":"","text":"Задача: Далее приведем более наглядный пример. Будет построена граница решения для каждого класса. В качестве данных будем использовать уже знакомый нам и встроенный в библиотеку sklearn набор данных ирисов Фишера. Этот набор данных стал уже классическим, и часто используется в литературе для иллюстрации работы различных статистических алгоритмов","keywords":["ai","данных","метрики","набор","уже","knn","notebook","sklearn","алгоритмов","библиотеку","ближайших","более"],"topics":["oop","algorithms"]},{"id":"chunk-43","heading":"","text":". Датасет содержит наблюдения за 150 разными цветками ирисов, данные по каждому цветку расположены в строках. В стобцах записаны длина и ширина чашелистика, длина и ширина лепестка, вид ириса.","keywords":["ai","длина","метрики","ширина","150","knn","notebook","ближайших","вид","данные","датасет","за"],"topics":[]},{"id":"chunk-44","heading":"","text":"python ьfrom sklearn.neighbors import KNeighborsClassifier iris = sns.load dataset('iris') iris","keywords":["ai","iris","метрики","dataset","import","kneighborsclassifier","knn","notebook","python","sklearn.neighbors","sns.load","ближайших"],"topics":["python"]},{"id":"chunk-45","heading":"","text":"````code sepal length sepal width petal length petal width species 0 5.1 3.5 1.4 0.2 setosa 1 4.9 3.0 1.4 0.2 setosa 2 4.7 3.2 1.3 0.2 setosa 3 4.6 3.1 1.5 0.2 setosa 4 5.0 3.6 1.4 0.2 setosa .. ... ... ... ... ... 145 6.7 3.0 5.2 2.3 virginica 146 6.3 2.5 5.0 1.9 virginica 147 6.5 3.0 5.2 2.0 virginica 148 6.2 3.4 5.4 2.3 virginica 149 5.9 3.0 5.1 1.8 virginica","keywords":["...","0.2","setosa","virginica","3.0","1.4","ai","2.3","5.0","5.1","5.2","length"],"topics":[]},{"id":"chunk-46","heading":"","text":"Задача: Покажем на графиках зависимости ширины лепестка от его длины, а также аналогичный график зависимость для длины и ширины чашелистика. Разные виды цветков отмечены разными цветами.","keywords":["ai","длины","метрики","ширины","knn","notebook","аналогичный","ближайших","виды","график","графиках","его"],"topics":[]},{"id":"chunk-47","heading":"","text":"plt.subplot(121) sns.scatterplot( data=iris, x = 'petal width', y='petal length', hue='species', s=70 )","keywords":["ai","petal","метрики","121","70","data","hue","iris","knn","length","notebook","plt.subplot"],"topics":[]},{"id":"chunk-48","heading":"","text":"plt.xlabel('Длинна лепестка, см') plt.ylabel('Ширина лепестка, см') plt.legend() plt.grid()","keywords":["ai","лепестка","метрики","см","knn","notebook","plt.grid","plt.legend","plt.xlabel","plt.ylabel","ближайших","длинна"],"topics":[]},{"id":"chunk-49","heading":"","text":"plt.subplot (122) sns.scatterplot(data=iris, x='sepal width', y='sepal length', hue='species', s=70) plt.xlabel('Длинна чашелепестка, см') plt.ylabel('Ширина чашелепестка, см') plt.legend() plt.grid(); ````","keywords":["ai","sepal","метрики","см","чашелепестка","122","70","data","hue","iris","knn","length"],"topics":[]},{"id":"chunk-50","heading":"","text":"Из графиков видно, что в первом случае классы визуально хорошо отделимы друг от друга, хотя два классе имеют небольшое пересечение. Во втором случае разделить два класса между собой уже намного труднее. Далее разделим датасет на обучающую и тестовую выборки в соотношении 80:20. Обучающая выборка (training sample) — выборка, по которой производится настройка (оптимизация параметров) модели зависимости","keywords":["ai","выборка","два","метрики","случае","20.","80","knn","notebook","sample","training","ближайших"],"topics":["ai","oop"]},{"id":"chunk-51","heading":"","text":". Тестовая (или контрольная) выборка (test sample) — выборка, по которой оценивается качество построенной модели.","keywords":["ai","выборка","метрики","knn","notebook","sample","test","ближайших","качества","качество","классификация","контрольная"],"topics":["testing"]},{"id":"chunk-52","heading":"","text":"````python from sklearn.model selection import train test split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy score","keywords":["ai","import","метрики","accuracy","kneighborsclassifier","knn","notebook","python","score","selection","sklearn.metrics","sklearn.model"],"topics":["python","ai","testing"]},{"id":"chunk-53","heading":"","text":"iris = sns.load dataset('iris') X train, X test, y train, y test = train test split( iris.iloc[:,: 1], iris.iloc[:, 1], test size = 0.20 )","keywords":["test","ai","train","iris","iris.iloc","метрики","0.20","dataset","knn","notebook","size","sns.load"],"topics":["ai","testing"]},{"id":"chunk-54","heading":"","text":"X train.shape, X test.shape, y train.shape, y test.shape X train.head() y train.head() ````","keywords":["ai","test.shape","train.head","train.shape","метрики","knn","notebook","ближайших","качества","классификация","расстояния","соседей."],"topics":["ai","testing"]},{"id":"chunk-55","heading":"","text":"code 70 versicolor 104 virginica 134 virginica 4 setosa 69 versicolor Name: species, dtype: object","keywords":["ai","versicolor","virginica","метрики","104","134","69","70","code","dtype","knn","name"],"topics":[]},{"id":"chunk-56","heading":"","text":"Для предыдущего примера поэкспериментируйте с параметрами классификатора: 1. Установите другое количество ближайших соседей (k = 1, 5, 10). 2. Установите размер тестовой выборки 15% от всего датасета. 3. Постройте графики и оцените качество моделей, проанализируйте результаты.","keywords":["ai","ближайших","метрики","установите","1.","10","15","2.","3.","knn","notebook","всего"],"topics":["knn","oop"]},{"id":"chunk-57","heading":"","text":"````python from sklearn.model selection import train test split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy score","keywords":["ai","import","метрики","accuracy","kneighborsclassifier","knn","notebook","python","score","selection","sklearn.metrics","sklearn.model"],"topics":["python","ai","testing"]},{"id":"chunk-58","heading":"","text":"iris = sns.load dataset('iris') X train, X test, y train, y test = train test split( iris.iloc[:,: 1], iris.iloc[:, 1], test size = 0.15 )","keywords":["test","ai","train","iris","iris.iloc","метрики","0.15","dataset","knn","notebook","size","sns.load"],"topics":["ai","testing"]},{"id":"chunk-59","heading":"","text":"plt.figure(figsize=(10,7)) sns.scatterplot(x = 'petal width', y = 'petal length', data=iris, hue='species', s = 70) plt.xlabel('Длинна лепестка, см') plt.ylabel('Ширина лепестка, см') plt.legend(loc=2) plt.grid","keywords":["ai","petal","лепестка","метрики","см","10","70","data","figsize","hue","iris","knn"],"topics":[]},{"id":"chunk-60","heading":"","text":"for i in range(len(y test)): if np.array(y test)[i] != y pred[i]: plt.scatter(X test.iloc[i, 3], X test.iloc[i, 2], color='r', s=150)","keywords":["ai","test","test.iloc","метрики","150","color","if","knn","len","notebook","np.array","plt.scatter"],"topics":["numpy","testing"]},{"id":"chunk-61","heading":"","text":"Часто бывает полезно разбивать объекты на категории не по количеству, а по качеству. Эта качественная информация нередко представляется как принадлежность наблюдения к отдельной категории, такой как пол, цвета или марка автомобиля. Однако не все категориальные данные одинаковые. Наборы категорий без внутреннего упорядочения называются номинальными. Примеры номинальных категорий включают:","keywords":["ai","категории","категорий","метрики","knn","notebook","автомобиля.","без","ближайших","бывает","включают","внутреннего"],"topics":[]},{"id":"chunk-62","heading":"","text":"С другой стороны, когда набор категорий имеет некое естественное упорядочение, мы называем его порядковым. Например:","keywords":["ai","метрики","knn","notebook","ближайших","другой","его","естественное","имеет","категорий","качества","классификация"],"topics":[]},{"id":"chunk-63","heading":"","text":"Более того, категориальная информация часто представлена в данных в виде вектора или столбца символьных значений (например, \"Москва\", \"Санкт Петербург\", \"Казань\"). Проблема в том, что большинство машинно\u0002обучающихся алгоритмов требуют ввода числовых значений.","keywords":["ai","метрики","knn","notebook","алгоритмов","ближайших","более","большинство","ввода","вектора","виде","данных"],"topics":["algorithms"]},{"id":"chunk-64","heading":"","text":"Алгоритм k ближайших соседей предоставляет простой пример. Одним из шагов в алгоритме является вычисление расстояний между наблюдениями — часто с использованием евклидова расстояния. Однако вычисление расстояния, очевидно, невозможно, если значение х, является строковым типом (например, \"Москва\")","keywords":["ai","ближайших","вычисление","метрики","расстояния","является","knn","notebook","алгоритм","алгоритме","евклидова","если"],"topics":["knn","algorithms"]},{"id":"chunk-65","heading":"","text":". Для того чтобы его можно было ввести в уравнение евклидова расстояния, нам нужно преобразовать это строковое значение в числовой формат. Наша цель — сделать преобразование, которое правильно передает информацию в категориях (упорядоченность, относительные интервалы между категориями и т. д.). Существует большое количество методов кодирования категориальных данных, среди которых нет универсальных","keywords":["ai","метрики","расстояния","knn","notebook","ближайших","большое","было","ввести","д.","данных","евклидова"],"topics":[]},{"id":"chunk-66","heading":"","text":". Выбирая метод, стоит отталкиваться от ваших данных, мощности множества категорий и алгоритма машинного обучения.","keywords":["ai","метрики","knn","notebook","алгоритма","ближайших","ваших","выбирая","данных","категорий","качества","классификация"],"topics":["algorithms"]},{"id":"chunk-67","heading":"","text":"Дан порядковый категориальный признак (например, высокий, средний, низкий). Выполнить его кодировку. Для решения задачи можно использовать метод replace фрейма данных pandas для преобразования строковых меток в числовые эквиваленты","keywords":["ai","метрики","knn","notebook","pandas","replace","ближайших","выполнить","высокий","дан","данных","его"],"topics":["pandas"]},{"id":"chunk-68","heading":"","text":"Создать признаки dataframe = pd.DataFrame({\"оценка\": [\"низкая\",\"низкая\",\"средняя\",\"средняя\",\"высокая\"]}) Создать словарь преобразования шкалы scale mapper = {\"низкая\":1,\"средняя\":2,\"высокая\":3}","keywords":["ai","низкая","средняя","высокая","метрики","создать","dataframe","knn","mapper","notebook","pd.dataframe","scale"],"topics":["pandas"]},{"id":"chunk-69","heading":"","text":"заменить значения признаков значениями словаря dataframe[\"оценка\"].replace(scale mapper) ````","keywords":["ai","метрики",".replace","dataframe","knn","mapper","notebook","scale","ближайших","заменить","значения","значениями"],"topics":["pandas"]},{"id":"chunk-70","heading":"","text":"Дан словарь, и требуется его конвертировать в матрицу признаков. Для решения задачи можно задействовать класс векторизатор словаря Dictvectorizer:","keywords":["ai","метрики","dictvectorizer","knn","notebook","ближайших","векторизатор","дан","его","задачи","задействовать","качества"],"topics":["oop"]},{"id":"chunk-71","heading":"","text":"python импортировать блок схему from sklearn.feature extraction import DictVectorizer Создать словарь data dict = [{\"карасный\":2,\"синий\":2}, {\"карасный\":4,\"синий\":3}, {\"карасный\":1,\"синий\":2}, {\"карасный\":2,\"синий\":2}] Создать векторизатор словаря dictvectorizer = DictVectorizer(sparse = False) Конвертировать словарь в матрицу признаков features = dictvectorizer.fit transform(data dict) features","keywords":["карасный","синий","ai","dictvectorizer","data","dict","features","метрики","словарь","создать","dictvectorizer.fit","extraction"],"topics":["python"]},{"id":"chunk-72","heading":"","text":"Определите набор признаков человека, по аналогии из РТ 1, – например, цвет глаз и конвертируйте его в матрицу признаков.","keywords":["ai","метрики","knn","notebook","аналогии","ближайших","глаз","его","качества","классификация","конвертируйте","матрицу"],"topics":[]},{"id":"chunk-73","heading":"","text":"data = [{\"глаз\":2, \"рука\":2}, {\"голова\":1, \"рука\":2}, {\"голова\":1, \"нога\":2}, {\"глаз\":1, \"нога\":2}, {\"нога\":1, \"рука\":3}]","keywords":["ai","нога","рука","глаз","голова","метрики","data","knn","notebook","ближайших","качества","классификация"],"topics":[]},{"id":"chunk-74","heading":"","text":"dictvectorizer = DictVectorizer(sparse = False) features = dictvectorizer.fit transform(data) features ````","keywords":["ai","dictvectorizer","features","метрики","data","dictvectorizer.fit","false","knn","notebook","sparse","transform","ближайших"],"topics":[]},{"id":"chunk-75","heading":"","text":"code array([[2., 0., 0., 2.], [0., 1., 0., 2.], [0., 1., 2., 0.], [1., 0., 2., 0.], [0., 0., 1., 3.]])","keywords":["0.","2.","1.","ai","метрики","3.","array","code","knn","notebook","ближайших","качества"],"topics":[]}]},{"id":"ai/notebook-04-regression","href":"/docs/ai/notebook-04-regression","slug":["ai","notebook-04-regression"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 4 — Регрессия","description":"Линейные модели, аппроксимация и визуализация регрессионных зависимостей.","preview":"Дата 22.04.2023 1.1. Теоретический материал – Линейные регрессионные модели Линейная регрессия Линейная регрессия (Linear regression) – модель зависимости переменной x от одной или нескольких других переменных (факторов, регрессоров, независимых переменных) с","keywords":["данные","зашумляем","beta","xdata","test","print","dataframe","opt","ydata","двух","01","списков","формируем","ax.set","train","lw","plt.show","dataset"],"topics":["python","ai","pandas","numpy","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"","text":"Линейная регрессия (Linear regression) – модель зависимости переменной x от одной или нескольких других переменных (факторов, регрессоров, независимых переменных) с линейной функцией зависимости. Линейная регрессия относится к задаче определения «линии наилучшего соответствия» через набор точек данных и стала простым предшественником нелинейных методов, которые используют для обучения нейронных сетей.","keywords":["ai","регрессия","линейная","переменных","linear","notebook","regression","аппроксимация","визуализация","данных","других","зависимостей."],"topics":["regression"]},{"id":"chunk-1","heading":"","text":"Цель линейной регрессии — поиск линии, которая наилучшим образом соответствует этим точкам. Напомним, что общее уравнение для прямой есть 𝑓 (𝑥) = 𝑏 + 𝑚 ⋅ 𝑥 +, где 𝑚 – наклон линии, а 𝑏 – его сдвиг.","keywords":["ai","𝑏","𝑚","𝑥","линии","𝑓","notebook","аппроксимация","визуализация","его","зависимостей.","которая"],"topics":[]},{"id":"chunk-2","heading":"","text":"Функция потерь – это мера количества ошибок, которые наша линейная регрессия делает на наборе данных. Хотя есть разные функции потерь, все они вычисляют расстояние между предсказанным значением 𝑦(х) и его фактическим значением. Одна очень распространенная функция потерь называется средней квадратичной ошибкой MSE. Чтобы вычислить MSE, мы просто берем все значения ошибок, считаем их квадраты длин и усредняем.","keywords":["ai","потерь","ошибок","регрессия","функция","mse","mse.","notebook","𝑦","аппроксимация","берем","визуализация"],"topics":["regression"]},{"id":"chunk-3","heading":"","text":"Допустим у нас есть много экспериментальных точек. Необходимо через них провести кривую, которая как можно ближе проходила к этим точкам. При этом необходимо минимизировать среднюю квадратичную ошибку (MSE). Для решения данной задачи в Python есть множество библиотек. Самыми распостраненными выступают:","keywords":["ai","необходимо","mse","notebook","python","аппроксимация","библиотек.","ближе","визуализация","выступают","данной","допустим"],"topics":["python"]},{"id":"chunk-4","heading":"","text":"numpy numpy.linalg.lstsq scipy scipy.linalg (содержит все функции из numpy.linalg плюс часть новых функций, которых нет в numpy.linalg).","keywords":["ai","numpy.linalg","notebook","numpy","numpy.linalg.lstsq","scipy","scipy.linalg","аппроксимация","визуализация","зависимостей.","которых","линейные"],"topics":["numpy"]},{"id":"chunk-5","heading":"","text":"перепишем линейное уравнение y = mx + c как y =Ap, где A = [[ x 1 ]] и p = [[m],[c]] Построим A по x:","keywords":["ai","ap","mx","notebook","аппроксимация","визуализация","зависимостей.","линейное","линейные","модели","перепишем","построим"],"topics":[]},{"id":"chunk-6","heading":"","text":"Используем метод lstsq для решения кго относительно вектора p. m, c = np.linalg.lstsq(A, y, rcond = None)[0] print(m,c)","keywords":["ai","lstsq","none","notebook","np.linalg.lstsq","p.","print","rcond","аппроксимация","вектора","визуализация","зависимостей."],"topics":["numpy"]},{"id":"chunk-7","heading":"","text":"Построим график полученной прямой и укажем на нем точки. plt.plot(x,y,'o',label = 'Исходные данные', markersize=10) plt.plot(x,m x+c,\"r\",label = 'Линейгая экстраполяция') plt.legend() plt.show() ````","keywords":["ai","label","plt.plot","10","markersize","notebook","plt.legend","plt.show","x+c","аппроксимация","визуализация","график"],"topics":[]},{"id":"chunk-8","heading":"","text":"Задача: Пусть 𝑥, 𝑦 – вектора длиной 𝑛 3 (точек 3). Задача заключается в построении эстраполяционного полинома второго порядка (параболы). Таким образом, необходимо найти такие коэффициенты поринома 𝑎, 𝑏, 𝑐 по методу наименьших квадратов. Данные мтогут быть получены в результате измерений. Покажем пример генерации данных случайным образом и загрузки их из файла.","keywords":["ai","задача","образом","𝑎","𝑏","𝑐","𝑛","notebook","𝑥","𝑦","аппроксимация","быть"],"topics":[]},{"id":"chunk-9","heading":"","text":"````python from numpy import from numpy.random import генерируем случайные x и y delta = 1.0 x = linspace( 5,5,11) y = x 2+delta (rand(11) 0.5) x += delta (rand(11) 0.5)","keywords":["11","ai","0.5","delta","import","rand","1.0","2+delta","linspace","notebook","numpy","numpy.random"],"topics":["python","numpy"]},{"id":"chunk-10","heading":"","text":"записываем данные в файл x.tofile('x data.txt', '\\n') y.tofile('y data.txt', '\\n')","keywords":["ai","data.txt","notebook","x.tofile","y.tofile","аппроксимация","визуализация","данные","зависимостей.","записываем","линейные","модели"],"topics":[]},{"id":"chunk-11","heading":"читаем данные из файлов","text":"x = fromfile('x data.txt', float, sep='\\n') y = fromfile('y data.txt', float, sep='\\n')","keywords":["ai","data.txt","float","fromfile","sep","notebook","аппроксимация","визуализация","данные","зависимостей.","линейные","модели"],"topics":[]},{"id":"chunk-12","heading":"рисуем кривую вида y = ax^2 + bx + c, подставляя из решения коэффициенты s[0], s[1], s[2]","text":"plt.plot(x prec, s[0] x prec 2 + s[1] x prec+s[2], ' ', lw=2) plt.grid() plt.savefig('парабола.png') ````","keywords":["ai","prec","ax","bx","lw","notebook","plt.grid","plt.plot","plt.savefig","prec+s","аппроксимация","вида"],"topics":[]},{"id":"chunk-13","heading":"рисуем кривую вида y = ax^2 + bx + c, подставляя из решения коэффициенты s[0], s[1], s[2]","text":"По данным предыдущего примера постройте эстраполяционного полинома третьего порядка","keywords":["ai","ax","bx","notebook","аппроксимация","вида","визуализация","данным","зависимостей.","коэффициенты","кривую","линейные"],"topics":[]},{"id":"chunk-14","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"plt.plot(x prec, s[0] x prec 3 + s[1] x prec 2 + s[2] x prec + s[3], ' ', lw=3) plt.grid() plt.savefig('полином 3 й степени.png') ````","keywords":["prec","ai","ax","bx","cx","lw","notebook","plt.grid","plt.plot","plt.savefig","аппроксимация","вида"],"topics":[]},{"id":"chunk-15","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"Представьте собственные данные и постройте эктраполяцию полиномами первой, второй и третьей степени.","keywords":["ai","ax","bx","cx","notebook","аппроксимация","вида","визуализация","второй","данные","зависимостей.","коэффициенты"],"topics":[]},{"id":"chunk-16","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"python x=array([0,7,12,15]) y=array([3,6,9,12]) A=vstack((x,ones(len(x)))).T m,c = np.linalg.lstsq(A,y,rcond=None)[0] plt.plot(x,y,'o',markersize=10) plt.plot(x,m x+c,'r') plt.legend() plt.show() delta = 1.0 x= linspace( 10,10,10) y= x 2+delta (rand(10)) x+= delta (rand(10)) m= vstack((x 2, x, ones(10))).T s= linalg.lstsq(m, y, rcond=None)[0] x prec=linspace( 10,10,100) plt.plot(x,y,'D') plt.plot(x prec,s[0] x prec","keywords":["10","plt.plot","ai","prec",".t","12","array","delta","linspace","none","ones","rand"],"topics":["python","numpy"]},{"id":"chunk-17","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"2+s[1] x prec+s[2],' ',lw=2) plt.grid() plt.savefig('парабола.png') plt.show() m=vstack((x 3, x 2, x, ones(10))).T s=linalg.lstsq(m, y, rcond=None)[0] x prec=linspace( 10,10,100) plt.plot(x,y,'D') plt.plot(x prec,s[0] x prec 3+s[1] x prec 2+s[2] x prec+s[3],' ',lw=3) plt.grid() plt.savefig('полином 3 й степени.png') plt.show()","keywords":["prec","10","ai","2+s","lw","plt.grid","plt.plot","plt.savefig","plt.show","prec+s",".t","100"],"topics":[]},{"id":"chunk-18","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"Задача: Необходимо проверить гипотезу, что наши точечно заданная функция ложится на кривую вида 𝑓(𝑥, 𝑏) = 𝑏0 + 𝑏1 𝑒𝑥𝑝(−𝑏^2 𝑥^2)","keywords":["ai","𝑏","𝑥","вида","кривую","ax","𝑏0","𝑏1","bx","cx","𝑒𝑥𝑝","𝑓"],"topics":[]},{"id":"chunk-19","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"````python Добавим шума в данные, сделанные по функции f(x, b) с коэффициентами b = (0.25, 0.75, 0.5) beta=(0.25,0.75,0.5) def f(x,b0,b1,b2): return b0+b1 exp( b2 x 2)","keywords":["ai","0.25","0.5","0.75","b2","ax","b0","b0+b1","b1","beta","bx","cx"],"topics":["python"]},{"id":"chunk-20","heading":"зашумляем эти данные","text":"Используем функцию для получения решения в виде коэффициентов функции f(x) для указанных xdata и ydata from scipy.optimize import curve fit beta opt,beta cov = curve fit(f, xdata, ydata) beta opt","keywords":["ai","beta","curve","fit","opt","xdata","ydata","cov","import","notebook","scipy.optimize","аппроксимация"],"topics":[]},{"id":"chunk-21","heading":"зашумляем эти данные","text":"Вычислим квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-22","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata,ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata,f(xdata, beta opt),'b',lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\",fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\",fontsize=18) plt.show()","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-23","heading":"зашумляем эти данные","text":"Задача: Необходимо проверить гипотезу, что наши точечно заданная функция ложится на кривые вида:","keywords":["ai","notebook","аппроксимация","вида","визуализация","гипотезу","данные","зависимостей.","заданная","задача","зашумляем","кривые"],"topics":[]},{"id":"chunk-24","heading":"зашумляем эти данные","text":"````python решение 1 Добавим шума в данные, сделанные по функции f(x,b) c коэффицентами beta=(0.25,0.75) beta=(0.25,0.75) def f(x,b0,b1): return b0+b1 x зададим массив точек xi xdata = linspace(0, 5, 50) cоздаем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 0.05 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt) Вычисляемлинейное","keywords":["beta","xdata","ai","данные","0.25","0.75","opt","ydata","зашумляем","точек","шума","0.05"],"topics":["python"]},{"id":"chunk-25","heading":"зашумляем эти данные","text":"отклонение lin dev = sum(beta cov[0]) print(lin dev) Вычисляем квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","beta","dev","fres","lin","print","residuals","sum","отклонение","cov","notebook","opt"],"topics":[]},{"id":"chunk-26","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show()","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-27","heading":"зашумляем эти данные","text":"решение 2 Добавим шума в данные, сделанные по функции f(x,b) с коэффицентами b = (0.25б 0.75,0.5) beta = (0.25, 0.75, 0.5) def f(x, b0, b1, b2): return b0 + b1 x + b2 x 2 зададим массив точек xi xdata = linspace(0, 5, 50) создаем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 0.05 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt)","keywords":["beta","xdata","ai","данные","0.5","0.75","b0","b1","b2","opt","ydata","зашумляем"],"topics":[]},{"id":"chunk-28","heading":"зашумляем эти данные","text":"Вычислим линейное отклонение lin dev = sum(beta cov[0]) print(lin dev)","keywords":["ai","dev","lin","beta","cov","notebook","print","sum","аппроксимация","визуализация","вычислим","данные"],"topics":[]},{"id":"chunk-29","heading":"зашумляем эти данные","text":"вычислить квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-30","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show()","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-31","heading":"зашумляем эти данные","text":"решение 3 Добавим шума в данные, сделанные по функции f(x,b) с коэффицентами b = (1,2) beta = (1, 2) def f(x, b0, b1): return b0 + b1 log(x) заданим массив точек xi xdata = linspace(1, 5, 50) созадем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 0.05 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt) Вычислим линейное отклонение","keywords":["beta","xdata","ai","данные","b0","b1","opt","ydata","зашумляем","точек","шума","0.05"],"topics":[]},{"id":"chunk-32","heading":"зашумляем эти данные","text":"lin dev = sum(beta cov[0]) print(lin dev)","keywords":["ai","dev","lin","beta","cov","notebook","print","sum","аппроксимация","визуализация","данные","зависимостей."],"topics":[]},{"id":"chunk-33","heading":"зашумляем эти данные","text":"Вычислим квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-34","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show()","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-35","heading":"зашумляем эти данные","text":"решение 4 Добавим шума в данные, сделанные по функции f(x,b) с коэффицентами b = (1,2) beta = (1, 2) def f(x, b0, b1): return b0 x b1 заданим массив точек xi xdata = linspace(1, 5, 50) созадем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 0.05 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt) Вычислим линейное отклонение lin dev","keywords":["beta","xdata","ai","данные","b0","b1","opt","ydata","зашумляем","точек","шума","0.05"],"topics":[]},{"id":"chunk-36","heading":"зашумляем эти данные","text":"= sum(beta cov[0]) print(lin dev)","keywords":["ai","beta","cov","dev","lin","notebook","print","sum","аппроксимация","визуализация","данные","зависимостей."],"topics":[]},{"id":"chunk-37","heading":"зашумляем эти данные","text":"Вычислим квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-38","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show() ````","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-39","heading":"зашумляем эти данные","text":"Подставьте собственные данные и поэкспериментируйте с представленными функциями. Проанализируйте динамику изменения данных.","keywords":["ai","данные","notebook","аппроксимация","визуализация","данных.","динамику","зависимостей.","зашумляем","изменения","линейные","модели"],"topics":[]},{"id":"chunk-40","heading":"зашумляем эти данные","text":"````python beta = (8,88,888) def f(x, b0, b1, b2): return b0 + b1 x + b2 x 2 заданим массив точек xi xdata = linspace( 7, 7, 100) созадем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 50 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt) Вычислим линейное отклонение lin dev = sum(beta cov[0]) print(lin dev)","keywords":["beta","xdata","ai","b0","b1","b2","cov","dev","lin","opt","print","ydata"],"topics":["python"]},{"id":"chunk-41","heading":"зашумляем эти данные","text":"Вычислим квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-42","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 50) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show() ````","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","50","ax","ax.scatter","fig"],"topics":[]},{"id":"chunk-43","heading":"зашумляем эти данные","text":"Линейная регрессия это широко используемый метод статистического анализа, который использует регрессионный анализ в математической статистике для определения количественной взаимосвязи между двумя или более переменными. Если регрессионный анализ включает две или более независимых переменных, а связь между зависимой и независимой переменными является линейной, тогда имееи дело с множественной линейной регрессией.","keywords":["ai","анализ","более","линейной","между","регрессионный","регрессия","notebook","анализа","аппроксимация","взаимосвязи","визуализация"],"topics":["regression"]},{"id":"chunk-44","heading":"зашумляем эти данные","text":"В этом разделе мы увидим, как библиотеку Scikit Learn в Python для машинного обучения можно использовать для реализации функций регрессии. Мы начнем с простой линейной регрессии с участием двух переменных, а затем перейдем к линейной регрессии с участием нескольких переменных.","keywords":["ai","линейной","мы","регрессии","участием","learn","notebook","python","scikit","аппроксимация","библиотеку","визуализация"],"topics":["python"]},{"id":"chunk-45","heading":"зашумляем эти данные","text":"Задача: Построим простую линейную регрессию в Python с использованием библиотеки scikit learn","keywords":["ai","learn","notebook","python","scikit","аппроксимация","библиотеки","визуализация","данные","зависимостей.","задача","зашумляем"],"topics":["python"]},{"id":"chunk-46","heading":"зашумляем эти данные","text":"````python Импортируем необходимые библиотеки используем pandas и numpy для обработки данных matpoltlib для визуализации и sklearn для обучения набора данных и импорта моделей from pandas import DataFrame, Series from sklearn.model selection import train test split from sklearn.linear model import LinearRegression","keywords":["ai","import","pandas","данных","dataframe","linearregression","matpoltlib","model","notebook","numpy","python","selection"],"topics":["python","ai","pandas","numpy","testing","regression"]},{"id":"chunk-47","heading":"зашумляем эти данные","text":"создадим набор данных для описания взаимосвязи между временем обучения студентов и успеваймостью my dict = { 'Учебное время': [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, 5.0, 5.5], 'Оценка': [10, 22, 13, 43, 20, 22, 33, 50, 62, 48, 55, 75, 62, 73, 81, 76, 64, 82, 90, 93] }","keywords":["ai","22","62","0.5","0.75","1.0","1.25","1.5","1.75","10","13","2.0"],"topics":[]},{"id":"chunk-48","heading":"зашумляем эти данные","text":"Рисуем диаграмму plt.scatter(dataset['Учебное время'], dataset['Оценка'], color='b', label='Данные экзамена') plt.xlabel('Часы') plt.ylabel('Оценка') plt.show() ````","keywords":["ai","dataset","данные","оценка","color","label","notebook","plt.scatter","plt.show","plt.xlabel","plt.ylabel","аппроксимация"],"topics":[]},{"id":"chunk-49","heading":"зашумляем эти данные","text":"После того как мы получили представление о данных, разделим информацию на «атрибуты» и «метки». Атрибуты – это независимые переменные, а метки – это зависимые переменные, значения которых должны быть предсказаны. В нашем наборе всего два столбца и необходимо предсказать оценку в зависимости от количества часов. Чтобы извлечь атрибуты и метки, выполните следующий скрипт:","keywords":["ai","атрибуты","метки","переменные","notebook","аппроксимация","быть","визуализация","всего","выполните","данные","данных"],"topics":[]},{"id":"chunk-50","heading":"зашумляем эти данные","text":"````python x = dataset.iloc[:, : 1].values y = dataset.iloc[:, 1].values print(x) print(y)","keywords":["ai",".values","dataset.iloc","print","notebook","python","аппроксимация","визуализация","данные","зависимостей.","зашумляем","линейные"],"topics":["python"]},{"id":"chunk-51","heading":"зашумляем эти данные","text":"Теперь когда у нас есть атрибуты и метки, необходимость разделить их на а обучающий и тестовый наборы. приведенный фрагмент 80 % данных на обучающий набор, а 20% данных на набор тестов x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0) далее можно обучить алгоритм линейной регрессии необходимо импортировать класс LinelRegression, создать его экземпляр и вызвать метод fit()","keywords":["test","ai","train","данных","набор","обучающий","0.2","20","80","fit","linelregression","notebook"],"topics":["ai","oop","testing","algorithms","regression"]},{"id":"chunk-52","heading":"зашумляем эти данные","text":"regressor = LinearRegression() regressor.fit(x train, y train) приведем полученные коэффиценты для линейной регрессии print(regressor.intercept ) print(regressor.coef ) ````","keywords":["ai","print","train","linearregression","notebook","regressor","regressor.coef","regressor.fit","regressor.intercept","аппроксимация","визуализация","данные"],"topics":["ai","regression"]},{"id":"chunk-53","heading":"зашумляем эти данные","text":"code [[1.0000e+00 3.5710e+03 1.9760e+03 5.2500e 01] [2.0000e+00 4.0920e+03 1.2500e+03 5.7200e 01] [3.0000e+00 3.8650e+03 1.5860e+03 5.8000e 01] [4.0000e+00 4.8700e+03 2.3510e+03 5.2900e 01] [3.0000e+00 4.3990e+03 4.3100e+02 5.4400e 01] [4.0000e+00 5.3420e+03 1.3330e+03 5.7100e 01] [5.0000e+00 5.3190e+03 1.1868e+04 4.5100e 01] [3.0000e+00 5.1260e+03 2.1380e+03 5.5300e 01] [5.0000e+00 4.4470e+03 8.5770e+03 5.2900e 01]","keywords":["01","3.0000e+00","ai","4.0000e+00","5.0000e+00","5.2900e","1.0000e+00","1.1868e+04","1.2500e+03","1.3330e+03","1.5860e+03","1.9760e+03"],"topics":[]},{"id":"chunk-54","heading":"зашумляем эти данные","text":"[5.0000e+00 4.5120e+03 8.5070e+03 5.5200e 01] [4.0000e+00 4.3910e+03 5.9390e+03 5.3000e 01] [5.0000e+00 5.1260e+03 1.4186e+04 5.2500e 01] [4.0000e+00 4.8170e+03 6.9300e+03 5.7400e 01] [5.0000e+00 4.2070e+03 6.5800e+03 5.4500e 01] [4.0000e+00 4.3320e+03 8.1590e+03 6.0800e 01] [5.0000e+00 4.3180e+03 1.0340e+04 5.8600e 01] [6.0000e+00 4.2060e+03 8.5080e+03 5.7200e 01] [0.0000e+00 3.7180e+03 4.7250e+03 5.4000e 01]","keywords":["01","5.0000e+00","4.0000e+00","ai","0.0000e+00","1.0340e+04","1.4186e+04","3.7180e+03","4.2060e+03","4.2070e+03","4.3180e+03","4.3320e+03"],"topics":[]},{"id":"chunk-55","heading":"зашумляем эти данные","text":"[6.0000e+00 4.7160e+03 5.9150e+03 7.2400e 01] [3.0000e+00 4.3410e+03 6.0100e+03 6.7700e 01] [1.0000e+00 4.5930e+03 7.8340e+03 6.6300e 01] [3.0000e+00 4.9830e+03 6.0200e+02 6.0200e 01] [1.0000e+00 4.8970e+03 2.4490e+03 5.1100e 01]] [3571. 4092. 3865. 4870. 4399. 5342. 5319. 5126. 4447. 4512. 4391. 5126. 4817. 4207. 4332. 4318. 4206. 3718. 4716. 4341. 4593. 4983","keywords":["01","ai","1.0000e+00","3.0000e+00","5126.","2.4490e+03","3571.","3718.","3865.","4.3410e+03","4.5930e+03","4.7160e+03"],"topics":[]},{"id":"chunk-56","heading":"зашумляем эти данные","text":". 4897.] 9.094947017729282e 13 [5.75398478e 13 1.00000000e+00 1.11022302e 16 3.44246345e 14]","keywords":["ai","13","1.00000000e+00","1.11022302e","14","16","3.44246345e","4897.","5.75398478e","9.094947017729282e","notebook","аппроксимация"],"topics":[]},{"id":"chunk-57","heading":"зашумляем эти данные","text":"Получившийся результат можно интерпретировать следующим образом: с каждым затраченным часом на обучение результат экзамена повышается приблизительно на 17 баллов. Далее можно построить прогнозы. Для этого мы будем использовать наши тестовые данные и посмотрим, насколько точно наш алгоритм предсказывает процентную оценку. Чтобы сделать прогноз на тестовых данных необходимо выполнить следующий код:","keywords":["ai","данные","можно","результат","17","notebook","алгоритм","аппроксимация","баллов.","будем","визуализация","выполнить"],"topics":["algorithms"]},{"id":"chunk-58","heading":"зашумляем эти данные","text":"````python y pred = regressor.predict(x test) сравним фактические значения с прогнозиремыми df = DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","df","pred","test","actual","dataframe","notebook","predicted","python","regressor.predict","аппроксимация","визуализация"],"topics":["python","pandas","testing"]},{"id":"chunk-59","heading":"зашумляем эти данные","text":"визуализируем результат сравнения df.plot(kind='bar') plt.grid(which='major', linestyle=' ', linewidth='0.5', color='green') plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') plt.show() построим линию регресии с тестовыми данными plt.scatter(x test, y test, color='gray') plt.plot(x test, y pred, color='red', linewidth=2) plt.show() ````","keywords":["color","ai","linewidth","test","0.5","linestyle","plt.grid","plt.show","which","bar","black","df.plot"],"topics":["testing"]},{"id":"chunk-60","heading":"зашумляем эти данные","text":"Постройте модель линейной регрессии для произвольных данных из двух столбцов. Для примера можно взять точечную зависимость заработной платы от опыта работы:","keywords":["ai","notebook","аппроксимация","взять","визуализация","данные","данных","двух","зависимостей.","зависимость","заработной","зашумляем"],"topics":[]},{"id":"chunk-61","heading":"зашумляем эти данные","text":"(https://raw.githubusercontent.com/AnnaShestova/salary years simple linear\u0002regression/master/Salary Data.csv).","keywords":["ai","salary","annashestova","data.csv","https","linear","master","notebook","raw.githubusercontent.com","regression","simple","years"],"topics":["python","regression"]},{"id":"chunk-62","heading":"зашумляем эти данные","text":"````python url = r'https://raw.githubusercontent.com/AnnaShestova/salary years simple linear regression/master/Salary Data.csv' dataset = pd.read csv(url) print(dataset.head(5))","keywords":["ai","salary","url","annashestova","csv","data.csv","dataset","dataset.head","https","linear","master","notebook"],"topics":["python","regression"]},{"id":"chunk-63","heading":"зашумляем эти данные","text":"plt.scatter(dataset['YearsExperience'], dataset['Salary'], color='b', label='Salary data') plt.xlabel('Experience') plt.ylabel('Salary') plt.show()","keywords":["ai","salary","dataset","color","data","experience","label","notebook","plt.scatter","plt.show","plt.xlabel","plt.ylabel"],"topics":[]},{"id":"chunk-64","heading":"зашумляем эти данные","text":"x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0)","keywords":["test","ai","train","0.2","notebook","random","size","split","state","аппроксимация","визуализация","данные"],"topics":["ai","testing"]},{"id":"chunk-65","heading":"зашумляем эти данные","text":"y pred = regressor.predict(x test) df = DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","df","pred","test","actual","dataframe","notebook","predicted","regressor.predict","аппроксимация","визуализация","данные"],"topics":["pandas","testing"]},{"id":"chunk-66","heading":"зашумляем эти данные","text":"df.plot(kind='bar') plt.grid(which='major', linestyle=' ', linewidth='0.5', color='green') plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') plt.show()","keywords":["ai","0.5","color","linestyle","linewidth","plt.grid","which","bar","black","df.plot","green","kind"],"topics":[]},{"id":"chunk-67","heading":"зашумляем эти данные","text":"plt.scatter(x test, y test, color='blue') plt.plot(x test, y pred, color='green', linewidth=2) plt.show() ````","keywords":["ai","test","color","blue","green","linewidth","notebook","plt.plot","plt.scatter","plt.show","pred","аппроксимация"],"topics":["testing"]},{"id":"chunk-68","heading":"зашумляем эти данные","text":"В предыдущем примере мы проиллюстрировали линейную регрессию с двумя переменными. Однако, почти все реальные задачи имеют больше параметров. Линейная регрессия с участием нескольких переменных называется «множественной линейной регрессией» или многомерной линейной регрессией. Шаги для выполнения множественной линейной регрессии аналогичны шагам для простой . Разница заключается в оценке","keywords":["ai","линейной","множественной","регрессия","notebook","аналогичны","аппроксимация","больше","визуализация","выполнения","данные","двумя"],"topics":["regression"]},{"id":"chunk-69","heading":"зашумляем эти данные","text":". Вы можете использовать множественную регрессию, чтобы узнать, какой фактор оказывает наибольшее влияние на прогнозируемый результат или как различные переменные связаны друг с другом.","keywords":["ai","notebook","аппроксимация","визуализация","влияние","вы","данные","друг","другом.","зависимостей.","зашумляем","использовать"],"topics":[]},{"id":"chunk-70","heading":"зашумляем эти данные","text":"Задача: Для решения задачи множественной регрессии можно задействовать уже известный метод numpy.linalg.lstsq.","keywords":["ai","notebook","numpy.linalg.lstsq.","аппроксимация","визуализация","данные","зависимостей.","задача","задачи","задействовать","зашумляем","известный"],"topics":["numpy"]},{"id":"chunk-71","heading":"зашумляем эти данные","text":"y = [1,2,3,4,3,4,5,3,5,5,4,5,4,5,4,5,6,0,6,3,1,3,1] x = [[0,2,4,1,5,4,5,9,9,9,3,7,8,8,6,6,5,5,5,6,6,5,5], [4,1,2,3,4,5,6,7,5,8,7,8,7,8,7,8,6,8,9,2,1,5,6], [4,1,2,5,6,7,8,9,7,8,7,8,7,4,3,1,2,3,4,1,3,9,7]]","keywords":["ai","notebook","аппроксимация","визуализация","данные","зависимостей.","зашумляем","линейные","модели","регрессионных","регрессия"],"topics":[]},{"id":"chunk-72","heading":"зашумляем эти данные","text":"x = np.transpose(x) transpose to input vectors x = np.c [x, np.ones(x.shape[0])] add bias term linreg = np.linalg.lstsq(x, y, rcond=None)[0] print(linreg) ````","keywords":["ai","linreg","add","bias","input","none","notebook","np.c","np.linalg.lstsq","np.ones","np.transpose","print"],"topics":["numpy"]},{"id":"chunk-73","heading":"зашумляем эти данные","text":"Задача: Для данных из предыдущей задачи построить модель множественной линейной регрессии с использованием средств библиотеки sсikit learn.","keywords":["ai","learn.","notebook","sсikit","аппроксимация","библиотеки","визуализация","данные","данных","зависимостей.","задача","задачи"],"topics":[]},{"id":"chunk-74","heading":"зашумляем эти данные","text":"````python from sklearn.model selection import train test split from sklearn.linear model import LinearRegression from sklearn import metrics","keywords":["ai","import","linearregression","metrics","model","notebook","python","selection","sklearn","sklearn.linear","sklearn.model","split"],"topics":["python","ai","testing","regression"]},{"id":"chunk-75","heading":"зашумляем эти данные","text":"y = [1,2,3,4,3,4,5,3,5,5,4,5,4,5,4,5,6,0,6,3,1,3,1] x = [[0,2,4,1,5,4,5,9,9,9,3,7,8,8,6,6,5,5,5,6,6,5,5], [4,1,2,3,4,5,6,7,5,8,7,8,7,8,7,8,6,8,9,2,1,5,6], [4,1,2,5,6,7,8,9,7,8,7,8,7,4,3,1,2,3,4,1,3,9,7]]","keywords":["ai","notebook","аппроксимация","визуализация","данные","зависимостей.","зашумляем","линейные","модели","регрессионных","регрессия"],"topics":[]},{"id":"chunk-76","heading":"формируем DataFrame из двух списков","text":"new y = np.array(y) new y = new y.transpose() df1 = pd.DataFrame(new y) new x = np.array(x) new x = new x.transpose() df2 = pd.DataFrame(new x) df1 = df1.rename(columns={0: 'y'}, inplace=False) df2 = df2.rename(columns={0: 'x1', 1: 'x2', 2: 'x3'}, inplace=False)","keywords":["new","ai","columns","df1","df2","false","inplace","np.array","pd.dataframe","dataframe","df1.rename","df2.rename"],"topics":["pandas","numpy"]},{"id":"chunk-77","heading":"формируем DataFrame из двух списков","text":"frames = [df1, df2] dataset = pd.concat([df1, df2], axis=1, join='inner') print(dataset.head())","keywords":["ai","df1","df2","axis","dataframe","dataset","dataset.head","frames","inner","join","notebook","pd.concat"],"topics":["pandas"]},{"id":"chunk-78","heading":"формируем DataFrame из двух списков","text":"разделим данные на метки и атрибуты x = dataset[['x1', 'x2', 'x3']] y = dataset['y']","keywords":["ai","dataset","dataframe","notebook","x1","x2","x3","аппроксимация","атрибуты","визуализация","данные","двух"],"topics":["pandas"]},{"id":"chunk-79","heading":"формируем DataFrame из двух списков","text":"раздлеим данные на обуччающую и тестовую выборки x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0)","keywords":["test","ai","train","0.2","dataframe","notebook","random","size","split","state","аппроксимация","визуализация"],"topics":["ai","pandas","testing"]},{"id":"chunk-80","heading":"формируем DataFrame из двух списков","text":"для обучения алгоритма мы выполняем тот же код, что и раньше, используя метод fit() класса LinearRegression regressor = LinearRegression() regressor.fit(x train, y train)","keywords":["ai","linearregression","train","dataframe","fit","notebook","regressor","regressor.fit","алгоритма","аппроксимация","визуализация","выполняем"],"topics":["ai","pandas","oop","algorithms","regression"]},{"id":"chunk-81","heading":"формируем DataFrame из двух списков","text":"выведем кожффициенты модели coeff df = pd.DataFrame(regressor.coef , x.columns, columns=['Coefficient']) coeff df","keywords":["ai","coeff","df","модели","coefficient","columns","dataframe","notebook","pd.dataframe","regressor.coef","x.columns","аппроксимация"],"topics":["pandas"]},{"id":"chunk-82","heading":"формируем DataFrame из двух списков","text":"Чтобы сделать прогнозы на тестовых данных, выполните следующий код y pred = regressor.predict(x test) df = pd.DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","df","pred","test","actual","dataframe","notebook","pd.dataframe","predicted","regressor.predict","аппроксимация","визуализация"],"topics":["pandas","testing"]},{"id":"chunk-83","heading":"формируем DataFrame из двух списков","text":"Последний шаг оценить производительность алгоритма. Мы сделаем это, найдя значения для MSE print('Mean Squared Error:', metrics.mean squared error(y test, y pred)) ````","keywords":["ai","error","squared","dataframe","mean","metrics.mean","mse","notebook","pred","print","test","алгоритма."],"topics":["pandas","testing","algorithms"]},{"id":"chunk-84","heading":"формируем DataFrame из двух списков","text":"code y x1 x2 x3 0 1 0 4 4 1 2 2 1 1 2 3 4 2 2 3 4 1 3 5 4 3 5 4 6 (23, 4) y x1 x2 x3 count 23.000000 23.000000 23.000000 23.000000 mean 3.565217 5.347826 5.521739 5.043478 std 1.674029 2.404706 2.428422 2.704849 min 0.000000 0.000000 1.000000 1.000000 25% 3.000000 4.500000 4.000000 3.000000 50% 4.000000 5.000000 6.000000 5.000000 75% 5.000000 6.500000 7.500000 7.000000 max 6.000000 9.000000 9.000000 9.000000 Mean","keywords":["23.000000","5.000000","9.000000","ai","0.000000","1.000000","3.000000","4.000000","6.000000","mean","x1","x2"],"topics":["pandas"]},{"id":"chunk-85","heading":"формируем DataFrame из двух списков","text":"Squared Error: 1.3272699242343076","keywords":["ai","1.3272699242343076","dataframe","error","notebook","squared","аппроксимация","визуализация","двух","зависимостей.","линейные","модели"],"topics":["pandas"]},{"id":"chunk-86","heading":"формируем DataFrame из двух списков","text":"Постройте модель множественной линейной регрессии для произвольных данных из нескольких столбцов. Для примера можно взять потребления газа (в миллионах галлонов) в 48 штатах США или набор данных о качестве красного вина (1) и (2) соответственно. Найдите коэффициенты множественной регрессии. Постройте прогноз. 1. https://raw.githubusercontent.com/likarajo/petrol consumption/master/data/pe trol consumption.csv 2","keywords":["ai","данных","множественной","постройте","1.","48","consumption","consumption.csv","data","dataframe","https","likarajo"],"topics":["python","pandas"]},{"id":"chunk-87","heading":"формируем DataFrame из двух списков","text":". https://raw.githubusercontent.com/aniruddhachoudhury/Red Wine\u0002Quality/master/winequality red.csv","keywords":["ai","aniruddhachoudhury","dataframe","https","master","notebook","quality","raw.githubusercontent.com","red","red.csv","wine","winequality"],"topics":["python","pandas"]},{"id":"chunk-88","heading":"формируем DataFrame из двух списков","text":"````python url = r'https://raw.githubusercontent.com/likarajo/petrol consumption/master/data/petrol consumption.csv' dataset = pd.read csv(url) print(dataset.head(5))","keywords":["ai","petrol","url","consumption","consumption.csv","csv","data","dataframe","dataset","dataset.head","https","likarajo"],"topics":["python","pandas"]},{"id":"chunk-89","heading":"формируем DataFrame из двух списков","text":"y = list(dataset['Petrol tax']) x = [list(dataset['Average income']), list(dataset['Paved Highways']), list(dataset['Population Driver licence(%)']), list(dataset['Petrol Consumption'])]","keywords":["dataset","list","ai","petrol","average","consumption","dataframe","driver","highways","income","licence","notebook"],"topics":["pandas"]},{"id":"chunk-90","heading":"формируем DataFrame из двух списков","text":"new y = new y.transpose() df1 = pd.DataFrame(new y) new x = np.array(x) new x = new x.transpose() df2 = pd.DataFrame(new x) df1 = df1.rename(columns={0: 'y'}, inplace=False) df2 = df2.rename(columns={0: 'x1', 1: 'x2', 2: 'x3'}, inplace=False)","keywords":["new","ai","columns","df1","df2","false","inplace","pd.dataframe","dataframe","df1.rename","df2.rename","notebook"],"topics":["pandas","numpy"]},{"id":"chunk-91","heading":"формируем DataFrame из двух списков","text":"frames = [df1, df2] dataset = pd.concat([df1, df2], axis=1, join='inner') print(dataset.head())","keywords":["ai","df1","df2","axis","dataframe","dataset","dataset.head","frames","inner","join","notebook","pd.concat"],"topics":["pandas"]},{"id":"chunk-92","heading":"формируем DataFrame из двух списков","text":"x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0)","keywords":["test","ai","train","0.2","dataframe","notebook","random","size","split","state","аппроксимация","визуализация"],"topics":["ai","pandas","testing"]},{"id":"chunk-93","heading":"формируем DataFrame из двух списков","text":"coeff df = pd.DataFrame(regressor.coef , x.columns, columns=['Coefficient']) coeff df","keywords":["ai","coeff","df","coefficient","columns","dataframe","notebook","pd.dataframe","regressor.coef","x.columns","аппроксимация","визуализация"],"topics":["pandas"]},{"id":"chunk-94","heading":"формируем DataFrame из двух списков","text":"y pred = regressor.predict(x test) df = pd.DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","df","pred","test","actual","dataframe","notebook","pd.dataframe","predicted","regressor.predict","аппроксимация","визуализация"],"topics":["pandas","testing"]},{"id":"chunk-95","heading":"формируем DataFrame из двух списков","text":"````code Petrol tax Average income Paved Highways Population Driver licence(%) \\ 0 9.0 3571 1976 0.525 1 9.0 4092 1250 0.572 2 9.0 3865 1586 0.580 3 7.5 4870 2351 0.529 4 8.0 4399 431 0.544","keywords":["9.0","ai","0.525","0.529","0.544","0.572","0.580","1250","1586","1976","2351","3571"],"topics":["pandas"]},{"id":"chunk-96","heading":"формируем DataFrame из двух списков","text":"Petrol Consumption 0 541 1 524 2 561 3 414 4 410 y x1 x2 x3 3 0 1 3571.0 1976.0 0.525 541.0 1 2 4092.0 1250.0 0.572 524.0 2 3 3865.0 1586.0 0.580 561.0 3 4 4870.0 2351.0 0.529 414.0 4 3 4399.0 431.0 0.544 410.0 (23, 5) y x1 x2 x3 3 count 23.000000 23.000000 23.000000 23.000000 23.000000 mean 3.565217 4529.913043 5573.652174 0.567957 540.869565 std 1.674029 484.049764 3856.453123 0.058789 112.101539 min 0.000000","keywords":["23.000000","ai","x1","x2","x3","0.000000","0.058789","0.525","0.529","0.544","0.567957","0.572"],"topics":["pandas"]},{"id":"chunk-97","heading":"формируем DataFrame из двух списков","text":"3571.000000 431.000000 0.451000 344.000000 25% 3.000000 4262.500000 2057.000000 0.529500 465.500000 50% 4.000000 4447.000000 5939.000000 0.553000 525.000000 75% 5.000000 4883.500000 8333.000000 0.583000 591.500000 max 6.000000 5342.000000 14186.000000 0.724000 865.000000 Mean Squared Error: 4.749488311367661 ````","keywords":["ai","0.451000","0.529500","0.553000","0.583000","0.724000","14186.000000","2057.000000","25","3.000000","344.000000","3571.000000"],"topics":["pandas"]}]},{"id":"ai/notebook-05-decision-trees","href":"/docs/ai/notebook-05-decision-trees","slug":["ai","notebook-05-decision-trees"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 5 — Деревья решений","description":"Деревья решений и базовые техники интерпретации классификаторов.","preview":"Теоретический материал – Деревья принятия решений Деревья решений являются одним из наиболее эффективных инструментов интеллектуального анализа данных и предсказательной аналитики, которые позволяют решать задачи классификации и регрессии. Перед тем как непоср","keywords":["print","self","def","test","d2","d1","setosa","данных","enter","if","мы","train","virginica","class","init","left","python","warrior"],"topics":["python","ai","knn","pandas","numpy","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"","text":"Деревья решений являются одним из наиболее эффективных инструментов интеллектуального анализа данных и предсказательной аналитики, которые позволяют решать задачи классификации и регрессии.","keywords":["ai","деревья","решений","notebook","анализа","аналитики","базовые","данных","задачи","инструментов","интеллектуального","интерпретации"],"topics":["oop"]},{"id":"chunk-1","heading":"","text":"Перед тем как непосредственно перейти к решению задач с использование данного инструмента рассмотрим общее понятие \"дерево\" в информатике и способы задания деревьев в языке Python.","keywords":["ai","деревья","решений","notebook","python.","базовые","данного","дерево","деревьев","задания","задач","инструмента"],"topics":["python"]},{"id":"chunk-2","heading":"","text":"Деревья принадлежат к числу основных структур данных, используемых в программировании. Древовидная структура является одним из способов представления иерархической структуры в графическом виде. Такое название она получила потому, что граф выглядит как перевернутое дерево. Корень дерева (корневой узел) находится на самом верху, а листья (потомки) — внизу.","keywords":["ai","деревья","решений","notebook","базовые","верху","виде.","внизу.","выглядит","граф","графическом","данных"],"topics":[]},{"id":"chunk-3","heading":"","text":"Деревья широко применяются в компьютерных технологиях. Примером является файловая система, представляющая собой иерархическую структуру из файлов и каталогов.","keywords":["ai","деревья","решений","notebook","базовые","иерархическую","интерпретации","каталогов.","классификаторов.","компьютерных","представляющая","применяются"],"topics":[]},{"id":"chunk-4","heading":"","text":"На рисунке изображены родительские отношения (ребра, ветви дерева) между узлами (вершинами) дерева. На верхнем уровне каждый «родитель» указывает на своих «потомков». То есть в этой иерархической структуре вершина всегда «знает» своих потомков.","keywords":["ai","деревья","решений","своих","notebook","базовые","верхнем","вершина","вершинами","ветви","всегда","дерева"],"topics":[]},{"id":"chunk-5","heading":"","text":"Для того чтобы более точно оперировать структурой Дерево, нужно дать определение некоторым ключевым понятиям:","keywords":["ai","деревья","решений","notebook","базовые","более","дать","дерево","интерпретации","классификаторов.","ключевым","некоторым"],"topics":[]},{"id":"chunk-6","heading":"","text":"− лист, листовой или терминальный узел — конечный узел, то есть не имеющий потомков;","keywords":["ai","деревья","решений","узел","notebook","базовые","имеющий","интерпретации","классификаторов.","конечный","лист","листовой"],"topics":[]},{"id":"chunk-7","heading":"","text":"С корневого узла начинается выполнение большинства операций над деревом. Чтобы получить доступ к любому элементу структуры, необходимо, переходя по ветвям, перебирать элементы, начиная с головы — корневого узла. Корневой узел — это своеобразный вход в дерево. Большинство алгоритмов работы с деревом строятся на том, что каждый узел дерева рассматриваются как корневой узел поддерева, «растущего» из этого узла","keywords":["ai","узел","деревья","корневого","корневой","решений","узла","notebook","алгоритмов","базовые","большинства","большинство"],"topics":["algorithms"]},{"id":"chunk-8","heading":"","text":". Такой подход дает возможность зацикливать выполнение операций при прохождении по элементам дерева. Но в связи с тем, что при прохождении по дереву (в отличие от массива) неизвестно сколько шагов будет в этом цикле, используется другой инструмент — рекурсивный вызов.","keywords":["ai","деревья","при","прохождении","решений","notebook","базовые","будет","возможность","вызов.","выполнение","дает"],"topics":[]},{"id":"chunk-9","heading":"","text":"Двоичное (бинарное) дерево — это древовидная структура данных, где каждый узел имеет не более двух детей. Этих детей называют левым (Л) и правым (П) потомком или «сыном». На рисунке выше дерево является двоичным.","keywords":["ai","дерево","деревья","решений","notebook","базовые","бинарное","более","выше","данных","двоичное","двоичным."],"topics":[]},{"id":"chunk-10","heading":"","text":"В предыдущих разделах мы рассматривали в основном традиционное программирование на Python, когда вся программа разбивается (или не разбивается) на отдельные модули, содержащие функции. Такое программирование соответствует парадигме структурного программирования. Само структурное программирование оказалось колоссальным шагом в построении программ","keywords":["ai","программирование","деревья","разбивается","решений","notebook","python","базовые","вся","интерпретации","классификаторов.","когда"],"topics":["python"]},{"id":"chunk-11","heading":"","text":". Однако еще большим шагом является парадигма объектно ориентированного программирования. В этом подходе программа состоит из отдельных классов, которые объединяют в себе как переменные, называемые полями класса, так и функции, называемые методами класса.","keywords":["ai","деревья","называемые","решений","notebook","базовые","большим","еще","интерпретации","класса","класса.","классификаторов."],"topics":["oop"]},{"id":"chunk-12","heading":"","text":"На самом деле мы уже сталкивались с классами, когда создавали объекты для решения задач классификации и регрессии в Scikit learn. В данном разделе подробнее познакомимся с основами объектно\u0002ориентированного программирования (ООП).","keywords":["ai","деревья","решений","learn.","notebook","scikit","базовые","данном","деле","задач","интерпретации","классами"],"topics":["oop"]},{"id":"chunk-13","heading":"","text":"Рассмотрим на примерах эти понятия. Первое инкапсуляция это объединение в одном объекте данных и программного кода таким образом, что для внешней работы внутренняя часть объекта может быть скрыта от пользователя. Инкапсуляция может быть реализована не только с помощью классов, но и с помощью модулей, но классы позволяют сделать инкапсуляцию естественным путем. Создадим класс в Python","keywords":["ai","быть","деревья","инкапсуляция","может","помощью","решений","notebook","python","базовые","внешней","внутренняя"],"topics":["python","oop"]},{"id":"chunk-14","heading":"","text":". Для этого необходимо определить класс (новый тип данных) и создать объект, называемый экземпляром класса. Мы рекомендуем имена классов начинать с заглавной буквы \"T\", подчеркивая тем самым, что речь идет о типе данных.","keywords":["ai","деревья","решений","notebook","базовые","буквы","данных","данных.","заглавной","идет","имена","интерпретации"],"topics":["oop"]},{"id":"chunk-15","heading":"","text":"class TAnimal: name = \"\" def init (self, name): self.name = name def say(self): print(self.name)","keywords":["ai","name","def","self","self.name","деревья","решений","class","init","notebook","print","say"],"topics":[]},{"id":"chunk-16","heading":"","text":"Теперь создадим экземпляр этого класса. Экземпляр класса представляет собой переменную, с которой можно работать обычным образом.","keywords":["ai","деревья","решений","экземпляр","notebook","базовые","интерпретации","класса","класса.","классификаторов.","которой","можно"],"topics":["oop"]},{"id":"chunk-17","heading":"","text":"Рассмотрим синтаксис Python при создании классов. Все начинается с ключевого слова class. Далее в блоке из отступов мы определяем переменные, которые будем называть полями и функции, которые называются методами. Методы определяются, как обычные функции и могут возвращать значения","keywords":["ai","деревья","которые","решений","функции","class.","notebook","python","базовые","блоке","будем","возвращать"],"topics":["python","oop"]},{"id":"chunk-18","heading":"","text":". Единственное отличие состоит в том, что у всех методов есть обязательный первый параметр, который по традиции всегда называем self в котором передается ссылка на экземпляр класса. Поэтому когда внутри класса метод хочет обратиться к своему полю, то необходимо использовать конструкцию self.name. Заметим, что при вызове методов мы первый параметр не задаем.","keywords":["ai","деревья","методов","параметр","первый","решений","notebook","self","self.name.","базовые","внутри","всегда"],"topics":["oop"]},{"id":"chunk-19","heading":"","text":"Далее, у каждого класса есть метод, с именем init , который называется конструктором класса. Этот метод вызывается в момент создания экземпляра Animal = TAnimal(\"Обезьяна\"). Конструктор может иметь любое количество параметров. Предположим, что теперь нам нужно сделать класс для описания конкретного животного кошки","keywords":["ai","деревья","метод","решений","animal","init","notebook","tanimal","базовые","вызывается","далее","животного"],"topics":["oop"]},{"id":"chunk-20","heading":"","text":". Для это мы используем наследование классов, когда можно определять новые классы, как наследники существующих. При этом новый класс будет иметь все поля и методы наследуемого класса. Вот как это делается:","keywords":["ai","деревья","решений","notebook","базовые","будет","вот","делается","иметь","интерпретации","используем","класс"],"topics":["oop"]},{"id":"chunk-21","heading":"","text":"class TAnimal: name = \"\" def init (self, name): self.name = name def say(self): print(self.name) class TCat(TAnimal): def may(self): print(\"Мяу!\") Cat = TCat(\"Кошка\") Cat.say() Cat.may()","keywords":["ai","def","name","self","class","print","self.name","tanimal","tcat","деревья","решений","cat"],"topics":[]},{"id":"chunk-22","heading":"","text":"Мы видим, что у наследованного класса сохранился конструктор и метод say. В последнем примере мы выдели, что наследный класс, также как и исходный имеет конструктор, который принимает в качестве параметра название животного тогда, что в данном случае излишне. Для решения этой проблемы мы воспользуемся объектно ориентированным механизмом полиморфизмом. Полиморфизм это возможность замены методов при наследовании","keywords":["ai","мы","деревья","конструктор","решений","notebook","say.","базовые","видим","возможность","воспользуемся","выдели"],"topics":["oop"]},{"id":"chunk-23","heading":"","text":". Сделаем так, чтобы не нужно было передавать в конструкторе название \"Кошка\".","keywords":["ai","деревья","решений","notebook","базовые","было","интерпретации","классификаторов.","конструкторе","кошка","название","нужно"],"topics":[]},{"id":"chunk-24","heading":"","text":"class TCat(TAnimal): def init (self): super(). init (\"Кошка\") def may(self): print(\"Мяу!\") Cat = TCat() Cat.say() Cat.may()","keywords":["ai","def","init","self","tcat","деревья","решений","cat","cat.may","cat.say","class","may"],"topics":[]},{"id":"chunk-25","heading":"","text":"Результат выполнения этой программы будет аналогичный, но теперь при использовании этого класса нам не нужно передавать в конструкторе никаких параметров. Полиморфное перекрытие методов делается простым объявлением метода (в данном случае конструктора). При этом нельзя можно менять входные параметры","keywords":["ai","деревья","при","решений","notebook","аналогичный","базовые","будет","входные","выполнения","данном","делается"],"topics":["oop"]},{"id":"chunk-26","heading":"","text":". Если в результате написания кода метода возникает необходимость вызвать перекрытый метод, то для этого необходимо использовать функцию super(), которая по сути просто возвращает ссылку на родительский класс. Самое удивительное в полиморфизме, что изменяя метод, он меняется даже когда на него есть ссылки родительского класса. Рассмотрим еще один пример. Пусть у нас есть класс:","keywords":["ai","деревья","метод","решений","notebook","super","базовые","возвращает","возникает","вызвать","даже","если"],"topics":["oop"]},{"id":"chunk-27","heading":"","text":"class TDo: def Operation(self, x, y): return x + y def Run(self): x = int(input(\"Enter x \")) y = int(input(\"Enter y \")) z = self.Operation(x, y) print(\"Result = \" + z. str ()) Do = TDo() Do.Run()","keywords":["ai","def","enter","input","int","self","tdo","деревья","решений","class","do","do.run"],"topics":["numpy"]},{"id":"chunk-28","heading":"","text":"Необходимо разработать виртуальную модель процесса обучения. В программе должны быть объекты ученики, учитель, кладезь знаний. Потребуется три класса – \"учитель\", \"ученик\", \"данные\". Учитель и ученик во многом похожи, оба – люди. Значит, их классы могут принадлежать одному надклассу \"человек\". Однако в контексте данной задачи у учителя и ученика вряд ли найдутся общие атрибуты","keywords":["ai","учитель","деревья","решений","ученик","notebook","атрибуты","базовые","быть","виртуальную","вряд","данной"],"topics":["oop"]},{"id":"chunk-29","heading":"","text":". Определим, что должны уметь объекты для решения задачи \"увеличить знания\":","keywords":["ai","деревья","решений","notebook","базовые","должны","задачи","знания","интерпретации","классификаторов.","объекты","определим"],"topics":[]},{"id":"chunk-30","heading":"","text":"• Данные могут представлять собой список знаний. Элементы будут извлекаться по индексу.","keywords":["ai","деревья","решений","notebook","базовые","будут","данные","знаний.","извлекаться","индексу.","интерпретации","классификаторов."],"topics":[]},{"id":"chunk-31","heading":"","text":"````python class Data: def init (self, info): self.info = list(info) def getitem (self, i): return self.info[i]","keywords":["ai","def","info","self","self.info","деревья","решений","class","data","getitem","init","list"],"topics":["python"]},{"id":"chunk-32","heading":"","text":"class Pupil: def init (self): self.knowledge = [] def take(self, info): self.knowledge.append(info)","keywords":["ai","def","info","self","деревья","решений","class","init","notebook","pupil","self.knowledge","self.knowledge.append"],"topics":[]},{"id":"chunk-33","heading":"","text":"lesson = Data('class', 'object', 'inheritance', 'polymorphism', 'encapsulation') marIvanna = Teacher() vasy = Pupil() pety = Pupil() marIvanna.teach(lesson[2], vasy, pety) marIvanna.teach(lesson[0], pety) print(vasy.knowledge) print(pety.knowledge) ````","keywords":["ai","lesson","pety","marivanna.teach","print","pupil","vasy","деревья","решений","class","data","encapsulation"],"topics":[]},{"id":"chunk-34","heading":"","text":"Напишите программу по следующему описанию. Есть класс \"Воин\". От него создаются два экземпляра юнита. Каждому устанавливается здоровье в 100 очков. В случайном порядке они бьют друг друга. Тот, кто бьет, здоровья не теряет. У того, кого бьют, оно уменьшается на 20 очков от одного удара. После каждого удара надо выводить сообщение, какой юнит атаковал, и сколько у противника осталось здоровья","keywords":["ai","бьют","деревья","здоровья","от","решений","100","20","notebook","атаковал","базовые","бьет"],"topics":["oop"]},{"id":"chunk-35","heading":"","text":". Как только у кого то заканчивается ресурс здоровья, программа завершается сообщением о том, кто одержал победу.","keywords":["ai","деревья","решений","notebook","базовые","завершается","заканчивается","здоровья","интерпретации","классификаторов.","кого","кто"],"topics":[]},{"id":"chunk-36","heading":"","text":"def hit(self, target, target1): if target.health 0: target.health = 20 if target1 == warrior1: target1 = 'Warrior1' if target1 == warrior2: target1 = 'Warrior2' print(target1, ' has attacked') print(target.health, ' left') if target.health == 0: print(target1, ' has won')","keywords":["target1","if","target.health","ai","print","has","warrior1","warrior2","деревья","решений","20","attacked"],"topics":[]},{"id":"chunk-37","heading":"","text":"warrior1 = Warrior(100) warrior2 = Warrior(100) q = int(input('Enter 1 to attack. Enter 2 to stop program:'))","keywords":["ai","100","enter","warrior","деревья","решений","attack.","input","int","notebook","program","stop"],"topics":["numpy"]},{"id":"chunk-38","heading":"","text":"while q != 2: if q == 1: j = random.randint(1, 2) if j % 2 == 0: warrior1.hit(warrior2, warrior1) q = int(input('Enter 1 to let some warrior attack:')) else: warrior2.hit(warrior1, warrior2) q = int(input('Enter 1 to let some warrior attack:')) else: print('Wrong input.') break ````","keywords":["ai","attack","else","enter","if","input","int","let","some","warrior","warrior1","warrior2"],"topics":["numpy"]},{"id":"chunk-39","heading":"","text":"code Enter 1 to attack. Enter 2 to stop program:1 Warrior2 has attacked 80 left Enter 1 to let some warrior attack:1 Warrior2 has attacked 60 left Enter 1 to let some warrior attack:1 Warrior2 has attacked 40 left Enter 1 to let some warrior attack:1 Warrior2 has attacked 20 left Enter 1 to let some warrior attack:1 Warrior1 has attacked 80 left Enter 1 to let some warrior attack:1 Warrior1 has attacked 60 left Enter","keywords":["enter","attacked","has","left","attack","let","some","warrior","warrior2","ai","60","80"],"topics":[]},{"id":"chunk-40","heading":"","text":"1 to let some warrior attack:1 Warrior1 has attacked 40 left Enter 1 to let some warrior attack:1 Warrior1 has attacked 20 left Enter 1 to let some warrior attack:1 Warrior2 has attacked 0 left Warrior2 has won Enter 1 to let some warrior attack:2","keywords":["attack","has","let","some","warrior","ai","attacked","enter","left","warrior1","warrior2","деревья"],"topics":[]},{"id":"chunk-41","heading":"","text":"Создайте класс по работе с дробями. В классе должна быть реализована следующая функциональность:","keywords":["ai","деревья","решений","notebook","базовые","быть","должна","дробями.","интерпретации","класс","классе","классификаторов."],"topics":["oop"]},{"id":"chunk-42","heading":"","text":"def init (self, n, d): if n == 0: self.num = 0 self.den = 1 else: z = self.sgn(n) self.sgn(d) n = abs(n) d = abs(d) k = self.gcd(n, d) self.num = z n // k self.den = d // k","keywords":["ai","abs","self.den","self.num","self.sgn","деревья","решений","def","else","if","init","notebook"],"topics":[]},{"id":"chunk-43","heading":"","text":"def str (self): if self.num == 0: return '0' else: return str(self.num) + '/' + str(self.den)","keywords":["ai","str","return","self.num","деревья","решений","def","else","if","notebook","self","self.den"],"topics":[]},{"id":"chunk-44","heading":"","text":"def add (self, o): n1 = self.num d1 = self.den if type(o) == int: n2 = o d2 = 1 else: n2 = o.num d2 = o.den n = n1 d2 + n2 d1 d = d1 d2 return Rational(n, d)","keywords":["d2","ai","d1","n2","n1","деревья","решений","add","def","else","if","int"],"topics":[]},{"id":"chunk-45","heading":"","text":"def radd (self, o): n1 = self.num d1 = self.num if type(o) == int: n2 = o d2 = 1 else: n2 = o.num d2 = o.den n = n1 d2 + n2 d1 d = d1 d2 return Rational(n, d)","keywords":["d2","ai","d1","n2","n1","self.num","деревья","решений","def","else","if","int"],"topics":[]},{"id":"chunk-46","heading":"","text":"def sub (self, o): n1 = self.num d1 = self.den n2 = o.num d2 = o.den n = n1 d2 n2 d1 d = d1 d2 return Rational(n, d)","keywords":["ai","d1","d2","n1","n2","деревья","решений","def","notebook","o.den","o.num","rational"],"topics":[]},{"id":"chunk-47","heading":"","text":"def mul (self, o): n1 = self.num d1 = self.den n2 = o.num d2 = o.den n = n1 n2 d = d1 d2 return Rational(n, d)","keywords":["ai","d1","d2","n1","n2","деревья","решений","def","mul","notebook","o.den","o.num"],"topics":[]},{"id":"chunk-48","heading":"","text":"def floordiv (self, o): n1 = self.num d1 = self.den n2 = o.num d2 = o.den n = n1 d2 d = d1 n2 return Rational(n, d)","keywords":["ai","d1","d2","n1","n2","деревья","решений","def","floordiv","notebook","o.den","o.num"],"topics":[]},{"id":"chunk-49","heading":"","text":"d1 = Rational(1, 2) d2 = Rational(1, 3) d3 = d1 + d2 print(d3) d4 = d1 d2 print(d4) d5 = d1 d2 print(d5) d6 = d1 d2 print(d6) d7 = d1 // d2 print(d7) d8 = 6 + d1 print(d8) ````","keywords":["d1","d2","print","ai","d3","d4","d5","d6","d7","d8","rational","деревья"],"topics":[]},{"id":"chunk-50","heading":"","text":"Создайте класс по работе с тригонометрическими функциями. В классе должны быть реализованы функции вычисления:","keywords":["ai","деревья","решений","notebook","базовые","быть","вычисления","должны","интерпретации","класс","классе","классификаторов."],"topics":["oop"]},{"id":"chunk-51","heading":"","text":"Любое представление графов, естественно, можно использовать для представления деревьев, потому что деревья — это особый вид графов. Однако, деревья играют свою большую роль в алгоритмах, и для них разработано много соответствующих структур и методов","keywords":["деревья","ai","решений","notebook","алгоритмах","базовые","большую","вид","графов","графов.","деревьев","естественно"],"topics":["algorithms"]},{"id":"chunk-52","heading":"","text":". Большинство алгоритмов на деревьях (например, поиск по деревьям) можно рассматривать в терминах теории графов, но специальные структуры данных делают их проще в реализации.","keywords":["ai","деревья","решений","notebook","алгоритмов","базовые","большинство","графов","данных","делают","деревьям","деревьях"],"topics":["algorithms"]},{"id":"chunk-53","heading":"","text":"Проще всего описать представление дерева с корнем, в котором ребра спускаются вниз от корня. Такие деревья часто отображают иерархическое ветвление данных, где корень отображает все объекты (которые, возможно, хранятся в листьях), а каждый внутренний узел показывает объекты, содержащиеся в дереве, корень которого — этот узел","keywords":["ai","деревья","корень","объекты","решений","узел","notebook","базовые","ветвление","вниз","внутренний","возможно"],"topics":[]},{"id":"chunk-54","heading":"","text":". Это описание можно использовать, представив каждое поддерево списком, содержащим все его поддеревья потомки. Рассмотрим простое дерево, показанное на рисунке ниже.","keywords":["ai","деревья","решений","notebook","базовые","дерево","его","интерпретации","использовать","каждое","классификаторов.","можно"],"topics":[]},{"id":"chunk-55","heading":"","text":"Каждый список в сущности является списком потомков каждого из внутренних узлов. Во втором примере мы обращаемся к третьему потомку корня, затем ко второму его потомку и в конце концов — к первому потомку предыдущего узла (этот путь отмечен на рисунке). В ряде случаев возможно заранее определить максимальное число потомков каждого узла. (Например, каждый узел бинарного дерева может иметь до двух потомков)","keywords":["ai","потомков","потомку","деревья","каждого","каждый","решений","notebook","базовые","бинарного","внутренних","возможно"],"topics":[]},{"id":"chunk-56","heading":"","text":". Поэтому можно использовать другие представления, скажем, объекты с отдельным атрибутом для каждого из потомков как в листинге ниже.","keywords":["ai","деревья","решений","notebook","атрибутом","базовые","другие","интерпретации","использовать","каждого","классификаторов.","листинге"],"topics":[]},{"id":"chunk-57","heading":"","text":"Определите класс бинарного дерева и задайте его объекты с отдельным атрибутом для каждого из потомков.","keywords":["ai","деревья","решений","notebook","атрибутом","базовые","бинарного","дерева","его","задайте","интерпретации","каждого"],"topics":["oop"]},{"id":"chunk-58","heading":"","text":"````python class Tree: def init (self, left, right): self.left = left self.right = right","keywords":["ai","left","right","деревья","решений","class","def","init","notebook","python","self","self.left"],"topics":["python"]},{"id":"chunk-59","heading":"","text":"Для обозначения отсутствующих потомков можно использовать None (в случае если у узла только один потомок). Само собой, можно комбинировать разные методы (например, использовать списки или множества потомков для каждого узла).","keywords":["ai","деревья","использовать","можно","потомков","решений","узла","none","notebook","базовые","если","интерпретации"],"topics":[]},{"id":"chunk-60","heading":"","text":"Распространенный способ реализации деревьев, особенно на языках, не имеющих встроенной поддержки списков, это так называемое представление «первый потомок, следующий брат». В нем каждый узел имеет два «указателя» или атрибута, указывающих на другие узлы, как в бинарном дереве. Однако, первый из этих атрибутов ссылается на первого потомка узла, а второй — на его следующего брата (т.е","keywords":["ai","деревья","первый","решений","notebook","атрибута","атрибутов","базовые","бинарном","брат","брата","встроенной"],"topics":[]},{"id":"chunk-61","heading":"","text":". узел, имеющий того же родителя, но находящийся правее, — прим. перев). Иными словами, каждый узел дерева имеет указатель на связанный список его потомков, а каждый из этих потомков ссылается на свой собственный аналогичный список. Таким образом, небольшая модификация бинарного дерева даст нам многопутевое дерево, показанное в листинге ниже.","keywords":["ai","дерева","деревья","каждый","потомков","решений","узел","notebook","аналогичный","базовые","бинарного","даст"],"topics":[]},{"id":"chunk-62","heading":"","text":"````python class Tree: def init (self, kids, next=None): self.kids = self.val = kids self.next = next","keywords":["ai","kids","next","деревья","решений","class","def","init","none","notebook","python","self"],"topics":["python"]},{"id":"chunk-63","heading":"","text":"Представьте дерево показанное на рисунке с использованием списка из списков. Выведите на печать корень дерева, а также его левое и правое поддеревья.","keywords":["ai","деревья","решений","notebook","базовые","выведите","дерева","дерево","его","интерпретации","использованием","классификаторов."],"topics":[]},{"id":"chunk-64","heading":"","text":"````python class Tree: def init (self, kids, next=None): self.kids = self.val = kids self.next = next","keywords":["ai","kids","next","деревья","решений","class","def","init","none","notebook","python","self"],"topics":["python"]},{"id":"chunk-65","heading":"","text":"print('left subtree = ', end='') print(t.kids.next.val, end=', ') print(t.kids.next.next.val, end=', ') print(t.kids.next.next.next)","keywords":["print","ai","end","деревья","решений","left","notebook","subtree","t.kids.next.next.next","t.kids.next.next.val","t.kids.next.val","базовые"],"topics":[]},{"id":"chunk-66","heading":"","text":"print('right subtree = ', end='') print(t.next.kids, end=', ') print(t.next.next.kids) ````","keywords":["ai","print","end","деревья","решений","notebook","right","subtree","t.next.kids","t.next.next.kids","базовые","интерпретации"],"topics":[]},{"id":"chunk-67","heading":"","text":"class Tree: def init (self, data): self.left = None self.right = None self.data = data def PrintTree(self): print(self.data)","keywords":["ai","data","def","none","self","self.data","деревья","решений","class","init","notebook","print"],"topics":[]},{"id":"chunk-68","heading":"","text":"Реализуйте в классе функцию для вставки нового элемента в дерево по следующим правилам:","keywords":["ai","деревья","решений","notebook","базовые","вставки","дерево","интерпретации","классе","классификаторов.","нового","правилам"],"topics":["oop"]},{"id":"chunk-69","heading":"","text":"• Левое поддерево узла содержит только узлы со значениями меньше, чем значение в узле.","keywords":["ai","деревья","решений","notebook","базовые","значение","значениями","интерпретации","классификаторов.","левое","меньше","поддерево"],"topics":[]},{"id":"chunk-70","heading":"","text":"• Правое поддерево узла содержит только узлы со значениями меньше, чем значение в узле.","keywords":["ai","деревья","решений","notebook","базовые","значение","значениями","интерпретации","классификаторов.","меньше","поддерево","правое"],"topics":[]},{"id":"chunk-71","heading":"","text":"• Каждое из левого и правого поддеревьев также должно быть бинарным деревом поиска.","keywords":["ai","деревья","решений","notebook","базовые","бинарным","быть","деревом","должно","интерпретации","каждое","классификаторов."],"topics":[]},{"id":"chunk-72","heading":"","text":"Метод вставки сравнивает значение узла с родительским узлом и решает куда доваить элемент (в левое или правое поддерево). Перепишите, метод PrintTree для печати полной версии дерева.","keywords":["ai","деревья","метод","решений","notebook","printtree","базовые","версии","вставки","дерева.","доваить","значение"],"topics":[]},{"id":"chunk-73","heading":"","text":"````python class Tree: def init (self, data): self.left = None self.right = None self.data = data","keywords":["ai","data","none","деревья","решений","class","def","init","notebook","python","self","self.data"],"topics":["python"]},{"id":"chunk-74","heading":"","text":"def PrintTree(self): if self.left: self.left.PrintTree() print(self.data) if self.right: self.right.PrintTree()","keywords":["ai","if","деревья","решений","def","notebook","print","printtree","self","self.data","self.left","self.left.printtree"],"topics":[]},{"id":"chunk-75","heading":"","text":"def AddItem(self, item): if self.data: if item self.data: if self.right is None: self.right = Tree(item) else: self.right.AddItem(item) else: self.data = item","keywords":["item","ai","if","self.data","else","self.right","деревья","решений","additem","def","none","notebook"],"topics":[]},{"id":"chunk-76","heading":"","text":"tree = Tree(5) tree.AddItem(1) tree.AddItem(2) tree.AddItem(6) tree.AddItem(7) tree.PrintTree() ````","keywords":["tree.additem","ai","tree","деревья","решений","notebook","tree.printtree","базовые","интерпретации","классификаторов.","техники"],"topics":[]},{"id":"chunk-77","heading":"","text":"Дерево решений – это один из наиболее часто и широко используемых алгоритмов контролируемого машинного обучения, который может выполнять как регрессионные, так и классификационные задачи. Использование деревьев решений для прогнозного анализа имеет ряд преимуществ:","keywords":["решений","ai","деревья","notebook","алгоритмов","анализа","базовые","выполнять","дерево","деревьев","задачи.","имеет"],"topics":["oop","algorithms"]},{"id":"chunk-78","heading":"","text":"1. Деревья решений могут быть использованы для прогнозирования как непрерывных, так и дискретных значений, т. е. они хорошо работают как для задач регрессии, так и для задач классификации.","keywords":["ai","деревья","решений","задач","так","1.","notebook","базовые","быть","дискретных","е.","значений"],"topics":["oop"]},{"id":"chunk-79","heading":"","text":"4. Они очень быстры и эффективны по сравнению с KNN и другими алгоритмами классификации.","keywords":["ai","деревья","решений","4.","knn","notebook","алгоритмами","базовые","быстры","другими","интерпретации","классификаторов."],"topics":["knn","oop","algorithms"]},{"id":"chunk-80","heading":"","text":"Решим модельные примеры классификации и регрессии, разобранные в предыдущих раочих тетрадях, но с использованием деревьев принятия решений.","keywords":["ai","деревья","решений","notebook","базовые","деревьев","интерпретации","использованием","классификаторов.","классификации","модельные","предыдущих"],"topics":["oop"]},{"id":"chunk-81","heading":"","text":"Построим дерево решений для задачи классификации, для этого, построим границу решения для каждого класса. В качестве данных будем использовать уже знакомый нам и встроенный в библиотеку sklearn набор данных ирисов Фишера. Импортируем библиотеки, набор данных и посмотрим его характеристики.","keywords":["ai","данных","решений","деревья","набор","построим","notebook","sklearn","базовые","библиотеки","библиотеку","будем"],"topics":["oop"]},{"id":"chunk-82","heading":"","text":"code sepal length sepal width petal length petal width species 0 5.1 3.5 1.4 0.2 setosa 1 4.9 3.0 1.4 0.2 setosa 2 4.7 3.2 1.3 0.2 setosa 3 4.6 3.1 1.5 0.2 setosa 4 5.0 3.6 1.4 0.2 setosa","keywords":["0.2","setosa","1.4","ai","length","petal","sepal","width","деревья","решений","1.3","1.5"],"topics":[]},{"id":"chunk-83","heading":"","text":"Далее, разделим наши данные на атрибуты и метки, а затем выделим в общей совокупности полученных данных обучающие и тестовые наборы. Таким образом, мы можем обучить наш алгоритм на одном наборе данных, а затем протестировать его на совершенно на другом наборе, который алгоритм еще не видел. Это дает вам более точное представление о том, как на самом деле будет работать ваш обученный алгоритм.","keywords":["ai","алгоритм","данных","деревья","затем","наборе","решений","notebook","алгоритм.","атрибуты","базовые","более"],"topics":["algorithms"]},{"id":"chunk-84","heading":"","text":"x train, x test, y train, y test = train test split( dataset.iloc[:, : 1], dataset.iloc[:, 1], test size = 0.20 )","keywords":["test","ai","train","dataset.iloc","деревья","решений","0.20","notebook","size","split","базовые","интерпретации"],"topics":["ai","testing"]},{"id":"chunk-85","heading":"","text":"x train.shape, x test.shape, y train.shape, y test.shape x train.head() y train.head() ````","keywords":["ai","test.shape","train.head","train.shape","деревья","решений","notebook","базовые","интерпретации","классификаторов.","техники"],"topics":["ai","testing"]},{"id":"chunk-86","heading":"","text":"code 49 setosa 58 versicolor 68 versicolor 35 setosa 122 virginica Name: species, dtype: object","keywords":["ai","setosa","versicolor","деревья","решений","122","35","49","58","68","code","dtype"],"topics":[]},{"id":"chunk-87","heading":"","text":"После того, как данные были разделены на обучающие и тестовые наборы, последний шаг состоит в том, чтобы обучить алгоритм дерева решений на этих данных и сделать прогнозы. Scikit Learn содержит библиотеку tree , которая содержит встроенные классы/методы для различных алгоритмов дерева решений","keywords":["решений","ai","дерева","деревья","содержит","learn","notebook","scikit","tree","алгоритм","алгоритмов","базовые"],"topics":["oop","algorithms"]},{"id":"chunk-88","heading":"","text":". Поскольку мы собираемся выполнить здесь задачу классификации, мы будем использовать класс DecisionTreeClassifier для этого примера. Метод fit этого класса вызывается для обучения алгоритма на обучающих данных, которые передаются в качестве параметра методу fit . Выполним следующий сценарий для обучения алгоритма.","keywords":["ai","fit","деревья","мы","обучения","решений","этого","decisiontreeclassifier","notebook","алгоритма","алгоритма.","базовые"],"topics":["oop","algorithms"]},{"id":"chunk-89","heading":"","text":"````python from sklearn.tree import DecisionTreeClassifier classifier = DecisionTreeClassifier() classifier.fit(x train, y train)","keywords":["ai","decisiontreeclassifier","train","деревья","решений","classifier","classifier.fit","import","notebook","python","sklearn.tree","базовые"],"topics":["python","ai"]},{"id":"chunk-90","heading":"","text":"Теперь, когда наш классификатор обучен, давайте сделаем прогнозы по тестовым данным. Для составления прогнозов используется метод predict класса Decision Tree Classifier. Взгляните на следующий код для использования.","keywords":["ai","деревья","решений","classifier.","decision","notebook","predict","tree","базовые","взгляните","давайте","данным."],"topics":["oop"]},{"id":"chunk-91","heading":"","text":"code array(['virginica', 'setosa', 'virginica', 'virginica', 'setosa', 'versicolor', 'virginica', 'versicolor', 'setosa', 'versicolor', 'virginica', 'virginica', 'versicolor', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'setosa', 'setosa', 'virginica', 'versicolor', 'setosa', 'versicolor'], dtype=object)","keywords":["virginica","setosa","versicolor","ai","деревья","решений","array","code","dtype","notebook","object","базовые"],"topics":[]},{"id":"chunk-92","heading":"","text":"На данный момент мы обучили наш алгоритм и сделали некоторые прогнозы. Теперь посмотрим, насколько точен наш алгоритм. Для задач классификации обычно используются такие метрики, как матрица путаницы, точность. Библиотека Scikit Learn metrics содержит методы classification report и confusion matrix, которые могут быть использованы для расчета этих метрик.","keywords":["ai","деревья","наш","решений","classification","confusion","learn","matrix","metrics","notebook","report","scikit"],"topics":["oop","algorithms"]},{"id":"chunk-93","heading":"","text":"python from sklearn.metrics import classification report, confusion matrix print(confusion matrix(y test, y pred)) print(classification report(y test, y pred))","keywords":["ai","classification","confusion","matrix","pred","print","report","test","деревья","решений","import","notebook"],"topics":["python","testing"]},{"id":"chunk-94","heading":"","text":"setosa 1.00 1.00 1.00 11 versicolor 1.00 0.78 0.88 9 virginica 0.83 1.00 0.91 10","keywords":["1.00","ai","деревья","решений","0.78","0.83","0.88","0.91","10","11","notebook","setosa"],"topics":[]},{"id":"chunk-95","heading":"","text":"accuracy 0.93 30 macro avg 0.94 0.93 0.93 30 weighted avg 0.94 0.93 0.93 30 ````","keywords":["0.93","30","ai","0.94","avg","деревья","решений","accuracy","macro","notebook","weighted","базовые"],"topics":[]},{"id":"chunk-96","heading":"","text":"Из матрицы оценок алгоритма вы можете видеть, что из 30 тестовых экземпляров наш алгоритм неправильно классифицировал только 3. Это приблизительно 91 % точности.","keywords":["ai","деревья","решений","3.","30","91","notebook","алгоритм","алгоритма","базовые","видеть","вы"],"topics":["oop","algorithms"]},{"id":"chunk-97","heading":"","text":"данные x = np.array([[ 1, 1], [ 2, 1], [ 3, 2], [1, 1], [2, 1], [3, 2]]) target = [0, 0, 0, 1, 1, 1]","keywords":["ai","деревья","решений","notebook","np.array","target","базовые","данные","интерпретации","классификаторов.","техники"],"topics":["numpy"]},{"id":"chunk-98","heading":"","text":"````python from sklearn.metrics import classification report, confusion matrix x = np.array([[ 1, 1], [ 2, 1], [ 3, 2], [1, 1], [2, 1], [3, 2]]) target = [0, 0, 0, 1, 1, 1]","keywords":["ai","деревья","решений","classification","confusion","import","matrix","notebook","np.array","python","report","sklearn.metrics"],"topics":["python","numpy"]},{"id":"chunk-99","heading":"","text":"x train, x test, y train, y test = train test split( dataset.iloc[:, : 1], dataset.iloc[:, 1], test size=0.20 )","keywords":["test","ai","train","dataset.iloc","деревья","решений","0.20","notebook","size","split","базовые","интерпретации"],"topics":["ai","testing"]},{"id":"chunk-100","heading":"","text":"x train.shape, x test.shape, y train.shape, y test.shape x train.head() y train.head()","keywords":["ai","test.shape","train.head","train.shape","деревья","решений","notebook","базовые","интерпретации","классификаторов.","техники"],"topics":["ai","testing"]},{"id":"chunk-101","heading":"","text":"print(confusion matrix(y test, y pred)) print(classification report(y test, y pred, zero division=0)) ````","keywords":["ai","pred","print","test","деревья","решений","classification","confusion","division","matrix","notebook","report"],"topics":["testing"]},{"id":"chunk-102","heading":"","text":"Процесс решения регрессионной задачи с деревом решений с помощью Scikit Learn очень похож на процесс классификации. Однако для регрессии мы используем класс DecisionTreeRegressor древовидной библиотеки. Кроме того, оценочные показатели регрессии отличаются от показателей классификации. В остальном процесс почти такой же. Построим регрессию с использованием дерева решений в Python и библиотеки scikit learn","keywords":["решений","ai","процесс","learn","scikit","деревья","классификации.","регрессии","decisiontreeregressor","notebook","python","базовые"],"topics":["python","oop"]},{"id":"chunk-103","heading":"","text":". В качестве исходного набора данных будем использовать зависимость заработной платы от опыта работы из предыдущей тетради:","keywords":["ai","деревья","решений","notebook","базовые","будем","данных","зависимость","заработной","интерпретации","использовать","исходного"],"topics":[]},{"id":"chunk-104","heading":"","text":"https://raw.githubusercontent.com/AnnaShestova/salary years simple\u0002linear regression/master/Salary Data.csv","keywords":["ai","salary","деревья","решений","annashestova","data.csv","https","linear","master","notebook","raw.githubusercontent.com","regression"],"topics":["python","regression"]},{"id":"chunk-105","heading":"","text":"url = r'https://raw.githubusercontent.com/AnnaShestova/salary years simple linear regression/master/Salary Data.csv' dataset = pd.read csv(url) print(dataset.head())","keywords":["ai","salary","url","деревья","решений","annashestova","csv","data.csv","dataset","dataset.head","https","linear"],"topics":["python","regression"]},{"id":"chunk-106","heading":"","text":"plt.scatter(dataset['YearsExperience'], dataset['Salary'], color='b', label='Заработная плата') plt.xlabel('Опыт(лет)') plt.ylabel('Заработная плата') plt.show()","keywords":["ai","dataset","деревья","заработная","плата","решений","color","label","notebook","plt.scatter","plt.show","plt.xlabel"],"topics":[]},{"id":"chunk-107","heading":"","text":"from sklearn.tree import DecisionTreeRegressor x = dataset.iloc[:, : 1].values y = dataset.iloc[:, 1].values print(x) print(y)","keywords":["ai",".values","dataset.iloc","print","деревья","решений","decisiontreeregressor","import","notebook","sklearn.tree","базовые","интерпретации"],"topics":[]},{"id":"chunk-108","heading":"","text":"x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0)","keywords":["test","ai","train","деревья","решений","0.2","notebook","random","size","split","state","базовые"],"topics":["ai","testing"]},{"id":"chunk-109","heading":"","text":"from sklearn import metrics print('Mean Squared Error:', metrics.mean squared error(y test, y pred)) print('Mean Absolute Error:', metrics.mean absolute error(y test, y pred))","keywords":["error","ai","absolute","mean","metrics.mean","pred","print","squared","test","деревья","решений","import"],"topics":["testing"]},{"id":"chunk-110","heading":"","text":"Средняя абсолютная ошибка для нашего алгоритма составляет 4120.66, что составляет менее 6 процентов от среднего значения всех значений в столбце.","keywords":["ai","деревья","решений","составляет","4120.66","notebook","абсолютная","алгоритма","базовые","всех","значений","значения"],"topics":["algorithms"]},{"id":"chunk-111","heading":"","text":"Постройте модель регрессии для данных из предыдущей рабочей тетради.Для примера можно взять потребления газа (в миллионах галлонов) в 48 штатах США или набор данных о качестве красного вина: https://raw.githubusercontent.com/likarajo/petrol consumption/master/data/petrol consumption.csv https://raw.githubusercontent.com/aniruddhachoudhury/Red WineQuality/master/winequality red.csv","keywords":["ai","https","master","petrol","raw.githubusercontent.com","winequality","данных","деревья","решений","48","aniruddhachoudhury","consumption"],"topics":["python"]},{"id":"chunk-112","heading":"","text":"````python from sklearn.tree import DecisionTreeRegressor from sklearn import tree from sklearn import metrics","keywords":["ai","import","sklearn","деревья","решений","decisiontreeregressor","metrics","notebook","python","sklearn.tree","tree","базовые"],"topics":["python"]},{"id":"chunk-113","heading":"","text":"url = r'https://raw.githubusercontent.com/likarajo/petrol consumption/master/data/petrol consumption.csv' dataset = pd.read csv(url) print(dataset.head())","keywords":["ai","petrol","url","деревья","решений","consumption","consumption.csv","csv","data","dataset","dataset.head","https"],"topics":["python"]},{"id":"chunk-114","heading":"","text":"plt.scatter(dataset['Petrol tax'],dataset['Average income'],color='b',label='Average income') plt.xlabel('Average income') plt.ylabel('Petrol tax') plt.show()","keywords":["ai","average","income","dataset","petrol","tax","деревья","решений","color","label","notebook","plt.scatter"],"topics":[]},{"id":"chunk-115","heading":"","text":"x train,x test,y train,y test = train test split(x,y,test size=0.2,random state=0)","keywords":["test","ai","train","деревья","решений","0.2","notebook","random","size","split","state","базовые"],"topics":["ai","testing"]},{"id":"chunk-116","heading":"","text":"tree.plot tree(regressor) y pred = regressor.predict(x test) y pred df = pd.DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","pred","df","test","деревья","решений","actual","notebook","pd.dataframe","predicted","regressor","regressor.predict"],"topics":["pandas","testing"]},{"id":"chunk-117","heading":"","text":"print('Squared ', metrics.mean squared error(y test, y pred)) print('Absolute', metrics.mean absolute error(y test, y pred)) metrics.mean absolute error(y test, y pred) / np.average(y) 100 ````","keywords":["absolute","ai","error","metrics.mean","pred","test","print","squared","деревья","решений","100","notebook"],"topics":["numpy","testing"]}]},{"id":"ai/notebook-06-genetic-and-annealing","href":"/docs/ai/notebook-06-genetic-and-annealing","slug":["ai","notebook-06-genetic-and-annealing"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 6 — Генетические и эволюционные методы","description":"Оптимизация: эволюционные стратегии и имитация отжига.","preview":"Теоретический материал – Эволюционные методы Эволюционные методы Эволюционные методы относятся к числу эффективных средств решения задач оптимизации и структурного синтеза проектных решений. Они основаны на использовании принципов оптимального приспособления о","keywords":["def","distances","item","bestroute","if","return","distance","range","arrv","decrement","else","news","sumlength","отжига","max","длины","len","newv"],"topics":["python","ai","oop","algorithms"],"chunks":[{"id":"chunk-0","heading":"","text":"Эволюционные методы относятся к числу эффективных средств решения задач оптимизации и структурного синтеза проектных решений. Они основаны на использовании принципов оптимального приспособления организмов в живой природе к условиям окружающей среды. К числу эволюционных относятся методы генетические, колонии муравьев, поведения толпы. Наиболее развиты и востребованы в настоящее время генетические алгоритмы","keywords":["ai","генетические","методы","эволюционные","относятся","числу","notebook","алгоритмы","востребованы","время","живой","задач"],"topics":["algorithms"]},{"id":"chunk-1","heading":"","text":". По мере развития техники и технологий растет доля сложных задач проектирования и управления, для решения которых применение традиционных методов проблематично. Поэтому все большее внимание уделяется применению методов искусственного интеллекта. Генетические алгоритмы Для применения ГА необходимо:","keywords":["ai","генетические","методов","эволюционные","notebook","алгоритмы","большее","внимание","га","доля","задач","имитация"],"topics":["algorithms"]},{"id":"chunk-2","heading":"","text":"1. выделить совокупность свойств объекта, характеризуемых внутренними параметрами и влияющих на его полезность, т.е. выделить множество управляемых параметровX=(x 1,x 2,…,x n) среди x i могут быть величины различных типов (real, integer, Boolean, enumeration). Наличие нечисловых величин (enumeration) обусловливает возможность решения задач не только параметрической, но и структурной оптимизации;","keywords":["ai","enumeration","выделить","эволюционные","1.","boolean","integer","notebook","real","быть","величин","величины"],"topics":[]},{"id":"chunk-3","heading":"","text":"2. сформулировать количественную оценку полезности вариантов объекта — функцию полезности F. Если в исходном виде задача многокритериальна, то такая формулировка означает выбор скалярного (обобщенного) критерия;","keywords":["ai","полезности","эволюционные","2.","f.","notebook","вариантов","виде","выбор","генетические","если","задача"],"topics":[]},{"id":"chunk-4","heading":"","text":"code for (k=0; k<G; k++) { for (j=0; j<N; j++) { Выбор родительской пары хромосом; Кроссовер; Мутации; Оценка функции полезности F потомков; Селекция; } Замена текущего поколения новым; }","keywords":["ai","эволюционные","code","j++","k++","notebook","выбор","генетические","замена","имитация","кроссовер","методы"],"topics":[]},{"id":"chunk-5","heading":"","text":"Пусть дана начальная популяция из четырех хромосом с двумя генами x и y. Показатель качества хромосомы оценивается функцией Z. При равном качестве хромосом предпочтение отдается хромосоме с большим номером. На каждом этапе хромосома a с высшим качеством порождает четыре новых хромосомы 𝑏1, 𝑐1, 𝑏2, 𝑐2, обмениваясь генами с двумя хромосомами b и c более низкого качества по указанной схеме:","keywords":["ai","генами","двумя","качества","хромосом","хромосомы","эволюционные","𝑏1","𝑏2","𝑐1","𝑐2","notebook"],"topics":[]},{"id":"chunk-6","heading":"","text":"Последняя хромоcома (с низшим качеством) выбывает из популяции. Найти максимальный показатель качества хромосомы в популяции и общее качество популяции после четырех этапов эволюции.","keywords":["ai","популяции","эволюционные","notebook","выбывает","генетические","имитация","качества","качество","качеством","максимальный","методы"],"topics":[]},{"id":"chunk-7","heading":"","text":"````python функция качества хромосомы def qZ(x, y): return (x 3 y + 1) / (3 x 2 + 3 y 2 + 1) сумма качества хромосом def qSumZ(Z): return sum(Z)","keywords":["ai","def","return","качества","эволюционные","notebook","python","qsumz","qz","sum","генетические","имитация"],"topics":["python"]},{"id":"chunk-8","heading":"","text":"def exchangeScheme(oldX, oldY, sortedId): x = [0 for i in range(4)] y = [0 for i in range(4)]","keywords":["ai","range","эволюционные","def","exchangescheme","notebook","oldx","oldy","sortedid","генетические","имитация","методы"],"topics":[]},{"id":"chunk-9","heading":"","text":"def sorting(Z): sortedId = sorted(range(len(Z)), key=lambda k: Z[k]) return sortedId","keywords":["ai","sortedid","эволюционные","def","key","lambda","len","notebook","range","return","sorted","sorting"],"topics":[]},{"id":"chunk-10","heading":"","text":"шаг эволюции def evoStep(x, y, z): , minId = min((value, id) for (id, value) in enumerate(z)) x = x[:] y = y[:] z = z[:]","keywords":["ai","id","value","эволюционные","def","enumerate","evostep","min","minid","notebook","генетические","имитация"],"topics":[]},{"id":"chunk-11","heading":"","text":"шаги эволюции (конечная функция), по умолчанию 4 шага def evoSteps(x, y, steps num=4): results = []","keywords":["ai","эволюционные","def","evosteps","notebook","num","results","steps","генетические","имитация","конечная","методы"],"topics":[]},{"id":"chunk-12","heading":"","text":"qualityArrZ = [] for i in range(len(results[2])): qualityArrZ += results[2][i][3]","keywords":["ai","qualityarrz","results","эволюционные","len","notebook","range","генетические","имитация","методы","оптимизация","отжига."],"topics":[]},{"id":"chunk-13","heading":"","text":"Выполните по вариантам соответственно реализацию генетического алгоритма в соответствии с приложенными начальными данными.","keywords":["ai","эволюционные","notebook","алгоритма","вариантам","выполните","генетические","генетического","данными.","имитация","методы","начальными"],"topics":["algorithms"]},{"id":"chunk-14","heading":"","text":"````python функция качества хромосомы def qZ(x, y): return (x 3 y + 1) / (3 x 2 + y 2 + 1) сумма качества хромосом def qSumZ(Z): return sum(Z)","keywords":["ai","def","return","качества","эволюционные","notebook","python","qsumz","qz","sum","генетические","имитация"],"topics":["python"]},{"id":"chunk-15","heading":"","text":"def exchangeScheme(oldX, oldY, sortedId): x = [0 for i in range(4)] y = [0 for i in range(4)]","keywords":["ai","range","эволюционные","def","exchangescheme","notebook","oldx","oldy","sortedid","генетические","имитация","методы"],"topics":[]},{"id":"chunk-16","heading":"","text":"def sorting(Z): sortedId = sorted(range(len(Z)), key=lambda k: Z[k]) return sortedId","keywords":["ai","sortedid","эволюционные","def","key","lambda","len","notebook","range","return","sorted","sorting"],"topics":[]},{"id":"chunk-17","heading":"","text":"шаг эволюции def evoStep(x, y, z): , minId = min((value, id) for (id, value) in enumerate(z)) x = x[:] y = y[:] z = z[:]","keywords":["ai","id","value","эволюционные","def","enumerate","evostep","min","minid","notebook","генетические","имитация"],"topics":[]},{"id":"chunk-18","heading":"","text":"шаги эволюции (конечная функция), по умолчанию 4 шага def evoSteps(x, y, steps num=4): results = []","keywords":["ai","эволюционные","def","evosteps","notebook","num","results","steps","генетические","имитация","конечная","методы"],"topics":[]},{"id":"chunk-19","heading":"","text":"qualityArrZ = [] for i in range(len(results[2])): qualityArrZ += results[2][i][3]","keywords":["ai","qualityarrz","results","эволюционные","len","notebook","range","генетические","имитация","методы","оптимизация","отжига."],"topics":[]},{"id":"chunk-20","heading":"","text":"code max 1 step: 2.2941176470588234 max 2 step: 0.9714285714285713 max 3 step: 4.823529411764706 max 4 step: 5.828571428571428 max Z: 2.0","keywords":["max","step","ai","эволюционные","0.9714285714285713","2.0","2.2941176470588234","4.823529411764706","5.828571428571428","code","notebook","генетические"],"topics":[]},{"id":"chunk-21","heading":"","text":"Алгоритм отжига – это метод оптимизации, который называется отжигом, или симуляцией восстановления (Simulated annealing). Как ясно из названия, метод поиска моделирует процесс восстановления. Восстановление – это физический процесс, который заключается в нагреве и последующем контролируемом охлаждении субстанции","keywords":["ai","который","метод","процесс","эволюционные","annealing","notebook","simulated","алгоритм","восстановление","восстановления","восстановления."],"topics":["algorithms"]},{"id":"chunk-22","heading":"","text":". В результате получается прочная кристаллическая структура, которая отличается от структуры с дефектами, образующейся при быстром беспорядочном охлаждении. Структура здесь представляет собой кодированное решение, а температура используется для того, чтобы указать, как и когда будут приниматься новые решения.","keywords":["ai","структура","эволюционные","notebook","беспорядочном","будут","быстром","генетические","дефектами","здесь","имитация","используется"],"topics":[]},{"id":"chunk-23","heading":"","text":"Метод отжига может быть эффективным при решении задач различных классов, требующих оптимизации. Ниже приводится их краткий список:","keywords":["ai","эволюционные","notebook","быть","генетические","задач","имитация","их","классов","краткий","метод","методы"],"topics":["oop"]},{"id":"chunk-24","heading":"","text":"Поскольку метод отжига представляет собой процесс генерации случайных чисел, поиск решения с использованием данного алгоритма может занять значительное время. В некоторых случаях алгоритм вообще не находит решение или выбирает не самое оптимальное. Алгоритм отжига как способ выполнения процедур поиска и оптимизации","keywords":["ai","алгоритм","отжига","эволюционные","notebook","алгоритма","вообще","время.","выбирает","выполнения","генерации","генетические"],"topics":["algorithms"]},{"id":"chunk-25","heading":"","text":". Данный метод является аналогом процесса нагревания тела до состояния плавления с последующим постепенным охлаждением. При высоких температурах поиск ведется по всему диапазону. При снижении температуры диапазон поиска уменьшается до небольшой области вокруг текущего решения.","keywords":["ai","до","при","эволюционные","notebook","аналогом","ведется","вокруг","всему","высоких","генетические","данный"],"topics":[]},{"id":"chunk-26","heading":"","text":"Рассмотрим решение задачи поиска оптимального маршрута на графе методом имитации отжига Для этого, представим формальную постановку задачи и рассмотрим пример, который иллюстрирует алгоритм решения.","keywords":["ai","задачи","рассмотрим","эволюционные","notebook","алгоритм","генетические","графе","иллюстрирует","имитации","имитация","который"],"topics":["algorithms"]},{"id":"chunk-27","heading":"","text":"Итак, необходимо Найти длину гамильтонова цикла 𝑆4 в полном графе 𝐾6 после четырех циклов решения задачи методом отжига. Даны расстояния 𝐿𝑖,𝑗 между вершинами. Даны также: начальная последовательность вершин 𝐿0, последовательность замен вершин 𝑍 и выпавшие при этом вероятности перехода 𝑃𝑘, 𝑘 = 1, . . . , 4.","keywords":["ai","вершин","даны","отжига.","последовательность","эволюционные","4.","𝑗","𝑘","𝐾6","𝐿0","𝐿𝑖"],"topics":[]},{"id":"chunk-28","heading":"","text":"Переход на худшее (∆𝑆𝑘 = 𝑆𝑘 − 𝑆𝑘−1 0) решение допустим, если 𝑃∗ = 100где снижение температуры происходит по закону 𝑇𝑘+1 = 0.5𝑇𝑘 от 𝑇1 = 100.","keywords":["ai","𝑆𝑘","эволюционные","0.5𝑇𝑘","100.","100где","notebook","𝑃","𝑇1","𝑇𝑘+1","генетические","допустим"],"topics":[]},{"id":"chunk-29","heading":"","text":"Итак, начальные условия задачи представляют собой следующий граф с расстояниями между ребрами: image.png","keywords":["ai","эволюционные","image.png","notebook","генетические","граф","задачи","имитация","итак","между","методы","начальные"],"topics":[]},{"id":"chunk-30","heading":"","text":"distances = [(1, 2, 20), (1, 3, 40), (1, 4, 42), (1, 5, 33), (1, 6, 21), (2, 3, 26), (2, 4, 38), (2, 5, 42), (2, 6, 17), (3, 4, 22), (3, 5, 43), (3, 6, 21), (4, 5, 27), (4, 6, 22), (5, 6, 26)] длины рёбер","keywords":["ai","21","22","26","42","эволюционные","17","20","27","33","38","40"],"topics":[]},{"id":"chunk-31","heading":"","text":"V = [1, 4, 5, 2, 6, 3, 1] последовательность прохождения маршрута Z = [(3, 4), (4, 6), (5, 2), (6, 2)] последовательность замен вершин P = [49, 54, 43, 54] случайные числа, выпавшие в процессе счёта","keywords":["ai","54","последовательность","эволюционные","43","49","notebook","вершин","выпавшие","генетические","замен","имитация"],"topics":[]},{"id":"chunk-32","heading":"","text":"функция изменения температуры def reductTemp(prevT): nextT = .5 prevT return nextT","keywords":["ai","nextt","prevt","эволюционные",".5","def","notebook","reducttemp","return","генетические","изменения","имитация"],"topics":[]},{"id":"chunk-33","heading":"","text":"graph = nx.Graph() создание пустого графа graph.add weighted edges from(distances) добавление весов рёбер","keywords":["ai","эволюционные","distances","edges","graph","graph.add","notebook","nx.graph","weighted","весов","генетические","графа"],"topics":[]},{"id":"chunk-34","heading":"","text":"отрисовка графа с заданными вершинами nx.draw kamada kawai(graph, node color=' fb7258', node size=2000, with labels=True) ````","keywords":["ai","node","эволюционные","2000","color","fb7258","graph","kamada","kawai","labels","notebook","nx.draw"],"topics":["ai"]},{"id":"chunk-35","heading":"","text":"````python вычисление длины ребра def edgeLength(i, j, distances, roundTrip=True): if roundTrip: return max([(item[2] if (item[0] == i and item[1] == j) or (item[1] == i and item[0] == j) else 1) for item in distances]) else: return max([(item[2] if (item[0] == i and item[1] == j) else 1) for item in distances])","keywords":["item","ai","distances","else","if","max","return","roundtrip","эволюционные","def","edgelength","notebook"],"topics":["python"]},{"id":"chunk-36","heading":"","text":"одна перестановка в пути def routeOneReplacement(arrV, Z, replacementByName=True): decrement = 1 if replacementByName else 0","keywords":["ai","replacementbyname","эволюционные","arrv","decrement","def","else","if","notebook","routeonereplacement","true","генетические"],"topics":[]},{"id":"chunk-37","heading":"","text":"arrV[Z[0] decrement], arrV[Z[1] decrement] = arrV[Z[1] decrement], arrV[Z[0] decrement]","keywords":["arrv","decrement","ai","эволюционные","notebook","генетические","имитация","методы","оптимизация","отжига.","стратегии"],"topics":[]},{"id":"chunk-38","heading":"","text":"перестановка в пути def routeReplacement(V, Z): for z in Z: V = routeOnereplacement(V, z) return V","keywords":["ai","эволюционные","def","notebook","return","routeonereplacement","routereplacement","генетические","имитация","методы","оптимизация","отжига."],"topics":[]},{"id":"chunk-39","heading":"","text":"выбор нужного пути методом отжига def chooseRoute(distances, V, Z, T, P): sumLength = routeLength(V, distances) нахождение длины пути arrSum = [sumLength] массив сумм длин","keywords":["ai","distances","sumlength","пути","эволюционные","arrsum","chooseroute","def","notebook","routelength","выбор","генетические"],"topics":["ai"]},{"id":"chunk-40","heading":"","text":"циклы методом отжига for i in range(len(Z)): newV = routeOneReplacement(V[:], Z[i]) новый маршрут после перестановки newS = routeLength(newV, distances) длина нового маршрута arrSum.append(newS) deltaS = newS sumLength разница между длиной нового и старого маршрутов","keywords":["ai","news","newv","нового","эволюционные","arrsum.append","deltas","distances","len","notebook","range","routelength"],"topics":["ai"]},{"id":"chunk-41","heading":"","text":"в случае, если разница между длинами больше 0, то вычисляется вероятность if deltaS 0: p = probability(deltaS, T) подсчёт вероятности","keywords":["ai","deltas","эволюционные","if","notebook","probability","больше","вероятности","вероятность","вычисляется","генетические","длинами"],"topics":[]},{"id":"chunk-42","heading":"","text":"если заданная вероятность попадает в интервал от 0 до p, то новый маршрут выбирается if p P[i]: V = newV sumLength = newS else: V = newV sumLength = newS","keywords":["ai","news","newv","sumlength","эволюционные","else","if","notebook","вероятность","выбирается","генетические","до"],"topics":["ai"]},{"id":"chunk-43","heading":"","text":"отрисовка графа по заданному маршруту def drawRouteGraph(distances, bestRoute): newDistances = [] прохождение по вектору for i in range(len(bestRoute) 1): for distance in distances: if distance[0] == bestRoute[i] and distance[1] == bestRoute[i + 1] or distance[1] == bestRoute[i] and distance[0] == bestRoute[i + 1]: newDistances.append(distance)","keywords":["bestroute","distance","ai","distances","эволюционные","def","drawroutegraph","if","len","newdistances","newdistances.append","notebook"],"topics":[]},{"id":"chunk-44","heading":"","text":"graph.add weighted edges from(newDistances) добавление весов рёбер отрисовка графа с заданными вершинами nx.draw kamada kawai(graph, node color=' fb7258', node size=2000, with labels=True)","keywords":["ai","node","эволюционные","2000","color","edges","fb7258","graph","graph.add","kamada","kawai","labels"],"topics":["ai"]},{"id":"chunk-45","heading":"","text":"print(f'Лучший выбранный маршрут: {bestRoute}') print(f'Длина лучшего выбранного маршрута: {routeLength(bestRoute, distances)}') print(f'Длины всех рассмотренных маршрутов: {arrLength}')","keywords":["ai","print","bestroute","эволюционные","arrlength","distances","notebook","routelength","всех","выбранного","выбранный","генетические"],"topics":[]},{"id":"chunk-46","heading":"","text":"Найти длину гамильтонова цикла S4 в полном графе K6 после четырех циклов решения задачи методом отжига по вариантам ниже.","keywords":["ai","эволюционные","k6","notebook","s4","вариантам","гамильтонова","генетические","графе","длину","задачи","имитация"],"topics":[]},{"id":"chunk-47","heading":"","text":"````python distances = [(1, 2, 24), (1, 3, 41), (1, 4, 36), (1, 5, 22), (1, 6, 19), (2, 3, 21), (2, 4, 33), (2, 5, 33), (2, 6, 14), (3, 4, 27), (3, 5, 39), (3, 6, 23), (4, 5, 20), (4, 6, 20), (5, 6, 19)] длины рёбер","keywords":["ai","19","20","33","эволюционные","14","21","22","23","24","27","36"],"topics":["python"]},{"id":"chunk-48","heading":"","text":"V = [1, 3, 4, 5, 6, 2, 1] последовательность прохождения маршрута Z = [(3, 4), (4, 6), (5, 2), (6, 2)] последовательность замен вершин P = [33, 82, 51, 76] случайные числа, выпавшие в процессе счёта","keywords":["ai","последовательность","эволюционные","33","51","76","82","notebook","вершин","выпавшие","генетические","замен"],"topics":[]},{"id":"chunk-49","heading":"","text":"функция изменения температуры def reductTemp(prevT): nextT = .5 prevT return nextT","keywords":["ai","nextt","prevt","эволюционные",".5","def","notebook","reducttemp","return","генетические","изменения","имитация"],"topics":[]},{"id":"chunk-50","heading":"","text":"вычисление длины ребра def edgeLength(i, j, distances, roundTrip=True): if roundTrip: return max([(item[2] if (item[0] == i and item[1] == j) or (item[1] == i and item[0] == j) else 1) for item in distances]) else: return max([(item[2] if (item[0] == i and item[1] == j) else 1) for item in distances])","keywords":["item","ai","distances","else","if","max","return","roundtrip","эволюционные","def","edgelength","notebook"],"topics":[]},{"id":"chunk-51","heading":"","text":"одна перестановка в пути def routeOneReplacement(arrV, Z, replacementByName=True): decrement = 1 if replacementByName else 0","keywords":["ai","replacementbyname","эволюционные","arrv","decrement","def","else","if","notebook","routeonereplacement","true","генетические"],"topics":[]},{"id":"chunk-52","heading":"","text":"arrV[Z[0] decrement], arrV[Z[1] decrement] = arrV[Z[1] decrement], arrV[Z[0] decrement]","keywords":["arrv","decrement","ai","эволюционные","notebook","генетические","имитация","методы","оптимизация","отжига.","стратегии"],"topics":[]},{"id":"chunk-53","heading":"","text":"перестановка в пути def routeReplacement(V, Z): for z in Z: V = routeOnereplacement(V, z) return V","keywords":["ai","эволюционные","def","notebook","return","routeonereplacement","routereplacement","генетические","имитация","методы","оптимизация","отжига."],"topics":[]},{"id":"chunk-54","heading":"","text":"выбор нужного пути методом отжига def chooseRoute(distances, V, Z, T, P): sumLength = routeLength(V, distances) нахождение длины пути arrSum = [sumLength] массив сумм длин","keywords":["ai","distances","sumlength","пути","эволюционные","arrsum","chooseroute","def","notebook","routelength","выбор","генетические"],"topics":["ai"]},{"id":"chunk-55","heading":"","text":"циклы методом отжига for i in range(len(Z)): newV = routeOneReplacement(V[:], Z[i]) новый маршрут после перестановки newS = routeLength(newV, distances) длина нового маршрута arrSum.append(newS) deltaS = newS sumLength разница между длиной нового и старого маршрутов","keywords":["ai","news","newv","нового","эволюционные","arrsum.append","deltas","distances","len","notebook","range","routelength"],"topics":["ai"]},{"id":"chunk-56","heading":"","text":"в случае, если разница между длинами больше 0, то вычисляется вероятность if deltaS 0: p = probability(deltaS, T) подсчёт вероятности","keywords":["ai","deltas","эволюционные","if","notebook","probability","больше","вероятности","вероятность","вычисляется","генетические","длинами"],"topics":[]},{"id":"chunk-57","heading":"","text":"если заданная вероятность попадает в интервал от 0 до p, то новый маршрут выбирается if p P[i]: V = newV sumLength = newS else: V = newV sumLength = newS","keywords":["ai","news","newv","sumlength","эволюционные","else","if","notebook","вероятность","выбирается","генетические","до"],"topics":["ai"]},{"id":"chunk-58","heading":"","text":"отрисовка графа по заданному маршруту def drawRouteGraph(distances, bestRoute): newDistances = [] прохождение по вектору for i in range(len(bestRoute) 1): for distance in distances: if distance[0] == bestRoute[i] and distance[1] == bestRoute[i + 1] or distance[1] == bestRoute[i] and distance[0] == bestRoute[i + 1]: newDistances.append(distance)","keywords":["bestroute","distance","ai","distances","эволюционные","def","drawroutegraph","if","len","newdistances","newdistances.append","notebook"],"topics":[]},{"id":"chunk-59","heading":"","text":"graph.add weighted edges from(newDistances) добавление весов рёбер отрисовка графа с заданными вершинами nx.draw kamada kawai(graph, node color=' fb7258', node size=2000, with labels=True)","keywords":["ai","node","эволюционные","2000","color","edges","fb7258","graph","graph.add","kamada","kawai","labels"],"topics":["ai"]},{"id":"chunk-60","heading":"","text":"print(f'Лучший выбранный маршрут: {bestRoute}') print(f'Длина лучшего выбранного маршрута: {routeLength(bestRoute, distances)}') print(f'Длины всех рассмотренных маршрутов: {arrLength}')","keywords":["ai","print","bestroute","эволюционные","arrlength","distances","notebook","routelength","всех","выбранного","выбранный","генетические"],"topics":[]},{"id":"chunk-61","heading":"","text":"code Лучший выбранный маршрут: [1, 6, 5, 4, 3, 2, 1] Длина лучшего выбранного маршрута: 130 Длины всех рассмотренных маршрутов: [145, 158, 183, 130, 146]","keywords":["ai","130","эволюционные","145","146","158","183","code","notebook","всех","выбранного","выбранный"],"topics":[]}]},{"id":"ai/notebook-07-neural-networks","href":"/docs/ai/notebook-07-neural-networks","slug":["ai","notebook-07-neural-networks"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 7 — Нейронные сети","description":"Персептрон, MLP и базовый цикл обучения нейросети.","preview":"1.1. Теоретический материал – Нейронные сети Обучение персептрона Персептрон представляет собой элементарную часть нейронной сети. Одиночный персептрон является линейным бинарным классификатором. В этой лекции мы рассмотрим процедуру обучения персептрона для к","keywords":["print","test","out","train","mlp","weights","bias","def","python","self","neuron","np.array","мы","10","h1","h2","init","𝑦"],"topics":["python","ai","pandas","numpy","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"","text":"Персептрон представляет собой элементарную часть нейронной сети. Одиночный персептрон является линейным бинарным классификатором. В этой лекции мы рассмотрим процедуру обучения персептрона для классификации данных. Поскольку персептрон представляет собой бинарный классификатор, то мы будем рассматривать лишь два класса","keywords":["персептрон","ai","мы","обучения","представляет","собой","mlp","notebook","базовый","бинарный","бинарным","будем"],"topics":["oop"]},{"id":"chunk-1","heading":"","text":". Пусть мы рассматриваем некоторое множество (конечное или бесконечное) n мерных векторов, которые будем обозначать 𝑥 = (𝑥1, 𝑥2, . . . , 𝑥𝑛)","keywords":["ai","mlp","notebook","𝑥","𝑥1","𝑥2","𝑥𝑛","базовый","бесконечное","будем","векторов","конечное"],"topics":[]},{"id":"chunk-2","heading":"","text":"Будем считать, что это множество разбивается на два класса, которые мы будем обозначать +1 и 1. Поэтому возникает задача построения функции, которая задана на нашем множестве векторов, и принимает значения в множестве \\{+1, −1\\}. В качестве такой функции может выступать персептрон. С алгебраической точки зрения персептрон состоит из вектора весов 𝑤 = (𝑤0, 𝑤1, 𝑤2, . . . , 𝑤𝑛).","keywords":["ai","+1","будем","множестве","персептрон","функции","1.","mlp","notebook","𝑤","𝑤0","𝑤1"],"topics":["oop"]},{"id":"chunk-3","heading":"","text":"Приведем алгоритм обучения персептрона. Пусть у нас есть набор обучающих данных \\{(𝑥, 𝑑)\\}, где 𝑥 это различные вектора, а 𝑑 из множества \\{+1, −1\\} указывает к какому классу относится наш вектор. 1. Положим вектор весов 𝑤 равным нулю. 2. Повторять 𝑁 раз следующие шаги: 3. Для каждого тестового набора (𝑥, 𝑑): 4. Вычислить 𝑦 = 𝑠𝑖𝑔𝑛[(𝑥, 𝑤)]. 5","keywords":["𝑥","ai","𝑑","𝑤","обучения","+1","1.","2.","3.","4.","mlp","𝑁"],"topics":["oop","algorithms"]},{"id":"chunk-4","heading":"","text":". Если 𝑦𝑑 < 0, то скорректировать веса 𝑤0 = 𝑤0 + 𝑎𝑑, 𝑤𝑖 = 𝑤𝑖 + 𝑎𝑑𝑥𝑖 , 𝑖 = 1,2, . . . , 𝑛.","keywords":["ai","𝑤0","𝑤𝑖","𝑎𝑑","𝑎𝑑𝑥𝑖","𝑖","mlp","𝑛.","notebook","𝑦𝑑","базовый","веса"],"topics":[]},{"id":"chunk-5","heading":"","text":"Рассмотрим программу обучения персептрона на языке Python. Сначала рассмотрим основной класс персептрона, который умеет учиться по тестовым данным.","keywords":["ai","обучения","персептрона","рассмотрим","mlp","notebook","python.","базовый","данным.","класс","который","нейронные"],"topics":["python","oop"]},{"id":"chunk-6","heading":"","text":"python класс который реализует персептрон и его обучение class Perceptron: def init (self, N): создать нулевые веса self.w = list() for i in range(N): self.w.append(0) метод для вычисления значения персептрона def calc(self, x): res = 0 for i in range(len(self.w)): res = res + self.w[i] x[i] return res пороговая функция активации персептрона def sign(self, x): if self.calc(x) 0: return 1 else: return 1 обучение на","keywords":["res","ai","def","return","self","self.w","range","обучение","персептрон","персептрона","calc","class"],"topics":["python","oop"]},{"id":"chunk-7","heading":"","text":"одном примере def learn(self, la, x, y): обучаем только, когда результат неверный if y self.calc(x) <= 0: for i in range(len(self.w)): self.w[i] = self.w[i] + la y x[i] обучение по всем данным Т кортеж примеров def learning(self, la, T): цикл обучения for n in range(100): обучение по всему набору примеров for t in T: self.learn(la, t[0], t[1])","keywords":["la","ai","self.w","def","range","self","обучение","обучения","примеров","цикл","100","if"],"topics":[]},{"id":"chunk-8","heading":"","text":"В строке 25 мы осуществляем корректировку весов. Посмотрим, как учится и работает наш персептрон.","keywords":["ai","25","mlp","notebook","базовый","весов.","корректировку","мы","наш","нейронные","нейросети.","обучения"],"topics":[]},{"id":"chunk-9","heading":"","text":"python создаем класс двумерного персептрона perceptron = Perceptron(2) la = 0.1 константа обучения создаем примеры T = list() T.append([[2,1], 1]) T.append([[3,2], 1]) T.append([[4,1], 1]) T.append([[1,2], 1]) T.append([[2,3], 1]) T.append([[5,7], 1]) perceptron.learning(la, T) обучение персептрона print(perceptron.w) печатаем веса проверим работу на тестовых примерах print(perceptron.sign([1.5, 2]))","keywords":["t.append","ai","la","perceptron","print","обучения","персептрона","создаем","0.1","1.5","list","mlp"],"topics":["python","oop"]},{"id":"chunk-10","heading":"","text":"print(perceptron.sign([3, 1.5])) print(perceptron.sign([5, 1])) print(perceptron.sign([5, 10]))","keywords":["ai","perceptron.sign","print","1.5","10","mlp","notebook","базовый","нейронные","нейросети.","обучения","персептрон"],"topics":[]},{"id":"chunk-11","heading":"","text":"Видим, что что наш персептрон отлично научился распознавать образы, относя к классу 1 те вектора, у которых первая компонента больше второй, и к классу 1 в противном случае. Хотя устройство персептронов довольно простое эти конструкции могут решать и практические задачи. Кроме того, из таких персептронов состоят нейронные сети.","keywords":["ai","классу","нейронные","персептрон","персептронов","mlp","notebook","базовый","больше","вектора","видим","второй"],"topics":["oop"]},{"id":"chunk-12","heading":"","text":"Нейронная сеть — это функциональная единица машинного или глубокого обучения. Она имитирует поведение человеческого мозга, поскольку основана на концепции биологических нейронных сетей.","keywords":["ai","mlp","notebook","базовый","биологических","глубокого","единица","имитирует","концепции","машинного","мозга","нейронная"],"topics":["ai"]},{"id":"chunk-13","heading":"","text":"Наиболее распространенный тип нейронной сети, называемый многослойным персептроном (MLP), представляет собой функцию, которая отображает входные данные в выходные данные. MLP имеет один входной слой и один выходной слой. Между ними может быть один или несколько скрытых слоев. Входной слой имеет тот же набор нейронов, что и признаки. Скрытые слои также могут иметь более одного нейрона","keywords":["ai","mlp","один","входной","имеет","сети","слой","notebook","базовый","более","быть","входные"],"topics":["ai"]},{"id":"chunk-14","heading":"","text":". Каждый нейрон представляет собой линейную функцию, к которой применяется функция активации для решения сложных задач. Выход каждого слоя подается в качестве входных данных для всех нейронов следующих слоев.","keywords":["ai","mlp","notebook","активации","базовый","всех","входных","выход","данных","задач.","каждого","каждый"],"topics":[]},{"id":"chunk-15","heading":"","text":"Нейронные сети способны решать множество задач. В основном они состоят из таких компонентов:","keywords":["ai","нейронные","сети","mlp","notebook","базовый","задач.","компонентов","множество","нейросети.","обучения","они"],"topics":[]},{"id":"chunk-16","heading":"","text":"Чтобы реализовать нейросеть, необходимо понимать, как ведут себя нейроны. Нейрон одновременно принимает несколько входов, обрабатывает эти данные и выдает один выход. Нейронная сеть представляет собой блоки ввода и вывода, где каждое соединение имеет соответствующие веса (это сила связи нейронов; чем вес больше, тем один нейрон сильнее влияет на другой). Данные всех входов умножаются на веса:","keywords":["ai","веса","входов","данные","нейрон","один","mlp","notebook","базовый","блоки","больше","ввода"],"topics":["ai"]},{"id":"chunk-17","heading":"","text":"Полученное значение пропускается через функцию активации (сигмоиду), которая преобразует входы в один выход:","keywords":["ai","mlp","notebook","активации","базовый","входы","выход","значение","которая","нейронные","нейросети.","обучения"],"topics":[]},{"id":"chunk-18","heading":"","text":"Интервал результатов сигмоиды — от 0 до 1. Отрицательные числа стремятся к нулю, а положительные — к единице.","keywords":["ai","1.","mlp","notebook","базовый","до","единице.","интервал","нейронные","нейросети.","нулю","обучения"],"topics":[]},{"id":"chunk-19","heading":"","text":"````python def sigmoid(x): функция активации: f(x) = 1/(1+e^( x)) return 1 / (1 + np.exp( x)) class Neuron: def init (self, weights, bias): self.weights = weights self.bias = bias def feedforward(self, inputs): total = np.dot(self.weights, inputs) + self.bias return sigmoid(total)","keywords":["ai","def","bias","inputs","return","self","self.bias","self.weights","sigmoid","total","weights","1+e"],"topics":["python","numpy"]},{"id":"chunk-20","heading":"","text":"weights = np.array([0, 1]) w1=0, w2=1 bias = 4 c=4 n = Neuron(weights, bias) x = np.array([2, 3]) x=2, y =3 print(n.feedforward(x)) 0.9990889488055994 ````","keywords":["ai","bias","np.array","weights","0.9990889488055994","mlp","n.feedforward","neuron","notebook","print","w1","w2"],"topics":["numpy"]},{"id":"chunk-21","heading":"","text":"Нейросеть состоит из множества соединенных между собой нейронов. Пример несложной нейронной сети где:","keywords":["ai","сети","mlp","notebook","базовый","между","множества","нейронной","нейронные","нейронов.","нейросети.","нейросеть"],"topics":["ai"]},{"id":"chunk-22","heading":"","text":"Представим, что нейроны из графика выше имеют веса [0, 1]. Пороговое значение (𝑏) у обоих нейронов равно 0 и они имеют идентичную сигмоиду.","keywords":["ai","имеют","𝑏","mlp","notebook","базовый","веса","выше","графика","значение","идентичную","нейронные"],"topics":[]},{"id":"chunk-23","heading":"","text":"Входные данные по нейронам передаются до тех пор, пока не получатся выходные значения.","keywords":["ai","mlp","notebook","базовый","входные","выходные","данные","до","значения.","нейронам","нейронные","нейросети."],"topics":[]},{"id":"chunk-24","heading":"","text":"````python class OurNeuralNetwork: Данные нейросети: два входа два нейрона в скрыттых слоях(h1,h2) выход(о1) Нейроны имеют идентичные веса и пороги: w=[0,1] b=0 def init (self): weights = np.array ([0, 1]) bias = 0 Класс Neuron из предыдущего раздела self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias) def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 =","keywords":["bias","neuron","weights","ai","def","h1","h2","out","self","два","class","feedforward"],"topics":["python","numpy","oop"]},{"id":"chunk-25","heading":"","text":"self.h2.feedforward(x) входы для о1 это выходы для h1 и h2 out o1 = self.o1.feedforward(np.array([out h1, out h2])) return out o1","keywords":["out","ai","h1","h2","o1","mlp","notebook","np.array","return","self.h2.feedforward","self.o1.feedforward","базовый"],"topics":["numpy"]},{"id":"chunk-26","heading":"","text":"network = OurNeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) 0.7216325609518421 ````","keywords":["ai","0.7216325609518421","mlp","network","network.feedforward","notebook","np.array","ourneuralnetwork","print","базовый","нейронные","нейросети."],"topics":["numpy"]},{"id":"chunk-27","heading":"","text":"Обучение нейросети — это подбор весов, которые соответствуют всем входам для решения поставленных задач. Класс нейронной сети:","keywords":["ai","сети","mlp","notebook","базовый","весов","всем","входам","задач.","класс","которые","нейронной"],"topics":["oop"]},{"id":"chunk-28","heading":"","text":"class NeuralNetwork: def init (self, x, y): self.input = x self.weights1 = np.random.rand(self.input.shape[1], 4) self.weights2 = np.random.rand(4,1) self.y = y self.output = np.zeros(y.shape) Каждый этап процесса обучения состоит из: прямого распространения (прогнозируемый выход); обратного распространения (обновление весов и смещений).","keywords":["ai","np.random.rand","обучения","распространения","class","def","init","mlp","neuralnetwork","notebook","np.zeros","self"],"topics":["numpy"]},{"id":"chunk-29","heading":"","text":"В данном случае на выход ŷ влияют только две переменные — 𝑤 (веса) и 𝑏 (смещение). Настройку весов и смещений из данных входа или процесс обучения нейросети можно изобразить так:","keywords":["ai","обучения","𝑏","mlp","notebook","𝑤","базовый","веса","весов","влияют","входа","выход"],"topics":[]},{"id":"chunk-30","heading":"","text":"Как видно, формула прямого распространения представляет собой несложное вычисление:","keywords":["ai","mlp","notebook","базовый","видно","вычисление","нейронные","нейросети.","несложное","обучения","персептрон","представляет"],"topics":[]},{"id":"chunk-31","heading":"","text":"``` class NeuralNetwork: def init (self, x, y): self.input = x self.weights1 = np.random.rand(self.input.shape[1], 4) self.weights2 = np.random.rand(4,1) self.y = y self.output = np.zeros(self.y.shape)","keywords":["ai","np.random.rand","class","def","init","mlp","neuralnetwork","notebook","np.zeros","self","self.input","self.input.shape"],"topics":["numpy"]},{"id":"chunk-32","heading":"","text":"def feedforward(self): self.layer1 = sigmoid(np.dot(self.input, self.weights1)) self.output = sigmoid(np.dot(self.layer1, self.weights2)) ``` Чтобы вычислить ошибку прогноза, необходимо использовать функцию потери. В примере уместно воспользоваться формулой суммы квадратов ошибок — средним значением между прогнозируемым и фактическим результатами:","keywords":["ai","np.dot","self.layer1","sigmoid","def","feedforward","mlp","notebook","self","self.input","self.output","self.weights1"],"topics":["numpy"]},{"id":"chunk-33","heading":"","text":"Обратное распространение позволяет измерить производные в обратном порядке — от конца к началу, и скорректировать веса и смещения. Для этого необходимо узнать производную функции потери — тангенс угла наклона.","keywords":["ai","mlp","notebook","базовый","веса","измерить","конца","наклона.","началу","нейронные","нейросети.","необходимо"],"topics":[]},{"id":"chunk-34","heading":"","text":"Производная функции по отношению к весам и смещениям позволяет узнать градиентный спуск. Производная функции потери не содержит весов и смещений, для ее вычисления необходимо добавить правило цепи:","keywords":["ai","производная","функции","mlp","notebook","базовый","весам","весов","вычисления","градиентный","добавить","ее"],"topics":[]},{"id":"chunk-35","heading":"","text":"𝐿𝑜𝑠𝑠 (𝑦, 𝑦̂) = ∑(𝑦 − 𝑦̂)2𝑛𝑖=1𝜕𝐿𝑜𝑠𝑠 (𝑦, 𝑦̂)𝜕𝑊=𝜕𝐿𝑜𝑠𝑠 (𝑦, 𝑦̂)𝜕𝑦̂∙𝜕𝑦̂𝜕𝑧∙𝜕𝑧𝜕𝑊 == 2(𝑦 − 𝑦̂)∙ производную сигмоиды ∙ 𝑥 == 2(𝑦 − 𝑦̂) ∙ 𝑧(1 − 𝑧) ∙ 𝑥,","keywords":["𝑦","𝑧","ai","𝐿𝑜𝑠𝑠","𝑊","𝑥","2𝑛𝑖","mlp","notebook","базовый","нейронные","нейросети."],"topics":[]},{"id":"chunk-36","heading":"","text":"Благодаря этому правилу можно регулировать веса. Добавляем в код Python функцию обратного распространения:","keywords":["ai","mlp","notebook","python","базовый","благодаря","веса.","добавляем","код","можно","нейронные","нейросети."],"topics":["python"]},{"id":"chunk-37","heading":"","text":"``` class NeuralNetwork: def init (self, x, y): self.input = x self.weights1 = np.random.rand(self.input.shape[1], 4) self.weights2 = np.random.rand(4,1) self.y = y self.output = np.zeros(self.y.shape)","keywords":["ai","np.random.rand","class","def","init","mlp","neuralnetwork","notebook","np.zeros","self","self.input","self.input.shape"],"topics":["numpy"]},{"id":"chunk-38","heading":"","text":"def feedforward(self): self.layer1 = sigmoid(np.dot(self.input, self.weights1)) self.output = sigmoid(np.dot(self.layer1, self.weights2))","keywords":["ai","np.dot","self.layer1","sigmoid","def","feedforward","mlp","notebook","self","self.input","self.output","self.weights1"],"topics":["numpy"]},{"id":"chunk-39","heading":"","text":"def backprop(self): d weights2 = np.dot(self.layer1.T, (2 (self.y self.output) sigmoid derevative(self.output))) d weights1 = np.dot(self.input.T, (np.dot(2 (self.y self.output) sigmoid derivative(self.output), self.weights2.T) sigmoid derivative(self.layer1)))","keywords":["self.output","ai","np.dot","sigmoid","derivative","self.y","backprop","def","derevative","mlp","notebook","self"],"topics":["numpy"]},{"id":"chunk-40","heading":"","text":"``` Нейронные сети базируются на определенных алгоритмах и математических функциях. Сначала может казаться, что разобраться в них довольно сложно. Но существуют готовые библиотеки машинного обучения для построения и тренировки нейросетей, позволяющие не углубляться в их устройство.","keywords":["ai","нейронные","обучения","сети","mlp","notebook","алгоритмах","базируются","базовый","библиотеки","готовые","довольно"],"topics":["algorithms"]},{"id":"chunk-41","heading":"","text":"Реализовать классы нейросетей по аналогии с классом OurNeuralNetwork. Данные нейросети:","keywords":["ai","mlp","notebook","ourneuralnetwork.","аналогии","базовый","данные","классом","классы","нейронные","нейросетей","нейросети"],"topics":["oop"]},{"id":"chunk-42","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([0.5, 0.5, 0.5]) bias = 0 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.h3 = Neuron(weights, bias) self.o1 = Neuron(weights, bias) def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out h3 = self.h3.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2, out h3])) return out","keywords":["out","bias","weights","neuron","0.5","ai","def","h1","h2","h3","np.array","self"],"topics":["python","numpy"]},{"id":"chunk-43","heading":"","text":"o1","keywords":["ai","mlp","notebook","o1","базовый","нейронные","нейросети.","обучения","персептрон","сети","цикл"],"topics":[]},{"id":"chunk-44","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3, 4]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-45","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([1, 0]) bias = 1 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias) self.o2 = Neuron(weights, bias) def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2])) out o2 = self.o2.feedforward(np.array([out h2, out h1]))","keywords":["out","bias","weights","neuron","ai","h1","h2","np.array","def","self","class","feedforward"],"topics":["python","numpy"]},{"id":"chunk-46","heading":"","text":"return np.array([out o1, out o2])","keywords":["ai","out","mlp","notebook","np.array","o1","o2","return","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-47","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-48","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([1, 0]) bias = 1 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias)","keywords":["bias","weights","ai","neuron","class","def","init","mlp","neuralnetwork","notebook","np.array","python"],"topics":["python","numpy"]},{"id":"chunk-49","heading":"","text":"def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2])) return sigmoid(out o1)","keywords":["out","ai","h1","h2","o1","def","feedforward","mlp","notebook","np.array","return","self"],"topics":["numpy"]},{"id":"chunk-50","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-51","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([1, 0]) bias = 1 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias)","keywords":["bias","weights","ai","neuron","class","def","init","mlp","neuralnetwork","notebook","np.array","python"],"topics":["python","numpy"]},{"id":"chunk-52","heading":"","text":"def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2])) return np.tanh(out o1)","keywords":["out","ai","h1","h2","o1","def","feedforward","mlp","notebook","np.array","np.tanh","return"],"topics":["numpy"]},{"id":"chunk-53","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-54","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([1, 0]) bias = 1 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias)","keywords":["bias","weights","ai","neuron","class","def","init","mlp","neuralnetwork","notebook","np.array","python"],"topics":["python","numpy"]},{"id":"chunk-55","heading":"","text":"def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2])) return max(0, out o1)","keywords":["out","ai","h1","h2","o1","def","feedforward","max","mlp","notebook","np.array","return"],"topics":["numpy"]},{"id":"chunk-56","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-57","heading":"","text":"Теперь мы знаем, что такое нейронные сети и какие шаги необходимо выполнить, чтобы построить простую нейронную сеть с плотными связями. В этом разделе мы попытаемся построить простую нейронную сеть, которая предсказывает класс, к которому принадлежит данное растение ириса. Мы будем использовать библиотеку Python Scikit Learn для создания нашей нейронной сети.","keywords":["ai","мы","нейронную","нейронные","построить","простую","сети","сеть","learn","mlp","notebook","python"],"topics":["python","oop"]},{"id":"chunk-58","heading":"","text":"Sklearn предоставляет 2 оценщика для задач классификации и регрессии соответственно:","keywords":["ai","mlp","notebook","sklearn","базовый","задач","классификации","нейронные","нейросети.","обучения","оценщика","персептрон"],"topics":["oop"]},{"id":"chunk-59","heading":"","text":"Набор данных цифр: мы будем использовать набор данных цифр, который имеет изображения размером 8x8 для цифр 0 9. Ниже мы будем использовать цифровые данные для задач классификации.","keywords":["ai","цифр","будем","данных","использовать","мы","набор","8x8","9.","mlp","notebook","базовый"],"topics":["oop"]},{"id":"chunk-60","heading":"","text":"Набор данных о жилье в Бостоне: мы будем использовать набор данных о жилье в Бостоне, который содержит информацию о различных свойствах дома, таких как среднее количество комнат, уровень преступности на душу населения в городе и т. д. Мы будем использовать его для задач регрессии.","keywords":["ai","бостоне","будем","данных","жилье","использовать","мы","набор","mlp","notebook","базовый","городе"],"topics":[]},{"id":"chunk-61","heading":"","text":"Sklearn предоставляет оба этих набора данных. Мы можем загрузить их, вызвав методы load digits() и load boston().","keywords":["ai","load","boston","digits","mlp","notebook","sklearn","базовый","вызвав","данных.","загрузить","их"],"topics":[]},{"id":"chunk-62","heading":"","text":"digits = load digits() X digits, Y digits = digits.data, digits.target print('Dataset Sizes : ' , X digits.shape, Y digits.shape) ````","keywords":["digits","ai","digits.shape","dataset","digits.data","digits.target","load","mlp","notebook","print","sizes","базовый"],"topics":[]},{"id":"chunk-63","heading":"","text":"python boston = load boston() X boston, Y boston = digits.data, digits.target print('Dataset Sizes : ', X boston.shape, Y boston.shape)","keywords":["boston","ai","boston.shape","dataset","digits.data","digits.target","load","mlp","notebook","print","python","sizes"],"topics":["python"]},{"id":"chunk-64","heading":"","text":"MLPClassifier — это класс, доступный как часть модуля neuro network sklearn для выполнения задач классификации с использованием многослойного персептрона.","keywords":["ai","mlp","mlpclassifier","network","neuro","notebook","sklearn","базовый","выполнения","доступный","задач","использованием"],"topics":["ai","oop"]},{"id":"chunk-65","heading":"","text":"Функция train test split модуля model selection sklearn поможет нам разделить данные на два набора: 80% для обучения и 20% для тестирования.","keywords":["ai","обучения","20","80","mlp","model","notebook","selection","sklearn","split","test","train"],"topics":["ai","testing"]},{"id":"chunk-66","heading":"","text":"Мы также используем seed(random state=123) с train test split, чтобы мы всегда получали одно и то же разделение и могли сравнивать и воспроизволить результаты в будущем.","keywords":["ai","мы","123","mlp","notebook","random","seed","split","state","test","train","базовый"],"topics":["ai","testing"]},{"id":"chunk-67","heading":"","text":"X train, X test, Y train, Y test = train test split(X digits, Y digits, train size= 0.80, test size= 0.20, stratify=Y digits, random state=123) print('Train/Test Sizes : ', X train.shape, X test.shape, Y train.shape, Y test.shape) ````","keywords":["test","train","ai","digits","size","test.shape","train.shape","0.20","0.80","123","mlp","notebook"],"topics":["ai","testing"]},{"id":"chunk-68","heading":"","text":"Для начала натренируем модель MLPClassifier с параметрами по умолчанию для тренировочных данных.","keywords":["ai","mlp","mlpclassifier","notebook","базовый","данных.","модель","натренируем","начала","нейронные","нейросети.","обучения"],"topics":["ai"]},{"id":"chunk-69","heading":"","text":"mlp classifier = MLPClassifier(random state=123) mlp classifier.fit(X train, Y train) MLPClassifier(activation='relu', alpha=0.0001, batch size='auto', beta 1=0.9, beta 2=0.999, early stopping=False, epsilon=1e 08, hidden layer sizes= (100,), learning rate='constant', learning rate init=0.001, max iter=200, momentum=0.9, n iter no change=10, nesterovs momentum=True, power t=0.5, random state=123, shuffle=True,","keywords":["ai","mlp","0.9","123","beta","iter","learning","mlpclassifier","momentum","random","rate","state"],"topics":["ai"]},{"id":"chunk-70","heading":"","text":"solver='adam' , tol=0.0001, validation fraction=0.1, verbose=False, warm start=False) ````","keywords":["ai","false","0.0001","0.1","adam","fraction","mlp","notebook","solver","start","tol","validation"],"topics":[]},{"id":"chunk-71","heading":"","text":"print(Y preds[:15]) print(Y test[:15]) Метод Score для оценки точностей моделей классификации print('Test Accuracy : %.3f'%mlp classifier.score(X test, Y test))","keywords":["test","ai","print","15","mlp",".3f","accuracy","classifier.score","notebook","preds","score","базовый"],"topics":["ai","oop","testing"]},{"id":"chunk-72","heading":"","text":"Cоздадим метод plot confusion matrix(), который принимает исходные и предсказанные метки данных по модели. Затем он строит матрицу путаницы, используя matplotlib.","keywords":["ai","confusion","cоздадим","matplotlib.","matrix","mlp","notebook","plot","базовый","данных","затем","используя"],"topics":[]},{"id":"chunk-73","heading":"","text":"def plot confusion matrix(Y test, Y preds): conf mat = confusion matrix(Y test, Y preds) fig = plt.figure(figsize=(6,6)) plt.matshow(conf mat, cmap=plt.cm.Blues, fignum=1) plt.yticks(range(10), range(10)) plt.xticks(range(10), range(10)) plt.colorbar(); for i in range(10): for j in range(10): plt.text(i 0.2, j+0.1, str(conf mat[j, i]), color ='tab:red')","keywords":["10","range","ai","conf","mat","confusion","matrix","preds","test","0.2","cmap","color"],"topics":["testing"]},{"id":"chunk-74","heading":"","text":"Ниже приведен список важных атрибутов, доступных с MLPClassifier, которые могут предоставить значимую информацию после обучения модели.","keywords":["ai","обучения","mlp","mlpclassifier","notebook","атрибутов","базовый","важных","доступных","значимую","информацию","которые"],"topics":["ai"]},{"id":"chunk-75","heading":"","text":"− coefs — возвращает массив длины n layers 1, где каждый элемент представляет веса, связанные с уровнем i.","keywords":["ai","coefs","i.","layers","mlp","notebook","базовый","веса","возвращает","длины","каждый","массив"],"topics":[]},{"id":"chunk-76","heading":"","text":"− intercepts — возвращает массив длины n layers 1, где каждый элемент представляет собой перехват, связанный с персептронами слоя i.","keywords":["ai","i.","intercepts","layers","mlp","notebook","базовый","возвращает","длины","каждый","массив","нейронные"],"topics":[]},{"id":"chunk-77","heading":"","text":"python print('Loss : ', mlp classifier.loss ) print('Number of Coefs : ',len(mlp classifier.coefs )) print('Number of Intercepts : ',len(mlp classifier.intercepts )) print('Number of Intercepts for which Estimator Ran : ', mlp classifier.n iter ) print('Name of Output layer Activation Function : ', mlp classifier.out activation )","keywords":["mlp","print","ai","number","activation","intercepts","len","classifier.coefs","classifier.intercepts","classifier.loss","classifier.n","classifier.out"],"topics":["python","ai"]},{"id":"chunk-78","heading":"","text":"MLPRegressor — это класс, доступный как часть библиотеки neuro network sklearn для выполнения задач регрессии с использованием многослойного персептрона. Также разделим набор данных на две части:","keywords":["ai","mlp","mlpregressor","network","neuro","notebook","sklearn","базовый","библиотеки","выполнения","данных","две"],"topics":["ai","oop"]},{"id":"chunk-79","heading":"","text":"− тестовые данные (20%), по которым будет проверяться точность обученной модели.","keywords":["ai","20","mlp","notebook","базовый","будет","данные","которым","модели.","нейронные","нейросети.","обучения"],"topics":[]},{"id":"chunk-80","heading":"","text":"python X train, X test, Y train, Y test = train test split(X boston, Y boston, train size= 0.80, test size= 0.20, stratify=Y digits, random state=123) print('Train/Test Sizes : ', X train.shape, X test.shape, Y train.shape, Y test.shape)","keywords":["test","train","ai","boston","size","test.shape","train.shape","0.20","0.80","123","digits","mlp"],"topics":["python","ai","testing"]},{"id":"chunk-81","heading":"","text":"mlp regressor = MLPRegressor(random state=123) mlp regressor.fit(X train, Y train) MLPClassifier(activation='relu', alpha=0.0001, batch size='auto', beta 1=0.9, beta 2=0.999, early stopping=False, epsilon=1e 08, hidden layer sizes= (100,), learning rate='constant', learning rate init=0.001, max iter=200, momentum=0.9, n iter no change=10, nesterovs momentum=True, power t=0.5, random state=123, shuffle=True,","keywords":["ai","mlp","0.9","123","beta","iter","learning","momentum","random","rate","state","train"],"topics":["ai"]},{"id":"chunk-82","heading":"","text":"solver='adam' , tol=0.0001, validation fraction=0.1, verbose=False, warm start=False) ````","keywords":["ai","false","0.0001","0.1","adam","fraction","mlp","notebook","solver","start","tol","validation"],"topics":[]},{"id":"chunk-83","heading":"","text":"print(Y preds[:10]) print(Y test[:10]) Метод Score оценивает точность модели классификации print('Test R^2 Score : %.3f'%mlp regressor.score(X test, Y test)) print('Training R^2 Score : %.3f'%mlp regressor.score(X train, Y train)) ````","keywords":["print","test","ai","mlp","score",".3f","10","regressor.score","train","notebook","preds","training"],"topics":["ai","oop","testing"]},{"id":"chunk-84","heading":"","text":"python print('Number of Coefs : ', len(mlp regressor.coefs )) [weights.shape for weights in mlp regressor.coefs ]","keywords":["ai","mlp","regressor.coefs","coefs","len","notebook","number","print","python","weights","weights.shape","базовый"],"topics":["python","ai"]},{"id":"chunk-85","heading":"","text":"python print('Number of Iterations for which estimator Ran : ', mlp regressor.n iter )","keywords":["ai","mlp","estimator","iter","iterations","notebook","number","print","python","ran","regressor.n","which"],"topics":["python","ai"]},{"id":"chunk-86","heading":"","text":"python print('Name of Output Layer Activation Function : ', mlp regressor.out activation )","keywords":["ai","activation","mlp","function","layer","name","notebook","output","print","python","regressor.out","базовый"],"topics":["python","ai"]},{"id":"chunk-87","heading":"","text":"Используйте классы MLPClassified и MLPRegressor для классификации и регрессии произвольных данных из интернета. Проведите анализ атрибуты, полученных моделей.","keywords":["ai","mlp","mlpclassified","mlpregressor","notebook","анализ","атрибуты","базовый","данных","интернета.","используйте","классификации"],"topics":["ai","oop"]},{"id":"chunk-88","heading":"","text":"https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv","keywords":["ai","6f9306ad21398ea43cba4f7d537619d0e07d5ae3","8836201","gist.githubusercontent.com","https","iris.csv","mlp","netj","notebook","raw","базовый","нейронные"],"topics":[]},{"id":"chunk-89","heading":"","text":"https://raw.githubusercontent.com/AnnaShestova/salary years simple linearregression/master/Salary Data.csv","keywords":["ai","salary","annashestova","data.csv","https","linearregression","master","mlp","notebook","raw.githubusercontent.com","simple","years"],"topics":["python","regression"]},{"id":"chunk-90","heading":"","text":"````python from sklearn.datasets import load iris from sklearn.neural network import MLPClassifier from sklearn.model selection import train test split","keywords":["ai","import","iris","load","mlp","mlpclassifier","network","notebook","python","selection","sklearn.datasets","sklearn.model"],"topics":["python","ai","testing"]},{"id":"chunk-91","heading":"","text":"iris = load iris() X iris, Y iris = iris.data, iris.target print('Dataset Sizes: ', X iris, Y iris) ````","keywords":["iris","ai","dataset","iris.data","iris.target","load","mlp","notebook","print","sizes","базовый","нейронные"],"topics":[]},{"id":"chunk-92","heading":"","text":"python X train, X test, Y train, Y test = train test split(X iris, Y iris, train size= 0.80, test size= 0.20, stratify=Y iris, random state=123) print('Train/Test Sizes : ', X train.shape, X test.shape, Y train.shape, Y test.shape)","keywords":["test","train","ai","iris","size","test.shape","train.shape","0.20","0.80","123","mlp","notebook"],"topics":["python","ai","testing"]},{"id":"chunk-93","heading":"","text":"python mlp classifier = MLPClassifier(random state=123) mlp classifier.fit(X train, Y train) MLPClassifier(activation='relu', alpha=0.0001, batch size='auto', beta 1=0.9, beta 2=0.999, early stopping=False, epsilon=1e 08, hidden layer sizes= (100,), learning rate='constant', learning rate init=0.001, max iter=200, momentum=0.9, n iter no change=10, nesterovs momentum=True, power t=0.5, random state=123, shuffle=True,","keywords":["ai","mlp","0.9","123","beta","iter","learning","mlpclassifier","momentum","random","rate","state"],"topics":["python","ai"]},{"id":"chunk-94","heading":"","text":"solver='adam' , tol=0.0001, validation fraction=0.1, verbose=False, warm start=False)","keywords":["ai","false","0.0001","0.1","adam","fraction","mlp","notebook","solver","start","tol","validation"],"topics":[]},{"id":"chunk-95","heading":"","text":"print(Y preds[:15]) print(Y test[:15]) print('Test Accuracy : %.3f'%mlp classifier.score(X test, Y test)) print('Training Accuracy : %.3f'%mlp classifier.score(X train, Y train)) ````","keywords":["print","test","ai","mlp",".3f","15","accuracy","classifier.score","train","notebook","preds","training"],"topics":["ai","testing"]},{"id":"chunk-96","heading":"","text":"````python from sklearn.neural network import MLPRegressor from sklearn.model selection import train test split","keywords":["ai","import","mlp","mlpregressor","network","notebook","python","selection","sklearn.model","sklearn.neural","split","test"],"topics":["python","ai","testing"]},{"id":"chunk-97","heading":"","text":"dataframe = pd.read csv('https://raw.githubusercontent.com/AnnaShestova/salary years simple linear regression/master/Salary Data.csv')","keywords":["ai","salary","annashestova","csv","data.csv","dataframe","https","linear","master","mlp","notebook","pd.read"],"topics":["python","pandas","regression"]},{"id":"chunk-98","heading":"","text":"python X train, X test, Y train, Y test = train test split(X salary, Y salary, train size = 0.8, test size=0.2, random state = 123) print('Train/Test Sizes : ', X train.shape, X test.shape, Y train.shape, Y test.shape)","keywords":["test","train","ai","salary","size","test.shape","train.shape","0.2","0.8","123","mlp","notebook"],"topics":["python","ai","testing"]},{"id":"chunk-99","heading":"","text":"python mlp regressor = MLPRegressor(random state=123) mlp regressor.fit(X train, Y train) MLPClassifier(activation='relu', alpha=0.0001, batch size='auto', beta 1=0.9, beta 2=0.999, early stopping=False, epsilon=1e 08, hidden layer sizes= (100,), learning rate='constant', learning rate init=0.001, max iter=200, momentum=0.9, n iter no change=10, nesterovs momentum=True, power t=0.5, random state=123, shuffle=True,","keywords":["ai","mlp","0.9","123","beta","iter","learning","momentum","random","rate","state","train"],"topics":["python","ai"]},{"id":"chunk-100","heading":"","text":"solver='adam' , tol=0.0001, validation fraction=0.1, verbose=False, warm start=False)","keywords":["ai","false","0.0001","0.1","adam","fraction","mlp","notebook","solver","start","tol","validation"],"topics":[]},{"id":"chunk-101","heading":"","text":"python Y preds = mlp regressor.predict(X test) print(Y preds[:10]) print(Y test[:10]) print('Test R^2 Score : %.3f'%mlp regressor.score(X test, Y test)) print('Training R^2 Score : %.3f'%mlp regressor.score(X train, Y train))","keywords":["test","mlp","print","ai",".3f","10","preds","regressor.score","score","train","notebook","python"],"topics":["python","ai","testing"]},{"id":"chunk-102","heading":"","text":"python print('Number of Coefs : ', len(mlp regressor.coefs )) [weights.shape for weights in mlp regressor.coefs ]","keywords":["ai","mlp","regressor.coefs","coefs","len","notebook","number","print","python","weights","weights.shape","базовый"],"topics":["python","ai"]},{"id":"chunk-103","heading":"","text":"python print('Number of Iterations for which estimator Ran : ', mlp regressor.n iter )","keywords":["ai","mlp","estimator","iter","iterations","notebook","number","print","python","ran","regressor.n","which"],"topics":["python","ai"]},{"id":"chunk-104","heading":"","text":"python print('Name of Output Layer Activation Function : ', mlp regressor.out activation )","keywords":["ai","activation","mlp","function","layer","name","notebook","output","print","python","regressor.out","базовый"],"topics":["python","ai"]}]},{"id":"ai/notebook-08-clustering","href":"/docs/ai/notebook-08-clustering","slug":["ai","notebook-08-clustering"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 8 — Кластеризация","description":"Неподконтрольное обучение и методы группировки данных.","preview":"Теоретический материал Кластеризация Кластеризация — разбиение множества объектов на подмножества, называемые кластерами. Кластеризация, будучи математическим алгоритм имеет широкое применение во многих сферах: начиная с таких естественно научных областей как","keywords":["данных","набор","кластеризацию","выполним","загрузим","centers","10","cluster","clusters","data","python","100","plt.scatter","кластеризация","кластеров","figsize","алгоритм","кластеризации"],"topics":["python","ai","pandas","numpy","oop","algorithms","clustering"],"chunks":[{"id":"chunk-0","heading":"","text":"Кластеризация — разбиение множества объектов на подмножества, называемые кластерами. Кластеризация, будучи математическим алгоритм имеет широкое применение во многих сферах: начиная с таких естественно научных областей как биология и физиология, и заканчивая маркетингом в социальных сетях и поисковой оптимизацией","keywords":["ai","кластеризация","notebook","алгоритм","биология","будучи","группировки","данных.","естественно","заканчивая","имеет","кластерами."],"topics":["algorithms","clustering"]},{"id":"chunk-1","heading":"","text":". Цель – разделить данные таким образом, чтобы точки, находящие в одном и том же кластере, были очень схожи друг с другом, а точки, находящиеся в разных кластерах, отличались друг от друга. Как и алгоритмы классификации, алгоритмы кластеризации присваивают (или прогнозируют) каждой точке данных номер кластера, которому она принадлежит.","keywords":["ai","алгоритмы","друг","точки","notebook","были","группировки","данные","данных","данных.","друга.","другом"],"topics":["oop","algorithms"]},{"id":"chunk-2","heading":"","text":"Задача кластеризации относится к широкому классу задач обучения без учителя. Кластеризацию применяют для анализа и поиска признаков по которым можно объединить объекты, сжатия данных и поиска новизны (что не входит ни в один кластер) В чем отличие классификации и кластеризации: при классификации у вас есть набор предопределенных классов, вы обучаете ИИ на наборе примеров и потом хотите знать, к какому классу","keywords":["ai","классификации","классу","кластеризации","поиска","notebook","анализа","без","вас","входит","вы","группировки"],"topics":["oop"]},{"id":"chunk-3","heading":"","text":"принадлежит новый объект. При кластеризации вы используете алгоритм, который пытается сгруппировать набор объектов и определить, существует ли какая либо взаимосвязь между объектами.","keywords":["ai","notebook","алгоритм","взаимосвязь","вы","группировки","данных.","используете","какая","кластеризации","кластеризация","который"],"topics":["algorithms"]},{"id":"chunk-4","heading":"","text":"Кластеризация k средних – один из самых простых и наиболее часто используемых алгоритмов кластеризации. Сначала выбирается число кластеров k. После выбора значения k алгоритм k средних отбирает точки, которые будут представлять центры кластеров (cluster centers). Затем для каждой точки данных вычисляется его евклидово расстояние до каждого центра кластера. Каждая точка назначается ближайшему центру кластера","keywords":["ai","кластеризация","кластеров","средних","точки","centers","cluster","k.","notebook","алгоритм","алгоритмов","ближайшему"],"topics":["algorithms","clustering"]},{"id":"chunk-5","heading":"","text":". Алгоритм вычисляет центроиды (centroids) – центры тяжести кластеров. Каждый центроид – это вектор, элементы которого представляют собой средние значения характеристик, вычисленные по всем точкам кластера. Центр кластера смещается в его центроид. Точки заново назначаются ближайшему центру кластера","keywords":["ai","кластера","centroids","notebook","алгоритм","ближайшему","вектор","всем","вычисленные","вычисляет","группировки","данных."],"topics":["algorithms"]},{"id":"chunk-6","heading":"","text":". Этапы изменения центров кластеров и переназначения точек итеративно повторяются до тех пор, пока границы кластеров и расположение центроидов не перестанут изменяться, т.е. на каждой итерации в каждый кластер будут попадать одни и те же точки данных.","keywords":["ai","данных.","кластеров","notebook","будут","границы","группировки","до","же","изменения","изменяться","итеративно"],"topics":[]},{"id":"chunk-7","heading":"","text":"Алгоритм k средних, наверное, самый популярный и простой алгоритм кластеризации и очень легко представляется в виде простого псевдокода:","keywords":["ai","алгоритм","notebook","виде","группировки","данных.","кластеризации","кластеризация","легко","методы","наверное","неподконтрольное"],"topics":["algorithms"]},{"id":"chunk-8","heading":"","text":"Выбрать количество кластеров k, которое нам кажется оптимальным для наших данных. Высыпать случайным образом в пространство наших данных k точек (центроидов). Для каждой точки нашего набора данных посчитать, к какому центроиду она ближе. Переместить каждый центроид в центр выборки, которую мы отнесли к этому центроиду","keywords":["ai","данных","данных.","наших","центроиду","notebook","ближе.","выборки","выбрать","высыпать","группировки","каждой"],"topics":[]},{"id":"chunk-9","heading":"","text":". Повторять последние два шага фиксированное число раз, либо до тех пор пока центроиды не \"сойдутся\" (обычно это значит, что их смещение относительно предыдущего положения не превышает какого то заранее заданного небольшого значения).","keywords":["ai","notebook","группировки","данных.","два","до","заданного","заранее","значения","значит","их","какого"],"topics":[]},{"id":"chunk-10","heading":"","text":"Стоит заметить, что можно рассчитывать расстояние между центройдами по любой метрике (Евклидовой, Хемминговой и т.д.).","keywords":["ai","notebook","группировки","данных.","евклидовой","заметить","кластеризация","любой","между","методы","метрике","можно"],"topics":[]},{"id":"chunk-11","heading":"","text":"Сгенерируем 2D набор данных, содержащий 4 разных больших объекта, а затем применим алгоритм k средних, чтобы увидеть результат.","keywords":["ai","2d","notebook","алгоритм","больших","группировки","данных","данных.","затем","кластеризация","методы","набор"],"topics":["algorithms"]},{"id":"chunk-12","heading":"сгенерируем данные","text":"from sklearn.datasets import make blobs x, y true = make blobs(n samples=400, centers=4, cluster std=0.60, random state=0) plt.scatter(x[:, 0], x[:, 1], s=20) plt.show() ````","keywords":["ai","blobs","make","0.60","20","400","centers","cluster","import","notebook","plt.scatter","plt.show"],"topics":["clustering"]},{"id":"chunk-13","heading":"Построим и визуализируем центры кластера, выбранные с помощью k средних оценки Python","text":"from sklearn.datasets import make blobs x, y true = make blobs(n samples=400, centers=4, cluster std=0.60, random state=0) plt.scatter(x[:, 0], x[:, 1], c=y kmeans, s=20, cmap='summer') centers = kmeans.cluster centers plt.scatter(centers[:, 0], centers[:, 1], c='blue', s=100, alpha=.9) plt.show() ````","keywords":["centers","ai","blobs","make","plt.scatter",".9","0.60","100","20","400","alpha","blue"],"topics":["python","clustering"]},{"id":"chunk-14","heading":"Построим и визуализируем центры кластера, выбранные с помощью k средних оценки Python","text":"Проведем кластеризацию K средних к набору простых цифр. K means попытается идентифицировать похожие цифры","keywords":["ai","средних","means","notebook","python","визуализируем","выбранные","группировки","данных.","идентифицировать","кластера","кластеризацию"],"topics":["python"]},{"id":"chunk-15","heading":"загрузим набор цифр из sklearn","text":"from sklearn.datasets import load digits digits = load digits() digits.data.shape ````","keywords":["ai","digits","load","digits.data.shape","import","notebook","sklearn","sklearn.datasets","группировки","данных.","загрузим","кластеризация"],"topics":[]},{"id":"chunk-16","heading":"выполним кластеризацию","text":"kmeans = KMeans(n clusters=10, random state=0) clusters = kmeans.fit predict(digits.data) kmeans.cluster centers .shape ````","keywords":["ai","clusters","kmeans",".shape","10","centers","digits.data","kmeans.cluster","kmeans.fit","notebook","predict","random"],"topics":["clustering"]},{"id":"chunk-17","heading":"выполним кластеризацию","text":"code /usr/local/lib/python3.10/dist packages/sklearn/cluster/ kmeans.py:870: FutureWarning: The default value of n init will change from 10 to 'auto' in 1.4. Set the value of n init explicitly to suppress the warning warnings.warn(","keywords":["ai","init","value","1.4.","10","870","auto","change","cluster","code","default","dist"],"topics":["python","clustering"]},{"id":"chunk-18","heading":"выполним кластеризацию","text":"python fig, ax = plt.subplots(2, 5, figsize=(8, 3)) centers = kmeans.cluster centers .reshape(10, 8, 8) for axi, center in zip(ax.flat, centers): axi.set(xticks=[], yticks=[]) axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)","keywords":["ai","centers","center",".reshape","10","ax","ax.flat","axi","axi.imshow","axi.set","cmap","fig"],"topics":["python","clustering"]},{"id":"chunk-19","heading":"выполним кластеризацию","text":"Дан массив данных. Требуется провести кластерный анализ данных методом k средних. Поэкспериментируйте с количеством кластеров.","keywords":["ai","данных.","notebook","анализ","выполним","группировки","дан","данных","кластеризацию","кластеризация","кластерный","кластеров."],"topics":[]},{"id":"chunk-20","heading":"выполним кластеризацию","text":"````python def cluster analysis(x, clusters): x, y true = make blobs(n samples=clusters 100, centers=clusters, cluster std=.6, random state=0) plt.scatter(x[:, 0], x[:, 1], s=20) plt.show()","keywords":["ai","clusters","cluster",".6","100","20","analysis","blobs","centers","def","make","notebook"],"topics":["python","clustering"]},{"id":"chunk-21","heading":"выполним кластеризацию","text":"x, y true = make blobs(n samples=clusters 100, centers=clusters, cluster std=.6, random state=0) plt.scatter(x[:, 0], x[:, 1], c=y kmeans, s=20, cmap='summer') centers = kmeans.cluster centers plt.scatter(centers[:, 0], centers[:, 1], c='blue', s=100, alpha=.9) plt.show()","keywords":["centers","ai","100","clusters","plt.scatter",".6",".9","20","alpha","blobs","blue","cluster"],"topics":["clustering"]},{"id":"chunk-22","heading":"выполним кластеризацию","text":"X = np.array([[5, 3], [10, 15], [15, 15], [24, 10], [30, 45], [85, 70], [71, 80], [60, 78], [55, 52], [80, 91]]) cluster analysis(X, 4) количество кластеров = 4 cluster analysis(X, 10) количество кластеров = 10 ````","keywords":["10","15","ai","80","analysis","cluster","кластеров","количество","24","30","45","52"],"topics":["numpy","clustering"]},{"id":"chunk-23","heading":"выполним кластеризацию","text":"Выполните кластеризацию для набора данных ирисов Фишера. Выполните предсказания для модели. Поэкспериментируйте с количеством кластеров.","keywords":["ai","выполните","кластеризацию","notebook","выполним","группировки","данных","данных.","ирисов","кластеризация","кластеров.","количеством"],"topics":[]},{"id":"chunk-24","heading":"выполним кластеризацию","text":"def clusterization(x, amount): print(x.data.shape) kmeans = KMeans(n clusters=amount, random state=0) clusters = kmeans.fit predict(x.data) print(kmeans.cluster centers .shape)","keywords":["ai","amount","clusters","kmeans","print",".shape","centers","clusterization","def","kmeans.cluster","kmeans.fit","notebook"],"topics":["clustering"]},{"id":"chunk-25","heading":"выполним кластеризацию","text":"fig, ax = plt.subplots(amount // 2, 2, figsize=(10, 3)) centers = kmeans.cluster centers .reshape(amount, 2, 2) for axi, center in zip(ax.flat, centers): axi.set(xticks=[], yticks=[]) axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)","keywords":["ai","centers","amount","center",".reshape","10","ax","ax.flat","axi","axi.imshow","axi.set","cmap"],"topics":["clustering"]},{"id":"chunk-26","heading":"выполним кластеризацию","text":"Алгомеративная кластеризация относится к семейству алгоритмов кластеризации, в основе которых лежат одинаковые принципы: алгоритм начинает свою работу с того, что каждую точку данных заносит в свой собственный кластер и по мере выполнения объединяет два наиболее схожих между собой кластера до тех пор, пока не будет удовлетворен определенный критерий остановки","keywords":["ai","кластеризация","notebook","алгомеративная","алгоритм","алгоритмов","будет","выполнения","выполним","группировки","данных","данных."],"topics":["algorithms","clustering"]},{"id":"chunk-27","heading":"выполним кластеризацию","text":". Зачастую данным критерием выступает это количество кластеров, поэтому схожие между собой кластеры объединяются до тех пор, пока не останется заданное число кластеров.","keywords":["ai","notebook","выполним","выступает","группировки","данным","данных.","до","заданное","зачастую","кластеризацию","кластеризация"],"topics":[]},{"id":"chunk-28","heading":"выполним кластеризацию","text":"Результатом агломеративной кластеризации является иерархическая кластеризация. Кластеризация выполняется итеративно, и каждая точка совершает путь от отдельной точки кластера до участника итогового кластера. На каждом промежуточном шаге происходит кластеризация данных (с разным количеством кластеров). Иногда полезно сразу взглянуть на все возможные кластеризации","keywords":["ai","кластеризация","кластеризации","notebook","агломеративной","взглянуть","возможные","выполним","выполняется","группировки","данных","данных."],"topics":["clustering"]},{"id":"chunk-29","heading":"выполним кластеризацию","text":". Следующий пример показывает наложение всех возможных кластеризаций, показанных на рис. и дает некоторое представление о том, как каждый кластер распадается на более мелкие кластеры.","keywords":["ai","notebook","более","возможных","всех","выполним","группировки","дает","данных.","каждый","кластер","кластеризаций"],"topics":[]},{"id":"chunk-30","heading":"выполним кластеризацию","text":"python X = np.array( [[7, 8], [12, 20], [17, 19], [26, 15], [32, 37], [87, 75], [73, 85], [62, 80], [73, 60], [87, 96]]) labels = range(1, 11) plt.figure(figsize=(10, 7)) plt.subplots adjust(bottom=.1) plt.scatter(X[:, 0], X[:, 1], label='True Position') for label, x, y in zip(labels, X[:, 0], X[:, 1]): plt.annotate( label, xy=(x, y), xytext=( 3, 3), textcoords='offset points', ha='right', va='bottom') plt.show()","keywords":["ai","label","73","87","bottom","labels",".1","10","11","12","15","17"],"topics":["python","numpy"]},{"id":"chunk-31","heading":"выполним кластеризацию","text":"python from scipy.cluster.hierarchy import dendrogram, linkage from matplotlib import pyplot as plt linked = linkage(X, 'single') label list = range(1, 11) plt.figure(figsize=(10, 7)) dendrogram(linked, orientation='top', labels=label list, distance sort='descending', show leaf counts=True) plt.show()","keywords":["ai","dendrogram","import","label","linkage","linked","list","10","11","counts","descending","distance"],"topics":["python","clustering"]},{"id":"chunk-32","heading":"выполним кластеризацию","text":"Далее нам нужно импортировать класс для кластеризации и вызвать его метод fit predict для прогнозирования кластера.","keywords":["ai","fit","notebook","predict","вызвать","выполним","группировки","далее","данных.","его","импортировать","класс"],"topics":["oop"]},{"id":"chunk-33","heading":"выполним кластеризацию","text":"python from sklearn.cluster import AgglomerativeClustering cluster = AgglomerativeClustering(n clusters=2, affinity='euclidean', linkage='ward') cluster.fit predict(X) plt.scatter(X[:, 0], X[:, 1], c=cluster.labels , cmap='rainbow')","keywords":["ai","agglomerativeclustering","affinity","cluster","cluster.fit","cluster.labels","clusters","cmap","euclidean","import","linkage","notebook"],"topics":["python","ai","clustering"]},{"id":"chunk-34","heading":"выполним кластеризацию","text":"В этом примере мы выполним иерархическую кластеризацию реальных данных и посмотрим, как ее можно использовать для решения реальной проблемы. Выполним кластеризацию данных по набору https://raw.githubusercontent.com/lucko515/clustering python/master/Customer%20in%20Mall%20clusterng/Mall Customers.csv","keywords":["ai","выполним","кластеризацию","данных","20clusterng","20in","20mall","clustering","customer","customers.csv","https","lucko515"],"topics":["python","clustering"]},{"id":"chunk-35","heading":"выполним кластеризацию","text":"Задача, которую мы собираемся решить в этом разделе, состоит в том, чтобы разделить клиентов на разные группы в зависимости от их покупательских тенденций.","keywords":["ai","notebook","выполним","группировки","группы","данных.","зависимости","задача","их","кластеризацию","кластеризация","клиентов"],"topics":[]},{"id":"chunk-36","heading":"загрузим набор данных","text":"url = r'https://raw.githubusercontent.com/lucko515/clustering python/master/Customer%20in%20Mall%20clusterng/Mall Customers.csv' customer data = pd.read csv(url) customer data.head() ````","keywords":["ai","customer","url","20clusterng","20in","20mall","clustering","csv","customers.csv","data","data.head","https"],"topics":["python","clustering"]},{"id":"chunk-37","heading":"загрузим набор данных","text":"code CustomerID Genre Age Annual Income (k$) Spending Score (1 100) 0 1 Male 19 15 39 1 2 Male 21 15 81 2 3 Female 20 16 6 3 4 Female 23 16 77 4 5 Female 31 17 40","keywords":["ai","female","15","16","male","100","17","19","20","21","23","31"],"topics":[]},{"id":"chunk-38","heading":"загрузим набор данных","text":"Наш набор данных состоит из пяти столбцов. Чтобы просмотреть результаты в двумерном пространстве, мы сохраним только два из них:«Годовой доход» (в тысячах долларов) и «Оценка расходов» (1–100). Столбец «Оценка расходов» показывает, как часто человек тратит деньги в торговом центре по шкале от 1 до 100, где 100 — это самый высокий расход","keywords":["100","ai","данных","набор","оценка","расходов","notebook","высокий","годовой","группировки","данных.","два"],"topics":[]},{"id":"chunk-39","heading":"загрузим набор данных","text":". Выполним следующий скрипт, чтобы отфильтровать первые три столбца из нашего набора данных:","keywords":["ai","данных","notebook","выполним","группировки","данных.","загрузим","кластеризация","методы","набор","набора","нашего"],"topics":[]},{"id":"chunk-40","heading":"загрузим набор данных","text":"Наш набор данных состоит из пяти столбцов. Чтобы просмотреть результаты в двумерном пространстве, мы сохраним только два из них:«Годовой доход» (в тысячах долларов) и «Оценка расходов» (1–100). Столбец «Оценка расходов» показывает, как часто человек тратит деньги в торговом центре по шкале от 1 до 100, где 100 — это самый высокий расход","keywords":["100","ai","данных","набор","оценка","расходов","notebook","высокий","годовой","группировки","данных.","два"],"topics":[]},{"id":"chunk-41","heading":"загрузим набор данных","text":". Выполним следующий скрипт, чтобы отфильтровать первые три столбца из нашего набора данных:","keywords":["ai","данных","notebook","выполним","группировки","данных.","загрузим","кластеризация","методы","набор","набора","нашего"],"topics":[]},{"id":"chunk-42","heading":"загрузим набор данных","text":"python figure(figsize=(28, 12), dpi=180) plt.figure(figsize=(10, 7)) plt.title('Customer Dendrograms') dend = shc.dendrogram(shc.linkage(data, method='ward'))","keywords":["ai","figsize","10","12","180","28","customer","data","dend","dendrograms","dpi","figure"],"topics":["python"]},{"id":"chunk-43","heading":"загрузим набор данных","text":"Если мы нарисуем горизонтальную линию, которая проходит через самое длинное расстояние без горизонтальной линии, мы получим 5 кластеров. Теперь мы знаем количество кластеров для нашего набора данных, следующим шагом будет группировка точек данных в эти пять кластеров.","keywords":["ai","данных","мы","кластеров.","notebook","без","будет","горизонтальной","горизонтальную","группировка","группировки","данных."],"topics":[]},{"id":"chunk-44","heading":"загрузим набор данных","text":"Для этого мы снова воспользуемся классом AgglomerativeClustering библиотеки sklearn.cluster.","keywords":["ai","agglomerativeclustering","notebook","sklearn.cluster.","библиотеки","воспользуемся","группировки","данных","данных.","загрузим","классом","кластеризация"],"topics":["oop","clustering"]},{"id":"chunk-45","heading":"загрузим набор данных","text":"cluster = AgglomerativeClustering(n clusters=5, affinity='euclidean', linkage='ward') cluster.fit predict(data) ````","keywords":["ai","affinity","agglomerativeclustering","cluster","cluster.fit","clusters","data","euclidean","linkage","notebook","predict","ward"],"topics":["clustering"]},{"id":"chunk-46","heading":"загрузим набор данных","text":"code /usr/local/lib/python3.10/dist packages/sklearn/cluster/ agglomerative.py:983: FutureWarning: Attribute affinity was deprecated in version 1.2 and will be removed in 1.4. Use metric instead warnings.warn(","keywords":["ai","1.2","1.4.","983","affinity","agglomerative.py","attribute","cluster","code","deprecated","dist","futurewarning"],"topics":["python","clustering"]},{"id":"chunk-47","heading":"загрузим набор данных","text":"code array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 0, 2, 0, 2, 1, 2, 0, 2,","keywords":["ai","array","code","notebook","группировки","данных","данных.","загрузим","кластеризация","методы","набор","неподконтрольное"],"topics":[]},{"id":"chunk-48","heading":"загрузим набор данных","text":"0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2])","keywords":["ai","notebook","группировки","данных","данных.","загрузим","кластеризация","методы","набор","неподконтрольное","обучение"],"topics":[]},{"id":"chunk-49","heading":"загрузим набор данных","text":"В качестве последнего шага давайте построим кластеры, чтобы увидеть, как на самом деле были сгруппированы наши данные:","keywords":["ai","notebook","были","группировки","давайте","данные","данных","данных.","деле","загрузим","качестве","кластеризация"],"topics":[]},{"id":"chunk-50","heading":"загрузим набор данных","text":"python plt.figure(figsize=(10, 7)) plt.scatter(data[:, 0], data[:, 1], c=cluster.labels , cmap='rainbow')","keywords":["ai","data","10","cluster.labels","cmap","figsize","notebook","plt.figure","plt.scatter","python","rainbow","группировки"],"topics":["python","ai","clustering"]},{"id":"chunk-51","heading":"загрузим набор данных","text":"Выполните иерархическую кластеризацию для набора данных об ирисах Фишера. При этом необходимо использовать любые два признака (всего их четыре).","keywords":["ai","данных","notebook","всего","выполните","группировки","данных.","два","загрузим","иерархическую","ирисах","использовать"],"topics":[]},{"id":"chunk-52","heading":"загрузим набор данных","text":"iris data = load iris() data = pd.DataFrame(data=iris data.data, columns=iris data.feature names) data.head() ````","keywords":["iris","ai","data","columns","data.data","data.feature","data.head","load","names","notebook","pd.dataframe","группировки"],"topics":["pandas"]},{"id":"chunk-53","heading":"загрузим набор данных","text":"code sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) 0 5.1 3.5 1.4 0.2 1 4.9 3.0 1.4 0.2 2 4.7 3.2 1.3 0.2 3 4.6 3.1 1.5 0.2 4 5.0 3.6 1.4 0.2","keywords":["0.2","cm","1.4","ai","length","petal","sepal","width","1.3","1.5","3.0","3.1"],"topics":[]},{"id":"chunk-54","heading":"загрузим набор данных","text":"python plt.figure(figsize=(10, 7)) plt.title('Iris Dendrograms') dend = shc.dendrogram(shc.linkage(data, method='ward'))","keywords":["ai","10","data","dend","dendrograms","figsize","iris","method","notebook","plt.figure","plt.title","python"],"topics":["python"]},{"id":"chunk-55","heading":"загрузим набор данных","text":"cluster = AgglomerativeClustering(n clusters=4, affinity='euclidean', linkage='ward') cluster.fit predict(data)","keywords":["ai","affinity","agglomerativeclustering","cluster","cluster.fit","clusters","data","euclidean","linkage","notebook","predict","ward"],"topics":["clustering"]},{"id":"chunk-56","heading":"загрузим набор данных","text":"plt.figure(figsize=(10, 7)) plt.scatter(data[:, 0], data[:, 1], c=cluster.labels , cmap='rainbow') ````","keywords":["ai","data","10","cluster.labels","cmap","figsize","notebook","plt.figure","plt.scatter","rainbow","группировки","данных"],"topics":["ai","clustering"]},{"id":"chunk-57","heading":"загрузим набор данных","text":"code /usr/local/lib/python3.10/dist packages/sklearn/cluster/ agglomerative.py:983: FutureWarning: Attribute affinity was deprecated in version 1.2 and will be removed in 1.4. Use metric instead warnings.warn(","keywords":["ai","1.2","1.4.","983","affinity","agglomerative.py","attribute","cluster","code","deprecated","dist","futurewarning"],"topics":["python","clustering"]}]},{"id":"algorithms","href":"/docs/algorithms","slug":["algorithms"],"section":"algorithms","sectionTitle":"Algorithms","title":"Algorithms","description":"Вводные и вспомогательные материалы StackMIREA Docs.","preview":"Что внутри Введение и навигация по проекту MDX структура и правила оформления Связь с Python и Java треками","keywords":["java","mdx","python","введение","внутри","навигация","оформления","правила","проекту","связь","структура","треками","algorithms","docs.","stackmirea","вводные","вспомогательные","материалы"],"topics":["python","java","algorithms"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"Введение и навигация по проекту MDX структура и правила оформления Связь с Python и Java треками","keywords":["algorithms","docs.","java","mdx","python","stackmirea","введение","вводные","внутри","вспомогательные","материалы","навигация"],"topics":["python","java"]}]},{"id":"bigdata","href":"/docs/bigdata","slug":["bigdata"],"section":"bigdata","sectionTitle":"BigData","title":"BigData","description":"Практики по анализу больших данных, ML, классификации и кластеризации.","preview":"BigData обзор В этом разделе собраны практические работы по дисциплине BigData в формате MDX. Материалы охватывают вводную часть, анализ данных, регрессию, прикладные кейсы со страховыми данными, классификацию, кластеризацию, ансамблевые методы и итоговую рабо","keywords":["практика","bigdata","данных","анализ","практики","данными","материалы","методы","работа","часть","mdx.","ансамблевое","ансамблевые","вводную","данных.","дисциплине","итоговая","итоговую"],"topics":["ai","oop","clustering","regression"],"chunks":[{"id":"chunk-0","heading":"BigData обзор","text":"В этом разделе собраны практические работы по дисциплине BigData в формате MDX. 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Документация собрана по исходному коду этой практики.","keywords":["java","алгоритмы","базовые","остановки.","рекурсивные","условия","10","task","документация","задание","исходному","коду"],"topics":["algorithms"]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task10/Recursion11.java Task10/Recursion12.java Task10/Recursion13.java Task10/Recursion14.java","keywords":["task10","java","10","recursion11.java","recursion12.java","recursion13.java","recursion14.java","task","алгоритмы","базовые","исходные","остановки."],"topics":["java"]},{"id":"chunk-2","heading":"Task10/Recursion11.java","text":"public class Recursion11 { public static int Recurs(){ Scanner s = new Scanner(System.in); int n = s.nextInt(); if (n==1){ int m = s.nextInt(); if (m == 1){ return Recurs()+n+m; } else{ int k = s.nextInt(); if(k == 1){ return Recurs()+m+n+k; } else{ return n+m+k; } } } else{ int m = s.nextInt(); if (m==1){ return Recurs()+m+n; } else{ return n+m","keywords":["int","return","else","if","recurs","s.nextint","java","public","scanner","+m+n","+m+n+k","+n+m"],"topics":["java"]},{"id":"chunk-3","heading":"Task10/Recursion11.java","text":"; } } } public static void main(String[] args){ System.out.println(Recurs()); } }","keywords":["java","10","args","main","public","recurs","recursion11.java","static","string","system.out.println","task","task10"],"topics":["ai","java"]},{"id":"chunk-4","heading":"Task10/Recursion12.java","text":"public class Recursion12 { public static void Recurs(){ Scanner s = new Scanner(System.in); int n = s.nextInt(); if (n 0){ if (n % 2 == 1){ System.out.println(n); Recurs(); } else{ Recurs(); } } } public static void main(String[] args){ Recurs(); } }","keywords":["recurs","public","if","java","scanner","static","void","10","args","class","else","int"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task10/Recursion13.java","text":"public class Recursion13 { public static void Recurs(){ Scanner s = new Scanner(System.in); int n = s.nextInt(); if (n 0){ System.out.println(n); int m = s.nextInt(); if (m 0){ Recurs(); } } } public static void main(String[] args){ Recurs(); } }","keywords":["public","recurs","if","int","java","s.nextint","scanner","static","void","10","args","class"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task10/Recursion14.java","text":"public class Recursion14 { public static int Recurs(int n){ if (n < 10){ return n; } else{ System.out.println(n % 10 + \" \"); return Recurs(n / 10); } } public static void main(String[] args){ System.out.println(Recurs(123)); } }","keywords":["10","public","recurs","int","java","return","static","system.out.println","123","args","class","else"],"topics":["ai","java"]},{"id":"chunk-7","heading":"Описание","text":"В этом модуле используется 4 Java файлов. Ключевые сущности: Recursion11, Recursion12, Recursion13, Recursion14.","keywords":["java","10","recursion11","recursion12","recursion13","recursion14.","task","алгоритмы","базовые","используется","ключевые","модуле"],"topics":["java"]},{"id":"chunk-8","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","10","task","алгоритмы","базовые","демо","задачи","запускайте","затем","кейсами.","класс","модель"],"topics":["oop"]}]},{"id":"java/task-11-sorting-students","href":"/docs/java/task-11-sorting-students","slug":["java","task-11-sorting-students"],"section":"java","sectionTitle":"Java","title":"Task 11 — Сортировка студентов","description":"Сортировка и сравнение объектов по метрикам.","preview":"Задание Сортировка и сравнение объектов по метрикам. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task11/Connection.java Task11/SortingStudentsByGPA.java Task11/Student.java Task11/Co","keywords":["new","task11","list","string","public","student","int","name","sortingstudentsbygpa","r.add","sortingstudentsbygpa.java","connection.java","idnumber","return","сортировка","c.add","mergesort","result.add"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Сортировка и сравнение объектов по метрикам. Документация собрана по исходному коду этой практики.","keywords":["сортировка","java","метрикам.","объектов","сравнение","11","task","документация","задание","исходному","коду","практики."],"topics":[]},{"id":"chunk-1","heading":"Task11/Connection.java","text":"c.add(new String[]{\"Max\", \"10\"}); c.add(new String[]{\"Nicolai\", \"20\"}); c.add(new String[]{\"Tolic\", \"19\"}); c.add(new String[]{\"Dane\", \"17\"});","keywords":["c.add","new","string","java","сортировка","10","11","17","19","20","connection.java","dane"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task11/Connection.java","text":"List r = new ArrayList< (); r.add(new String[]{\"Denes\",\"23\"}); r.add(new String[]{\"Dasha\", \"30\"}); r.add(new String[]{\"Oleg\", \"27\"}); //c.addAll(r); //System.out.print(c); List t = mergeSort(c, r); for (String[] d: t){ System.out.println(\"[\" +d[0]+\", \"+d[1] +\"]\"); } } private static List mergeSort(List c, List r) { if (c.size() left1 = c.subList(0, m1); List right1 = c.subList(m1, c.size())","keywords":["list","new","string","r.add","+d","c.size","c.sublist","java","m1","mergesort","сортировка","11"],"topics":["java"]},{"id":"chunk-3","heading":"Task11/Connection.java","text":"; List left2 = r.subList(0,m2); List right2 = r.subList(m2, r.size()); List sortleft = mergeSort(left1, left2); List sortright = mergeSort(right1, right2); return merge(sortleft, sortright); } public static List merge(List left, List right) { List result = new ArrayList< (); int i = 0; int j = 0","keywords":["list","int","java","left2","m2","merge","mergesort","r.sublist","right2","sortleft","sortright","сортировка"],"topics":["java"]},{"id":"chunk-4","heading":"Task11/Connection.java","text":"; while (i < left.size() && j < right.size()) { if (Integer.parseInt(left.get(i)[1]) < Integer.parseInt(right.get(j)[1])) { result.add(left.get(i)); i++; } else { result.add(right.get(j)); j++; } } while (i < left.size()) { result.add(left.get(i)); i++; } while (j < right.size()) { result.add(right.get(j)); j++; } return result; } }","keywords":["result.add","left.get","right.get","while","i++","integer.parseint","j++","java","left.size","right.size","сортировка","11"],"topics":["java"]},{"id":"chunk-5","heading":"Task11/SortingStudentsByGPA.java","text":"```java title=\"Task11/SortingStudentsByGPA.java\" package Task11; public class SortingStudentsByGPA implements Comparable { public String name; public int bal;","keywords":["java","public","task11","sortingstudentsbygpa.java","сортировка","11","bal","class","comparable","implements","int","name"],"topics":["java"]},{"id":"chunk-6","heading":"Task11/SortingStudentsByGPA.java","text":"public SortingStudentsByGPA(String name, int bal) { this.name = name; this.bal = bal; }","keywords":["bal","java","name","сортировка","11","int","public","sortingstudentsbygpa","sortingstudentsbygpa.java","string","task","task11"],"topics":["java"]},{"id":"chunk-7","heading":"Task11/SortingStudentsByGPA.java","text":"@Override public int compareTo(SortingStudentsByGPA o) { return o.bal this.bal; } public static void main(String[] args){ List r = new ArrayList< (); r.add(new SortingStudentsByGPA(\"Nicolai\", 123)); r.add(new SortingStudentsByGPA(\"Oleg\", 200)); r.add(new SortingStudentsByGPA(\"Sasha\", 260)); r.add(new SortingStudentsByGPA(\"Nikita\", 250)); Collections.sort(r)","keywords":["new","sortingstudentsbygpa","r.add","java","public","сортировка","11","123","200","250","260","args"],"topics":["ai","java"]},{"id":"chunk-8","heading":"Task11/SortingStudentsByGPA.java","text":"; for (SortingStudentsByGPA s : r){ System.out.println(s.name+ \" Баллы \"+s.bal); } } }","keywords":["java","сортировка","+s.bal","11","s.name+","sortingstudentsbygpa","sortingstudentsbygpa.java","system.out.println","task","task11","баллы","метрикам."],"topics":["java"]},{"id":"chunk-9","heading":"Task11/Student.java","text":"public Student(String name, int iDNumber) { this.name = name; this.iDNumber = iDNumber; } public void setName(String name){ this.name=name; } public String getName(){ return name; } public int getiDNumber() { return iDNumber; } public void setiDNumber(int iDNumber) { this.iDNumber = iDNumber; } public static void main(String[] args){ Student[] s = new Student[6]; s[0]=new Student(\"Sol\",1); s[1]=new Student(\"Pol\",2)","keywords":["public","idnumber","name","student","string","int","new","void","java","return","this.idnumber","this.name"],"topics":["ai","java"]},{"id":"chunk-10","heading":"Task11/Student.java","text":"; s[2]=new Student(\"Kol\",3); s[3]=new Student(\"Tol\",7); s[4]=new Student(\"Fol\",0); s[5]=new Student(\"Rol\",4); for(int i = 1; i =0 && s[j].getiDNumber() current.getiDNumber()){ s[j+1]=s[j]; j ; } s[j+1]=current; } System.out.println(\" \"); for(Student f: s){ System.out.println(\"Name: \"+f.getName()+\" id: \"+f.getiDNumber()); } } }","keywords":["student","new","j+1","java","system.out.println","сортировка",".getidnumber","+f.getidnumber","+f.getname","11","current","current.getidnumber"],"topics":["java"]},{"id":"chunk-11","heading":"Описание","text":"В этом модуле используется 3 Java файлов. Ключевые сущности: Connection, SortingStudentsByGPA, Student.","keywords":["java","сортировка","11","connection","sortingstudentsbygpa","student.","task","используется","ключевые","метрикам.","модуле","объектов"],"topics":["java"]},{"id":"chunk-12","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","сортировка","11","task","демо","задачи","запускайте","затем","кейсами.","класс","метрикам.","модель"],"topics":["oop"]},{"id":"chunk-13","heading":"Вывод","text":"Task 11 — Сортировка студентов документирует реальное решение из исходного кода.","keywords":["сортировка","11","java","task","студентов","вывод","документирует","исходного","кода.","метрикам.","объектов","реальное"],"topics":[]}]},{"id":"java/task-12-drunkard-game","href":"/docs/java/task-12-drunkard-game","slug":["java","task-12-drunkard-game"],"section":"java","sectionTitle":"Java","title":"Task 12 — Игра Пьяница","description":"Моделирование игры через очереди и коллекции.","preview":"Задание Моделирование игры через очереди и коллекции. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task12/Drunkard.java Task12/Drunkard.java java title=\"Task12/Drunkard.java\" package","keywords":["int","b1","b1.length","new","b2.length","drunkard.java","newb1","newb2","system.out.println","task12","b2","count","system.arraycopy","card2","t1","t2","10","card1"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Моделирование игры через очереди и коллекции. Документация собрана по исходному коду этой практики.","keywords":["java","игры","коллекции.","моделирование","очереди","через","12","task","документация","задание","игра","исходному"],"topics":[]},{"id":"chunk-1","heading":"Task12/Drunkard.java","text":"public class Drunkard { public static void main(String[] args) throws InterruptedException { System.out.println(\"Ведите 5 карт для первого игрока: \"); Scanner s = new Scanner(System.in); Scanner r = new Scanner(System.in); int t1 = s.nextInt(); System.out.println(\"Ведите 5 карт для второго игрока: \"); int t2 = r.nextInt(); int[] b1 = new int[5]; int[] b2 = new int[5]; while (t1 0) { for (int i = b1.length 1; i = 0","keywords":["int","new","scanner","java","public","system.in","system.out.println","t1","ведите","игрока","карт","12"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task12/Drunkard.java","text":"; i ) { b1[i] = t1 % 10; t1 = t1 / 10; } } while (t2 0) { for (int i = b2.length 1; i = 0; i ) { b2[i] = t2 % 10; t2 = t2 / 10; } } int count = 0; while (b1.length != 0 && b2.length != 0 && count card2 (card1 == 0 && card2 == 9)) && (card1!=9&&card2!=0)) { int[] newb1 = new int[b1.length + 1]; System.arraycopy(b1, 1, newb1, 0, b1.length 1); newb1[newb1.length 2] = card1; newb1[newb1.length 1] = card2; b1 = newb1","keywords":["newb1","10","card2","int","t2","b1","b1.length","card1","t1","b2.length","count","java"],"topics":["java"]},{"id":"chunk-3","heading":"Task12/Drunkard.java","text":"; int[] newb2 = new int[b2.length 1]; System.arraycopy(b2, 1, newb2, 0, b2.length 1); b2 = newb2; count += 1; for(int i =0; i<b2.length;i++) { System.out.println(b2[i]); } System.out.println(\"==========================\"); } else { int[] newb2 = new int[b2.length + 1]; System.arraycopy(b2, 1, newb2, 0, b2.length 1); newb2[newb2.length 2] = card2; newb2[newb2.length 1] = card1; b2 = newb2","keywords":["newb2","b2","b2.length","int","java","new","newb2.length","system.arraycopy","system.out.println","12","card1","card2"],"topics":["java"]},{"id":"chunk-4","heading":"Task12/Drunkard.java","text":"; int[] newb1 = new int[b1.length 1]; System.arraycopy(b1, 1, newb1, 0, b1.length 1); b1 = newb1; count += 1; } } if (count == 106) { System.out.println(\"botva\"); } if (b2.length == 0) { System.out.println(\"first \" + count); } else { System.out.println(\"second \" + count); } //Удаление переменной из массива / int index = 0; int[] newArray = new int[b1.length 1]; System.arraycopy(b1,0,newArray,0,index)","keywords":["int","count","b1","b1.length","newb1","system.out.println","if","index","java","new","newarray","system.arraycopy"],"topics":["java"]},{"id":"chunk-5","heading":"Task12/Drunkard.java","text":"; System.arraycopy(b1, index+1,newArray,index,b1.length index 1); b1=newArray; for(int i= 0; i<b1.length;i++) { System.out.println(b1[i]); } / } }","keywords":["b1","b1.length","index","java","newarray","12","drunkard.java","i++","index+1","int","system.arraycopy","system.out.println"],"topics":["java"]},{"id":"chunk-6","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","12","task","демо","задачи","запускайте","затем","игра","игры","кейсами.","класс","коллекции."],"topics":["oop"]}]},{"id":"java/task-13-file-io","href":"/docs/java/task-13-file-io","slug":["java","task-13-file-io"],"section":"java","sectionTitle":"Java","title":"Task 13 — Файловый ввод-вывод","description":"Чтение и запись файлов, обработка ошибок I/O.","preview":"Задание Чтение и запись файлов, обработка ошибок I/O. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task13/file.java Task13/test.txt Task13/file.java java title=\"Task13/file.java\" pack","keywords":["task13","file.java","задание","new","filewriter","scanner","system.out.println","test.txt","catch","ex","ex.getmessage","ideaprojects","ioexception","o.","src","string","try","untitled"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Чтение и запись файлов, обработка ошибок I/O. Документация собрана по исходному коду этой практики.","keywords":["java","o.","запись","обработка","ошибок","файлов","чтение","13","task","ввод-вывод","документация","задание"],"topics":[]},{"id":"chunk-1","heading":"Task13/file.java","text":"public class file { public static void main(String[] args) { //Задание 1 и 3 try (FileWriter writer = new FileWriter(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task13\\\\test.txt\", false)){ Scanner s = new Scanner(System.in); String w = s.nextLine(); writer.write(w); writer.flush(); } catch (IOException ex) { System.out.println(ex.getMessage())","keywords":["filewriter","java","new","public","scanner","string","task13","13","args","catch","class","ex"],"topics":["ai","java","testing"]},{"id":"chunk-2","heading":"Task13/file.java","text":"; } //Задание 2 try (FileReader reader = new FileReader(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task13\\\\test.txt\")){ int c; while ((c=reader.read())!= 1){ System.out.println((char)c); } }catch (IOException ex){ System.out.println(ex.getMessage()); } //Задание 4 try (FileWriter writer = new FileWriter(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task13\\\\test.txt\", true)){ Scanner s = new Scanner(System.in); String w = s.nextLine()","keywords":["new","task13","filereader","filewriter","ideaprojects","java","scanner","src","system.out.println","test.txt","try","untitled"],"topics":["java","testing"]},{"id":"chunk-3","heading":"Task13/file.java","text":"; writer.append(\"\\n\"); writer.write(w); writer.flush(); }catch (IOException ex){ System.out.println(ex.getMessage()); } //Задание 4 } }","keywords":["java","13","catch","ex","ex.getmessage","file.java","ioexception","o.","system.out.println","task","task13","writer.append"],"topics":["java"]},{"id":"chunk-4","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","13","o.","task","ввод-вывод","демо","задачи","запись","запускайте","затем","кейсами.","класс"],"topics":["oop"]}]},{"id":"java/task-14-generics-and-queues","href":"/docs/java/task-14-generics-and-queues","slug":["java","task-14-generics-and-queues"],"section":"java","sectionTitle":"Java","title":"Task 14 — Generics и очереди","description":"Обобщения и безопасная работа с очередями.","preview":"Задание Обобщения и безопасная работа с очередями. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task14/BoundedWaitList.java Task14/IWaitList.java Task14/Main.java Task14/UnfairWaitLis","keywords":["task14","public","system.out.println","element","res","new","waitlist","boolean","if","int","bout.add","capacity","return","boundedwaitlist","content","content.add","main.java","unfairwaitlist"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Обобщения и безопасная работа с очередями. Документация собрана по исходному коду этой практики.","keywords":["java","безопасная","обобщения","очередями.","работа","14","generics","task","документация","задание","исходному","коду"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task14/BoundedWaitList.java Task14/IWaitList.java Task14/Main.java Task14/UnfairWaitList.java Task14/WaitList.java","keywords":["task14","java","14","boundedwaitlist.java","generics","iwaitlist.java","main.java","task","unfairwaitlist.java","waitlist.java","безопасная","исходные"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task14/BoundedWaitList.java","text":"public class BoundedWaitList extends WaitList { private int capacity; public BoundedWaitList(int capacity){ if (capacity 0) this.capacity = capacity; else System.out.println(\"Очередь должна состоять больше чем из 0 элементов\"); } public int getCapacity() { return capacity; } public void add(E element){ if(capacity content.size()) content.add(element); else System.out.println(\"Очередь переполнена\"); }","keywords":["capacity","public","int","boundedwaitlist","element","else","if","java","system.out.println","очередь","14","add"],"topics":["ai","java"]},{"id":"chunk-3","heading":"Task14/IWaitList.java","text":"public interface IWaitList { void add(E element); E remove(); boolean contains(E element); boolean containsAll(Collection c); boolean isEmpty(); }","keywords":["boolean","element","java","14","add","collection","contains","containsall","generics","interface","isempty","iwaitlist"],"topics":["ai","java"]},{"id":"chunk-4","heading":"Task14/Main.java","text":"public class Main { static public void main(String[] args){ WaitList wait = new WaitList< (); ArrayList al = new ArrayList< (); al.add(\"O\"); wait.add(\"L\"); wait.add(\"O\"); wait.add(\"L\"); System.out.println(wait); wait.remove(); System.out.println(wait); System.out.println(\"Есть ли 'L' ? \" + wait.contains(\"L\")); al.add(\"D\"); System.out.println(\"Есть ли 'D'? \" + wait.containsAll(al)); System.out.println(\"Список пуст","keywords":["system.out.println","wait","wait.add","al","al.add","arraylist","java","main","new","public","waitlist","ли"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task14/Main.java","text":"? \" + wait.isEmpty()); System.out.println(\" \"); BoundedWaitList bout = new BoundedWaitList< (4); bout.add(\"f\"); bout.add(\"a\"); bout.add(\"B\"); bout.add(\"a\"); bout.add(\"!\"); System.out.println(bout); bout.remove(); System.out.println(bout); bout.add(\"!\"); System.out.println(bout); System.out.println(\"Объем списка: \"+bout.getCapacity()); System.out.println(\" \"); UnfairWaitList unf = new UnfairWaitList< (); unf.add(\"L\")","keywords":["bout.add","system.out.println","bout","boundedwaitlist","java","new","unfairwaitlist","+bout.getcapacity","14","bout.remove","generics","main.java"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task14/Main.java","text":"; unf.add(\"O\"); unf.add(\"T\"); unf.add(\"p\"); System.out.println(unf); unf.remove(\"L\"); System.out.println(\"Попытка удаления первого элемента: \"+unf); unf.remove(\"O\"); System.out.println(\"Удаление элемента: \"+unf); unf.moveToBack(\"T\"); System.out.println(\"Перенос элемента в конец: \"+unf);","keywords":["system.out.println","+unf","unf.add","элемента","java","unf.remove","14","generics","main.java","task","task14","unf"],"topics":["ai","java"]},{"id":"chunk-7","heading":"Task14/UnfairWaitList.java","text":"public class UnfairWaitList extends WaitList { public UnfairWaitList(){} public void remove(E element){ for(int i =0; i<content.size();i = i) { E el = content.remove(); if(!el.equals(element) (el.equals(element) && i == 0)){ content.add(el); i++; } } } public void moveToBack(E element){ remove(element); content.add(element); } }","keywords":["element","public","content.add","el","el.equals","java","remove","unfairwaitlist","void","14","class","content.remove"],"topics":["ai","java"]},{"id":"chunk-8","heading":"Task14/WaitList.java","text":"public class WaitList implements IWaitList { protected ConcurrentLinkedQueue content; public WaitList(){ content = new ConcurrentLinkedQueue< (); } public WaitList(Collection c){ content = new ConcurrentLinkedQueue< (c); } @Override public String toString() { return \"WaitList{\" + \"content=\" + content + '}'; }","keywords":["content","public","waitlist","concurrentlinkedqueue","java","new","14","class","collection","generics","implements","iwaitlist"],"topics":["ai","java"]},{"id":"chunk-9","heading":"Task14/WaitList.java","text":"@Override public E remove() { if(isEmpty()){ throw new IllegalStateException(\"Очередь пуста\"); } return content.remove(); }","keywords":["java","14","content.remove","generics","if","illegalstateexception","isempty","new","override","public","remove","return"],"topics":["ai","java"]},{"id":"chunk-10","heading":"Task14/WaitList.java","text":"@Override public boolean contains(E element) { boolean res = false; for (int i = 0; i < content.size(); i++) { E cur = content.remove(); if (cur.equals(element)) res = true; content.add(cur); } return res; }","keywords":["res","boolean","cur","element","java","14","contains","content.add","content.remove","content.size","cur.equals","false"],"topics":["ai","java"]},{"id":"chunk-11","heading":"Task14/WaitList.java","text":"@Override public boolean containsAll(Collection c) { ArrayList al = new ArrayList< (c); boolean res = false; for(int i = 0; i < c.size(); i++){ res = false; for(int j = 0; j < content.size(); j++){ E el = content.remove(); if(el.equals(al.get(i))) res = true; content.add(el); } if(!res) return res; } return res; }","keywords":["res","arraylist","boolean","el","false","if","int","java","return","14","al","al.get"],"topics":["ai","java"]},{"id":"chunk-12","heading":"Описание","text":"В этом модуле используется 5 Java файлов. Ключевые сущности: BoundedWaitList, IWaitList, Main, UnfairWaitList, WaitList.","keywords":["java","14","boundedwaitlist","generics","iwaitlist","main","task","unfairwaitlist","waitlist.","безопасная","используется","ключевые"],"topics":["ai","java"]},{"id":"chunk-13","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","14","generics","task","безопасная","демо","задачи","запускайте","затем","кейсами.","класс","модель"],"topics":["oop"]}]},{"id":"java/task-15-mvc-pattern","href":"/docs/java/task-15-mvc-pattern","slug":["java","task-15-mvc-pattern"],"section":"java","sectionTitle":"Java","title":"Task 15 — MVC паттерн","description":"Разделение модели, представления и контроллера.","preview":"Задание Разделение модели, представления и контроллера. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task15/Employee.java Task15/EmployeeController.java Task15/EmployeeView.java Task1","keywords":["task15","public","string","new","void","model","name","view","private","mvcpatterndemomovie.java","class","return","employee","movieview.java","student","system.out.println","movie","255"],"topics":["ai","mvc","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Разделение модели, представления и контроллера. Документация собрана по исходному коду этой практики.","keywords":["java","контроллера.","модели","представления","разделение","15","mvc","task","документация","задание","исходному","коду"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task15/Employee.java Task15/EmployeeController.java Task15/EmployeeView.java Task15/Movie.java Task15/MovieController.java Task15/MovieView.java Task15/MVCPatternDemoEmployee.java Task15/MVCPatternDemoMovie.java Task15/MVCPatternDemoStudent.java Task15/Student.java Task15/StudentController.java Task15/StudentView.java","keywords":["task15","java","15","employee.java","employeecontroller.java","employeeview.java","movie.java","moviecontroller.java","movieview.java","mvc","mvcpatterndemoemployee.java","mvcpatterndemomovie.java"],"topics":["mvc","java"]},{"id":"chunk-2","heading":"Task15/Employee.java","text":"```java title=\"Task15/Employee.java\" package Task15; public class Employee { private String Name; private double salary; public String getName() { return Name; } public void setName(String name) { Name = name; } public double getSalary() { return salary; }","keywords":["name","public","java","string","task15","double","employee.java","private","return","salary","15","class"],"topics":["java"]},{"id":"chunk-3","heading":"Task15/EmployeeController.java","text":"```java title=\"Task15/EmployeeController.java\" package Task15; public class EmployeeController { private Employee model; private EmployeeView view; public EmployeeController(Employee model, EmployeeView view) { this.model = model; this.view = view; } public void setEmployeeName(String name){ model.setName(name); } public String getEmployeeName(){ return model.getName()","keywords":["public","java","model","task15","view","employee","employeecontroller","employeecontroller.java","employeeview","name","private","string"],"topics":["java"]},{"id":"chunk-4","heading":"Task15/EmployeeController.java","text":"; } public void setEmployeeSalary(double salary){ model.setSalary(salary); } public double getEmployeeSalary(){ return model.getSalary(); } public void updateView(){ view.printEmployeeDetails(model.getName(), model.getSalary()); } public void nds(){ model.setSalary(model.getSalary() (model.getSalary() 0.13)); } public void theend(String e){ view.printEnd(e); } }","keywords":["public","model.getsalary","void","double","java","model.setsalary","salary","0.13","15","employeecontroller.java","getemployeesalary","model.getname"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task15/EmployeeView.java","text":"public class EmployeeView { public void printEmployeeDetails(String name, double salary){ System.out.println(\"Имя: \" + name); System.out.println(\"Зарплата: \"+salary); } public void printEnd(String e){ System.out.println(\"End: \"+e); } }","keywords":["public","system.out.println","java","name","string","void","+e","+salary","15","class","double","employeeview"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task15/Movie.java","text":"public void setOpis(String opis) { this.opis = opis; } public String getDate() { return date; }","keywords":["java","opis","public","string","15","date","getdate","movie.java","mvc","return","setopis","task"],"topics":["java"]},{"id":"chunk-7","heading":"Task15/MovieController.java","text":"public class MovieController { private Movie model; private MovieView view; public MovieController(Movie model, MovieView view){ this.model = model; this.view = view; } public void updet(String imagePath, String date, String opis){ model.setDate(date); model.setOpis(opis); view.updateView(imagePath, date, opis); }","keywords":["date","model","opis","public","string","view","imagepath","java","movie","moviecontroller","movieview","private"],"topics":["java"]},{"id":"chunk-8","heading":"Task15/MovieView.java","text":"public class MovieView extends JFrame { private JLabel name movie; private JLabel prince; private JLabel time;","keywords":["jlabel","private","java","15","class","extends","jframe","movie","movieview","movieview.java","mvc","name"],"topics":["java"]},{"id":"chunk-9","heading":"Task15/MovieView.java","text":"MovieView() throws IOException, InterruptedException { super(\"Расписание кино\"); setDefaultCloseOperation(JFrame.EXIT ON CLOSE);","keywords":["java","15","close","interruptedexception","ioexception","jframe.exit","movieview","movieview.java","mvc","setdefaultcloseoperation","super","task"],"topics":["java"]},{"id":"chunk-10","heading":"Task15/MovieView.java","text":"JPanel panel = new JPanel(); panel.setLayout(new BoxLayout(panel, BoxLayout.Y AXIS)); panel.setBackground(new Color(255,255,255)); panel.add(Box.createVerticalGlue());","keywords":["255","new","java","jpanel","panel","15","axis","box.createverticalglue","boxlayout","boxlayout.y","color","movieview.java"],"topics":["java"]},{"id":"chunk-11","heading":"Task15/MovieView.java","text":"JPanel imagePanel = new JPanel(); imagePanel.setPreferredSize(new Dimension(800, 600)); imagePanel.setMaximumSize(new Dimension(800, 600)); imagePanel.setBackground(new Color(255,255,255)); imagePanel.add(name movie);","keywords":["new","255","600","800","dimension","java","jpanel","15","color","imagepanel","imagepanel.add","imagepanel.setbackground"],"topics":["java"]},{"id":"chunk-12","heading":"Task15/MovieView.java","text":"prince.setAlignmentX(Component.CENTER ALIGNMENT); time.setAlignmentX(Component.CENTER ALIGNMENT);","keywords":["alignment","component.center","java","15","movieview.java","mvc","prince.setalignmentx","task","task15","time.setalignmentx","контроллера.","модели"],"topics":["java"]},{"id":"chunk-13","heading":"Task15/MovieView.java","text":"getContentPane().add(panel); setSize(800, 800); setDefaultCloseOperation(JFrame.EXIT ON CLOSE); setVisible(true); }","keywords":["800","java",".add","15","close","getcontentpane","jframe.exit","movieview.java","mvc","panel","setdefaultcloseoperation","setsize"],"topics":["java"]},{"id":"chunk-14","heading":"Task15/MovieView.java","text":"try { BufferedImage img = ImageIO.read(new File(filename)); JLabel picture = new JLabel(); picture.setIcon(new ImageIcon(img)); } catch (IOException e) { System.out.println(\"Ошибка при получении картинки\"); } } public void updateView(String imagePath, String date, String description){ name movie.setIcon(new ImageIcon(imagePath)); prince.setText(date); time.setText(\" \"+ description +\" \"); } }","keywords":["new","string","date","description","imageicon","imagepath","img","java","jlabel","15","bufferedimage","catch"],"topics":["java"]},{"id":"chunk-15","heading":"Task15/MVCPatternDemoEmployee.java","text":"public class MVCPatternDemoEmployee { public static void main(String[] args){ Employee model = retriveEmployeeFromDa(); EmployeeView view = new EmployeeView(); EmployeeController controller = new EmployeeController(model, view); controller.updateView(); controller.nds(); //controller.setEmployeeSalary(30000); System.out.println(\"С вычитом подоходового налога\"); controller.updateView()","keywords":["controller.updateview","employeecontroller","employeeview","java","model","new","public","view","15","30000","args","class"],"topics":["ai","mvc","java"]},{"id":"chunk-16","heading":"Task15/MVCPatternDemoEmployee.java","text":"; controller.theend(\"Завершение работы\"); }","keywords":["java","15","controller.theend","mvc","mvcpatterndemoemployee.java","task","task15","завершение","контроллера.","модели","паттерн","представления"],"topics":["mvc","java"]},{"id":"chunk-17","heading":"Task15/MVCPatternDemoEmployee.java","text":"private static Employee retriveEmployeeFromDa() { Employee model = new Employee(); model.setName(\"Артем\"); model.setSalary(30000); return model; } }","keywords":["employee","java","model","15","30000","model.setname","model.setsalary","mvc","mvcpatterndemoemployee.java","new","private","retriveemployeefromda"],"topics":["mvc","java"]},{"id":"chunk-18","heading":"Task15/MVCPatternDemoMovie.java","text":"public class MVCPatternDemoMovie { public static void main(String[] args) throws IOException, InterruptedException { Movie model = new Movie(\"Дата показа 12.12.2023\", \"Когда речь идет о деньгах, совесть молчит. А уж если речь об огромных деньгах!.","keywords":["java","movie","public","деньгах","речь","12.12.2023","15","args","class","interruptedexception","ioexception","main"],"topics":["ai","mvc","java"]},{"id":"chunk-19","heading":"Task15/MVCPatternDemoMovie.java","text":". Это основанная на реальных событиях история нескольких провидцев, которые независимо друг от друга предсказали мировой экономический кризис 2008 года задолго до того, как о нем зашептались в кулуарах на Уолл стрит. И предсказав, стали на нем зарабатывать. Сами того не желая.\"); MovieView view = new MovieView(); MovieController controller = new MovieController(model, view)","keywords":["java","moviecontroller","movieview","new","view","нем","того","15","2008","controller","model","mvc"],"topics":["mvc","java"]},{"id":"chunk-20","heading":"Task15/MVCPatternDemoMovie.java","text":"; controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\2.jpg\", model.getOpis(), model.getDate()); view.addMouseListener(new MouseAdapter() { int click = 1;","keywords":["java","task15","15","2.jpg","click","controller.updet","ideaprojects","int","model.getdate","model.getopis","mouseadapter","mvc"],"topics":["mvc","java"]},{"id":"chunk-21","heading":"Task15/MVCPatternDemoMovie.java","text":"@Override public void mouseClicked(MouseEvent e) { switch (click) { case 0: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\2.jpg\", \"Дата показа 13.12.2023\", \"Когда речь идет о деньгах, совесть молчит. А уж если речь об огромных деньгах!.","keywords":["java","task15","деньгах","речь","13.12.2023","15","2.jpg","case","click","controller.updet","ideaprojects","mouseclicked"],"topics":["mvc","java"]},{"id":"chunk-22","heading":"Task15/MVCPatternDemoMovie.java","text":". Это основанная на реальных событиях история нескольких провидцев, которые независимо друг от друга предсказали мировой экономический кризис 2008 года задолго до того, как о нем зашептались в кулуарах на Уолл стрит. И предсказав, стали на нем зарабатывать. Сами того не желая.\"); break","keywords":["java","нем","того","15","2008","break","mvc","mvcpatterndemomovie.java","task","task15","года","до"],"topics":["mvc","java"]},{"id":"chunk-23","heading":"Task15/MVCPatternDemoMovie.java","text":"; case 1: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\3.jpg\", \"Дата показа 14.12.2023\", \"Нью йоркский финансист Джордан Белфорт в конце 80 х годов основал одну из крупнейших брокерских компаний, но через десять лет его осудили за мошенничество с ценными бумагами","keywords":["java","task15","14.12.2023","15","3.jpg","80","case","controller.updet","ideaprojects","mvc","mvcpatterndemomovie.java","src"],"topics":["mvc","java"]},{"id":"chunk-24","heading":"Task15/MVCPatternDemoMovie.java","text":". С самых высот финансового мира Белфорду приходиться опуститься на самое дно, он борется с алкоголизмом и наркотической зависимостью, но после тюремного заключения бывший брокер переосмысливает свою жизнь, начинает читать лекции и выпускает книгу.\"); break","keywords":["java","15","break","mvc","mvcpatterndemomovie.java","task","task15","алкоголизмом","белфорду","борется","брокер","бывший"],"topics":["mvc","java"]},{"id":"chunk-25","heading":"Task15/MVCPatternDemoMovie.java","text":"; case 2: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\4.jpg\", \"Дата показа 15.12.2023\",\"Американское семейство отправляется из Чикаго в Европу, но в спешке сборов бестолковые родители забывают дома… одного из своих детей. Юное создание, однако, не теряется и демонстрирует чудеса изобретательности. И когда в дом залезают грабители, им приходится не раз пожалеть о встрече с милым крошкой.\"); break","keywords":["java","task15","15","15.12.2023","4.jpg","break","case","controller.updet","ideaprojects","mvc","mvcpatterndemomovie.java","src"],"topics":["mvc","java"]},{"id":"chunk-26","heading":"Task15/MVCPatternDemoMovie.java","text":"; case 3: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\5.jpg\", \"Дата показа 16.12.2023\", \"Кобб — талантливый вор, лучший из лучших в опасном искусстве извлечения: он крадет ценные секреты из глубин подсознания во время сна, когда человеческий разум наиболее уязвим","keywords":["java","task15","15","16.12.2023","5.jpg","case","controller.updet","ideaprojects","mvc","mvcpatterndemomovie.java","src","task"],"topics":["mvc","java"]},{"id":"chunk-27","heading":"Task15/MVCPatternDemoMovie.java","text":". Редкие способности Кобба сделали его ценным игроком в привычном к предательству мире промышленного шпионажа, но они же превратили его в извечного беглеца и лишили всего, что он когда либо любил.\"); break; case 4: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\1.png\", \"Дата показа 17.12.2023\", \"Вскоре после отмщения за смерть жены и сына, Макс Рокатански покинул ряды «Основного силового патруля» и уехал","keywords":["java","task15","его","1.png","15","17.12.2023","break","case","controller.updet","ideaprojects","mvc","mvcpatterndemomovie.java"],"topics":["mvc","java"]},{"id":"chunk-28","heading":"Task15/MVCPatternDemoMovie.java","text":"в глушь, где скитается в одиночестве, пока мир медленно падает впоследствии нефтяного кризиса и глобальной войны. Не имея ничего, кроме своей машины «Перехватчик», Максу предстоит научиться, как выжить в пост апокалиптической пустоши и сражаться с жестокими, безжалостными воинами, которые населяют её.\"); break; } click++; if (click == 5) { click = 0; } } }); } }","keywords":["click","java","15","break","click++","if","mvc","mvcpatterndemomovie.java","task","task15","апокалиптической","безжалостными"],"topics":["mvc","java"]},{"id":"chunk-29","heading":"Task15/MVCPatternDemoStudent.java","text":"public class MVCPatternDemoStudent { public static void main(String[] args){ Student model = retriveStudentFromDa(); StudentView view = new StudentView(); StudentController controller = new StudentController(model, view); controller.updateView(); controller.setStudentRollNo(\"01\"); System.out.println(\" \"); controller.updateView(); } private static Student retriveStudentFromDa() { Student student = new Student()","keywords":["student","new","controller.updateview","java","model","public","retrivestudentfromda","static","studentcontroller","studentview","view","01"],"topics":["ai","mvc","java"]},{"id":"chunk-30","heading":"Task15/MVCPatternDemoStudent.java","text":"; student.setName(\"Николай\"); student.setRollNo(\"09\"); return student; } }","keywords":["java","09","15","mvc","mvcpatterndemostudent.java","return","student","student.setname","student.setrollno","task","task15","контроллера."],"topics":["mvc","java"]},{"id":"chunk-31","heading":"Task15/StudentController.java","text":"public class StudentController { private Student model; private StudentView view;","keywords":["java","private","15","class","model","mvc","public","student","studentcontroller","studentcontroller.java","studentview","task"],"topics":["java"]},{"id":"chunk-32","heading":"Task15/StudentController.java","text":"public StudentController(Student model, StudentView view) { this.model = model; this.view = view; } public void setStudentName(String name){ model.setName(name); } public String getStudentName(){ return model.getName(); } public void setStudentRollNo(String rollNo){ model.setRollNo(rollNo); } public String getStudentRollNo(){ return model.getRollNo()","keywords":["public","string","java","model","name","return","rollno","view","void","15","getstudentname","getstudentrollno"],"topics":["java"]},{"id":"chunk-33","heading":"Task15/StudentController.java","text":"; } public void updateView(){ view.printStudentDetails(model.getName(), model.getRollNo()); } }","keywords":["java","15","model.getname","model.getrollno","mvc","public","studentcontroller.java","task","task15","updateview","view.printstudentdetails","void"],"topics":["ai","java"]},{"id":"chunk-34","heading":"Task15/StudentView.java","text":"public class StudentView { public void printStudentDetails(String name, String rollNo){ System.out.println(\"Name: \" + name); System.out.println(\"RollNo: \" + rollNo); } }","keywords":["name","rollno","java","public","string","system.out.println","15","class","mvc","printstudentdetails","studentview","studentview.java"],"topics":["ai","java"]},{"id":"chunk-35","heading":"Описание","text":"В этом модуле используется 12 Java файлов. Ключевые сущности: Employee, EmployeeController, EmployeeView, Movie, MovieController, MovieView.","keywords":["java","12","15","employee","employeecontroller","employeeview","movie","moviecontroller","movieview.","mvc","task","используется"],"topics":["java"]},{"id":"chunk-36","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","15","mvc","task","демо","задачи","запускайте","затем","кейсами.","класс","контроллера.","модели"],"topics":["oop"]}]},{"id":"java/task-16-exceptions","href":"/docs/java/task-16-exceptions","slug":["java","task-16-exceptions"],"section":"java","sectionTitle":"Java","title":"Task 16 — Исключения","description":"Обработка исключений и безопасное выполнение кода.","preview":"Задание Обработка исключений и безопасное выполнение кода. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task16/Exception1.java Task16/Exception2.java Task16/Exception3.java Task16/Exc","keywords":["string","key","public","task16","system.out.println","void","static","new","scanner","exception","getdetails","null","args","class","exceptiondemo","intstring","main","catch"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Обработка исключений и безопасное выполнение кода. Документация собрана по исходному коду этой практики.","keywords":["java","безопасное","выполнение","исключений","кода.","обработка","16","task","документация","задание","исключения","исходному"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task16/Exception1.java Task16/Exception2.java Task16/Exception3.java Task16/Exception4.java Task16/ThrowsDemo5.java Task16/ThrowsDemo6.java Task16/ThrowsDemo7.java Task16/ThrowsDemo8.java","keywords":["task16","java","16","exception1.java","exception2.java","exception3.java","exception4.java","task","throwsdemo5.java","throwsdemo6.java","throwsdemo7.java","throwsdemo8.java"],"topics":["java"]},{"id":"chunk-2","heading":"Task16/Exception1.java","text":"public class Exception1 { public static void exceptionDemo(){ System.out.println(2/0); } public static void exceptionDemo1(){ System.out.println(2.0/0.0); } public static void exceptionDemo2(){ try { System.out.println(2/0); }catch (ArithmeticException e){ System.out.println(\"Attempted division by zero\"); } } public static void main(String[] args){ exceptionDemo1(); exceptionDemo2(); exceptionDemo(); } }","keywords":["public","static","system.out.println","void","exceptiondemo","exceptiondemo1","exceptiondemo2","java","0.0","16","2.0","args"],"topics":["ai","java"]},{"id":"chunk-3","heading":"Task16/Exception2.java","text":"public class Exception2 { public static void exceptionDemo1(){ Scanner myScanner = new Scanner(System.in); System.out.println(\"Enter an integer \"); String intString = myScanner.next(); int i = Integer.parseInt(intString); System.out.println(2/i); } public static void exceptionDemo(){ try { Scanner myScanner = new Scanner(System.in); System.out.println(\"Enter an integer \"); String intString = myScanner.next()","keywords":["scanner","intstring","public","system.out.println","enter","integer","java","myscanner","myscanner.next","new","static","string"],"topics":["java"]},{"id":"chunk-4","heading":"Task16/Exception2.java","text":"; int i = Integer.parseInt(intString); System.out.println(2/i); }catch (NumberFormatException e){ System.out.println(\"error\"); } } public static void main(String[] args){ exceptionDemo(); exceptionDemo1(); } }","keywords":["java","system.out.println","16","args","catch","error","exception2.java","exceptiondemo","exceptiondemo1","int","integer.parseint","intstring"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task16/Exception3.java","text":"public class Exception3 { public static void exceptionDemo() { try { Scanner myScanner = new Scanner(System.in); System.out.println(\"Enter an integer \"); String intString = myScanner.next(); int i = Integer.parseInt(intString); System.out.println(2 / i); } catch (Exception e) { System.err.println(\"error\"); } } public static void main(String[] args){ exceptionDemo(); } }","keywords":["public","exceptiondemo","intstring","java","scanner","static","string","system.out.println","void","16","args","catch"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task16/Exception4.java","text":"public class Exception4 { public static void exceptionDemo(){ try { Scanner myScanner = new Scanner(System.in); System.out.println(\"Enter an integer \"); String intString = myScanner.next(); int i = Integer.parseInt(intString); System.out.println(2/i); }catch (NumberFormatException e){ System.out.println(\"error\"); }finally { System.out.println(\"Проверка работы блока finally\")","keywords":["system.out.println","finally","intstring","java","public","scanner","16","catch","class","enter","error","exception4"],"topics":["java"]},{"id":"chunk-7","heading":"Task16/Exception4.java","text":"; } } public static void main(String[] args){ exceptionDemo(); } }","keywords":["java","16","args","exception4.java","exceptiondemo","main","public","static","string","task","task16","void"],"topics":["ai","java"]},{"id":"chunk-8","heading":"Task16/ThrowsDemo5.java","text":"public class ThrowsDemo5 { public static String getDetails1(String key){ if (key == null) { throw new NullPointerException(\"null key in getDetails\"); } return key; } public static String getDetails(String key){ try { if (key == null){ throw new NullPointerException(\"null key in getDetails\");","keywords":["key","null","string","getdetails","public","if","java","new","nullpointerexception","static","throw","16"],"topics":["ai","java"]},{"id":"chunk-9","heading":"Task16/ThrowsDemo5.java","text":"} }catch (NullPointerException e){} return \"data \" + key; } public static void main(String[] args){ System.out.println(getDetails(null)); System.out.println(getDetails1(null)); } }","keywords":["java","null","system.out.println","16","args","catch","data","getdetails","getdetails1","key","main","nullpointerexception"],"topics":["ai","java"]},{"id":"chunk-10","heading":"Task16/ThrowsDemo6.java","text":"```java title=\"Task16/ThrowsDemo6.java\" package Task16; public class ThrowsDemo6 { public void printMessage(String key){ try { String message = getDetails(key); System.out.println(message); }catch (Exception e){","keywords":["java","task16","key","message","public","string","throwsdemo6.java","16","catch","class","exception","getdetails"],"topics":["ai","java"]},{"id":"chunk-11","heading":"Task16/ThrowsDemo6.java","text":"} System.out.println(key); } public String getDetails(String key){ if (key == null){ throw new NullPointerException(\"null key in getDetails\"); } return \"data for \" + key; } public static void main(String[] args){ ThrowsDemo6 n = new ThrowsDemo6(); n.printMessage(\"tol\"); n.printMessage(null); n.getDetails(\"kol\"); n.getDetails(null); } }","keywords":["key","null","string","getdetails","java","n.getdetails","n.printmessage","new","public","throwsdemo6","16","args"],"topics":["ai","java"]},{"id":"chunk-12","heading":"Task16/ThrowsDemo7.java","text":"public class ThrowsDemo7 { public void getKey() throws Exception { Scanner myScanner = new Scanner(System.in); String key = myScanner.next(); printDetails(key); } public void printDetails(String key) throws Exception { String message = getDetails(key); System.out.println(message);","keywords":["key","public","string","exception","java","message","printdetails","scanner","throws","void","16","class"],"topics":["ai","java"]},{"id":"chunk-13","heading":"Task16/ThrowsDemo7.java","text":"} private String getDetails(String key) throws Exception { if(Objects.equals(key, \"!\")){ throw new Exception(\"Key set to empty string\"); } return \"data for \" + key; } public static void main(String[] args) throws Exception { ThrowsDemo7 n = new ThrowsDemo7(); n.getKey(); } }","keywords":["key","string","exception","java","new","throws","throwsdemo7","16","args","data","empty","getdetails"],"topics":["ai","java"]},{"id":"chunk-14","heading":"Task16/ThrowsDemo8.java","text":"public class ThrowsDemo8 { public void getKey() { boolean r = true; while (r) try { Scanner myScanner = new Scanner(System.in); String key = myScanner.next(); printDetails(key); r = false; }catch (Exception e){ r = true; } } public void printDetails(String key) throws Exception { String message = getDetails(key); System.out.println(message)","keywords":["key","public","string","exception","java","message","printdetails","scanner","true","void","16","boolean"],"topics":["ai","java"]},{"id":"chunk-15","heading":"Task16/ThrowsDemo8.java","text":"; } private String getDetails(String key) throws Exception { if(Objects.equals(key,\"!\")){ throw new Exception(\"Key set to empty string\"); } return \"data for \" + key; } public static void main(String[] args) { ThrowsDemo8 n = new ThrowsDemo8(); n.getKey(); } }","keywords":["key","string","exception","java","new","throwsdemo8","16","args","data","empty","getdetails","if"],"topics":["ai","java"]},{"id":"chunk-16","heading":"Описание","text":"В этом модуле используется 8 Java файлов. Ключевые сущности: Exception1, Exception2, Exception3, Exception4, ThrowsDemo5, ThrowsDemo6.","keywords":["java","16","exception1","exception2","exception3","exception4","task","throwsdemo5","throwsdemo6.","безопасное","выполнение","исключений"],"topics":["java"]},{"id":"chunk-17","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","16","task","безопасное","выполнение","демо","задачи","запускайте","затем","исключений","исключения","кейсами."],"topics":["oop"]}]},{"id":"java/task-17-gui-and-exceptions","href":"/docs/java/task-17-gui-and-exceptions","slug":["java","task-17-gui-and-exceptions"],"section":"java","sectionTitle":"Java","title":"Task 17 — GUI + исключения","description":"Связка UI-слоя, доменной логики и пользовательских ошибок.","preview":"Задание Связка UI слоя, доменной логики и пользовательских ошибок. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task17/EmptyStringException.java Task17/FIOINN.java Task17/LabClass.jav","keywords":["task17","public","new","void","fio","string","int","gpa","labclassui.java","students","student","emptystringexception","arr.get","idnumber","override","return","table","this"],"topics":["ai","numpy","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Связка UI слоя, доменной логики и пользовательских ошибок. Документация собрана по исходному коду этой практики.","keywords":["java","доменной","логики","ошибок.","пользовательских","связка","17","gui","task","ui","ui-слоя","документация"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task17/EmptyStringException.java Task17/FIOINN.java Task17/LabClass.java Task17/LabClassDriver.java Task17/LabClassUI.java Task17/Proverka.java Task17/Student.java Task17/StudentNotFoundException.java","keywords":["task17","java","17","emptystringexception.java","fioinn.java","gui","labclass.java","labclassdriver.java","labclassui.java","proverka.java","student.java","studentnotfoundexception.java"],"topics":["java"]},{"id":"chunk-2","heading":"Task17/EmptyStringException.java","text":"public class EmptyStringException extends IllegalArgumentException { public EmptyStringException(LabClassUI t) { JOptionPane.showMessageDialog(t, \"Введите необходимые данные в поле ввода!\"); } }","keywords":["emptystringexception","java","public","17","class","emptystringexception.java","extends","gui","illegalargumentexception","joptionpane.showmessagedialog","labclassui","task"],"topics":["numpy","java"]},{"id":"chunk-3","heading":"Task17/FIOINN.java","text":"public class FIOINN { public void red() { System.out.println(\"Введите ФИО\"); Scanner fi = new Scanner(System.in); String fio = fi.nextLine(); System.out.println(\"Введите ИНН\"); Scanner in = new Scanner(System.in); String[] inn = in.nextLine().split(\"\"); try { if (inn.length == 12) { System.out.println(\"ИНН \" + fio + \" верный!\"); } else { throw new Proverka(fio)","keywords":["scanner","fio","new","system.out.println","java","public","string","system.in","введите","инн",".split","12"],"topics":["java"]},{"id":"chunk-4","heading":"Task17/FIOINN.java","text":"; } }catch (Proverka e){ System.out.println(e.getMessage()); } } public static void main(String[] args){ FIOINN n = new FIOINN(); n.red(); } }","keywords":["fioinn","java","17","args","catch","e.getmessage","fioinn.java","gui","main","n.red","new","proverka"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task17/LabClass.java","text":"public class LabClass { public static void main(String[] args) { ArrayList new stud = new ArrayList< (); new stud.add(new Student(\"Иванов Иван Иванович\", 123456, 4.0)); new LabClassUI(new stud); } }","keywords":["new","arraylist","java","public","stud","123456","17","4.0","args","class","gui","labclass"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task17/LabClassDriver.java","text":"public class LabClassDriver { public int compare(Student stud1, Student stud2) { return Double.compare(stud1.getGPA(),stud2.getGPA()); } public void quick sort(ArrayList arr, int begin, int end) { if (begin arr, int begin, int end) { Student pt = arr.get(end); int i = (begin 1); for (int j = begin; j = 0) { i++; Student swapTemp = arr.get(i); arr.set(i, arr.get(j)); arr.set(j, swapTemp)","keywords":["int","begin","student","arr.get","end","public","arr","arr.set","java","swaptemp","17","arraylist"],"topics":["java"]},{"id":"chunk-7","heading":"Task17/LabClassDriver.java","text":"; } } Student swapt = arr.get(i + 1); arr.set(i + 1, arr.get(end)); arr.set(end, swapt); return i + 1; } }","keywords":["arr.get","arr.set","end","java","swapt","17","gui","labclassdriver.java","return","student","task","task17"],"topics":["java"]},{"id":"chunk-8","heading":"Task17/LabClassUI.java","text":"public LabClassUI(ArrayList students arr){ super(\"Сведения о студентах\"); setDefaultCloseOperation(EXIT ON CLOSE); setSize(520, 330); setResizable(false); this.students arr = students arr; JPanel panel = new JPanel(new FlowLayout()); JButton add btn = new JButton(\"Добавить\"); JButton sort btn = new JButton(\"Отсортировать по GPA\"); JButton search btn = new JButton(\"Поиск\"); panel.add(add btn); panel.add(search btn)","keywords":["jbutton","btn","new","arr","add","java","jpanel","panel.add","search","students","17","330"],"topics":["java"]},{"id":"chunk-9","heading":"Task17/LabClassUI.java","text":"; panel.add(sort btn);","keywords":["java","17","btn","gui","labclassui.java","panel.add","sort","task","task17","ui-слоя","доменной","исключения"],"topics":["java"]},{"id":"chunk-10","heading":"Task17/LabClassUI.java","text":"Object [][] exist stud = new String[students arr.size()][3]; for(int i = 0; i { try { add(); }catch (IllegalArgumentException ex){ JOptionPane.showMessageDialog(this, ex.getMessage()); } }); search btn.addActionListener(e { try{ find(); } catch (StudentNotFoundException ex){ JOptionPane.showMessageDialog(this, ex.getMessage()); } })","keywords":["catch","ex","ex.getmessage","java","joptionpane.showmessagedialog","this","try","17","add","arr.size","btn.addactionlistener","exist"],"topics":["numpy","java"]},{"id":"chunk-11","heading":"Task17/LabClassUI.java","text":"; sort btn.addMouseListener(new MouseInputListener() { @Override public void mouseClicked(MouseEvent e) { LabClassDriver ms = new LabClassDriver(); ms.quick sort(students arr,0,students arr.size() 1); DefaultTableModel table mod = (DefaultTableModel) table.getModel(); for(int i = 0; i<students arr.size();i++) { table mod.removeRow(i); Student st = students arr.get(i)","keywords":["students","arr.size","defaulttablemodel","java","labclassdriver","new","sort","table","17","arr","arr.get","btn.addmouselistener"],"topics":["numpy","java"]},{"id":"chunk-12","heading":"Task17/LabClassUI.java","text":"; table mod.insertRow(i, new Object[]{st.getFio(), st.getiDNumber(), st.getGPA()}); } } @Override public void mousePressed(MouseEvent e) {} @Override public void mouseReleased(MouseEvent e) {} @Override public void mouseEntered(MouseEvent e) {} @Override public void mouseExited(MouseEvent e) {} @Override public void mouseDragged(MouseEvent e) {} @Override public void mouseMoved(MouseEvent e) {} });","keywords":["mouseevent","override","public","void","java","17","gui","labclassui.java","mod.insertrow","mousedragged","mouseentered","mouseexited"],"topics":["java"]},{"id":"chunk-13","heading":"Task17/LabClassUI.java","text":"getContentPane().add(panel, BorderLayout.SOUTH); getContentPane().add(new JScrollPane(table), BorderLayout.CENTER); setVisible(true); }","keywords":[".add","getcontentpane","java","17","borderlayout.center","borderlayout.south","gui","jscrollpane","labclassui.java","new","panel","setvisible"],"topics":["java"]},{"id":"chunk-14","heading":"Task17/LabClassUI.java","text":"private void add() { String fio = JOptionPane.showInputDialog(\"ФИО студента: \"); if(fio.isEmpty()) try { throw new EmptyStringException(this); } catch (EmptyStringException e) { throw new RuntimeException(e); } String number = JOptionPane.showInputDialog(\"Номер дела:\");","keywords":["emptystringexception","java","joptionpane.showinputdialog","new","string","throw","17","add","catch","fio","fio.isempty","gui"],"topics":["numpy","java"]},{"id":"chunk-15","heading":"Task17/LabClassUI.java","text":"if(number.isEmpty()) try { throw new EmptyStringException(this); } catch (EmptyStringException e) { throw new RuntimeException(e); }","keywords":["emptystringexception","java","new","throw","17","catch","gui","if","labclassui.java","number.isempty","runtimeexception","task"],"topics":["java"]},{"id":"chunk-16","heading":"Task17/LabClassUI.java","text":"String GPA = JOptionPane.showInputDialog(\"GPA:\"); if(GPA.isEmpty()) try { throw new EmptyStringException(this); } catch (EmptyStringException e) { throw new RuntimeException(e); } students arr.add(new Student(fio, Integer.parseInt(number), Double.parseDouble(GPA))); DefaultTableModel table mod = (DefaultTableModel) table.getModel(); table mod.addRow(new Object[]{fio, number,GPA}); }","keywords":["gpa","new","defaulttablemodel","emptystringexception","fio","java","number","table","throw","17","arr.add","catch"],"topics":["numpy","java"]},{"id":"chunk-17","heading":"Task17/LabClassUI.java","text":"private void find() throws StudentNotFoundException { String search val = JOptionPane.showInputDialog(\"Введите ФИО для поиска:\"); for(int i = 0; i < students arr.size(); i++){ if(students arr.get(i).getFio().equals(search val)){ JOptionPane.showMessageDialog(this, \"Результат: \"+ students arr.get(i).toString()); return; } } throw new StudentNotFoundException(search val, this); }","keywords":["search","students","val","arr.get","java","studentnotfoundexception","this",".equals",".getfio",".tostring","17","arr.size"],"topics":["numpy","java"]},{"id":"chunk-18","heading":"Task17/LabClassUI.java","text":"private void remove() { int cnt = table.getSelectedRowCount(); if(cnt != 1) { JOptionPane.showMessageDialog(this, \"Необходимо выбрать строку для удаления\"); return; } cnt = table.getSelectedRow(); students arr.remove(cnt); DefaultTableModel table mod = (DefaultTableModel) table.getModel(); table mod.removeRow(cnt);","keywords":["cnt","defaulttablemodel","java","table","17","arr.remove","gui","if","int","joptionpane.showmessagedialog","labclassui.java","mod"],"topics":["numpy","java"]},{"id":"chunk-19","heading":"Task17/Proverka.java","text":"public class Proverka extends Exception{ public Proverka(String fio){ super(\"ИНН \"+fio+\" не верный\"); } }","keywords":["java","proverka","public","+fio+","17","class","exception","extends","fio","gui","proverka.java","string"],"topics":["java"]},{"id":"chunk-20","heading":"Task17/Student.java","text":"public class Student { private int iDNumber; private String fio; private double GPA; public Student(String fio,int iDNumber, double GPA) { this.iDNumber = iDNumber; this.fio = fio; this.GPA = GPA; } @Override public String toString() { return fio + \" (iDNumber: \" + iDNumber + \", GPA: \" + GPA + \")\" ; } public int getiDNumber() { return iDNumber; } public void setiDNumber(int iDNumber) { this.iDNumber = iDNumber","keywords":["idnumber","gpa","public","fio","int","private","string","double","java","return","student","this.idnumber"],"topics":["java"]},{"id":"chunk-21","heading":"Task17/Student.java","text":"; } public String getFio() { return fio; } public void setFio(String fio) { this.fio = fio; } public double getGPA() { return GPA; } public void setGPA(double GPA) { this.GPA = GPA; } }","keywords":["public","fio","gpa","double","java","return","string","void","17","getfio","getgpa","gui"],"topics":["java"]},{"id":"chunk-22","heading":"Task17/StudentNotFoundException.java","text":"public class StudentNotFoundException extends Throwable { public StudentNotFoundException(String fio, LabClassUI t) { JOptionPane.showMessageDialog(t, \"Студент \" + fio + \" не найден\"); } }","keywords":["fio","java","public","studentnotfoundexception","17","class","extends","gui","joptionpane.showmessagedialog","labclassui","string","studentnotfoundexception.java"],"topics":["numpy","java"]},{"id":"chunk-23","heading":"Описание","text":"В этом модуле используется 8 Java файлов. 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Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task2/Task.java Task2/Task.java java title=\"Task2/Task.java\" package Task2; p","keywords":["int","ty","task2","task.java","структура","j+1","java","system.out.println","входа","минимальная","точка","этой","factorial","input","public","task","tmp","документация"],"topics":["numpy","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Точка входа и минимальная структура Java приложения. 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Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task20/calculator.java Task20/calculator.java java title=\"Task20/calculator.j","keywords":["stack.pop","stack.push","task20","break","calculator.java","case","elem","new","stack","string","исключений.","калькулятора","обработкой","ооп","этой","0.0","double","exception"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"ООП реализация калькулятора с обработкой исключений. 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Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task21/AbstractQueue.java Task21/ArrayQueue.java Task21/ArrayQueueADT.java Ta","keywords":["public","task21","front","int","system.out.println","rear","new","return","if","isempty","queue","size","void","arr","count","element","throw","arrayqueuemodule"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Абстрактные типы данных и разные реализации очереди. Документация собрана по исходному коду этой практики.","keywords":["java","абстрактные","данных","очереди.","разные","реализации","типы","21","adt","queue","task","документация"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task21/AbstractQueue.java Task21/ArrayQueue.java Task21/ArrayQueueADT.java Task21/ArrayQueueModule.java Task21/LinkedQueue.java Task21/Queue.java Task21/TestArray.java Task21/TestLinked.java","keywords":["task21","java","21","abstractqueue.java","adt","arrayqueue.java","arrayqueueadt.java","arrayqueuemodule.java","linkedqueue.java","queue","queue.java","task"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task21/ArrayQueue.java","text":"public ArrayQueue(){ arr = new int[SIZE]; front=rear= 1; count = 0; } public void enqueue(int element){ if(count==SIZE){ throw new IllegalStateException(\"Переполнение массива\"); } if(isEmpty()){ front=rear=0; }else{ rear=(rear+1)%SIZE; } arr[rear] = element; count++; } public int element(){ if (isEmpty()) { throw new IllegalStateException(\"Queue is empty\"); } return arr[front]; }","keywords":["rear","arr","element","front","if","int","new","public","size","count","illegalstateexception","isempty"],"topics":["java"]},{"id":"chunk-3","heading":"Task21/ArrayQueue.java","text":"public int dequeue() { if (isEmpty()) { throw new IllegalStateException(\"Queue is empty\"); } int value = arr[front]; if (front == rear) { front = rear = 1; } else { front = (front + 1) % SIZE; }","keywords":["front","if","int","java","queue","rear","21","adt","arr","arrayqueue.java","dequeue","else"],"topics":["java"]},{"id":"chunk-4","heading":"Task21/ArrayQueue.java","text":"public int size() { return count; } public void clear() { front = rear = 1; count = 0; } public boolean isEmpty(){ if (front== 1){ return true; }else { return false; } } }","keywords":["public","return","count","front","java","21","adt","arrayqueue.java","boolean","clear","else","false"],"topics":["java"]},{"id":"chunk-5","heading":"Task21/ArrayQueueADT.java","text":"public class ArrayQueueADT { public ArrayQueueADT(ArrayQueueModule queue){ this.queue = queue; } private ArrayQueueModule queue; public void enqueue(int elemen) { queue.enqueue(elemen); } public int element(){ return queue.element(); } public int dequeue(){ return queue.dequeue(); } public int size(){ return queue.size(); } public void clear(){ queue.clear(); } public boolean isEmpty(){ return queue.isEmpty(); } }","keywords":["public","int","queue","return","arrayqueueadt","arrayqueuemodule","elemen","java","void","21","adt","arrayqueueadt.java"],"topics":["java"]},{"id":"chunk-6","heading":"Task21/ArrayQueueModule.java","text":"public class ArrayQueueModule { int SIZE = 5; int[] arr; int front, rear; int count;","keywords":["int","java","21","adt","arr","arrayqueuemodule","arrayqueuemodule.java","class","count","front","public","queue"],"topics":["java"]},{"id":"chunk-7","heading":"Task21/ArrayQueueModule.java","text":"public ArrayQueueModule(){ arr = new int[SIZE]; front=rear= 1; count = 0; } public void enqueue(int element){ if(count==SIZE){ throw new IllegalStateException(\"Переполнение массива\"); } if(isEmpty()){ front=rear=0; }else{ rear=(rear+1)%SIZE; } arr[rear] = element; count++; } public int element(){ if (isEmpty()) { throw new IllegalStateException(\"Queue is empty\"); } return arr[front]; }","keywords":["rear","arr","element","front","if","int","new","public","size","count","illegalstateexception","isempty"],"topics":["java"]},{"id":"chunk-8","heading":"Task21/ArrayQueueModule.java","text":"public int dequeue() { if (isEmpty()) { throw new IllegalStateException(\"Queue is empty\"); } int value = arr[front]; if (front == rear) { front = rear = 1; } else { front = (front + 1) % SIZE; }","keywords":["front","if","int","java","queue","rear","21","adt","arr","arrayqueuemodule.java","dequeue","else"],"topics":["java"]},{"id":"chunk-9","heading":"Task21/ArrayQueueModule.java","text":"public int size() { return count; } public void clear() { front = rear = 1; count = 0; } public boolean isEmpty(){ if (front== 1){ return true; }else { return false; } } }","keywords":["public","return","count","front","java","21","adt","arrayqueuemodule.java","boolean","clear","else","false"],"topics":["java"]},{"id":"chunk-10","heading":"Task21/LinkedQueue.java","text":"public class LinkedQueue extends AbstractQueue implements Queue { private LinkedList queue; public LinkedQueue(){ queue=new LinkedList< (); front=0; rear=0; } @Override public void enqueue(Object o) { queue.add(o); rear++; }","keywords":["queue","public","java","linkedlist","linkedqueue","21","abstractqueue","adt","class","enqueue","extends","front"],"topics":["java"]},{"id":"chunk-11","heading":"Task21/LinkedQueue.java","text":"@Override public Object element() { if(isEmpty())throw new IndexOutOfBoundsException(\"Массив пуст!!\"); return queue.get(front); } @Override public Object dequeue() { if (isEmpty()) throw new IndexOutOfBoundsException(\"Массив пуст!!\"); System.out.println(front+\" \"+rear); return queue.remove(front); }","keywords":["front","if","indexoutofboundsexception","isempty","java","new","object","override","public","return","throw","массив"],"topics":["java"]},{"id":"chunk-12","heading":"Task21/LinkedQueue.java","text":"@Override public boolean clear() { queue.clear(); front=0; rear=0; return false; } }","keywords":["java","21","adt","boolean","clear","false","front","linkedqueue.java","override","public","queue","queue.clear"],"topics":["java"]},{"id":"chunk-13","heading":"Task21/Queue.java","text":"public interface Queue { void enqueue(Object o); Object element(); Object dequeue(); boolean isEmpty(); boolean clear(); }","keywords":["object","boolean","java","queue","21","adt","clear","dequeue","element","enqueue","interface","isempty"],"topics":["java"]},{"id":"chunk-14","heading":"Task21/TestArray.java","text":"public class TestArray { public static void main(String[] args) { ArrayQueueModule m = new ArrayQueueModule(); // b.dequeue(); m.enqueue(1); m.enqueue(2); m.enqueue(3); m.enqueue(1); m.enqueue(2); System.out.println(m.size()); System.out.println(m.element()); System.out.println(m.dequeue()); System.out.println(m.isEmpty()); ArrayQueueADT a = new ArrayQueueADT(m); System.out.println(a.size())","keywords":["m.enqueue","system.out.println","arrayqueueadt","arrayqueuemodule","java","new","public","21","a.size","adt","args","b.dequeue"],"topics":["ai","java","testing"]},{"id":"chunk-15","heading":"Task21/TestArray.java","text":"; System.out.println(a.element()); System.out.println(a.dequeue()); System.out.println(a.isEmpty()); ArrayQueue q = new ArrayQueue(); q.enqueue(1); q.enqueue(2); q.enqueue(3); q.enqueue(1); q.enqueue(2); System.out.println(q.size()); System.out.println(q.element()); System.out.println(q.dequeue()); System.out.println(q.isEmpty()); } }","keywords":["system.out.println","q.enqueue","arrayqueue","java","21","a.dequeue","a.element","a.isempty","adt","new","q.dequeue","q.element"],"topics":["java","testing"]},{"id":"chunk-16","heading":"Task21/TestLinked.java","text":"public class TestLinked { public static void main(String[] args){ LinkedQueue l = new LinkedQueue(); l.enqueue(1); l.enqueue(3); l.enqueue(2); System.out.println(l.dequeue()); //System.out.println(l.element()); System.out.println(l.dequeue()); System.out.println(\"Пустой ли массив ? \"+l.isEmpty()); //System.out.println(l.clear()); System.out.println(l.dequeue()); System.out.println(\"Пустой ли массив ? \"+l.isEmpty())","keywords":["system.out.println","l.dequeue","l.enqueue","+l.isempty","java","linkedqueue","public","ли","массив","пустой","21","adt"],"topics":["ai","java","testing"]},{"id":"chunk-17","heading":"Task21/TestLinked.java","text":"; } }","keywords":["java","21","adt","queue","task","task21","testlinked.java","абстрактные","данных","очереди.","разные","реализации"],"topics":["java","testing"]},{"id":"chunk-18","heading":"Описание","text":"В этом модуле используется 8 Java файлов. Ключевые сущности: AbstractQueue, ArrayQueue, ArrayQueueADT, ArrayQueueModule, LinkedQueue, Queue.","keywords":["java","21","abstractqueue","adt","arrayqueue","arrayqueueadt","arrayqueuemodule","linkedqueue","queue","queue.","task","абстрактные"],"topics":["java"]},{"id":"chunk-19","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","21","adt","queue","task","абстрактные","данных","демо","задачи","запускайте","затем","кейсами."],"topics":["oop"]}]},{"id":"java/task-22-design-patterns","href":"/docs/java/task-22-design-patterns","slug":["java","task-22-design-patterns"],"section":"java","sectionTitle":"Java","title":"Task 22 — Паттерны проектирования","description":"Factory, Abstract Factory и MVC в прикладных задачах.","preview":"Задание Factory, Abstract Factory и MVC в прикладных задачах. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task22/one/Complex.java Task22/one/ComplexAbstractFactory.java Task22/one/Co","keywords":["task22","public","three","new","void","string","two","int","class","actionlistener","carcasview.java","override","return","idocument","one","textdocument","complex","image"],"topics":["ai","numpy","mvc","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Factory, Abstract Factory и MVC в прикладных задачах. Документация собрана по исходному коду этой практики.","keywords":["factory","abstract","java","mvc","задачах.","прикладных","22","task","документация","задание","исходному","коду"],"topics":["mvc"]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task22/one/Complex.java Task22/one/ComplexAbstractFactory.java Task22/one/ConcreteFactory.java Task22/one/TestComplex.java Task22/three/CarcasController.java Task22/three/CarcasModel.java Task22/three/CarcasView.java Task22/three/CreateTextDoc.java Task22/three/ICreateDocument.java Task22/three/IDocument.java Task22/three/TestWindow.java Task22/three/TextDocument.java Task22/two/AbstractChairFactory.java","keywords":["task22","three","one","factory","java","22","abstract","abstractchairfactory.java","carcascontroller.java","carcasmodel.java","carcasview.java","complex.java"],"topics":["ai","java","testing"]},{"id":"chunk-2","heading":"Исходные файлы решения","text":"Task22/two/Chair.java Task22/two/ChairFactory.java Task22/two/Client.java Task22/two/FunctionalChair.java Task22/two/MagicChair.java Task22/two/TestChair.java Task22/two/VictorianChair.java","keywords":["task22","two","factory","java","22","abstract","chair.java","chairfactory.java","client.java","functionalchair.java","magicchair.java","mvc"],"topics":["ai","java","testing"]},{"id":"chunk-3","heading":"Task22/one/Complex.java","text":"public class Complex { private int real; private int image; public Complex(){ this.real=0; this.image=0; } public Complex(int real, int image) { this.real=real; this.image=image; } public int getReal() { return real; } public void setReal(int real) { this.real = real; } public int getImage() { return image; } public void setImage(int image) { this.image = image","keywords":["int","public","image","real","complex","this.image","this.real","factory","java","private","return","void"],"topics":["java"]},{"id":"chunk-4","heading":"Task22/one/Complex.java","text":"; } @Override public String toString() { return \"Комплексное число = \"+ real + \" + \" + image + \"i\"; } }","keywords":["factory","java","22","abstract","complex.java","image","mvc","one","override","public","real","return"],"topics":["java"]},{"id":"chunk-5","heading":"Task22/one/ComplexAbstractFactory.java","text":"public interface ComplexAbstractFactory { Complex createComplex(); Complex CreateComplex(int real, int image); }","keywords":["complex","createcomplex","factory","int","java","22","abstract","complexabstractfactory","complexabstractfactory.java","image","interface","mvc"],"topics":["java"]},{"id":"chunk-6","heading":"Task22/one/ConcreteFactory.java","text":"public class ConcreteFactory implements ComplexAbstractFactory { @Override public Complex createComplex() { return new Complex(); } @Override public Complex CreateComplex(int real, int image) { return new Complex(real, image); } }","keywords":["complex","public","createcomplex","factory","image","int","java","new","override","real","return","22"],"topics":["java"]},{"id":"chunk-7","heading":"Task22/one/TestComplex.java","text":"public class TestComplex { public static void main(String[] args){ ComplexAbstractFactory c = new ConcreteFactory(); System.out.println(c.CreateComplex(1,7)); System.out.println(c.createComplex()); } }","keywords":["c.createcomplex","factory","java","public","system.out.println","22","abstract","args","class","complexabstractfactory","concretefactory","main"],"topics":["ai","java","testing"]},{"id":"chunk-8","heading":"Task22/three/CarcasController.java","text":"```java title=\"Task22/three/CarcasController.java\" package Task22.three; public class CarcasController { private final CarcasModel model; private final CarcasView view; public CarcasController() { this.model = new CarcasModel(); this.view = new CarcasView< ();","keywords":["java","carcascontroller","carcascontroller.java","carcasmodel","carcasview","factory","final","new","private","public","task22","three"],"topics":["java"]},{"id":"chunk-9","heading":"Task22/three/CarcasController.java","text":"view.addNewListener(e { model.setDocument(new CreateTextDoc().CreateNew()); view.showDocText((TextDocument) model.getDocument()); });","keywords":["factory","java",".createnew","22","abstract","carcascontroller.java","createtextdoc","model.getdocument","model.setdocument","mvc","new","task"],"topics":["java"]},{"id":"chunk-10","heading":"Task22/three/CarcasController.java","text":"view.addOpenListener(e { try { model.setDocument(new CreateTextDoc().CreateOpen(((CarcasView.openDocA) e).getPath())); view.showDocText((TextDocument) model.getDocument()); } catch (FileNotFoundException exc) { JOptionPane.showMessageDialog(view, \"File does not exist\"); } });","keywords":["factory","java",".createopen",".getpath","22","abstract","carcascontroller.java","carcasview.opendoca","catch","createtextdoc","does","exc"],"topics":["numpy","java"]},{"id":"chunk-11","heading":"Task22/three/CarcasController.java","text":"view.addSaveListener(e { var document = ((CarcasView.saveDocA)e).getDocument(); System.out.println(\"Saved\"); try { document.save(); } catch (IOException ex) { throw new RuntimeException(ex); } }); }","keywords":["ex","factory","java",".getdocument","22","abstract","carcascontroller.java","carcasview.savedoca","catch","document","document.save","ioexception"],"topics":["java"]},{"id":"chunk-12","heading":"Task22/three/CarcasView.java","text":"public class CarcasView extends JFrame { IDocument doc; class newDocA extends ActionEvent { public newDocA(Object obj) { super(obj, 0, \"\"); } } class openDocA extends ActionEvent { private String path;","keywords":["class","extends","actionevent","factory","java","newdoca","obj","public","22","abstract","carcasview","carcasview.java"],"topics":["java"]},{"id":"chunk-13","heading":"Task22/three/CarcasView.java","text":"public String getPath() { return path; } public openDocA(Object source, String path) { super(source, 0, \"\"); this.path = path; } } JMenuBar menu = new JMenuBar(); ArrayList saveListener = new ArrayList< (); ArrayList newListener = new ArrayList< (); ArrayList openListener = new ArrayList< (); class saveDocA extends ActionEvent { private IDocument doc; public saveDocA(Object obj, IDocument doc) { super(obj, 0, \"\")","keywords":["arraylist","new","path","public","doc","factory","idocument","java","jmenubar","obj","object","savedoca"],"topics":["java"]},{"id":"chunk-14","heading":"Task22/three/CarcasView.java","text":"; this.doc = doc; } public IDocument getDocument() { return doc; } }","keywords":["doc","factory","java","22","abstract","carcasview.java","getdocument","idocument","mvc","public","return","task"],"topics":["java"]},{"id":"chunk-15","heading":"Task22/three/CarcasView.java","text":"void addNewListener(ActionListener actionListener) { newListener.add(actionListener); } void addOpenListener(ActionListener actionListener) { openListener.add(actionListener); } void addSaveListener(ActionListener actionListener) { saveListener.add(actionListener); } void setOpenDoc(String path) { for (ActionListener actionListener: openListener) { actionListener.actionPerformed(new openDocA(this, path)); } }","keywords":["actionlistener","void","factory","java","path","22","abstract","actionlistener.actionperformed","addnewlistener","addopenlistener","addsavelistener","carcasview.java"],"topics":["numpy","java"]},{"id":"chunk-16","heading":"Task22/three/CarcasView.java","text":"void setNewDoc() { for (ActionListener actionListener: newListener) { actionListener.actionPerformed(new newDocA(this)); } }","keywords":["actionlistener","factory","java","22","abstract","actionlistener.actionperformed","carcasview.java","mvc","new","newdoca","newlistener","setnewdoc"],"topics":["numpy","java"]},{"id":"chunk-17","heading":"Task22/three/CarcasView.java","text":"void setSaveDoc() { for (ActionListener actionListener: saveListener) { actionListener.actionPerformed(new saveDocA(this, doc)); } }","keywords":["actionlistener","factory","java","22","abstract","actionlistener.actionperformed","carcasview.java","doc","mvc","new","savedoca","savelistener"],"topics":["numpy","java"]},{"id":"chunk-18","heading":"Task22/three/CarcasView.java","text":"CarcasView() { setTitle(\"Каркас\"); setDefaultCloseOperation(EXIT ON CLOSE); setSize(400, 200); JMenu fileItem = new JMenu(\"Файл\");","keywords":["factory","java","jmenu","200","22","400","abstract","carcasview","carcasview.java","close","exit","fileitem"],"topics":["java"]},{"id":"chunk-19","heading":"Task22/three/CarcasView.java","text":"JMenuItem newPosition = new JMenuItem(\"Создать\"); newPosition.addActionListener(e { setNewDoc(); });","keywords":["factory","java","jmenuitem","22","abstract","carcasview.java","mvc","new","newposition","newposition.addactionlistener","setnewdoc","task"],"topics":["java"]},{"id":"chunk-20","heading":"Task22/three/CarcasView.java","text":"JMenuItem openPosition = new JMenuItem(\"Открыть\"); openPosition.addActionListener(e { JFileChooser file choose = new JFileChooser(); if (file choose.showDialog(getContentPane(), \"OK\") == JFileChooser.APPROVE OPTION) { setOpenDoc(file choose.getSelectedFile().getAbsolutePath()); } });","keywords":["file","factory","java","jfilechooser","jmenuitem","new",".getabsolutepath","22","abstract","carcasview.java","choose","choose.getselectedfile"],"topics":["numpy","java"]},{"id":"chunk-21","heading":"Task22/three/CarcasView.java","text":"JMenuItem savePosition = new JMenuItem(\"Сохранить\"); savePosition.addActionListener(e { setSaveDoc(); });","keywords":["factory","java","jmenuitem","22","abstract","carcasview.java","mvc","new","saveposition","saveposition.addactionlistener","setsavedoc","task"],"topics":["java"]},{"id":"chunk-22","heading":"Task22/three/CarcasView.java","text":"JMenuItem exitPosition = new JMenuItem(\"Выход\"); exitPosition.addActionListener(e { setVisible(false); }); fileItem.add(newPosition); fileItem.add(openPosition); fileItem.add(savePosition); fileItem.add(exitPosition); menu.add(fileItem); setJMenuBar(menu); setBounds(0, 0, 390, 200); }","keywords":["fileitem.add","exitposition","factory","java","jmenuitem","200","22","390","abstract","carcasview.java","exitposition.addactionlistener","false"],"topics":["numpy","java"]},{"id":"chunk-23","heading":"Task22/three/CarcasView.java","text":"void showDocText(TextDocument textDocument) { this.doc = (IDocument ) textDocument; setTitle(\"Текущий документ \" + textDocument.getName()); getContentPane().removeAll(); repaint(); JTextArea textFieldBig = new JTextArea(textDocument.getContent())","keywords":["textdocument","factory","java","jtextarea",".removeall","22","abstract","carcasview.java","getcontentpane","idocument","mvc","new"],"topics":["ai","java"]},{"id":"chunk-24","heading":"Task22/three/CarcasView.java","text":"; textFieldBig.getDocument().addDocumentListener(new DocumentListener() { @Override public void insertUpdate(DocumentEvent documentEvent) { ((TextDocument)doc).setContent(textFieldBig.getText()); } @Override public void removeUpdate(DocumentEvent documentEvent) { ((TextDocument)doc).setContent(textFieldBig.getText())","keywords":["documentevent",".setcontent","doc","factory","java","override","public","textdocument","textfieldbig.gettext","void",".adddocumentlistener","22"],"topics":["java"]},{"id":"chunk-25","heading":"Task22/three/CarcasView.java","text":"; } @Override public void changedUpdate(DocumentEvent documentEvent) { ((TextDocument)doc).setContent(textFieldBig.getText()); } }); add(textFieldBig); setBounds(0, 0, 450, 200); }","keywords":["documentevent","factory","java",".setcontent","200","22","450","abstract","add","carcasview.java","changedupdate","doc"],"topics":["java"]},{"id":"chunk-26","heading":"Task22/three/CreateTextDoc.java","text":"public class CreateTextDoc implements ICreateDocument{ public IDocument CreateNew() { return (IDocument ) new TextDocument(\"Безымянный\"); }","keywords":["factory","idocument","java","public","22","abstract","class","createnew","createtextdoc","createtextdoc.java","icreatedocument","implements"],"topics":["java"]},{"id":"chunk-27","heading":"Task22/three/CreateTextDoc.java","text":"@Override public IDocument CreateOpen(String path) throws FileNotFoundException { BufferedReader file = new BufferedReader(new FileReader(path)); return (IDocument ) new TextDocument(path, file.lines().collect(Collectors.joining(\"\\n\"))); }","keywords":["new","path","bufferedreader","factory","idocument","java",".collect","22","abstract","collectors.joining","createopen","createtextdoc.java"],"topics":["java"]},{"id":"chunk-28","heading":"Task22/three/ICreateDocument.java","text":"```java title=\"Task22/three/ICreateDocument.java\" package Task22.three; public interface ICreateDocument { IDocument CreateNew(); IDocument CreateOpen(String path) throws FileNotFoundException;","keywords":["java","factory","icreatedocument.java","idocument","task22","three","22","abstract","createnew","createopen","filenotfoundexception","icreatedocument"],"topics":["java"]},{"id":"chunk-29","heading":"Task22/three/IDocument.java","text":"public interface IDocument { String getName(); void setName(String name); String getContent(); void setContent(String content);","keywords":["string","factory","java","void","22","abstract","content","getcontent","getname","idocument","idocument.java","interface"],"topics":["java"]},{"id":"chunk-30","heading":"Task22/three/TestWindow.java","text":"public class TestWindow { public static void main(String[] args) { try { UIManager.setLookAndFeel(UIManager.getSystemLookAndFeelClassName()); } catch (Exception ignored){}","keywords":["factory","java","public","22","abstract","args","catch","class","exception","ignored","main","mvc"],"topics":["ai","java","testing"]},{"id":"chunk-31","heading":"Task22/three/TestWindow.java","text":"CarcasController controller = new CarcasController(); controller.showWindow(); } }","keywords":["carcascontroller","factory","java","22","abstract","controller","controller.showwindow","mvc","new","task","task22","testwindow.java"],"topics":["java","testing"]},{"id":"chunk-32","heading":"Task22/three/TextDocument.java","text":"public class TextDocument implements IDocument{ private String text; private String name;","keywords":["factory","java","private","string","22","abstract","class","idocument","implements","mvc","name","public"],"topics":["java"]},{"id":"chunk-33","heading":"Task22/three/TextDocument.java","text":"TextDocument(String name, String text) { this.name = name; this.text = text; } @Override public String getName() { return name; } @Override public void setName(String name) { this.name = name; } @Override public String getContent() { return text; } @Override public void setContent(String text) { this.text = text","keywords":["string","name","text","override","public","factory","java","return","this.name","this.text","void","22"],"topics":["java"]},{"id":"chunk-34","heading":"Task22/three/TextDocument.java","text":"; } @Override public void save() throws IOException { BufferedWriter writer = new BufferedWriter(new FileWriter(name)); writer.write(text); writer.close(); }","keywords":["bufferedwriter","factory","java","new","22","abstract","filewriter","ioexception","mvc","name","override","public"],"topics":["java"]},{"id":"chunk-35","heading":"Task22/two/AbstractChairFactory.java","text":"public interface AbstractChairFactory { VictorianChair createVictorianChair(); MagicChair createMagicanChair(); FunctionalChair createFunctionalChair(); }","keywords":["factory","java","22","abstract","abstractchairfactory","abstractchairfactory.java","createfunctionalchair","createmagicanchair","createvictorianchair","functionalchair","interface","magicchair"],"topics":["ai","java"]},{"id":"chunk-36","heading":"Task22/two/ChairFactory.java","text":"@Override public VictorianChair createVictorianChair() { return new VictorianChair(35); }","keywords":["factory","java","victorianchair","22","35","abstract","chairfactory.java","createvictorianchair","mvc","new","override","public"],"topics":["ai","java"]},{"id":"chunk-37","heading":"Task22/two/ChairFactory.java","text":"@Override public MagicChair createMagicanChair() { return new MagicChair(); } @Override public FunctionalChair createFunctionalChair() { return new FunctionalChair(); } }","keywords":["factory","functionalchair","java","magicchair","new","override","public","return","22","abstract","chairfactory.java","createfunctionalchair"],"topics":["ai","java"]},{"id":"chunk-38","heading":"Task22/two/Client.java","text":"public class Client implements Chair { Chair chair; public void sit(){ System.out.println(\"Клиент сел на стул.\"); } public void setChair(Chair chair){ this.chair=chair; } }","keywords":["chair","public","factory","java","void","22","abstract","class","client","client.java","implements","mvc"],"topics":["ai","java"]},{"id":"chunk-39","heading":"Task22/two/FunctionalChair.java","text":"```java title=\"Task22/two/FunctionalChair.java\" package Task22.two; public class FunctionalChair implements Chair { public int sum(int a, int b){ return a+b; } }","keywords":["int","java","factory","functionalchair.java","public","task22","two","22","a+b","abstract","chair","class"],"topics":["ai","java"]},{"id":"chunk-40","heading":"Task22/two/MagicChair.java","text":"```java title=\"Task22/two/MagicChair.java\" package Task22.two; public class MagicChair implements Chair { public void doMagic(){ System.out.println(\"Магический стул\"); } }","keywords":["java","factory","magicchair.java","public","task22","two","22","abstract","chair","class","domagic","implements"],"topics":["ai","java"]},{"id":"chunk-41","heading":"Task22/two/TestChair.java","text":"public class TestChair { public static void main(String[] args){ AbstractChairFactory af = new ChairFactory(); Scanner s = new Scanner(System.in); Client c = new Client(); System.out.println(\"Введите 1если нужен старинный стул, 2если нужен магический стул, 3если нужен функциональный стул.\"); int a = s.nextInt(); if (a ==1){ VictorianChair v = af.createVictorianChair(); System.out.println(v.getAge()+\" лет\")","keywords":["new","нужен","client","factory","java","public","scanner","system.out.println","стул","1если","22","2если"],"topics":["ai","java","testing"]},{"id":"chunk-42","heading":"Task22/two/TestChair.java","text":"; } if (a ==2){ MagicChair m = af.createMagicanChair(); m.doMagic(); } if (a==3){ FunctionalChair f = af.createFunctionalChair(); System.out.println(\"Сумма = \"+f.sum(39,12)); } if (a==1 a==2 a==3){ c.sit(); }else{ System.out.println(\"В каталоге нет такого вида стульев\"); } } }","keywords":["if","factory","java","system.out.println","+f.sum","12","22","39","abstract","af.createfunctionalchair","af.createmagicanchair","c.sit"],"topics":["ai","java","testing"]},{"id":"chunk-43","heading":"Task22/two/VictorianChair.java","text":"public class VictorianChair implements Chair { private int age; public VictorianChair(int age) { this.age = age; }","keywords":["age","factory","int","java","public","victorianchair","22","abstract","chair","class","implements","mvc"],"topics":["ai","java"]},{"id":"chunk-44","heading":"Описание","text":"В этом модуле используется 20 Java файлов. Ключевые сущности: Complex, ComplexAbstractFactory, ConcreteFactory, TestComplex, CarcasController, CarcasModel.","keywords":["java","factory","20","22","abstract","carcascontroller","carcasmodel.","complex","complexabstractfactory","concretefactory","mvc","task"],"topics":["java","testing"]},{"id":"chunk-45","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["factory","java","22","abstract","mvc","task","демо","задачах.","задачи","запускайте","затем","кейсами."],"topics":["oop"]},{"id":"chunk-46","heading":"Вывод","text":"Task 22 — Паттерны проектирования документирует реальное решение из исходного кода.","keywords":["22","factory","java","task","паттерны","проектирования","abstract","mvc","вывод","документирует","задачах.","исходного"],"topics":[]}]},{"id":"java/task-23-orders-and-menu","href":"/docs/java/task-23-orders-and-menu","slug":["java","task-23-orders-and-menu"],"section":"java","sectionTitle":"Java","title":"Task 23 — Заказы и меню","description":"Моделирование меню, заказов и интерфейсов предметной области.","preview":"Задание Моделирование меню, заказов и интерфейсов предметной области. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task23/Dish.java Task23/Drink.java Task23/InternetOrder.java Task23/","keywords":["public","string","task23","return","dish","name","description","int","new","order","private","coast","override","this.description","dish.java","drink.java","getcoast","system.out.println"],"topics":["python","ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Моделирование меню, заказов и интерфейсов предметной области. Документация собрана по исходному коду этой практики.","keywords":["меню","java","заказов","интерфейсов","моделирование","области.","предметной","23","task","документация","задание","заказы"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task23/Dish.java Task23/Drink.java Task23/InternetOrder.java Task23/Item.java Task23/Order.java Task23/Test.java","keywords":["task23","java","меню","23","dish.java","drink.java","internetorder.java","item.java","order.java","task","test.java","заказов"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task23/Dish.java","text":"public final class Dish implements Item{ private int coast; private String name; private String description; Dish(String name,String description){ this.coast = 0; this.name = name; this.description = description; } Dish(int coast,String name,String description){ this.coast = coast; this.name = name; this.description = description; } @Override public int getCoast() { return coast","keywords":["string","description","name","coast","dish","int","private","java","public","this.coast","this.description","this.name"],"topics":["python","java"]},{"id":"chunk-3","heading":"Task23/Dish.java","text":"; } @Override public String getName() { return name; } @Override public String getDescription() { return description; }","keywords":["java","override","public","return","string","меню","23","description","dish.java","getdescription","getname","name"],"topics":["java"]},{"id":"chunk-4","heading":"Task23/Dish.java","text":"} @Override public void setDescription(String description) { this.description = description; } public String toString(){ return \"Coast = \" + getCoast() + \", name = \" + getName() + \", description = \" + getDescription() + \"\\n\"; } } ```","keywords":["description","java","public","string","меню","23","coast","dish.java","getcoast","getdescription","getname","name"],"topics":["python","java"]},{"id":"chunk-5","heading":"Task23/Drink.java","text":"private int coast; private String name; private String description; Drink(String name,String description){ this.coast = 0; this.name = name; this.description = description; } Drink(int coast,String name,String description){ this.coast = coast; this.name = name; this.description = description; } @Override public int getCoast() { return coast; } @Override public String getName() { return name","keywords":["string","name","description","coast","int","private","drink","java","override","public","return","this.coast"],"topics":["python","java"]},{"id":"chunk-6","heading":"Task23/Drink.java","text":"; } @Override public String getDescription() { return description; }","keywords":["java","меню","23","description","drink.java","getdescription","override","public","return","string","task","task23"],"topics":["java"]},{"id":"chunk-7","heading":"Task23/Drink.java","text":"@Override public void setDescription(String description) { this.description = description; } }","keywords":["description","java","меню","23","drink.java","override","public","setdescription","string","task","task23","this.description"],"topics":["java"]},{"id":"chunk-8","heading":"Task23/InternetOrder.java","text":"public class InternetOrder { private Order[] orders; public Order getOrder(String name){ return new Order(1); } public void addDish(Dish dish,String name){ return; } public boolean removeOrder(String name){ return true; } public int freeTableNumber(){ return 0; } public int[] freeTableNumbers(){ return new int[]{}; } public Order[] getOrders(){ return new Order[]{}; } public double orderCoastSummary(){ return 0.0","keywords":["public","return","order","int","name","new","string","dish","java","меню","0.0","23"],"topics":["python","java"]},{"id":"chunk-9","heading":"Task23/InternetOrder.java","text":"; } public int dishQuantity(String dishName){ return 0; } }","keywords":["java","меню","23","dishname","dishquantity","int","internetorder.java","public","return","string","task","task23"],"topics":["java"]},{"id":"chunk-10","heading":"Task23/Order.java","text":"public class Order { private List items; private int size; private Dish[] dishes; Order(int size){ this.items = new ArrayList< (); } public boolean add(Dish dish){ return items.add(dish); } public boolean remove(String Name){ for(int i = 0; i n.getName().equals(Name)).count(); }","keywords":["dish","int","private","public","boolean","java","name","order","size","меню",".count",".equals"],"topics":["java"]},{"id":"chunk-11","heading":"Task23/Order.java","text":"public Dish[] getDishes() { return items.toArray(new Dish[0]); } public double costTotal(){ return items.stream().mapToDouble(Item::getCoast).sum(); } public String[] dishesName(){ return new String[]{items.stream().filter(n n.getName() != \"\").toArray().toString()}; } public Dish[] sortedDishesByCostDesc(){ return items.stream().sorted(Comparator.comparingInt(Item::getCoast)).toList().toArray(new Dish[0]); } }","keywords":["dish","public","return","items.stream","new",".toarray","getcoast","item","java","string","меню",".filter"],"topics":["python","java"]},{"id":"chunk-12","heading":"Task23/Test.java","text":"public class Test { public static void main(String[] args) { Order order = new Order(5); order.add(new Dish(450,\"Оливье\",\"Холодное блюдо\")); order.add(new Dish(300,\"Лимонад\",\"Напиток\")); order.add(new Dish(600,\"Рёбра\",\"Горячее блюдо\")); order.add(new Dish(400,\"Борщ\",\"Горячее блюдо\")); System.out.println(order.remove(\"Лимонад\")); System.out.println(order.dishQuantity()); System.out.println(order.costTotal())","keywords":["new","dish","order.add","order","system.out.println","блюдо","java","public","горячее","лимонад","меню","23"],"topics":["ai","java","testing"]},{"id":"chunk-13","heading":"Task23/Test.java","text":"; System.out.println(order.dishQuantity(\"Борщ\")); System.out.println(Arrays.stream(order.sortedDishesByCostDesc()).toList().toString()); } }","keywords":["java","system.out.println","меню",".tolist",".tostring","23","arrays.stream","order.dishquantity","order.sorteddishesbycostdesc","task","task23","test.java"],"topics":["java","testing"]},{"id":"chunk-14","heading":"Описание","text":"В этом модуле используется 6 Java файлов. Ключевые сущности: Dish, Drink, InternetOrder, Item, Order, Test.","keywords":["java","меню","23","dish","drink","internetorder","item","order","task","test.","заказов","заказы"],"topics":["java","testing"]},{"id":"chunk-15","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","меню","23","task","демо","задачи","заказов","заказы","запускайте","затем","интерфейсов","кейсами."],"topics":["oop"]}]},{"id":"java/task-24-final-order-system","href":"/docs/java/task-24-final-order-system","slug":["java","task-24-final-order-system"],"section":"java","sectionTitle":"Java","title":"Task 24 — Финальный проект","description":"Расширенная система заказов с менеджерами и клиентскими сущностями.","preview":"Задание Расширенная система заказов с менеджерами и клиентскими сущностями. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения task24/Address.java task24/AlcohoTable.java task24/Customer.ja","keywords":["public","task24","int","return","string","override","null","new","private","order","if","menuitem","itemname","head","temp","item","name","system.out.println"],"topics":["python","ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Расширенная система заказов с менеджерами и клиентскими сущностями. Документация собрана по исходному коду этой практики.","keywords":["java","заказов","клиентскими","менеджерами","расширенная","система","сущностями.","24","task","документация","задание","исходному"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"task24/Address.java task24/AlcohoTable.java task24/Customer.java task24/Dish.java task24/Drink.java task24/DrinkTypeEnum.java task24/InternetOrder.java task24/InternetOrdersManager.java task24/MenuItem.java task24/Order.java task24/OrdersManager.java task24/TableOrder.java task24/TableOrdersManager.java task24/Test.java","keywords":["task24","java","24","address.java","alcohotable.java","customer.java","dish.java","drink.java","drinktypeenum.java","internetorder.java","internetordersmanager.java","menuitem.java"],"topics":["java","testing"]},{"id":"chunk-2","heading":"task24/Address.java","text":"public final class Address extends Customer{ private String cityName; private int zipCode; private String streetName; private int buildingNumber; private int buildingLetter; private int apartmentNumber; public Address EMPTY ADDRESSS;","keywords":["private","int","address","java","public","string","24","address.java","addresss","apartmentnumber","buildingletter","buildingnumber"],"topics":["java"]},{"id":"chunk-3","heading":"task24/AlcohoTable.java","text":"interface AlcohoTable { public boolean isAlcoholicDrink(); public double getAlcoholVol();","keywords":["java","public","24","alcohotable","alcohotable.java","boolean","double","getalcoholvol","interface","isalcoholicdrink","task","task24"],"topics":["java"]},{"id":"chunk-4","heading":"task24/Customer.java","text":"public class Customer implements Order { private String firstName; private String secondName; private int age; private Address address; private Customer MATURE UNKNOWN CUSTOMER; private Customer NOT MATURE UNKNOWN CUSTOMER;","keywords":["private","customer","address","java","mature","string","unknown","24","age","class","customer.java","firstname"],"topics":["java"]},{"id":"chunk-5","heading":"task24/Dish.java","text":"public final class Dish extends MenuItem { private int coast; private String name; private String description; Dish(String name,String description){ super(); if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["string","private","description","dish","if","illegalargumentexception","java","name","new","throw","быть","должно"],"topics":["python","java"]},{"id":"chunk-6","heading":"task24/Dish.java","text":"}else { this.coast = 0; this.name = name; this.description = description; } } Dish(int coast,String name,String description){ super(); if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["description","else","if","illegalargumentexception","java","name","new","string","throw","быть","должно","пустым"],"topics":["python","java"]},{"id":"chunk-7","heading":"task24/Dish.java","text":"} else if ( coast < 0){ throw new IllegalArgumentException(\"Стоимость должны быть больше 0\");","keywords":["java","24","coast","dish.java","else","if","illegalargumentexception","new","task","task24","throw","больше"],"topics":["python","java"]},{"id":"chunk-8","heading":"task24/Dish.java","text":"}else { this.coast = coast; this.name = name; this.description = description; } } public Dish(double cost, Scanner name, Scanner opis) { super(); }","keywords":["java","name","scanner","24","coast","cost","description","dish","dish.java","double","else","opis"],"topics":["python","java"]},{"id":"chunk-9","heading":"task24/Drink.java","text":"final class Drink extends MenuItem implements AlcohoTable { private double alcoholVol; private DrinkTypeEnum type; private int coast; private String name; private String description;","keywords":["private","java","string","24","alcoholvol","alcohotable","class","coast","description","double","drink","drink.java"],"topics":["python","java"]},{"id":"chunk-10","heading":"task24/Drink.java","text":"public DrinkTypeEnum getType() { return type; } Drink(String name,String description){ if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["if","illegalargumentexception","java","new","string","throw","быть","должно","пустым","24","description","description.isempty"],"topics":["java"]},{"id":"chunk-11","heading":"task24/Drink.java","text":"}else { this.coast = 0; this.name = name; this.description = description; } } Drink(int coast,String name,String description){ if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["description","else","if","illegalargumentexception","java","name","new","string","throw","быть","должно","пустым"],"topics":["python","java"]},{"id":"chunk-12","heading":"task24/Drink.java","text":"} else if ( coast < 0){ throw new IllegalArgumentException(\"Стоимость должны быть больше 0\");","keywords":["java","24","coast","drink.java","else","if","illegalargumentexception","new","task","task24","throw","больше"],"topics":["python","java"]},{"id":"chunk-13","heading":"task24/Drink.java","text":"}else { this.coast = coast; this.name = name; this.description = description; } } Drink(int coast,String name,String description, int alcoholVol,DrinkTypeEnum type){ if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["coast","description","else","if","illegalargumentexception","int","java","name","new","string","throw","быть"],"topics":["python","java"]},{"id":"chunk-14","heading":"task24/Drink.java","text":"} else if ( coast < 0){ throw new IllegalArgumentException(\"Стоимость должны быть больше 0\");","keywords":["java","24","coast","drink.java","else","if","illegalargumentexception","new","task","task24","throw","больше"],"topics":["python","java"]},{"id":"chunk-15","heading":"task24/Drink.java","text":"}else { this.coast = coast; this.name = name; this.description = description; this.alcoholVol = alcoholVol; this.type = type; } }","keywords":["java","24","alcoholvol","coast","description","drink.java","else","name","task","task24","this.alcoholvol","this.coast"],"topics":["python","java"]},{"id":"chunk-16","heading":"task24/DrinkTypeEnum.java","text":"public class DrinkTypeEnum { public static String Proverc(String n){ String t = \"\"; if (Objects.equals(n, \"AL\")){ t = \"Предъявите паспор\"; } return t; } }","keywords":["string","java","public","24","al","class","drinktypeenum","drinktypeenum.java","if","objects.equals","proverc","return"],"topics":["java"]},{"id":"chunk-17","heading":"task24/InternetOrder.java","text":"public class InternetOrder implements Order{ private int size; private ListNode head; private ListNode tail; class ListNode{ private ListNode next; private ListNode prev; private MenuItem value; ListNode(MenuItem value){ this.next = null; this.prev = null; this.value = value; }","keywords":["listnode","private","value","class","java","menuitem","null","24","head","implements","int","internetorder"],"topics":["ai","java"]},{"id":"chunk-18","heading":"task24/InternetOrder.java","text":"} InternetOrder(int size){ this.tail = null; this.head = null; this.size = size; } @Override public boolean add(MenuItem item) { ListNode node = new ListNode(item); if(head == null){ head = node; tail = node; return true; }else{ node.next = head; head.prev = node; head = node; return true; } } @Override public String[] itemsNames() { String str[] = new String[size]; ListNode temp = head; int i = 0","keywords":["head","node","listnode","null","size","string","int","item","java","new","override","public"],"topics":["ai","java"]},{"id":"chunk-19","heading":"task24/InternetOrder.java","text":"; while (temp != null & i < size){ str[i] = temp.value.getName(); i++; temp = temp.next; } return str; } @Override public int itemsQuantity() { ListNode temp = head; int count = 0; while (temp != null){ count++; temp = temp.next; } return count; } @Override public int itemsQuantity(String itemName) { ListNode temp = head; int count = 0; while (temp != null){ if(temp.value.getName().equals(itemName)) { count++","keywords":["temp","int","count","null","while","count++","head","itemname","itemsquantity","java","listnode","override"],"topics":["java"]},{"id":"chunk-20","heading":"task24/InternetOrder.java","text":"; } temp = temp.next; } return count; }","keywords":["java","24","count","internetorder.java","return","task","task24","temp","temp.next","заказов","клиентскими","менеджерами"],"topics":["java"]},{"id":"chunk-21","heading":"task24/InternetOrder.java","text":"@Override public int itemsQuantity(MenuItem itemName) { ListNode temp = head; int count = 0; while (temp != null){ if(temp.value.equals(itemName)) { count++; } temp = temp.next; } return count; }","keywords":["temp","count","int","itemname","java","24","count++","head","if","internetorder.java","itemsquantity","listnode"],"topics":["java"]},{"id":"chunk-22","heading":"task24/InternetOrder.java","text":"@Override public Order[] getOrders() { return orderManager.values().toArray(new Order[0]); }","keywords":["java","order",".toarray","24","getorders","internetorder.java","new","ordermanager.values","override","public","return","task"],"topics":["java"]},{"id":"chunk-23","heading":"task24/InternetOrder.java","text":"@Override public int ordersCostSummary() { int sum = 0; ListNode temp = head; while (temp != null){ sum += temp.value.getCost(); temp = temp.next; } return sum; }","keywords":["sum","temp","int","java","24","head","internetorder.java","listnode","null","orderscostsummary","override","public"],"topics":["java"]},{"id":"chunk-24","heading":"task24/InternetOrder.java","text":"@Override public MenuItem[] getItems() { return orderManager.values().toArray(new MenuItem[0]); }","keywords":["java","menuitem",".toarray","24","getitems","internetorder.java","new","ordermanager.values","override","public","return","task"],"topics":["java"]},{"id":"chunk-25","heading":"task24/InternetOrder.java","text":"@Override public boolean remove(MenuItem item) { if (head == null){ return false; } if (head == tail && head.value.equals(item)){ head = null; tail = null; return true; } ListNode temp = head; while (temp != null & temp.value.equals(item)){ temp = temp.next; } temp.prev.next = temp.next; temp.next.prev = temp.prev; temp.prev = null; temp.next = null; return true; }","keywords":["null","head","item","return","temp","temp.next","if","java","tail","temp.prev","true","24"],"topics":["ai","java"]},{"id":"chunk-26","heading":"task24/InternetOrder.java","text":"@Override public int removeAll(String itemName) { if (head == null){ return 0; } if (head == tail & head.value.getName().equals(itemName)){ head = null; tail = null; return 1; } int count = 0; ListNode temp = head; while (temp != null){ if(temp.value.getName().equals(itemName)){ count++; temp.prev.next = temp.next; temp.next.prev = temp.prev; temp.prev = null; temp.next = null; } temp = temp.next; } return count; }","keywords":["null","head","if","itemname","return","temp","temp.next",".equals","count","int","java","tail"],"topics":["ai","java"]},{"id":"chunk-27","heading":"task24/InternetOrder.java","text":"@Override public int removeAll(MenuItem item) { if (head == null){ return 0; } if (head == tail & head.value.equals(item)){ head = null; tail = null; return 1; } int count = 0; ListNode temp = head; while (temp != null){ if(temp.value.equals(item)){ count++; temp.prev.next = temp.next; temp.next.prev = temp.prev; temp.prev = null; temp.next = null; } temp = temp.next; } return count; }","keywords":["null","head","if","item","return","temp","temp.next","count","int","java","tail","temp.prev"],"topics":["ai","java"]},{"id":"chunk-28","heading":"task24/InternetOrder.java","text":"@Override public boolean remove(String itemName) { if (head == null){ return false; } if (head == tail && head.value.getName().equals(itemName)){ head = null; tail = null; return true; } ListNode temp = head; while (temp != null & temp.value.getName().equals(itemName)){ temp = temp.next; } temp.prev.next = temp.next; temp.next.prev = temp.prev; temp.prev = null; temp.next = null; return true; }","keywords":["null","head","itemname","return","temp","temp.next",".equals","if","java","tail","temp.prev","true"],"topics":["ai","java"]},{"id":"chunk-29","heading":"task24/InternetOrder.java","text":"@Override public int costTotal() { orderManager.values().stream().filter(value orderManager.containsValue(value)).toList(); return 0; }","keywords":["java","value",".filter",".stream",".tolist","24","costtotal","int","internetorder.java","ordermanager.containsvalue","ordermanager.values","override"],"topics":["ai","java"]},{"id":"chunk-30","heading":"task24/InternetOrdersManager.java","text":"```java title=\"task24/InternetOrdersManager.java\" package task24; class QueueNode{ private QueueNode next; private QueueNode prev; private Order value; } public class InternetOrdersManager implements OrdersManager{ private QueueNode head; private QueueNode tail; private int size; public boolean add(Order order){ return true; } public Order remove(){ return null; } public Order order(){ return null","keywords":["order","private","queuenode","public","java","return","task24","class","internetordersmanager.java","null","24","add"],"topics":["ai","java"]},{"id":"chunk-31","heading":"task24/InternetOrdersManager.java","text":"; } @Override public int itemsQuantity(String itemName) { return 0; }","keywords":["java","24","int","internetordersmanager.java","itemname","itemsquantity","override","public","return","string","task","task24"],"topics":["java"]},{"id":"chunk-32","heading":"task24/MenuItem.java","text":"public class MenuItem implements Order{ private int cost; private String name; private String description;","keywords":["private","java","string","24","class","cost","description","implements","int","menuitem","menuitem.java","name"],"topics":["java"]},{"id":"chunk-33","heading":"task24/MenuItem.java","text":"@Override public boolean add(MenuItem item) { orderManager.put(item.getName(),item); return true; }","keywords":["item","java","24","add","boolean","item.getname","menuitem","menuitem.java","ordermanager.put","override","public","return"],"topics":["java"]},{"id":"chunk-34","heading":"task24/MenuItem.java","text":"@Override public String[] itemsNames() { return orderManager.keySet().stream().filter(key orderManager.get(key) != null).toArray(String[]::new); }","keywords":["java","key","string",".filter",".stream",".toarray","24","itemsnames","menuitem.java","new","null","ordermanager.get"],"topics":["java"]},{"id":"chunk-35","heading":"task24/MenuItem.java","text":"@Override public int itemsQuantity(String itemName) { return (int) orderManager.keySet().stream().filter(key key.equals(itemName)).count(); }","keywords":["int","itemname","java",".count",".filter",".stream","24","itemsquantity","key","key.equals","menuitem.java","ordermanager.keyset"],"topics":["java"]},{"id":"chunk-36","heading":"task24/MenuItem.java","text":"@Override public int itemsQuantity(MenuItem itemName) { int count = 0; for (Map.Entry entry : orderManager.entrySet()) { Order order = entry.getValue(); if (order.equals(itemName)) { count++; } } return count; }","keywords":["count","int","itemname","java","order","24","count++","entry","entry.getvalue","if","itemsquantity","map.entry"],"topics":["java"]},{"id":"chunk-37","heading":"task24/MenuItem.java","text":"@Override public Order[] getOrders() { return orderManager.values().toArray(new Order[0]); }","keywords":["java","order",".toarray","24","getorders","menuitem.java","new","ordermanager.values","override","public","return","task"],"topics":["java"]},{"id":"chunk-38","heading":"task24/MenuItem.java","text":"@Override public MenuItem[] getItems() { return orderManager.values().toArray(new MenuItem[0]); }","keywords":["java","menuitem",".toarray","24","getitems","menuitem.java","new","ordermanager.values","override","public","return","task"],"topics":["java"]},{"id":"chunk-39","heading":"task24/MenuItem.java","text":"@Override public boolean remove(MenuItem item) { return orderManager.remove(item.getName(),item); }","keywords":["item","java","24","boolean","item.getname","menuitem","menuitem.java","ordermanager.remove","override","public","remove","return"],"topics":["java"]},{"id":"chunk-40","heading":"task24/MenuItem.java","text":"@Override public int removeAll(String itemName) { int initialSize = orderManager.size(); // Запоминаем начальный размер HashMap orderManager.entrySet().removeIf(entry entry.getKey().equals(itemName)); int countDel = initialSize orderManager.size(); // Вычисляем количество удаленных элементов return countDel; }","keywords":["int","countdel","initialsize","itemname","java","ordermanager.size",".equals",".removeif","24","entry","entry.getkey","hashmap"],"topics":["java"]},{"id":"chunk-41","heading":"task24/MenuItem.java","text":"@Override public int removeAll(MenuItem item) { int count = 0; for(int i = 0 ; i < orderManager.size();i++){ if(orderManager.remove(item.name,item)) { count++; } } return count; }","keywords":["int","count","item","java","24","count++","i++","if","item.name","menuitem","menuitem.java","ordermanager.remove"],"topics":["java"]},{"id":"chunk-42","heading":"task24/MenuItem.java","text":"@Override public boolean remove(String itemName) { return orderManager.remove(itemName) != null; } @Override public int costTotal() { return 0; }","keywords":["itemname","java","override","public","return","24","boolean","costtotal","int","menuitem.java","null","ordermanager.remove"],"topics":["java"]},{"id":"chunk-43","heading":"task24/MenuItem.java","text":"@Override public Customer setCustomer(Customer customer) { return null; } @Override public String toString() { return \"cost = \" + cost + \", name = \" + name + \", description = \" + description; } public String prints(){ return orderManager.toString(); } }","keywords":["customer","public","return","cost","description","java","name","override","string","24","menuitem.java","null"],"topics":["java"]},{"id":"chunk-44","heading":"task24/Order.java","text":"public interface Order extends OrdersManager{ public boolean add(MenuItem item); public String[] itemsNames(); public int itemsQuantity(); public int itemsQuantity(String itemName); public int itemsQuantity(MenuItem itemName); public MenuItem[] getItems(); public boolean remove(MenuItem item); public int removeAll(String itemName); public int removeAll(MenuItem item); public boolean remove(String itemName);","keywords":["public","int","menuitem","itemname","string","boolean","item","itemsquantity","java","remove","removeall","24"],"topics":["java"]},{"id":"chunk-45","heading":"task24/Order.java","text":"public int costTotal(); public Customer getCustomer(); public Customer setCustomer(Customer customer);","keywords":["customer","public","java","24","costtotal","getcustomer","int","order.java","setcustomer","task","task24","заказов"],"topics":["java"]},{"id":"chunk-46","heading":"task24/OrdersManager.java","text":"interface OrdersManager{ HashMap orderManager = new HashMap< (); public int itemsQuantity(String itemName); public int itemsQuantity(MenuItem item); public Order[] getOrders(); public int ordersCostSummary(); public int ordersQuantity();","keywords":["public","int","hashmap","itemsquantity","java","24","getorders","interface","item","itemname","menuitem","new"],"topics":["java"]},{"id":"chunk-47","heading":"task24/TableOrder.java","text":"public class TableOrder implements Order{ private int size; private MenuItem[] items;","keywords":["java","private","24","class","implements","int","items","menuitem","order","public","size","tableorder"],"topics":["java"]},{"id":"chunk-48","heading":"task24/TableOrder.java","text":"@Override public boolean add(MenuItem item) { orderManager.put(item.getName(),item); return true; }","keywords":["item","java","24","add","boolean","item.getname","menuitem","ordermanager.put","override","public","return","tableorder.java"],"topics":["java"]},{"id":"chunk-49","heading":"task24/TableOrder.java","text":"@Override public String[] itemsNames() { return orderManager.keySet().stream().filter(key orderManager.get(key) != null).toArray(String[]::new); } @Override public int itemsQuantity() { return orderManager.keySet().size(); }","keywords":["java","key","ordermanager.keyset","override","public","return","string",".filter",".size",".stream",".toarray","24"],"topics":["java"]},{"id":"chunk-50","heading":"task24/TableOrder.java","text":"@Override public int itemsQuantity(String itemName) { return (int) orderManager.keySet().stream().filter(key key.equals(itemName)).count(); } @Override public int itemsQuantity(MenuItem itemName) { int count = 0; for (Map.Entry entry : orderManager.entrySet()) { Order order = entry.getValue(); if (order.equals(itemName)) { count++; } } return count","keywords":["int","itemname","count","itemsquantity","java","order","override","public","return",".count",".filter",".stream"],"topics":["java"]},{"id":"chunk-51","heading":"task24/TableOrder.java","text":"; } @Override public Order[] getOrders() { return orderManager.values().toArray(new Order[0]); } @Override public int ordersCostSummary() { return 0; }","keywords":["java","order","override","public","return",".toarray","24","getorders","int","new","ordermanager.values","orderscostsummary"],"topics":["java"]},{"id":"chunk-52","heading":"task24/TableOrder.java","text":"@Override public MenuItem[] getItems() { return orderManager.values().toArray(new MenuItem[0]); }","keywords":["java","menuitem",".toarray","24","getitems","new","ordermanager.values","override","public","return","tableorder.java","task"],"topics":["java"]},{"id":"chunk-53","heading":"task24/TableOrder.java","text":"@Override public boolean remove(MenuItem item) { return orderManager.remove(item.getName(),item); }","keywords":["item","java","24","boolean","item.getname","menuitem","ordermanager.remove","override","public","remove","return","tableorder.java"],"topics":["java"]},{"id":"chunk-54","heading":"task24/TableOrder.java","text":"@Override public int removeAll(String itemName) { int initialSize = orderManager.size(); // Запоминаем начальный размер HashMap orderManager.entrySet().removeIf(entry entry.getKey().equals(itemName)); int countDel = initialSize orderManager.size(); // Вычисляем количество удаленных элементов return countDel; }","keywords":["int","countdel","initialsize","itemname","java","ordermanager.size",".equals",".removeif","24","entry","entry.getkey","hashmap"],"topics":["java"]},{"id":"chunk-55","heading":"task24/TableOrder.java","text":"@Override public int removeAll(MenuItem item) { int count = 0; for(int i = 0 ; i < orderManager.size();i++){ if(orderManager.remove(item.getName(),item)) { count++; } } return count; }","keywords":["int","count","item","java","24","count++","i++","if","item.getname","menuitem","ordermanager.remove","ordermanager.size"],"topics":["java"]},{"id":"chunk-56","heading":"task24/TableOrder.java","text":"@Override public boolean remove(String itemName) { return orderManager.remove(itemName) != null; } @Override public int costTotal() { return 0; }","keywords":["itemname","java","override","public","return","24","boolean","costtotal","int","null","ordermanager.remove","remove"],"topics":["java"]},{"id":"chunk-57","heading":"task24/TableOrdersManager.java","text":"public class TableOrdersManager implements OrdersManager{ private Order[] orders; public void add(Order order, int tableNumber){} public void addItem(MenuItem item, int tableNumber){} public int freeTableNumber(Order order, int tableNumber){ return 0;} public int[] freeTableNumbers(Order order, int tableNumber){ return null;}","keywords":["order","int","public","tablenumber","java","return","void","24","add","additem","class","freetablenumber"],"topics":["java"]},{"id":"chunk-58","heading":"task24/TableOrdersManager.java","text":"public Order getOrder(int tableNumber){ return null;} public void remove(int tableNumber){} public int remove(Order order){ return 0;} public int removeAll(Order order){return 0;}","keywords":["order","int","public","return","java","remove","tablenumber","24","getorder","null","removeall","tableordersmanager.java"],"topics":["java"]},{"id":"chunk-59","heading":"task24/TableOrdersManager.java","text":"@Override public int itemsQuantity(MenuItem item) { return 0; } @Override public Order[] getOrders() { return new Order[0]; } @Override public int ordersCostSummary() { return 0; } @Override public int ordersQuantity() { return 0; } }","keywords":["override","public","return","int","java","order","24","getorders","item","itemsquantity","menuitem","new"],"topics":["java"]},{"id":"chunk-60","heading":"task24/Test.java","text":"public class Test { public static void main(String[] args) { Scanner s = new Scanner(System.in); Scanner r = new Scanner(System.in); Scanner w = new Scanner(System.in); MenuItem menuItem = new MenuItem(); double cost; boolean bool = true; String name, opis; Scanner nomder = new Scanner(System.in)","keywords":["scanner","new","system.in","menuitem","java","public","string","24","args","bool","boolean","class"],"topics":["ai","java","testing"]},{"id":"chunk-61","heading":"task24/Test.java","text":"; System.out.println(\"Ввелитед 1 если вы делаете заказ в ресторане:\\n\" + \"Введите 2 если делаете заказ на сайте:\\n\" + \"Введите 3 если хотите популярные блюда ресторана:\"); switch (nomder.nextInt()) { case 1: while (bool) { System.out.println(\"Что хотите добавить в заказ \\n1 если еду: \\n2 если напиток:\"); switch (nomder.nextInt()) { case 1: System.out.println(\"Введите название товара: \"); name = r.nextLine()","keywords":["если","system.out.println","введите","заказ","case","java","nomder.nextint","switch","делаете","хотите","24","bool"],"topics":["java","testing"]},{"id":"chunk-62","heading":"task24/Test.java","text":"; System.out.println(\"Введите цену товара: \"); cost = s.nextDouble(); System.out.println(\"Введите описание товара: \"); opis = w.nextLine(); Dish d = new Dish((int) cost, name, opis); menuItem.add(d); System.out.println(\"Ввелите 1 если вы хотите заказать еще или 0 если вы закончили с заказом\"); if (nomder.nextInt() == 1){ bool = true; break; }else { bool=false; break","keywords":["system.out.println","bool","break","cost","dish","java","opis","введите","вы","если","товара","24"],"topics":["java","testing"]},{"id":"chunk-63","heading":"task24/Test.java","text":"; } case 2: System.out.println(\"Введите название напитка: \"); name = r.nextLine(); System.out.println(\"Введите цену напитка: \"); cost = s.nextDouble(); System.out.println(\"Введите описание напитка если он алкагольный введите AL: \"); opis = w.nextLine(); Drink dr = new Drink((int) cost, name, opis); menuItem.add(dr); System.out.println(Proverc(opis))","keywords":["system.out.println","введите","opis","напитка","cost","dr","drink","java","name","24","al","case"],"topics":["java","testing"]},{"id":"chunk-64","heading":"task24/Test.java","text":"; System.out.println(\"Ввелите 1 если вы хотите заказать еще или 0 если вы закончили с заказом\"); if (nomder.nextInt() == 1){ bool = true; break; }else { bool=false; break; } } } break; case 2: while (bool) { System.out.println(\"Что хотите добавить в заказ \\n1 если еду: \\n2 если напиток:\"); switch (nomder.nextInt()) { case 1: System.out.println(\"Введите название товара: \"); name = r.nextLine()","keywords":["если","bool","break","system.out.println","case","java","nomder.nextint","вы","хотите","24","else","false"],"topics":["java","testing"]},{"id":"chunk-65","heading":"task24/Test.java","text":"; System.out.println(\"Введите цену товара: \"); cost = s.nextDouble(); System.out.println(\"Введите описание товара: \"); opis = w.nextLine(); Dish d = new Dish((int) cost, name, opis); menuItem.add(d); System.out.println(\"Ввелите 1 если вы хотите заказать еще или 0 если вы закончили с заказом\"); if (nomder.nextInt() == 1){ bool = true; break; }else { bool=false; break","keywords":["system.out.println","bool","break","cost","dish","java","opis","введите","вы","если","товара","24"],"topics":["java","testing"]},{"id":"chunk-66","heading":"task24/Test.java","text":"; } case 2: System.out.println(\"Введите название напитка: \"); name = r.nextLine(); System.out.println(\"Введите цену напитка: \"); cost = s.nextDouble(); System.out.println(\"Введите описание напитка если он алкагольный введите AL: \"); opis = w.nextLine(); Drink dr = new Drink((int) cost, name, opis); menuItem.add(dr); System.out.println(Proverc(opis))","keywords":["system.out.println","введите","opis","напитка","cost","dr","drink","java","name","24","al","case"],"topics":["java","testing"]},{"id":"chunk-67","heading":"task24/Test.java","text":"; System.out.println(\"Ввелите 1 если вы хотите заказать еще или 0 если вы закончили с заказом\"); if (nomder.nextInt() == 1){ bool = true; break; }else { bool=false; break; } } } break; case 3: Dish dishOne = new Dish(10,\"Название№1\",\"Описание№1\"); Dish dishTwo = new Dish(500,\"Название№2\",\"Описание№2\"); Dish dishFree = new Dish(1000,\"Название№3\",\"Описание№3\"); Drink drink = new Drink(120,\"dsd\",\"dsds\")","keywords":["dish","new","break","drink","название","описание","bool","java","вы","если","10","1000"],"topics":["java","testing"]},{"id":"chunk-68","heading":"task24/Test.java","text":"; menuItem.add(dishOne); menuItem.add(dishTwo); menuItem.add(dishFree); menuItem.add(drink); } System.out.println(Arrays.stream(menuItem.itemsNames()).toList()); } }","keywords":["menuitem.add","java",".tolist","24","arrays.stream","dishfree","dishone","dishtwo","drink","menuitem.itemsnames","system.out.println","task"],"topics":["java","testing"]},{"id":"chunk-69","heading":"Описание","text":"В этом модуле используется 14 Java файлов. Ключевые сущности: Address, AlcohoTable, Customer, Dish, Drink, DrinkTypeEnum.","keywords":["java","14","24","address","alcohotable","customer","dish","drink","drinktypeenum.","task","заказов","используется"],"topics":["java"]},{"id":"chunk-70","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","24","task","демо","задачи","заказов","запускайте","затем","кейсами.","класс","клиентскими","менеджерами"],"topics":["oop"]}]},{"id":"java/task-03-constructors-and-encapsulation","href":"/docs/java/task-03-constructors-and-encapsulation","slug":["java","task-03-constructors-and-encapsulation"],"section":"java","sectionTitle":"Java","title":"Task 3 — Конструкторы и инкапсуляция","description":"Инициализация объектов и контроль состояния через инкапсуляцию.","preview":"Задание Инициализация объектов и контроль состояния через инкапсуляцию. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task3/Author.java Task3/Ball.java Task3/TestAuthor.java Task3/Test","keywords":["public","double","string","task3","void","ball","return","author","email","gender","name","private","char","class","author.java","b1","ball.java","system.out.println"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Инициализация объектов и контроль состояния через инкапсуляцию. Документация собрана по исходному коду этой практики.","keywords":["java","инициализация","инкапсуляцию.","контроль","объектов","состояния","через","task","документация","задание","инкапсуляция","исходному"],"topics":[]},{"id":"chunk-1","heading":"Task3/Author.java","text":"public class Author { private String name; private String email; private char gender; public Author(String n, String e, char g){ name = n; email = e; gender = g; } public void setName(String name){ this.name = name; } public String getName(){ return name; } public void setEmail(String email){ this.email = email; } public String getEmail(){ return email; } public void getGender(char gender){ this.gender = gender","keywords":["string","public","email","name","gender","char","private","void","author","java","return","author.java"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task3/Author.java","text":"; } public char setGender(){ return gender; } public String toString(){ return this.name+\" (\"+gender+\") at \"+email; }","keywords":["java","public","return","+email","+gender+","author.java","char","gender","setgender","string","task","task3"],"topics":["ai","java"]},{"id":"chunk-3","heading":"Task3/Ball.java","text":"public class Ball { private double x = 0.0; private double y = 0.0; public Ball(){} public Ball(double x, double y){ this.x = x; this.y = y; } public double getX(){ return x; } public double getY(){ return y; } public void setX(double x){ this.x = x; } public void setY(double y){ this.y = y; } public void setXY(double x, double y){ this.x = x; this.y = y; } public void move(double xDisp, double yDisp){ x += xDisp","keywords":["double","public","void","ball","this.x","this.y","0.0","java","private","return","xdisp","ball.java"],"topics":["java"]},{"id":"chunk-4","heading":"Task3/Ball.java","text":"; y += yDisp; } public String toString(){ return \"Ball (\"+this.x+\", \"+this.y+\").\"; } }","keywords":["java","+this.x+","+this.y+","ball","ball.java","public","return","string","task","task3","tostring","ydisp"],"topics":["java"]},{"id":"chunk-5","heading":"Task3/TestAuthor.java","text":"public class TestAuthor { public static void main(String[] args){ Author a1 = new Author(\"Petr Grob\", \"peGrob@somewhere.com\", 'M'); System.out.println(a1); }","keywords":["a1","author","java","public","args","class","grob","main","new","pegrob","petr","somewhere.com"],"topics":["ai","java","testing"]},{"id":"chunk-6","heading":"Task3/TestBall.java","text":"public class TestBall { public static void main(String[] args){ Ball b1 = new Ball(100, 100); System.out.println(b1); b1.move(30, 15); System.out.println(b1); } }","keywords":["b1","100","ball","java","public","system.out.println","15","30","args","b1.move","class","main"],"topics":["ai","java","testing"]},{"id":"chunk-7","heading":"Описание","text":"В этом модуле используется 4 Java файлов. Ключевые сущности: Author, Ball, TestAuthor, TestBall.","keywords":["java","author","ball","task","testauthor","testball.","инициализация","инкапсуляцию.","инкапсуляция","используется","ключевые","конструкторы"],"topics":["java","testing"]},{"id":"chunk-8","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","task","демо","задачи","запускайте","затем","инициализация","инкапсуляцию.","инкапсуляция","кейсами.","класс","конструкторы"],"topics":["oop"]},{"id":"chunk-9","heading":"Вывод","text":"Task 3 — Конструкторы и инкапсуляция документирует реальное решение из исходного кода.","keywords":["java","task","инкапсуляция","конструкторы","вывод","документирует","инициализация","инкапсуляцию.","исходного","кода.","контроль","объектов"],"topics":["oop"]}]},{"id":"java/task-04-inheritance-and-composition","href":"/docs/java/task-04-inheritance-and-composition","slug":["java","task-04-inheritance-and-composition"],"section":"java","sectionTitle":"Java","title":"Task 4 — Наследование и композиция","description":"Переиспользование поведения через наследование и композицию.","preview":"Задание Переиспользование поведения через наследование и композицию. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task4/Book.java Task4/BookTest.java Task4/Circle.java Task4/CircleTes","keywords":["public","task4","string","name","return","rost","int","void","author","human.java","new","system.out.println","year","circle","class","static","наследование","book"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Переиспользование поведения через наследование и композицию. Документация собрана по исходному коду этой практики.","keywords":["наследование","java","композицию.","переиспользование","поведения","через","task","документация","задание","исходному","коду","композиция"],"topics":["oop"]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task4/Book.java Task4/BookTest.java Task4/Circle.java Task4/CircleTest.java Task4/Human.java","keywords":["task4","java","наследование","book.java","booktest.java","circle.java","circletest.java","human.java","task","исходные","композицию.","композиция"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task4/Book.java","text":"public Book(String author, String name, int year) { this.author = author; this.name = name; this.year = year; } public String getAuthor() { return author; } public void setAuthor(String author) { this.author = author; } public String getName() { return name; } public void setName(String name) { this.name = name; } public int getYear() { return year; } public void setYear(int year) { this.year = year; } }","keywords":["public","string","author","name","year","int","return","void","java","this.author","this.name","this.year"],"topics":["java"]},{"id":"chunk-3","heading":"Task4/BookTest.java","text":"public class BookTest { public static void main(String[] args){ Book b = new Book(\"Lev\", \"Man\", 2000); b.setAuthor(\"Pol\"); b.setName(\"Dog\"); b.setYear(2000); System.out.println(\"Author: \"+b.getAuthor()+\" name: \"+b.getName()+\" Year: \"+b.getYear()); } }","keywords":["2000","book","java","public","наследование","+b.getauthor","+b.getname","+b.getyear","args","author","b.setauthor","b.setname"],"topics":["ai","java","testing"]},{"id":"chunk-4","heading":"Task4/Circle.java","text":"public class Circle { private double radius; private float diametr; public Circle(double radius, float diametr) { this.radius = radius; this.diametr = diametr; } public double getRadius() { return radius; } public void setRadius(double r) { this.radius = r; }","keywords":["double","public","radius","diametr","circle","float","java","private","this.radius","наследование","circle.java","class"],"topics":["java"]},{"id":"chunk-5","heading":"Task4/CircleTest.java","text":"public class CircleTest { public static void main(String[] args){ Circle c = new Circle(20, 40); c.setRadius(30); System.out.println(\"Новый радиус: \" +c.getRadius()); } }","keywords":["circle","java","public","наследование","+c.getradius","20","30","40","args","c.setradius","circletest","circletest.java"],"topics":["ai","java","testing"]},{"id":"chunk-6","heading":"Task4/Human.java","text":"@Override public String toString() { return \"Head{\" + \"eye='\" + eye + '\\'' + \", volos='\" + volos + '\\'' + '}'; } } static class Leg{ public int Razmer;","keywords":["eye","java","public","volos","наследование","class","head","human.java","int","leg","override","razmer"],"topics":["java"]},{"id":"chunk-7","heading":"Task4/Human.java","text":"@Override public String toString() { return \"Leg{\" + \"Razmer=\" + Razmer + '}'; } } static class Hand{ public int ling;","keywords":["java","public","razmer","наследование","class","hand","human.java","int","leg","ling","override","return"],"topics":["java"]},{"id":"chunk-8","heading":"Task4/Human.java","text":"@Override public String toString() { return \"Hand{\" + \"ling=\" + ling + '}'; } } public int Rost; public String name; public Human(int rost, String name) { Rost = rost; this.name = name; } public int getRost() { return Rost; } public void setRost(int rost) { Rost = rost; } public String getName() { return name; } public void setName(String name) { this.name = name; }","keywords":["public","rost","name","string","int","return","java","ling","this.name","void","наследование","getname"],"topics":["java"]},{"id":"chunk-9","heading":"Task4/Human.java","text":"@Override public String toString() { return \"Human{\" + \"Rost=\" + Rost + \", name='\" + name + '\\'' + '}'; }","keywords":["java","name","rost","наследование","human","human.java","override","public","return","string","task","task4"],"topics":["java"]},{"id":"chunk-10","heading":"Task4/Human.java","text":"static public void main(String[] args){ Human h = new Human(170, \"LOK\"); System.out.println(h); Hand h2 = new Hand(12); System.out.println(h2); Leg l = new Leg(12); System.out.println(l); Head h3 = new Head(\"red\", 12); System.out.println(h3);","keywords":["new","system.out.println","12","h2","h3","hand","head","human","java","leg","наследование","170"],"topics":["ai","java"]},{"id":"chunk-11","heading":"Описание","text":"В этом модуле используется 5 Java файлов. Ключевые сущности: Book, BookTest, Circle, CircleTest, Human.","keywords":["java","наследование","book","booktest","circle","circletest","human.","task","используется","ключевые","композицию.","композиция"],"topics":["java","testing"]},{"id":"chunk-12","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","наследование","task","демо","задачи","запускайте","затем","кейсами.","класс","композицию.","композиция","модель"],"topics":["oop"]},{"id":"chunk-13","heading":"Вывод","text":"Task 4 — Наследование и композиция документирует реальное решение из исходного кода.","keywords":["наследование","java","task","композиция","вывод","документирует","исходного","кода.","композицию.","переиспользование","поведения","реальное"],"topics":["oop"]}]},{"id":"java/task-05-interfaces-and-abstract-classes","href":"/docs/java/task-05-interfaces-and-abstract-classes","slug":["java","task-05-interfaces-and-abstract-classes"],"section":"java","sectionTitle":"Java","title":"Task 5 — Интерфейсы и абстрактные классы","description":"Контракты поведения и разделение абстракций от реализации.","preview":"Задание Контракты поведения и разделение абстракций от реализации. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task5/Circle.java Task5/Movable.java Task5/MovableCircle.java Task5/Mov","keywords":["public","task5","system.out.println","double","movablepoint","void","int","override","return","radius","string","rectangle","width","circle","color","new","false","filled"],"topics":["python","ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Контракты поведения и разделение абстракций от реализации. Документация собрана по исходному коду этой практики.","keywords":["java","абстракций","контракты","от","поведения","разделение","реализации.","task","абстрактные","документация","задание","интерфейсы"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task5/Circle.java Task5/Movable.java Task5/MovableCircle.java Task5/MovablePoint.java Task5/MovableRectangle.java Task5/Rectangle.java Task5/Shape.java Task5/Square.java Task5/TestMovable.java Task5/TestShape.java","keywords":["task5","java","circle.java","movable.java","movablecircle.java","movablepoint.java","movablerectangle.java","rectangle.java","shape.java","square.java","task","testmovable.java"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task5/Circle.java","text":"public class Circle extends Shape { protected double radius; public Circle(){ this.filled = false; this.color = \"blue\"; this.radius = 1; } public Circle(double radius){ this.filled = false; this.color = \"blue\"; this.radius = radius; } public Circle(double radius, String color, boolean filled){ this.radius = radius; this.color = color; this.filled = filled; }","keywords":["radius","circle","public","double","this.color","this.filled","this.radius","blue","color","false","filled","java"],"topics":["java"]},{"id":"chunk-3","heading":"Task5/Circle.java","text":"public double getRadius() { return radius; } public void setRadius(double radius){ this.radius = radius; } @Override public double getArea() { return Math.PI radius radius; } @Override public double getPerimeter() { return 2 Math.PI radius; } @Override public String toString() { return \"Shape: circle radius: \"+this.radius+\", color: \"+this.color+\" Plosh \"+getArea(); } }","keywords":["radius","public","double","return","override","java","math.pi","+getarea","+this.color+","+this.radius+","circle","circle.java"],"topics":["java"]},{"id":"chunk-4","heading":"Task5/Movable.java","text":"public interface Movable { void moveUp(); void moveDown(); void moveLeft(); void moveRight(); }","keywords":["void","java","interface","movable","movable.java","movedown","moveleft","moveright","moveup","public","task","task5"],"topics":["java"]},{"id":"chunk-5","heading":"Task5/MovableCircle.java","text":"public class MovableCircle implements Movable{ private int radius; MovablePoint center; public MovableCircle(int x, int y, int xSpeed, int ySpeed, int radius){ this.center = new MovablePoint(x, y, xSpeed, ySpeed); this.radius = radius","keywords":["int","radius","java","movablecircle","movablepoint","public","xspeed","yspeed","center","class","implements","movable"],"topics":["java"]},{"id":"chunk-6","heading":"Task5/MovableCircle.java","text":"; } @Override public String toString() { return \"MovableCircle{\" + \"radius=\" + this.radius + \", center=\" + this.center.y + \" \"+this.center.ySpeed+\" \"+ this.center.xSpeed+\" \"+ this.center.x+ '}'; } @Override public void moveUp() { MovablePoint ret = this.center; ret.y += this.center.ySpeed; } @Override public void moveDown() { MovablePoint ret = this.center; ret.y = this.center.ySpeed","keywords":["override","public","java","movablepoint","ret","ret.y","this.center","this.center.yspeed","void","+this.center.yspeed+","center","movablecircle"],"topics":["java"]},{"id":"chunk-7","heading":"Task5/MovableCircle.java","text":"; } @Override public void moveLeft() { MovablePoint ret = this.center; ret.x += this.center.xSpeed; } @Override public void moveRight() { MovablePoint ret = this.center; ret.x = this.center.xSpeed; } }","keywords":["java","movablepoint","override","public","ret","ret.x","this.center","this.center.xspeed","void","movablecircle.java","moveleft","moveright"],"topics":["java"]},{"id":"chunk-8","heading":"Task5/MovablePoint.java","text":"public class MovablePoint implements Movable { int x; int y; int xSpeed; int ySpeed; public MovablePoint(int x, int y, int xSpeed, int ySpeed){ this.x=x; this.y=y; this.xSpeed=xSpeed; this.ySpeed=ySpeed; }","keywords":["int","xspeed","yspeed","java","movablepoint","public","class","implements","movable","movablepoint.java","task","task5"],"topics":["java"]},{"id":"chunk-9","heading":"Task5/MovablePoint.java","text":"@Override public String toString() { return \"MovablePoint{\" + \"x=\" + this.x + \", y=\" + this.y + \", xSpeed=\" + this.xSpeed + \", ySpeed=\" + this.ySpeed + '}'; }","keywords":["java","movablepoint","movablepoint.java","override","public","return","string","task","task5","this.x","this.xspeed","this.y"],"topics":["java"]},{"id":"chunk-10","heading":"Task5/MovableRectangle.java","text":"public class MovableRectangle implements Movable{ MovablePoint topLeft; MovablePoint bottomRight; public MovableRectangle(int x1, int y1, int x2, int y2, int xSpeed, int ySpeed){ this.topLeft = new MovablePoint(x1, y1, xSpeed, ySpeed); this.bottomRight = new MovablePoint(x2, y2, xSpeed, ySpeed); } public void proverkX(){ if (this.topLeft.xSpeed != this.bottomRight.xSpeed){ MovablePoint terx","keywords":["int","movablepoint","public","xspeed","yspeed","java","movablerectangle","new","x1","x2","y1","y2"],"topics":["java"]},{"id":"chunk-11","heading":"Task5/MovableRectangle.java","text":"; if(this.topLeft.xSpeed this.bottomRight.xSpeed){ terx = this.topLeft; terx.xSpeed = this.bottomRight.xSpeed; this.topLeft.xSpeed = this.bottomRight.xSpeed; }else{ terx = this.bottomRight; terx.xSpeed = this.topLeft.xSpeed; this.bottomRight.xSpeed = this.topLeft.xSpeed; } } } public void proverkY(){ if (this.topLeft.ySpeed != this.bottomRight.ySpeed){ MovablePoint tery","keywords":["this.bottomright.xspeed","this.topleft.xspeed","if","java","terx","terx.xspeed","else","movablepoint","movablerectangle.java","proverky","public","task"],"topics":["java"]},{"id":"chunk-12","heading":"Task5/MovableRectangle.java","text":"; if(this.topLeft.ySpeed this.bottomRight.ySpeed){ tery = this.topLeft; tery.ySpeed = this.bottomRight.ySpeed; this.topLeft.ySpeed = this.bottomRight.ySpeed; }else{ tery = this.bottomRight; tery.ySpeed = this.topLeft.ySpeed; this.bottomRight.ySpeed = this.topLeft.ySpeed; } } } @Override public String toString() { return \"MovableRectangle{\" + \"topLeft=\" + topLeft + \", bottomRight=\" + bottomRight + '}'","keywords":["this.bottomright.yspeed","this.topleft.yspeed","bottomright","java","tery","tery.yspeed","topleft","else","if","movablerectangle","movablerectangle.java","override"],"topics":["java"]},{"id":"chunk-13","heading":"Task5/MovableRectangle.java","text":"; } @Override public void moveUp() { this.proverkY(); MovablePoint rety = this.topLeft; rety.y += this.topLeft.ySpeed; rety = this.bottomRight; rety.y += this.bottomRight.ySpeed; } @Override public void moveDown() { this.proverkY(); MovablePoint rety = this.topLeft; rety.y = this.topLeft.ySpeed; rety = this.bottomRight; rety.y = this.bottomRight.ySpeed; } @Override public void moveLeft() { this.proverkX()","keywords":["rety","rety.y","override","public","void","java","movablepoint","this.bottomright","this.bottomright.yspeed","this.proverky","this.topleft","this.topleft.yspeed"],"topics":["java"]},{"id":"chunk-14","heading":"Task5/MovableRectangle.java","text":"; MovablePoint retX = this.topLeft; retX.x = this.topLeft.xSpeed; retX = this.bottomRight; retX.x = this.bottomRight.xSpeed; }","keywords":["java","retx","retx.x","movablepoint","movablerectangle.java","task","task5","this.bottomright","this.bottomright.xspeed","this.topleft","this.topleft.xspeed","абстрактные"],"topics":["java"]},{"id":"chunk-15","heading":"Task5/MovableRectangle.java","text":"@Override public void moveRight() { this.proverkX(); MovablePoint retX = this.topLeft; retX.x += this.topLeft.xSpeed; retX = this.bottomRight; retX.x += this.bottomRight.xSpeed; } }","keywords":["java","retx","retx.x","movablepoint","movablerectangle.java","moveright","override","public","task","task5","this.bottomright","this.bottomright.xspeed"],"topics":["java"]},{"id":"chunk-16","heading":"Task5/Rectangle.java","text":"```java title=\"Task5/Rectangle.java\" package Task5; public class Rectangle extends Shape{ protected double width; protected double length; public Rectangle(){ this.width = 6; this.length = 5; this.color = \"red\"; this.filled = false; } public Rectangle(double width, double length){ this.width = width; this.length = length; this.color = \"red\"; this.filled = false","keywords":["double","java","length","public","rectangle","task5","width","false","protected","rectangle.java","red","this.color"],"topics":["java"]},{"id":"chunk-17","heading":"Task5/Rectangle.java","text":"; } public Rectangle(double width, double length, String color, boolean filled){ this.width = width; this.length = length; this.color = color; this.filled = filled; } public double getWidth(){ return width; } public double getLength(){ return length; } public void setWidth(double width){ this.width = width; } public void setLength(double length){ this.length = length","keywords":["double","length","public","width","color","filled","java","return","this.length","this.width","void","boolean"],"topics":["java"]},{"id":"chunk-18","heading":"Task5/Rectangle.java","text":"; } @Override public double getArea() { return width length; } @Override public double getPerimeter() { return 2 (width+length); } @Override public String toString() { return \"Shape: rectangle width: \"+this.width+\", color: \"+this.color+\" Plosh \"+getArea(); } }","keywords":["override","public","return","double","java","width","+getarea","+this.color+","+this.width+","color","getarea","getperimeter"],"topics":["java"]},{"id":"chunk-19","heading":"Task5/Shape.java","text":"```java title=\"Task5/Shape.java\" package Task5; public abstract class Shape { protected String color; protected boolean filled; public Shape(){","keywords":["java","task5","protected","public","shape","shape.java","abstract","boolean","class","color","filled","package"],"topics":["java"]},{"id":"chunk-20","heading":"Task5/Shape.java","text":"} public Shape(String c, boolean f){ this.color = c; this.filled = f; } public String getColor(){ return color; } public void setColor(String color){ this.color = color; } public boolean isFilled(){ return filled; } public void setFilled(boolean filled){ this.filled = filled; } public abstract double getArea(); public abstract double getPerimeter(); public abstract String toString(); }","keywords":["public","string","abstract","boolean","color","filled","double","java","return","this.color","this.filled","void"],"topics":["java"]},{"id":"chunk-21","heading":"Task5/Square.java","text":"```java title=\"Task5/Square.java\" package Task5; public class Square extends Rectangle { public Square(){ this.filled = false; this.color = \"red\"; this.width = 1; } public Square(double side){ this.filled = false; this.color = \"red\"; this.width = side; } public Square(double side, String color, boolean filled){ this.width = side; this.filled = filled; this.color = color; } public double getSide(){ return width","keywords":["public","side","square","double","java","task5","this.color","this.filled","this.width","color","false","filled"],"topics":["java"]},{"id":"chunk-22","heading":"Task5/Square.java","text":"; } public void setSide(double side){ this.width = side; } @Override public double getArea() { return width width; } @Override public double getPerimeter() { return 4 width; } @Override public String toString() { return \"Shape: rectangle width: \"+this.width+\", color: \"+this.color+\" Plosh \"+getArea(); } }","keywords":["public","width","double","override","return","java","side","+getarea","+this.color+","+this.width+","color","getarea"],"topics":["java"]},{"id":"chunk-23","heading":"Task5/TestMovable.java","text":"public class TestMovable { public static void main(String[] args){ MovablePoint m1 = new MovablePoint(3, 4, 6, 7); MovableRectangle r1 = new MovableRectangle(5,0,5, 0,9, 8);","keywords":["java","movablepoint","movablerectangle","new","public","args","class","m1","main","r1","static","string"],"topics":["ai","java","testing"]},{"id":"chunk-24","heading":"Task5/TestMovable.java","text":"MovableCircle c1 = new MovableCircle(4, 6, 8, 9, 10); //System.out.println(((MovableCircle)c1).toString()); System.out.println(r1); c1.moveDown(); r1.moveUp(); m1.moveLeft(); //System.out.println(((MovableCircle)c1).toString()); System.out.println(r1); //System.out.println(m1); } }","keywords":["system.out.println","movablecircle","c1",".tostring","java","r1","10","c1.movedown","m1","m1.moveleft","new","r1.moveup"],"topics":["java","testing"]},{"id":"chunk-25","heading":"Task5/TestShape.java","text":"```java title=\"Task5/TestShape.java\" package Task5; public class TestShape { public static void main(String[] args) { / Scanner scanner = new Scanner(System.in); String color = scanner.nextLine(); boolean filled = scanner.nextBoolean(); double radius = scanner.nextDouble();","keywords":["java","scanner","task5","public","string","testshape.java","args","boolean","class","color","double","filled"],"topics":["ai","java","testing"]},{"id":"chunk-26","heading":"Task5/TestShape.java","text":"Circle c1 = new Circle(34, \"red\", false); Square s1 = new Square(32, \"red\", false); Rectangle r1 = new Rectangle(12, 11, \"red\", false); System.out.println(c1.toString()); System.out.println(s1.toString()); System.out.println(r1.toString()); } }","keywords":["false","new","red","system.out.println","circle","java","rectangle","square","11","12","32","34"],"topics":["java","testing"]},{"id":"chunk-27","heading":"Task5/TestShape.java","text":"Shape s1 = new Circle(5.5, \"RED\", false); // Upcast Circle toShape System.out.println(s1); // which version? System.out.println(s1.getArea()); // which version? System.out.println(s1.getPerimeter()); // which version? System.out.println(s1.getColor()); System.out.println(s1.isFilled()); System.out.println(((Circle)s1).getRadius()); Circle c1 = (Circle)s1; // downcast back to Circle System.out.println(c1)","keywords":["system.out.println","circle","s1","version","which","c1","java",".getradius","5.5","back","downcast","false"],"topics":["python","java","testing"]},{"id":"chunk-28","heading":"Task5/TestShape.java","text":"; System.out.println(c1.getArea()); System.out.println(c1.getPerimeter()); System.out.println(c1.getColor()); System.out.println(c1.isFilled()); System.out.println(c1.getRadius()); Shape s3 = new Rectangle(1.0, 2.0, \"RED\", false); // upcast System.out.println(s3); System.out.println(s3.getArea()); System.out.println(s3.getPerimeter()); System.out.println(s3.getColor()); System.out.println(((Rectangle)s3).getLength())","keywords":["system.out.println","s3","java","rectangle",".getlength","1.0","2.0","c1.getarea","c1.getcolor","c1.getperimeter","c1.getradius","c1.isfilled"],"topics":["python","java","testing"]},{"id":"chunk-29","heading":"Task5/TestShape.java","text":"; Rectangle r1 = (Rectangle)s3; // downcast System.out.println(r1); System.out.println(r1.getArea()); System.out.println(r1.getColor()); System.out.println(r1.getLength()); Shape s4 = new Square(6.6); // Upcast System.out.println(s4); System.out.println(s4.getArea()); System.out.println(s4.getColor()); System.out.println(((Square) s4).getSide()); Rectangle r2 = (Rectangle)s4; System.out.println(r2)","keywords":["system.out.println","rectangle","s4","java","r1","r2","square",".getside","6.6","downcast","new","r1.getarea"],"topics":["python","java","testing"]},{"id":"chunk-30","heading":"Task5/TestShape.java","text":"; System.out.println(r2.getArea()); System.out.println(r2.getColor()); System.out.println(((Square)r2).getSide()); System.out.println(r2.getLength()); Square sq1 = (Square)r2; System.out.println(sq1); System.out.println(sq1.getArea()); System.out.println(sq1.getColor()); System.out.println(sq1.getSide()); System.out.println(sq1.getLength()); } } ```","keywords":["system.out.println","square","java","r2","sq1",".getside","r2.getarea","r2.getcolor","r2.getlength","sq1.getarea","sq1.getcolor","sq1.getlength"],"topics":["java","testing"]},{"id":"chunk-31","heading":"Описание","text":"В этом модуле используется 10 Java файлов. Ключевые сущности: Circle, Movable, MovableCircle, MovablePoint, MovableRectangle, Rectangle.","keywords":["java","10","circle","movable","movablecircle","movablepoint","movablerectangle","rectangle.","task","абстрактные","абстракций","интерфейсы"],"topics":["java"]},{"id":"chunk-32","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","task","абстрактные","абстракций","демо","задачи","запускайте","затем","интерфейсы","кейсами.","класс","классы"],"topics":["oop"]},{"id":"chunk-33","heading":"Вывод","text":"Task 5 — Интерфейсы и абстрактные классы документирует реальное решение из исходного кода.","keywords":["java","task","абстрактные","интерфейсы","классы","абстракций","вывод","документирует","исходного","кода.","контракты","от"],"topics":["oop"]}]},{"id":"java/task-06-class-hierarchies","href":"/docs/java/task-06-class-hierarchies","slug":["java","task-06-class-hierarchies"],"section":"java","sectionTitle":"Java","title":"Task 6 — Иерархии классов","description":"Построение связанных сущностей в единой иерархии.","preview":"Задание Построение связанных сущностей в единой иерархии. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task6/Bed.java Task6/Bylidog.java Task6/Chair.java Task6/Cup.java Task6/Dish.jav","keywords":["public","string","task6","double","name","return","color","material","override","filled","class","furniture","void","year","dish","new","int","tostring"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Построение связанных сущностей в единой иерархии. Документация собрана по исходному коду этой практики.","keywords":["java","единой","иерархии.","построение","связанных","сущностей","task","документация","задание","иерархии","исходному","классов"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task6/Bed.java Task6/Bylidog.java Task6/Chair.java Task6/Cup.java Task6/Dish.java Task6/Dog.java Task6/Furniture.java Task6/FurnitureShop.java Task6/Haski.java Task6/Pan.java Task6/Plate.java Task6/TestDish.java Task6/TestDog.java Task6/TestFurnitureShop.java","keywords":["task6","java","bed.java","bylidog.java","chair.java","cup.java","dish.java","dog.java","furniture.java","furnitureshop.java","haski.java","pan.java"],"topics":["ai","java","testing"]},{"id":"chunk-2","heading":"Task6/Bed.java","text":"public class Bed extends Furniture{ private int count, maxw; private String NameMatras; public Bed(double h, double w, double l, String Name, String material, int count, int maxw, String NameMatras) { super(h, w, l, Name, material); this.count = count; this.maxw = maxw; this.NameMatras = NameMatras; }","keywords":["string","count","double","int","maxw","namematras","bed","java","material","name","private","public"],"topics":["java"]},{"id":"chunk-3","heading":"Task6/Bed.java","text":"@Override public String toString() { return \"Bed{\" + \"count=\" + count + \", maxw=\" + maxw + \", NameMatras='\" + NameMatras + '\\'' + \", h=\" + h + \", w=\" + w + \", l=\" + l + \", Name='\" + Name + '\\'' + \", material='\" + material + '\\'' + '}'; } }","keywords":["count","java","material","maxw","name","namematras","bed","bed.java","override","public","return","string"],"topics":["java"]},{"id":"chunk-4","heading":"Task6/Bylidog.java","text":"public class Bylidog extends Dog { public Bylidog(String name, int year) { super(name, year); }","keywords":["bylidog","java","name","public","year","bylidog.java","class","dog","extends","int","string","super"],"topics":["java"]},{"id":"chunk-5","heading":"Task6/Bylidog.java","text":"@Override public String toString() { return \"Bylidog{\" + \"name='\" + name + '\\'' + \", year=\" + year + '}'; } }","keywords":["java","name","year","bylidog","bylidog.java","override","public","return","string","task","task6","tostring"],"topics":["java"]},{"id":"chunk-6","heading":"Task6/Chair.java","text":"public class Chair extends Furniture{ public String nameMaterial; public Chair(double h, double w, double l, String Name, String material, String nameMaterial) { super(h, w, l, Name, material); this.nameMaterial = nameMaterial; }","keywords":["string","double","namematerial","public","chair","java","material","name","chair.java","class","extends","furniture"],"topics":["ai","java"]},{"id":"chunk-7","heading":"Task6/Chair.java","text":"public void setNameMaterial(String nameMaterial) { this.nameMaterial = nameMaterial; }","keywords":["java","namematerial","chair.java","public","setnamematerial","string","task","task6","this.namematerial","void","единой","иерархии"],"topics":["ai","java"]},{"id":"chunk-8","heading":"Task6/Chair.java","text":"@Override public String toString() { return \"Chair{\" + \"nameMaterial='\" + nameMaterial + '\\'' + \", h=\" + h + \", w=\" + w + \", l=\" + l + \", Name='\" + Name + '\\'' + \", material='\" + material + '\\'' + '}'; } }","keywords":["java","material","name","namematerial","chair","chair.java","override","public","return","string","task","task6"],"topics":["ai","java"]},{"id":"chunk-9","heading":"Task6/Cup.java","text":"public class Cup extends Dish{ protected double h; protected double r; public Cup(String color, boolean filled, double h, double r) { super(color, filled); this.r = r; this.h = h; } public double getH() { return h; } public void setH(double h) { this.h = h; } public double getR() { return r; } public void setR(double r) { this.r = r; }","keywords":["double","public","color","cup","filled","java","protected","return","this.h","this.r","void","boolean"],"topics":["java"]},{"id":"chunk-10","heading":"Task6/Cup.java","text":"@Override public String toString() { return \"Chahca{\" + \"h=\" + h + \", r=\" + r + \", color='\" + color + '\\'' + \", filled=\" + filled + '}'; } / @Override public double Radus() { return 2 r; } @Override public double Diametr() { return 0; }","keywords":["override","public","return","color","double","filled","java","chahca","cup.java","diametr","radus","string"],"topics":["java"]},{"id":"chunk-11","heading":"Task6/Dish.java","text":"public class Dish { String color; boolean filled; public Dish(String c, boolean f) { this.color = c; this.filled = f; } public String getC() { return color; } public void setC(String c) { this.color = c; } public boolean getFilled() { return filled; } public void setFilled(boolean f) { this.filled = f; } @Override public String toString() { return \"Dish{\" + \"color = \" + color + \", r=\" + filled + '}'; } }","keywords":["public","string","boolean","color","dish","filled","return","java","this.color","this.filled","void","class"],"topics":["java"]},{"id":"chunk-12","heading":"Task6/Dog.java","text":"public class Dog { public String name; public int year; public Dog(String name, int year) { this.name = name; this.year = year; } public String getName() { return name; }","keywords":["public","name","string","year","dog","int","java","class","dog.java","getname","return","task"],"topics":["java"]},{"id":"chunk-13","heading":"Task6/Dog.java","text":"@Override public String toString() { return \"Dog{\" + \"name='\" + name + '\\'' + \", year=\" + year + '}'; } }","keywords":["java","name","year","dog","dog.java","override","public","return","string","task","task6","tostring"],"topics":["java"]},{"id":"chunk-14","heading":"Task6/Furniture.java","text":"public abstract class Furniture { public double h, w, l; public String Name, material;","keywords":["public","java","abstract","class","double","furniture","furniture.java","material","name","string","task","task6"],"topics":["java"]},{"id":"chunk-15","heading":"Task6/Furniture.java","text":"public Furniture(double h, double w, double l, String Name, String material) { this.h = h; this.w = w; this.l = l; this.Name = Name; this.material = material; } public double getH() { return h; } public void setH(double h) { this.h = h; } public double getW() { return w; } public void setW(double w) { this.w = w; } public double getL() { return l; } public void setL(double l) { this.l = l","keywords":["double","public","return","void","java","material","name","string","this.h","this.l","this.w","furniture"],"topics":["java"]},{"id":"chunk-16","heading":"Task6/Furniture.java","text":"; } public String getName() { return Name; } public void setName(String Name) { this.Name = Name; } public String getMaterial() { return material; } public void setMaterial(String material) { this.material = material; } @Override public String toString() { return \"Furniture{\" + \"h=\" + h + \", w=\" + w + \", l=\" + l + \", name='\" + Name + '\\'' + \", material='\" + material + '\\'' + '}'; } }","keywords":["material","name","public","string","return","java","void","furniture","furniture.java","getmaterial","getname","override"],"topics":["java"]},{"id":"chunk-17","heading":"Task6/FurnitureShop.java","text":"public class FurnitureShop { private Furniture[] goods; public FurnitureShop(int count){ goods = new Furniture[count]; for(int i=0; i =count) break; goods[i++] = new Bed(43, 23, 45, \"lul\", \"derevo\", 2, 45, \"len\"); if (i =count) break; goods[i++] = new Chair(43, 23, 45, \"lyl\", \"derevo\", \"derevo12\"); } } public Furniture[] getGoods() { return goods; } public Furniture buy(int number){ Furniture f = goods[number]","keywords":["goods","furniture","count","public","45","int","new","23","43","break","derevo","furnitureshop"],"topics":["ai","java"]},{"id":"chunk-18","heading":"Task6/FurnitureShop.java","text":"; Furniture[] prod = new Furniture[goods.length 1]; for (int i=0; i number) prod[i 1] = goods[i]; } goods=prod; return f; } public String Asortiment(){ String vr = \"Асортимент магазина: \\n\"; for (Furniture f : goods){ vr += f.toString()+\"\\n\"; } return vr; } }","keywords":["furniture","goods","prod","vr","java","return","string","asortiment","f.tostring","furnitureshop.java","goods.length","int"],"topics":["java"]},{"id":"chunk-19","heading":"Task6/Haski.java","text":"public class Haski extends Dog{ public Haski(String name, int year) { super(name, year); }","keywords":["haski","java","name","public","year","class","dog","extends","haski.java","int","string","super"],"topics":["java"]},{"id":"chunk-20","heading":"Task6/Haski.java","text":"@Override public String toString() { return \"Haski{\" + \"name='\" + name + '\\'' + \", year=\" + year + '}'; } }","keywords":["java","name","year","haski","haski.java","override","public","return","string","task","task6","tostring"],"topics":["java"]},{"id":"chunk-21","heading":"Task6/Pan.java","text":"public class Pan extends Dish{ protected double r; protected double h; public Pan (String color, boolean filled, double r, double h) { super(color, filled); this.r = r; this.h = h; }","keywords":["double","color","filled","java","pan","protected","public","boolean","class","dish","extends","pan.java"],"topics":["java"]},{"id":"chunk-22","heading":"Task6/Pan.java","text":"@Override public String toString() { return \"Pan{\" + \"r=\" + r + \", h=\" + h + \", color='\" + color + '\\'' + \", filled=\" + filled + '}'; } / @Override public double Radus() { return 2; }","keywords":["color","filled","java","override","public","return","double","pan","pan.java","radus","string","task"],"topics":["java"]},{"id":"chunk-23","heading":"Task6/Plate.java","text":"public class Plate extends Dish{ protected String name; protected double r; public Plate(String color, boolean filled, String name, double r) { super(color, filled); this.name = name; this.r = r; }","keywords":["name","string","color","double","filled","java","plate","protected","public","boolean","class","dish"],"topics":["java"]},{"id":"chunk-24","heading":"Task6/Plate.java","text":"@Override public String toString() { return \"Plate{\" + \"name='\" + name + '\\'' + \", r=\" + r + \", color='\" + color + '\\'' + \", filled=\" + filled + '}'; } / @Override public double Radus() { return 0; } @Override public double Diametr() { return 0; } @Override public double Scovoroda() { return 0; }","keywords":["override","public","return","double","color","filled","java","name","diametr","plate","plate.java","radus"],"topics":["java"]},{"id":"chunk-25","heading":"Task6/TestDish.java","text":"public class TestDish { public static void main(String[] args) { Dish d = new Cup(\"red\", true, 4, 5); Dish d1 = new Dish(\"black\", false); Dish d2 = new Pan(\"white\", false, 78, 56); Dish d3 = new Plate(\"blue\", true, \"Boorg\", 32); System.out.println(d); System.out.println(d1); System.out.println(d2); System.out.println(d3); } }","keywords":["dish","new","system.out.println","d1","d2","d3","false","java","public","true","32","56"],"topics":["ai","java","testing"]},{"id":"chunk-26","heading":"Task6/TestDog.java","text":"public class TestDog { public static void main(String[] args){ Dog d1 = new Dog(\"kol\", 4); Dog d2 = new Bylidog(\"ser\", 5); Dog d3 = new Haski(\"pol\", 6); System.out.println(d1); System.out.println(d2); System.out.println(d3); } }","keywords":["dog","new","system.out.println","d1","d2","d3","java","public","args","bylidog","class","haski"],"topics":["ai","java","testing"]},{"id":"chunk-27","heading":"Task6/TestFurnitureShop.java","text":"public class TestFurnitureShop { public static void main(String[] args){ FurnitureShop f = new FurnitureShop(4); System.out.println(f.Asortiment()); //f.buy(3); System.out.println(\"Куплено: \"+\"\\n\"+f.buy(2));","keywords":["furnitureshop","java","public","system.out.println","+f.buy","args","class","f.asortiment","f.buy","main","new","static"],"topics":["ai","java","testing"]},{"id":"chunk-28","heading":"Описание","text":"В этом модуле используется 14 Java файлов. Ключевые сущности: Bed, Bylidog, Chair, Cup, Dish, Dog.","keywords":["java","14","bed","bylidog","chair","cup","dish","dog.","task","единой","иерархии","иерархии."],"topics":["ai","java"]},{"id":"chunk-29","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","task","демо","единой","задачи","запускайте","затем","иерархии","иерархии.","кейсами.","класс","классов"],"topics":["oop"]}]},{"id":"java/task-07-basic-gui","href":"/docs/java/task-07-basic-gui","slug":["java","task-07-basic-gui"],"section":"java","sectionTitle":"Java","title":"Task 7 — Базовый GUI","description":"Создание простого графического интерфейса на Java.","preview":"Задание Создание простого графического интерфейса на Java. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task7/Windows.java Task7/Windows.java java title=\"Task7/Windows.java\" package T","keywords":["100","task7","new","windows.java","jlabel","add","public","winner","12","if","jbutton","madrid","milan","result","windows","windows.this.lbl3.settext","windows.this.madrid","windows.this.milan"],"topics":["python","ai","numpy","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Создание простого графического интерфейса на Java. Документация собрана по исходному коду этой практики.","keywords":["java","java.","графического","интерфейса","простого","создание","gui","task","базовый","документация","задание","исходному"],"topics":["java"]},{"id":"chunk-1","heading":"Task7/Windows.java","text":"package Task7; public class Windows extends JFrame{ int milan; int madrid; JButton but1 = new JButton(\"AC Milan\"); JButton but2 = new JButton(\"Real Madrid\"); JLabel lbl1 = new JLabel(\"Result: 0 X 0\"); JLabel lbl2 = new JLabel(\"Last Scorer: N/A\"); JLabel lbl3 = new JLabel(\"Winner: DRAW\"); public Windows(){ super(\"Result\"); setLayout((LayoutManager) null); but1.setBounds(12, 12, 100, 100)","keywords":["jlabel","new","jbutton","100","12","int","java","madrid","milan","public","result","task7"],"topics":["python","java"]},{"id":"chunk-2","heading":"Task7/Windows.java","text":"; but2.setBounds(300,12,100,100); lbl1.setBounds(112, 12,100,100); lbl2.setBounds(112,30,100,100); lbl3.setBounds(112,50,100,100); add(but1); add(but2); add(lbl1); add(lbl2); add(lbl3); but1.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent ae) { try { ++Windows.this.milan; Windows.this.lbl1.setText(\"Result: \"+Windows.this.milan+\" X \"+Windows.this.madrid)","keywords":["100","add","112","12","java","++windows.this.milan","+windows.this.madrid","+windows.this.milan+","30","300","50","actionevent"],"topics":["numpy","java"]},{"id":"chunk-3","heading":"Task7/Windows.java","text":"; Windows.this.lbl2.setText(\"Last Scorer: AC Milan\"); if(Windows.this.milan Windows.this.madrid){ Windows.this.lbl3.setText(\"Winner: AC Milan\"); } if (Windows.this.milan == Windows.this.madrid){ Windows.this.lbl3.setText(\"Winner: DRAW\"); } } catch (Exception e){ } } }); but2.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent ae) { try { ++Windows.this.madrid","keywords":["ac","if","java","milan","windows.this.lbl3.settext","windows.this.madrid","windows.this.milan","winner","++windows.this.madrid","actionevent","actionlistener","actionperformed"],"topics":["python","numpy","java"]},{"id":"chunk-4","heading":"Task7/Windows.java","text":"; Windows.this.lbl1.setText(\"Result: \"+Windows.this.milan+\" X \"+Windows.this.madrid); Windows.this.lbl2.setText(\"Last Scorer: Real Madrid\"); if(Windows.this.milan < Windows.this.madrid){ Windows.this.lbl3.setText(\"Winner: Real Madrid\"); } if (Windows.this.milan == Windows.this.madrid){ Windows.this.lbl3.setText(\"Winner: DRAW\"); } } catch (Exception e){} } }); setSize(200,200)","keywords":["200","if","java","madrid","real","windows.this.lbl3.settext","windows.this.madrid","windows.this.milan","winner","+windows.this.madrid","+windows.this.milan+","catch"],"topics":["python","java"]},{"id":"chunk-5","heading":"Task7/Windows.java","text":"; } public static void main(String[] args){ Windows w = new Windows(); w.setVisible(true); w.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); } } ```","keywords":["java","windows","args","close","gui","java.","jframe.exit","main","new","public","static","string"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","gui","java.","task","базовый","графического","демо","задачи","запускайте","затем","интерфейса","кейсами."],"topics":["oop"]}]},{"id":"java/task-08-graphics-and-animation","href":"/docs/java/task-08-graphics-and-animation","slug":["java","task-08-graphics-and-animation"],"section":"java","sectionTitle":"Java","title":"Task 8 — Графика и анимация","description":"Работа с графическими примитивами и обновлением сцены.","preview":"Задание Работа с графическими примитивами и обновлением сцены. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task8/Animation.java Task8/Picture.java Task8/RandomShapes.java Task8/tret.","keywords":["new","int","task8","public","imageicon","color","private","jlabel","count","jpanel","randomshapes.java","void","height","random.nextint","static","width","final","jframe"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Работа с графическими примитивами и обновлением сцены. Документация собрана по исходному коду этой практики.","keywords":["java","графическими","обновлением","примитивами","работа","сцены.","task","анимация","графика","документация","задание","исходному"],"topics":[]},{"id":"chunk-1","heading":"Task8/Animation.java","text":"public class Animation { public static void main(String[] args) { JFrame frame = new JFrame(\"Анимация\"); frame.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); final ImageIcon[] icon = {new ImageIcon(\"D:\\\\Обои\\\\1234.jpeg\")}; //ImageIcon icon1 = new ImageIcon(\"D:\\\\Обои\\\\1680.jpg\"); JLabel label1; final JLabel[] label2 = new JLabel[1]; label1 = new JLabel(\"lol\", icon[0], SwingConstants.CENTER)","keywords":["new","imageicon","jlabel","final","icon","java","jframe","label1","public","анимация","обои","1234.jpeg"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task8/Animation.java","text":"; //label2 = new JLabel(\"lop\", icon1, SwingConstants.CENTER); JPanel panel = new JPanel(); panel.setBackground(Color.BLACK); panel.setPreferredSize(new Dimension(1919, 1079)); panel.add(label1); //panel.add(label2); frame.getContentPane().add(panel); frame.pack(); frame.setVisible(true); final int[] count = {0}; Timer t = new Timer(); TimerTask animate = null; t.schedule(animate, 1, 200)","keywords":["new","animate","java","jpanel","label2","panel","panel.add","timer",".add","1079","1919","200"],"topics":["java"]},{"id":"chunk-3","heading":"Task8/Animation.java","text":"; animate = new TimerTask() { @Override public void run() { switch (count[0]) { case 0: ImageIcon icon = new ImageIcon(\"D:\\\\Обои\\\\1680.jpg\"); JLabel label2 = new JLabel(\"lop\", icon, SwingConstants.CENTER); JPanel panel1 = new JPanel(); panel1.add(label2); break; case 1: ImageIcon icon1 = new ImageIcon(\"D:\\\\Обои\\\\1234.jpeg\"); JLabel label3 = new JLabel(\"lop\", icon1, SwingConstants.CENTER); JPanel panel2 = new JPanel()","keywords":["new","imageicon","jlabel","jpanel","case","icon","icon1","java","label2","lop","swingconstants.center","обои"],"topics":["java"]},{"id":"chunk-4","heading":"Task8/Animation.java","text":"; panel2.add(label3); break; } count[0]++; if(count[0] == 2) { count[0] = 0; } } }; } }","keywords":["count","java","++","animation.java","break","if","label3","panel2.add","task","task8","анимация","графика"],"topics":["java"]},{"id":"chunk-5","heading":"Task8/Picture.java","text":"public class Picture { public static void main(String[] args){ String r = \"\"; JFrame frame = new JFrame (\"Картинка\"); frame.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); for (int i=0; i<args.length; i++){ r=args[i]; } ImageIcon icon = new ImageIcon(r); JLabel label1; label1 = new JLabel(\"FreeBSD Left\", icon, SwingConstants.CENTER); //label1.setHorizontalTextPosition(SwingConstants.LEFT)","keywords":["new","args","icon","imageicon","java","jframe","jlabel","label1","public","string","args.length","class"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task8/Picture.java","text":"; //label1.setVerticalTextPosition(SwingConstants.BOTTOM); JPanel panel = new JPanel(); panel.setBackground(Color.BLACK); panel.setPreferredSize(new Dimension(200, 300)); panel.add(label1); frame.getContentPane().add(panel); frame.pack(); frame.setVisible(true); } }","keywords":["java","jpanel","new","panel",".add","200","300","color.black","dimension","frame.getcontentpane","frame.pack","frame.setvisible"],"topics":["java"]},{"id":"chunk-7","heading":"Task8/RandomShapes.java","text":"public Shape(Color color, int x, int y) { this.color = color; this.x = x; this.y = y; }","keywords":["color","int","java","public","randomshapes.java","shape","task","task8","this.color","this.x","this.y","анимация"],"topics":["java"]},{"id":"chunk-8","heading":"Task8/RandomShapes.java","text":"public RectangleShape(Color color, int x, int y, int width, int height) { super(color, x, y); this.width = width; this.height = height; } public Color getColor(){ return null; } @Override public void draw(Graphics g) { g.setColor(getColor()); g.fillRect(x, y, width, height); } }","keywords":["color","int","height","public","width","getcolor","java","draw","g.fillrect","g.setcolor","graphics","null"],"topics":["java"]},{"id":"chunk-9","heading":"Task8/RandomShapes.java","text":"class CircleShape extends Shape { private int radius; private Random random = new Random(); public CircleShape(Color color, int x, int y, int radius) { super(color, x, y); this.radius = radius; }","keywords":["int","color","radius","random","circleshape","java","private","class","extends","new","public","randomshapes.java"],"topics":["java"]},{"id":"chunk-10","heading":"Task8/RandomShapes.java","text":"@Override public void draw(Graphics g) { g.setColor(getColor()); g.fillOval(x, y, 2 radius, 2 radius); }","keywords":["java","radius","draw","g.filloval","g.setcolor","getcolor","graphics","override","public","randomshapes.java","task","task8"],"topics":["java"]},{"id":"chunk-11","heading":"Task8/RandomShapes.java","text":"private Color getColor() { Color randomColor = new Color(random.nextInt(256), random.nextInt(256), random.nextInt(256)); return randomColor; } }","keywords":["256","color","random.nextint","java","randomcolor","getcolor","new","private","randomshapes.java","return","task","task8"],"topics":["java"]},{"id":"chunk-12","heading":"Task8/RandomShapes.java","text":"public class RandomShapes extends JPanel { private static final int WIDTH = 800; private static final int HEIGHT = 600; private static final int NUM SHAPES = 20;","keywords":["final","int","private","static","java","20","600","800","class","extends","height","jpanel"],"topics":["java"]},{"id":"chunk-13","heading":"Task8/RandomShapes.java","text":"@Override protected void paintComponent(Graphics g) { super.paintComponent(g); for (int i = 0; i < NUM SHAPES; i++) { int x = random.nextInt(WIDTH); int y = random.nextInt(HEIGHT); int shapeType = random.nextInt(2); if (shapeType == 0) { int width = random.nextInt(100) + 20; int height = random.nextInt(100) + 20; RectangleShape rectangle = new RectangleShape(Color.red, x, y, width, height); rectangle.draw(g)","keywords":["int","random.nextint","height","width","100","20","java","rectangleshape","shapetype","color.red","graphics","i++"],"topics":["ai","java"]},{"id":"chunk-14","heading":"Task8/RandomShapes.java","text":"; } else { int radius = random.nextInt(50) + 10; CircleShape circle = new CircleShape(Color.red, x, y, radius); circle.draw(g); } } }","keywords":["circleshape","java","radius","10","50","circle","circle.draw","color.red","else","int","new","random.nextint"],"topics":["java"]},{"id":"chunk-15","heading":"Task8/RandomShapes.java","text":"public static void main(String[] args) { SwingUtilities.invokeLater(() { JFrame frame = new JFrame(\"Random Shapes\"); frame.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); frame.add(new RandomShapes()); frame.pack(); frame.setLocationRelativeTo(null); frame.setVisible(true); }); } }","keywords":["java","jframe","new","args","close","frame","frame.add","frame.pack","frame.setdefaultcloseoperation","frame.setlocationrelativeto","frame.setvisible","jframe.exit"],"topics":["ai","java"]},{"id":"chunk-16","heading":"Task8/tret.java","text":"```java title=\"Task8/tret.java\" package Task8; public class tret extends JPanel{ private static final int HEIGHT = 1080; private static final int WIDTH = 1920; private JFrame frame; private Timer timer; private Image image; public tret() { frame = new JFrame(\"Application name\"); frame.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); frame.setSize(WIDTH, HEIGHT); frame.add(this); frame.setVisible(true)","keywords":["private","java","task8","final","frame","height","image","int","jframe","public","static","timer"],"topics":["java"]},{"id":"chunk-17","heading":"Task8/tret.java","text":"; image = new ImageIcon(\"D:\\\\Обои\\\\1234.jpeg\").getImage(); count = 0; timer = new Timer(); timer.schedule(animate, 1,200); } private int count; TimerTask animate = new TimerTask() {","keywords":["new","animate","count","java","timer","timertask",".getimage","1234.jpeg","200","image","imageicon","int"],"topics":["java"]},{"id":"chunk-18","heading":"Task8/tret.java","text":"@Override public void run() { switch (count) { case 20: image = new ImageIcon(\"D:\\\\Обои\\\\1680.jpg\").getImage(); break; case 40: image = new ImageIcon(\"D:\\\\Обои\\\\03.jpg\").getImage(); break; case 60: image = new ImageIcon(\"D:\\\\Обои\\\\1234.jpeg\").getImage(); break; default: break; } count++; if (count == 80) { count = 0; } repaint(); } }; public void paint(Graphics canvas) { canvas.drawImage(image, 0, 0, null)","keywords":["break","image",".getimage","case","count","imageicon","new","обои","java","public","void","03.jpg"],"topics":["ai","java"]},{"id":"chunk-19","heading":"Task8/tret.java","text":"; } public static void main(String[] args){ new tret(); }","keywords":["java","args","main","new","public","static","string","task","task8","tret","tret.java","void"],"topics":["ai","java"]},{"id":"chunk-20","heading":"Описание","text":"В этом модуле используется 4 Java файлов. Ключевые сущности: Animation, Picture, RandomShapes, tret.","keywords":["java","animation","picture","randomshapes","task","tret.","анимация","графика","графическими","используется","ключевые","модуле"],"topics":["java"]},{"id":"chunk-21","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","task","анимация","графика","графическими","демо","задачи","запускайте","затем","кейсами.","класс","модель"],"topics":["oop"]}]},{"id":"java/task-09-nameable-priceable","href":"/docs/java/task-09-nameable-priceable","slug":["java","task-09-nameable-priceable"],"section":"java","sectionTitle":"Java","title":"Task 9 — Интерфейсы Nameable/Priceable","description":"Единые API для объектов с именем и ценой.","preview":"Задание Единые API для объектов с именем и ценой. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task9/Car.java Task9/Dishes.java Task9/Dog.java Task9/Nameable.java Task9/Priceable.java","keywords":["task9","public","nameable","string","car","name","price","priceable","dishes","api","car.java","class","dishes.java","new","return","system.out.println","void","единые"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Единые API для объектов с именем и ценой. Документация собрана по исходному коду этой практики.","keywords":["api","java","единые","именем","объектов","ценой.","nameable","priceable","task","документация","задание","интерфейсы"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task9/Car.java Task9/Dishes.java Task9/Dog.java Task9/Nameable.java Task9/Priceable.java Task9/TestNameable.java","keywords":["task9","java","api","car.java","dishes.java","dog.java","nameable","nameable.java","priceable","priceable.java","task","testnameable.java"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task9/Car.java","text":"public class Car implements Nameable{ String name; public Car(String name){ this.name = name; } @Override public String getName() { return name; } public String toString(){ return \"Car = \"+ name; } }","keywords":["name","public","string","car","java","nameable","return","api","car.java","class","getname","implements"],"topics":["java"]},{"id":"chunk-3","heading":"Task9/Dishes.java","text":"public class Dishes implements Priceable{ double price; public Dishes(double price){ this.price = price; } @Override public void getPrice() { System.out.printf(\"Цена = %s\", price); //return price; } public static void main(String[] args){ Priceable p = new Dishes(12345); //System.out.println(p); p.getPrice(); } }","keywords":["price","public","dishes","priceable","double","java","void","12345","api","args","class","dishes.java"],"topics":["ai","java"]},{"id":"chunk-4","heading":"Task9/TestNameable.java","text":"public class TestNameable { public static void main(String[] args){ Nameable n = new Dog(\"KOL\"); Nameable n1 = new Car(\"TOYOTA\"); System.out.println(n); System.out.println(n1); } }","keywords":["nameable","java","n1","new","public","system.out.println","api","args","car","class","dog","kol"],"topics":["ai","java","testing"]},{"id":"chunk-5","heading":"Описание","text":"В этом модуле используется 6 Java файлов. Ключевые сущности: Car, Dishes, Dog, Nameable, Priceable, TestNameable.","keywords":["java","nameable","priceable","api","car","dishes","dog","task","testnameable.","единые","именем","интерфейсы"],"topics":["java","testing"]},{"id":"chunk-6","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","api","nameable","priceable","task","демо","единые","задачи","запускайте","затем","именем","интерфейсы"],"topics":["oop"]},{"id":"chunk-7","heading":"Вывод","text":"Task 9 — Интерфейсы Nameable/Priceable документирует реальное решение из исходного кода.","keywords":["java","nameable","priceable","task","интерфейсы","api","вывод","документирует","единые","именем","исходного","кода."],"topics":[]}]},{"id":"systems-analysis-and-conceptual-modeling-part-1","href":"/docs/systems-analysis-and-conceptual-modeling-part-1","slug":["systems-analysis-and-conceptual-modeling-part-1"],"section":"systems-analysis-and-conceptual-modeling-part-1","sectionTitle":"Анализ и концептуальное моделирование систем (часть 1)","title":"Анализ и концептуальное моделирование систем (часть 1)","description":"Введение в системный анализ и концептуальное моделирование, часть 1.","preview":"О разделе Раздел Анализ и концептуальное моделирование систем (часть 1) добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["анализ","концептуальное","моделирование","часть","систем","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","1.","введение","задач","кода","конспекты"],"topics":[],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Анализ и концептуальное моделирование систем (часть 1) добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["анализ","концептуальное","моделирование","часть","систем","1.","stackmirea","systems-analysis-and-conceptual-modeling-part-1","введение","готов","добавлен","материалами."],"topics":[]}]},{"id":"algorithms/getting-started","href":"/docs/algorithms/getting-started","slug":["algorithms","getting-started"],"section":"algorithms","sectionTitle":"Algorithms","title":"Введение","description":"Навигация по учебной документации проекта StackMIREA.","preview":"StackMIREA Docs Этот сайт собирает практики в формате учебных модулей. Что внутри: раздел Python с практиками по ООП/ФВП и отдельным блоком AI ноутбуков; раздел Java с 24 задачами: от основ до паттернов проектирования; унифицированная структура каждой практики","keywords":["раздел","java","python","практики","24","ai","docs","stackmirea","блоком","до","задание","задачами","каждой","ноутбуков","ооп","основ","от","отдельным"],"topics":["python","ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"StackMIREA Docs","text":"раздел Python с практиками по ООП/ФВП и отдельным блоком AI ноутбуков; раздел Java с 24 задачами: от основ до паттернов проектирования; унифицированная структура каждой практики: задание, решение, описание, важные моменты и вывод.","keywords":["algorithms","раздел","24","ai","docs","java","python","stackmirea","stackmirea.","блоком","важные","введение"],"topics":["python","ai","oop","java"]},{"id":"chunk-1","heading":"Как пользоваться","text":"1. Выберите трек: Python или Java . 2. Откройте нужную практику по номеру. 3. Сначала прочитайте задание, затем разберите пример кода. 4. Сопоставьте материал с исходниками в репозитории.","keywords":["algorithms","1.","2.","3.","4.","java","python","stackmirea.","введение","выберите","документации","задание"],"topics":["python","java"]},{"id":"chunk-2","heading":"Как пользоваться","text":"Используйте правый блок On this page для быстрого перехода по большим документам.","keywords":["algorithms","page","stackmirea.","this","блок","большим","быстрого","введение","документам.","документации","используйте","навигация"],"topics":[]}]},{"id":"procedural-programming/homework-01-basics","href":"/docs/procedural-programming/homework-01-basics","slug":["procedural-programming","homework-01-basics"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 1 — Ввод, арифметика и уравнения","description":"Пять стартовых задач по консольному вводу, базовой арифметике и простым уравнениям.","preview":"Что внутри В первом домашнем задании собраны самые базовые упражнения: ввод строки, арифметические действия, решение линейного и квадратного уравнений, а также логическая задача про освещённость комнаты. 1.1 «Имя» Условие. Нужно запросить имя пользователя, счи","keywords":["cout","else","if","endl","уравнение","return","int","name","ошибка","перезапустите","приложение","double","include","имя","1.4","арифметика","еще","лампа"],"topics":["ai"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"В первом домашнем задании собраны самые базовые упражнения: ввод строки, арифметические действия, решение линейного и квадратного уравнений, а также логическая задача про освещённость комнаты.","keywords":["ввод","procedural-programming","арифметика","арифметике","арифметические","базовой","базовые","вводу","внутри","действия","дз","домашнем"],"topics":[]},{"id":"chunk-1","heading":"1.1 «Имя»","text":"Условие. Нужно запросить имя пользователя, считать одно слово и вывести приветствие.","keywords":["имя","1.1","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","вывести","дз","задач","запросить"],"topics":[]},{"id":"chunk-2","heading":"1.1 «Имя»","text":"Как устроено решение. Исходник читает строку в переменную Name , а затем выводит её обратно в консоль. Это минимальный пример на работу с cin и cout .","keywords":["1.1","cin","cout","name","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","выводит","дз"],"topics":[]},{"id":"chunk-3","heading":"1.1 «Имя»","text":"Что учесть. В сохранённой версии строка приветствия записана в кодировке старого исходника, но сама логика решения остаётся простой и корректной.","keywords":["1.1","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","версии","дз","задач","записана","имя"],"topics":[]},{"id":"chunk-4","heading":"1.1 «Имя»","text":"cpp title=\"resources/procedural programming/home work 1/name.cpp\" include using namespace std; string Name; int main(){ cout Name) { cout << \"Привет, \" << Name; } else { cout << \"Eror\"; } return 0; }","keywords":["cout","name","1.1","cpp","else","eror","home","include","int","main","name.cpp","namespace"],"topics":["ai"]},{"id":"chunk-5","heading":"1.2 «Арифметика»","text":"Условие. По двум введённым числам требуется получить сумму, разность, произведение и, если возможно, частное.","keywords":["арифметика","1.2","procedural-programming","арифметике","базовой","введенным","ввод","вводу","возможно","двум","дз","если"],"topics":[]},{"id":"chunk-6","heading":"1.2 «Арифметика»","text":"Как устроено решение. Сохранённый файл реализует калькулятор на одну выбранную операцию: пользователь вводит два числа и знак + , , или / , после чего программа считает результат.","keywords":["арифметика","1.2","procedural-programming","арифметике","базовой","ввод","вводит","вводу","выбранную","два","дз","задач"],"topics":[]},{"id":"chunk-7","heading":"1.2 «Арифметика»","text":"Что учесть. Это решение не печатает все четыре операции подряд, как просит формулировка из PDF, зато хорошо показывает проверку ввода и отдельную обработку деления на ноль.","keywords":["арифметика","1.2","pdf","procedural-programming","арифметике","базовой","ввод","ввода","вводу","деления","дз","задач"],"topics":[]},{"id":"chunk-8","heading":"1.2 «Арифметика»","text":"cpp title=\"resources/procedural programming/home work 1/Arifmetica.cpp\" include using namespace std; string Name; int a, b, d; int main() { cout a) { cout b) { cout Name) { if (Name == \"+\") { d = a + b; cout << \"Answer: \" << d; } else if (Name == \" \") { d = a b; cout << \"Answer: \" << d; } else if (Name == \" \") { d = a b; cout << \"Answer: \" << d; } else if (Name == \"/\") { if (b == 0) { cout << \"Error\"","keywords":["cout","name","if","answer","else","int","арифметика","1.2","arifmetica.cpp","cpp","error","home"],"topics":["ai"]},{"id":"chunk-9","heading":"1.2 «Арифметика»","text":"; } else { d = a / b; cout << \"Answer: \" << d; } } else { cout << \"Only: +, , , /\"; } } else { cout << \"Error\"; } } else { cout << \"Error\"; } } else { cout << \"Error\"; } }","keywords":["cout","else","error","арифметика","1.2","answer","only","procedural-programming","арифметике","базовой","ввод","вводу"],"topics":[]},{"id":"chunk-10","heading":"1.3 «Уравнение»","text":"Как устроено решение. Программа читает коэффициенты, проверяет случай b != 0 , вычисляет корень x = c / b , а затем отдельно рассматривает вырожденные случаи b = 0 .","keywords":["1.3","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","вырожденные","вычисляет","дз","задач","затем"],"topics":[]},{"id":"chunk-11","heading":"1.3 «Уравнение»","text":"Что учесть. В текущем коде ответ сохраняется в int , поэтому дробная часть может потеряться. Для защиты полезно проговорить, что математически здесь лучше использовать double .","keywords":["1.3","double","int","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","дз","дробная","задач"],"topics":[]},{"id":"chunk-12","heading":"1.3 «Уравнение»","text":"cpp title=\"resources/procedural programming/home work 1/maindesc.cpp\" include include using namespace std; int main() { setlocale(0, \"\"); double b, c; cout b) { cout c) { if (b != 0) { int m = ( c) / b; int r; if (m == 0) { r = 0; } else r = m; cout << \"Решение уравнения вида bx + c = 0 равно: \" << r << endl; } else if (b == 0) { if (c == 0) { cout << \"x любое число\"; } else cout << \"нет решений\"","keywords":["cout","if","else","int","include","уравнения","1.3","bx","cpp","double","endl","home"],"topics":["ai"]},{"id":"chunk-13","heading":"1.3 «Уравнение»","text":"; } } else { cout << \"Ошибка! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } } else { cout << \"Ошибка! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } return 0; }","keywords":["cout","else","endl","введите","ввели","вы","вычисления","данных","ошибка","перезапустите","пожалуйста","приложение"],"topics":[]},{"id":"chunk-14","heading":"1.4 «Ещё уравнение»","text":"Как устроено решение. Код проверяет несколько веток: полное квадратное уравнение, частные случаи b = 0 или c = 0 , а также вырожденный вариант, когда уравнение превращается в линейное или вообще теряет неизвестную.","keywords":["уравнение","1.4","procedural-programming","арифметика","арифметике","базовой","вариант","ввод","вводу","веток","вообще","вырожденный"],"topics":[]},{"id":"chunk-15","heading":"1.4 «Ещё уравнение»","text":"Что учесть. Это самая объёмная задача в первом ДЗ. Хорошо видно, как через if и else if раскладывается задача на отдельные случаи, а корни находятся через дискриминант.","keywords":["if","задача","через","1.4","else","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","видно"],"topics":[]},{"id":"chunk-16","heading":"1.4 «Ещё уравнение»","text":"cpp title=\"resources/procedural programming/home work 1/onemoresolution.cpp\" include include using namespace std; int main() { setlocale(0, \"\"); double a, b, c; cout a) { cout b) { cout c) { if (b == 0 && a != 0 && c 0) { if ((2 a) == 0) { cout << \"Ошибка, деление на ноль! Перезапустите приложение!\" << endl; return 0; } double x1 = ((( b) + (sqrt(d))) / (2 a)); double x2 = ((( b) (sqrt(d))) / (2 a))","keywords":["cout","double","if","include","sqrt","1.4","cpp","endl","home","int","main","namespace"],"topics":["ai"]},{"id":"chunk-17","heading":"1.4 «Ещё уравнение»","text":"; cout << \"Первый корень уравнения равен:\" << x1 << endl; cout << \"Второй корень уравнения равен:\" << x2 << endl; return 0; } } else if (a == 0 && b == 0) { if (c == 0) { cout << \"x любое число\" << endl; } else if (c != 0) { cout << \"нет решений\" << endl; } return 0; } else if (c == 0) { if (a == 0) { cout << \"Ошибка, деление на ноль! Перезапустите приложение!\" << endl; return 0; } double x = 1 (b / a)","keywords":["cout","endl","if","else","return","уравнения","корень","равен","1.4","double","procedural-programming","x1"],"topics":[]},{"id":"chunk-18","heading":"1.4 «Ещё уравнение»","text":"; cout << \"Корень уравнения равен:\" << x << endl; return 0; } else { if (b == 0) { cout << \"Ошибка, деление на ноль! Перезапустите приложение!\" << endl; return 0; } double x = (( c) / b); cout << \"Корень уравнения равен:\" << x << endl; return 0; } } else { cout << \"Ошибка! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } } else { cout << \"Ошибка","keywords":["cout","endl","else","return","ошибка","уравнения","корень","перезапустите","приложение","равен","1.4","double"],"topics":[]},{"id":"chunk-19","heading":"1.4 «Ещё уравнение»","text":"! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } } else { cout << \"Ошибка! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } return 0; }","keywords":["endl","введите","ввели","вы","вычисления","данных","перезапустите","пожалуйста","приложение","тип","число.","1.4"],"topics":[]},{"id":"chunk-20","heading":"1.5 «Лампа со шторой»","text":"Условие. В комнате светло, если включена лампа или если на улице день и открыты шторы.","keywords":["если","лампа","1.5","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","включена","день","дз"],"topics":[]},{"id":"chunk-21","heading":"1.5 «Лампа со шторой»","text":"Как устроено решение. Программа последовательно спрашивает про день, окно и лампу, валидирует ввод и затем проверяет комбинации состояний через вложенные условия.","keywords":["ввод","1.5","procedural-programming","арифметика","арифметике","базовой","валидирует","вводу","вложенные","день","дз","задач"],"topics":[]},{"id":"chunk-22","heading":"1.5 «Лампа со шторой»","text":"Что учесть. В сохранённой версии при открытых шторах выводится Light и днём, и ночью. Для защиты лучше отдельно проговорить, что по условию стоило бы дополнительно учитывать именно время суток.","keywords":["1.5","light","procedural-programming","арифметика","арифметике","базовой","бы","ввод","вводу","версии","время","выводится"],"topics":[]},{"id":"chunk-23","heading":"1.5 «Лампа со шторой»","text":"```cpp title=\"resources/procedural programming/home work 1/lamp.cpp\" include using namespace std; int Num, a, b, c;","keywords":["1.5","cpp","home","include","int","lamp.cpp","namespace","num","procedural","procedural-programming","programming","resources"],"topics":[]},{"id":"chunk-24","heading":"1.5 «Лампа со шторой»","text":"int main() { cout a; if ((a == 1) (a == 2)) { } else { cout b; if (b == 1 b == 2) { } else { cout c; if (c == 1 c == 2) { } else { cout << \"Error\"; return 0; } if (c == 1) { cout << \"Light\"; } else { if (b == 1) { if (a == 1) { cout << \"Light\"; } else { cout << \"Light\"; } } else { cout << \"Dark\"; } } } ```","keywords":["cout","else","if","light","1.5","dark","error","int","main","procedural-programming","return","арифметика"],"topics":["ai"]}]},{"id":"procedural-programming/homework-02-formulas-and-branching","href":"/docs/procedural-programming/homework-02-formulas-and-branching","slug":["procedural-programming","homework-02-formulas-and-branching"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 2 — Формулы, ветвления и табуляция","description":"Задачи на вычисление выражений, проверку области определения и вывод таблиц значений.","preview":"Что внутри Во втором домашнем задании акцент смещается с простого ввода на вычисление формул, проверку корректности аргументов и вывод серий значений. 2.1 «Конус» Условие. Нужно вычислить объём и полную поверхность усечённого конуса по R , r и h . Как устроено","keywords":["cout","include","return","else","int","2.1","конус","устроено","2.3","2.4","cpp","home","if","namespace","pi","procedural","programming","resources"],"topics":["ai"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"Во втором домашнем задании акцент смещается с простого ввода на вычисление формул, проверку корректности аргументов и вывод серий значений.","keywords":["вывод","вычисление","значений.","проверку","procedural-programming","акцент","аргументов","ввода","ветвления","внутри","втором","выражений"],"topics":[]},{"id":"chunk-1","heading":"2.1 «Конус»","text":"Условие. Нужно вычислить объём и полную поверхность усечённого конуса по R , r и h .","keywords":["2.1","procedural-programming","ветвления","вывод","выражений","вычисление","вычислить","дз","задачи","значений.","конус","конуса"],"topics":[]},{"id":"chunk-2","heading":"2.1 «Конус»","text":"Как устроено решение. Программа читает размеры, вычисляет образующую l , затем по формулам получает объём V и площадь S .","keywords":["2.1","procedural-programming","ветвления","вывод","выражений","вычисление","вычисляет","дз","задачи","затем","значений.","конус"],"topics":[]},{"id":"chunk-3","heading":"2.1 «Конус»","text":"Что учесть. В сохранённом исходнике pi объявлена как int , поэтому значение 3.14 обрезается до 3 . В разборе важно понимать, что сама идея верна, но точность страдает.","keywords":["2.1","3.14","int","pi","procedural-programming","важно","верна","ветвления","вывод","выражений","вычисление","дз"],"topics":[]},{"id":"chunk-4","heading":"2.1 «Конус»","text":"```cpp title=\"resources/procedural programming/home work 2/conus.cpp\" include include include using namespace std;","keywords":["include","2.1","conus.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-5","heading":"2.1 «Конус»","text":"int main() { int l; int pi = 3.14; float R, r, h, V, S; cout h && h = 0) { cout r && r = 0) { cout R && R = 0) { l = sqrt(h h + (R r) (R r)); pi = 3.14; V = (((pi h) / 3) (R R + r R + r r)); S = (pi (R R + (R + r) l + (r r))); cout << \"V: \" << V << \"\\n\"; cout << \"S: \" << S; } else { cout << \"Immposible\"; return 0; } } else { cout << \"Immposible\"; return 0; } } else { cout << \"Immposible\"; return 0; } } ```","keywords":["cout","pi","else","immposible","int","return","3.14","2.1","float","main","procedural-programming","sqrt"],"topics":["ai"]},{"id":"chunk-6","heading":"2.2 «Разветвление»","text":"Как устроено решение. Сохранённый файл работает по двум веткам: если x < 1 , считается w = a log( x ) , иначе программа пытается найти w = sqrt(a x^2) .","keywords":["2.2","log","procedural-programming","sqrt","ветвления","веткам","вывод","выражений","вычисление","двум","дз","если"],"topics":[]},{"id":"chunk-7","heading":"2.2 «Разветвление»","text":"Что учесть. Здесь хорошо видно, зачем проверять область определения: логарифм требует положительный аргумент, а корень требует неотрицательное подкоренное выражение.","keywords":["определения","требует","2.2","procedural-programming","аргумент","ветвления","видно","вывод","выражение.","выражений","вычисление","дз"],"topics":[]},{"id":"chunk-8","heading":"2.2 «Разветвление»","text":"cpp title=\"resources/procedural programming/home work 2/branchingout.cpp\" include include using namespace std; int main() { setlocale(0, \"\"); double x, a, w, xm; cout x; cout a; if (cin.fail()) { cin.clear(); cin.ignore(); cout = 1) { if ((a x x) < 0) { cout << \"Ответ: отрицательное подкоренное выражение! Нет ответа.\"; return 0; } else { w = sqrt(a x x); } } } cout << \"Ответ: \" << w; return 0; }","keywords":["cout","if","include","return","ответ","2.2","branchingout.cpp","cin.clear","cin.fail","cin.ignore","cpp","double"],"topics":["ai"]},{"id":"chunk-9","heading":"2.3 «Функция»","text":"Условие. Нужно вычислить выражение от b , x и y с логарифмом и квадратным корнем.","keywords":["2.3","procedural-programming","ветвления","вывод","выражение","выражений","вычисление","вычислить","дз","задачи","значений.","квадратным"],"topics":[]},{"id":"chunk-10","heading":"2.3 «Функция»","text":"Как устроено решение. В текущем варианте сначала считаются j = b y и u = b x , затем при корректной области определения программа выводит z = log(j) sqrt(u) .","keywords":["области","определения","2.3","log","procedural-programming","sqrt","варианте","ветвления","вывод","выводит","выражений","вычисление"],"topics":[]},{"id":"chunk-11","heading":"2.3 «Функция»","text":"Что учесть. Для такой задачи критично уметь назвать ограничения: b y 0 , а b x = 0 . Именно эти проверки защищают программу от некорректных вычислений.","keywords":["задачи","2.3","procedural-programming","ветвления","вывод","выражений","вычисление","вычислений.","дз","защищают","значений.","именно"],"topics":[]},{"id":"chunk-12","heading":"2.3 «Функция»","text":"```cpp title=\"resources/procedural programming/home work 2/main.cpp\" include include include using namespace std;","keywords":["include","2.3","cpp","home","main.cpp","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":["ai"]},{"id":"chunk-13","heading":"2.3 «Функция»","text":"int main() { float x, y, b, z; int j, u; cout b) { cout x) { cout y) { j = b y; u = b x; if (j 0) { if (u != 0) { z = log(j) sqrt(u); cout << \"Answer: \" << z; return 0; } else if (u = 0) { cout << \"Net resh\"; } } else { cout << \"Net resh\"; return 0; } } else { cout << \"Eror\"; return 0; } } else { cout << \"Eror\"; return 0; } } else { cout << \"Eror\"; return 0; } } ```","keywords":["cout","else","return","eror","if","int","net","resh","2.3","answer","float","log"],"topics":["ai"]},{"id":"chunk-14","heading":"2.4 «Порядок»","text":"Условие. Нужно вывести 10 натуральных чисел подряд, начиная с корректного значения N .","keywords":["10","2.4","procedural-programming","ветвления","вывести","вывод","выражений","вычисление","дз","задачи","значений.","значения"],"topics":[]},{"id":"chunk-15","heading":"2.4 «Порядок»","text":"Как устроено решение. Исходник читает N , задаёт границу N + 10 и печатает числа в цикле while .","keywords":["10","2.4","procedural-programming","while","ветвления","вывод","выражений","вычисление","границу","дз","задает","задачи"],"topics":[]},{"id":"chunk-16","heading":"2.4 «Порядок»","text":"Что учесть. В PDF отдельно оговариваются дробные, нулевые и отрицательные случаи. В текущем файле они не обработаны: решение работает как простая печать десяти последовательных целых чисел от введённого N .","keywords":["2.4","pdf","procedural-programming","введенного","ветвления","вывод","выражений","вычисление","десяти","дз","дробные","задачи"],"topics":[]},{"id":"chunk-17","heading":"2.4 «Порядок»","text":"```cpp title=\"resources/procedural programming/home work 2/linerow.cpp\" include include include using namespace std;","keywords":["include","2.4","cpp","home","linerow.cpp","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-18","heading":"2.4 «Порядок»","text":"while (N != N10) { cout << N << \", \"; N = N + 1; } } else { cout << \"Eror\"; return 0; } } ```","keywords":["cout","2.4","else","eror","n10","procedural-programming","return","while","ветвления","вывод","выражений","вычисление"],"topics":[]},{"id":"chunk-19","heading":"2.5 «Табуляция»","text":"Как устроено решение. В сохранённой программе табулируется выражение y = (x^2 2x + 2) / (x 1) . Значения от 4 до 0 и от 1.5 до 4 печатаются отдельно, чтобы не попасть на деление на ноль при x = 1 .","keywords":["до","от","табуляция","1.5","2.5","2x","procedural-programming","ветвления","вывод","выражение","выражений","вычисление"],"topics":[]},{"id":"chunk-20","heading":"2.5 «Табуляция»","text":"Что учесть. Это хороший пример, где область определения влияет не только на вычисление, но и на организацию цикла.","keywords":["вычисление","определения","табуляция","2.5","procedural-programming","ветвления","влияет","вывод","выражений","дз","задачи","значений."],"topics":[]},{"id":"chunk-21","heading":"2.5 «Табуляция»","text":"```cpp title=\"resources/procedural programming/home work 2/tabulation.cpp\" include include include using namespace std;","keywords":["include","табуляция","2.5","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","tabulation.cpp"],"topics":[]},{"id":"chunk-22","heading":"2.5 «Табуляция»","text":"int main() { setlocale(0, \"\"); double y; for (double x = 4; x <= 0; x = x + 0.5) { y = (((x x) 2 x + 2) / (x 1)); cout << \"Ответ для x = \" << x << \" y = \" << y << endl; } cout << \"Ответ для x = 1 не существует\" << endl; for (double x = 1.5; x <= 4; x = x + 0.5) { y = (((x x) 2 x + 2) / (x 1)); cout << \"Ответ для x = \" << x << \" y = \" << y << endl; } return 0; } ```","keywords":["cout","double","endl","ответ","0.5","табуляция","1.5","2.5","int","main","procedural-programming","return"],"topics":["ai"]}]},{"id":"procedural-programming/homework-03-files-and-strings","href":"/docs/procedural-programming/homework-03-files-and-strings","slug":["procedural-programming","homework-03-files-and-strings"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 3 — Заём, файлы и работа со строками","description":"Формулы по кредитам, чтение файлов, фильтрация чисел и сортировка символов.","preview":"Что внутри Третья домашняя работа совмещает два блока тем: расчётные задачи по формулам займа и базовую работу с файлами и строками. 3.1 «Заём» Условие. Нужно вычислить ежемесячный платёж m по сумме S , проценту p и сроку n . Как устроено решение. Программа пр","keywords":["cout","include","else","заем","3.1","if","return","решение.","устроено","3.2","cpp","home","int","main","namespace","procedural","programming","resources"],"topics":["ai","algorithms"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"Третья домашняя работа совмещает два блока тем: расчётные задачи по формулам займа и базовую работу с файлами и строками.","keywords":["работа","procedural-programming","базовую","блока","внутри","два","дз","домашняя","задачи","заем","займа","кредитам"],"topics":[]},{"id":"chunk-1","heading":"3.1 «Заём»","text":"Условие. Нужно вычислить ежемесячный платёж m по сумме S , проценту p и сроку n .","keywords":["заем","3.1","procedural-programming","вычислить","дз","ежемесячный","кредитам","нужно","платеж","программирование","процедурное","проценту"],"topics":[]},{"id":"chunk-2","heading":"3.1 «Заём»","text":"Как устроено решение. Программа проверяет диапазоны входных данных, отдельно рассматривает случай p = 0 , а в общем случае подставляет значения в формулу аннуитетного платежа.","keywords":["заем","3.1","procedural-programming","аннуитетного","входных","данных","дз","диапазоны","значения","кредитам","общем","отдельно"],"topics":[]},{"id":"chunk-3","heading":"3.1 «Заём»","text":"Что учесть. Это хороший пример, где сначала надо проверить корректность параметров, и только потом переходить к вычислению формулы.","keywords":["заем","3.1","procedural-programming","вычислению","дз","корректность","кредитам","надо","параметров","переходить","потом","пример"],"topics":[]},{"id":"chunk-4","heading":"3.1 «Заём»","text":"```cpp title=\"resources/procedural programming/home work 3/3.1.cpp\" include include using namespace std;","keywords":["include","заем","3.1","3.1.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std"],"topics":[]},{"id":"chunk-5","heading":"3.1 «Заём»","text":"int main() { double S, p, n, r; double m, q, y; cout S; if (S p; if (p = 1 pow(10, 4) && p n; if (n = 1 && n < pow(10, 2) && n != 0) { if (p == 0) { m = S / n / 12; cout << \"Mi nashli : \" << m << endl; return 0; } else { r = p / 100; } q = S r pow((1 + r), n); y = 12 (pow(1 + r, n) 1); m = q / y; cout << \"Mi nashli: \" << m << \" Mecyachnaya viplata.\"; return 0;","keywords":["if","pow","cout","10","12","double","mi","nashli","return","заем","100","3.1"],"topics":["ai"]},{"id":"chunk-6","heading":"3.1 «Заём»","text":"} else { cout << \"Nevenie dannie, restart\"; return 0; } } else { cout << \"Dannie ne verni, perezapustite\"; return 0; } } else { cout << \"Dannie ne verni, perezapustite\"; return 0; } } ```","keywords":["cout","dannie","else","return","ne","perezapustite","verni","заем","3.1","nevenie","procedural-programming","restart"],"topics":[]},{"id":"chunk-7","heading":"3.2 «Ссуда»","text":"Условие. По сумме S , месячному платежу m и сроку n нужно определить процент p .","keywords":["3.2","procedural-programming","дз","заем","кредитам","месячному","нужно","определить","платежу","программирование","процедурное","процент"],"topics":[]},{"id":"chunk-8","heading":"3.2 «Ссуда»","text":"Как устроено решение. Сохранённый код не выводит процент аналитически, а перебирает его от 100 до 100 с маленьким шагом и сравнивает расчётный платёж с заданным.","keywords":["100","3.2","procedural-programming","аналитически","выводит","дз","до","его","заданным.","заем","код","кредитам"],"topics":[]},{"id":"chunk-9","heading":"3.2 «Ссуда»","text":"Что учесть. Такой способ медленнее формульного, но хорошо показывает идею численного подбора.","keywords":["3.2","procedural-programming","дз","заем","идею","кредитам","медленнее","подбора.","показывает","программирование","процедурное","работа"],"topics":[]},{"id":"chunk-10","heading":"3.2 «Ссуда»","text":"```cpp title=\"resources/procedural programming/home work 3/3.2.cpp\" include include using namespace std;","keywords":["include","3.2","3.2.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-11","heading":"3.2 «Ссуда»","text":"int main() { setlocale(0, \"\"); float s, n, r, m, p = 0, i = 100000, m1; cout s) { cout m) { cout n) { while (( 100 = m) { cout << \"Процент займа = \" << p << \"%\" << endl; break; } } } else { cout << \"Перезапустите приложение и введите числа\"; } } else { cout << \"Перезапустите приложение и введите числа\"; } } else { cout << \"Перезапустите приложение и введите числа\"; } return 0; } ```","keywords":["cout","else","введите","перезапустите","приложение","числа","100","100000","3.2","break","endl","float"],"topics":["ai"]},{"id":"chunk-12","heading":"3.3 «Копирование файла»","text":"Условие. Нужно открыть текстовый файл, проверить его существование и вывести содержимое на экран без потери форматирования.","keywords":["3.3","procedural-programming","без","вывести","дз","его","заем","копирование","кредитам","нужно","открыть","потери"],"topics":[]},{"id":"chunk-13","heading":"3.3 «Копирование файла»","text":"Как устроено решение. В ресурсе сохранён закомментированный черновик. Ниже приведён тот же вариант в читаемом виде: файл открывается через fstream , затем посимвольно печатается в консоль.","keywords":["3.3","fstream","procedural-programming","вариант","виде","дз","же","заем","закомментированный","затем","консоль.","копирование"],"topics":[]},{"id":"chunk-14","heading":"3.3 «Копирование файла»","text":"```cpp title=\"resources/procedural programming/home work 3/3.3.cpp\" include include using namespace std;","keywords":["include","3.3","3.3.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-15","heading":"3.3 «Копирование файла»","text":"int main() { fstream fl; fl.open(\"f.txt\"); if (fl.is open()) { char c; while (fl.get(c)) { cout << c; } fl.close(); } else { cout << \"ne\"; } } ```","keywords":["cout","3.3","char","else","f.txt","fl","fl.close","fl.get","fl.is","fl.open","fstream","if"],"topics":["ai"]},{"id":"chunk-16","heading":"3.4 «Фильтр»","text":"Как устроено решение. В исходнике файл читается посимвольно, а на экран попадают только символы, для которых isdigit(c) возвращает истину.","keywords":["3.4","isdigit","procedural-programming","возвращает","дз","заем","истину.","исходнике","которых","кредитам","попадают","посимвольно"],"topics":[]},{"id":"chunk-17","heading":"3.4 «Фильтр»","text":"Что учесть. Такой вариант извлекает именно цифры, а не полноценные целые числа как отдельные токены. Для базовой задачи на фильтрацию этого достаточно, но на защите стоит понимать разницу.","keywords":["3.4","procedural-programming","базовой","вариант","дз","достаточно","задачи","заем","защите","извлекает","именно","кредитам"],"topics":[]},{"id":"chunk-18","heading":"3.4 «Фильтр»","text":"```cpp title=\"resources/procedural programming/home work 3/3.4.cpp\" include include using namespace std;","keywords":["include","3.4","3.4.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-19","heading":"3.4 «Фильтр»","text":"int main() { fstream fl; fl.open(\"2.txt\"); if (fl.is open()) { char c; while (fl.get(c)) { if (isdigit(c)) { cout << c; } } fl.close(); } else { cout << \"ne rabotaet\"; } } ```","keywords":["cout","if","2.txt","3.4","char","else","fl","fl.close","fl.get","fl.is","fl.open","fstream"],"topics":["ai"]},{"id":"chunk-20","heading":"3.5 «Сортировка букв»","text":"Как устроено решение. Пользователь вводит строку, после чего вызывается стандартная sort , которая упорядочивает символы в диапазоне begin() — end() .","keywords":["сортировка","3.5","begin","end","procedural-programming","sort","букв","вводит","вызывается","дз","диапазоне","заем"],"topics":[]},{"id":"chunk-21","heading":"3.5 «Сортировка букв»","text":"Что учесть. Это короткая, но очень полезная задача на работу со строками и стандартными алгоритмами STL.","keywords":["со","сортировка","строками","3.5","procedural-programming","stl.","алгоритмами","букв","дз","задача","заем","короткая"],"topics":["algorithms"]},{"id":"chunk-22","heading":"3.5 «Сортировка букв»","text":"```cpp title=\"resources/procedural programming/home work 3/3.5.cpp\" include include include using namespace std;","keywords":["include","сортировка","3.5","3.5.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std"],"topics":[]},{"id":"chunk-23","heading":"3.5 «Сортировка букв»","text":"int main() { system(\"CHCP 1251\"); cout line; sort(line.begin(), line.end()); cout << line << endl; return 0; } ```","keywords":["cout","line","сортировка","1251","3.5","chcp","endl","int","line.begin","line.end","main","procedural-programming"],"topics":["ai"]}]},{"id":"procedural-programming/homework-04-functions-and-automation","href":"/docs/procedural-programming/homework-04-functions-and-automation","slug":["procedural-programming","homework-04-functions-and-automation"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 4 — Функции, файлы, графика и системы счисления","description":"Девять задач на подпрограммы, файлы, псевдографику, матрицы и преобразование чисел.","preview":"Что внутри Четвёртая домашняя работа самая объёмная: здесь есть файловый ввод вывод, собственные функции, псевдографика, генератор чисел, матрицы и перевод между системами счисления. 4.1 «Файл» Условие. Нужно записать 10 вещественных чисел в файл, затем открыт","keywords":["if","return","else","int","cout","include","ch","i++","cf","endl","main","cpp","home","procedural","programming","resources","title","work"],"topics":["ai"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"Четвёртая домашняя работа самая объёмная: здесь есть файловый ввод вывод, собственные функции, псевдографика, генератор чисел, матрицы и перевод между системами счисления.","keywords":["матрицы","файлы","функции","procedural-programming","ввод","внутри","вывод","генератор","графика","девять","дз","домашняя"],"topics":[]},{"id":"chunk-1","heading":"4.1 «Файл»","text":"Условие. Нужно записать 10 вещественных чисел в файл, затем открыть его снова и посчитать сумму.","keywords":["файл","файлы","10","4.1","procedural-programming","вещественных","графика","девять","дз","его","задач","записать"],"topics":[]},{"id":"chunk-2","heading":"4.1 «Файл»","text":"Как устроено решение. Программа сначала пишет десять введённых чисел в 1.txt , затем вторым проходом считывает их из файла и накапливает сумму в переменной sum .","keywords":["файлы","1.txt","4.1","procedural-programming","sum","введенных","вторым","графика","девять","десять","дз","задач"],"topics":[]},{"id":"chunk-3","heading":"4.1 «Файл»","text":"```cpp title=\"resources/procedural programming/home work 4/4.1 main.cpp\" include include using namespace std;","keywords":["4.1","include","файлы","cpp","home","main.cpp","namespace","procedural","procedural-programming","programming","resources","std"],"topics":["ai"]},{"id":"chunk-4","heading":"4.1 «Файл»","text":"int main() { double q, a, sum = 0; int i = 0; ofstream file(\"1.txt\"); setlocale(0, \"\"); cout q) { i++; file << q << endl; } else { cout << \"Введите только числа\"; return 0; } } file.close();","keywords":["cout","file","int","файлы","1.txt","4.1","double","else","endl","file.close","i++","main"],"topics":["ai"]},{"id":"chunk-5","heading":"4.1 «Файл»","text":"ifstream fin(\"1.txt\"); while (fin a) { sum += a; } cout << \"Сумма=\" << sum; fin.close(); } ```","keywords":["fin","sum","файлы","1.txt","4.1","cout","fin.close","ifstream","procedural-programming","while","графика","девять"],"topics":[]},{"id":"chunk-6","heading":"4.2 «Знак числа»","text":"Условие. Нужно определить знак числа через отдельную подпрограмму функцию sign .","keywords":["знак","файлы","числа","4.2","procedural-programming","sign","графика","девять","дз","задач","матрицы","нужно"],"topics":[]},{"id":"chunk-7","heading":"4.2 «Знак числа»","text":"Как устроено решение. Функция ptt принимает число и печатает 1 , 0 или 1 в зависимости от его значения.","keywords":["файлы","4.2","procedural-programming","ptt","графика","девять","дз","его","зависимости","задач","знак","значения."],"topics":[]},{"id":"chunk-8","heading":"4.2 «Знак числа»","text":"```cpp title=\"resources/procedural programming/home work 4/4.2 main.cpp\" include include","keywords":["4.2","include","файлы","cpp","home","main.cpp","procedural","procedural-programming","programming","resources","title","work"],"topics":["ai"]},{"id":"chunk-9","heading":"4.2 «Знак числа»","text":"int main() { setlocale(0, \"\"); double S; cout S) { ptt(S); } else { cout << \"Введите число\"; } } ```","keywords":["cout","файлы","4.2","double","else","int","main","procedural-programming","ptt","setlocale","введите","графика"],"topics":["ai"]},{"id":"chunk-10","heading":"4.3 «Геометрические фигуры»","text":"Условие. Нужно вычислять площадь прямоугольника, треугольника или круга через отдельные функции.","keywords":["файлы","4.3","procedural-programming","вычислять","геометрические","графика","девять","дз","задач","круга","матрицы","нужно"],"topics":[]},{"id":"chunk-11","heading":"4.3 «Геометрические фигуры»","text":"Как устроено решение. Для каждой фигуры выделена собственная подпрограмма: prog1 , prog2 и prog3 . В main пользователь выбирает, какую именно фигуру считать.","keywords":["файлы","фигуры","4.3","main","procedural-programming","prog1","prog2","prog3","выбирает","выделена","геометрические","графика"],"topics":["ai"]},{"id":"chunk-12","heading":"4.3 «Геометрические фигуры»","text":"```cpp title=\"resources/procedural programming/home work 4/4.3 main.cpp\" include include using namespace std; void prog1() { double a, b; cout a; cin b; cout a ; cin h; cout r; cout << \"S=\" << 3.14 r r; }","keywords":["cout","4.3","cin","include","файлы","3.14","cpp","double","home","main.cpp","namespace","procedural"],"topics":["ai"]},{"id":"chunk-13","heading":"4.3 «Геометрические фигуры»","text":"int main() { setlocale(0, \"\"); string S; cout S; if (S == \"prog1\") { prog1(); } else if (S == \"prog2\") { prog2(); } else if (S == \"prog3\") { prog3(); } } ```","keywords":["if","else","prog1","prog2","prog3","файлы","4.3","cout","int","main","procedural-programming","setlocale"],"topics":["ai"]},{"id":"chunk-14","heading":"4.4 «Былая слава»","text":"Условие. Нужно вывести в консоль флаг в виде псевдографики, обязательно используя цикл или рекурсию.","keywords":["файлы","4.4","procedural-programming","былая","виде","вывести","графика","девять","дз","задач","используя","консоль"],"topics":[]},{"id":"chunk-15","heading":"4.4 «Былая слава»","text":"Как устроено решение. Программа печатает первые шесть строк с блоком звёзд и полосами, затем ещё шесть строк только из полос.","keywords":["строк","файлы","шесть","4.4","procedural-programming","блоком","былая","графика","девять","дз","еще","задач"],"topics":[]},{"id":"chunk-16","heading":"4.4 «Былая слава»","text":"cpp title=\"resources/procedural programming/home work 4/4.4 main.cpp\" include include using namespace std; int main() { cout << endl; for (int i = 0; i < 6; i++) { for (int i = 0; i < 8; i++) { cout << \" \"; } for (int i = 0; i < 41; i++) { cout << \" \"; } cout << endl; } for (int i = 0; i < 6; i++) { for (int i = 0; i < 49; i++) { cout << \" \"; } cout << endl; } }","keywords":["cout","int","i++","endl","4.4","include","файлы","41","49","cpp","home","main"],"topics":["ai"]},{"id":"chunk-17","heading":"4.5 «Синусоида»","text":"Как устроено решение. Сначала в массив sinx сохраняются значения синуса по точкам, затем двумерный массив graf заполняется символами или пробелами, после чего печатается построчно.","keywords":["массив","файлы","4.5","graf","procedural-programming","sinx","графика","двумерный","девять","дз","задач","заполняется"],"topics":[]},{"id":"chunk-18","heading":"4.5 «Синусоида»","text":"cpp title=\"resources/procedural programming/home work 4/4.5 main.cpp\" include include using namespace std; int main() { const int size = 89, height = 23; char graf[height][size]; double sinx[size]; for (int i = 0; i < size; i++) sinx[i] = 10 sin(i / 10.0); for (int i = 0; i < height; i++) for (int j = 0; j < size; j++) if ( 1 < 10.0 i round(sinx[j]) and 10.0 i round(sinx[j]) < 1) graf[i][j] = ' '","keywords":["int","size","sinx","10.0","height","4.5","graf","i++","include","round","файлы","10"],"topics":["ai"]},{"id":"chunk-19","heading":"4.5 «Синусоида»","text":"; else graf[i][j] = ' '; for (int i = 0; i < height; i++) { for (int j = 0; j < size; j++) cout << graf[i][j]; cout << endl; } }","keywords":["cout","graf","int","файлы","4.5","else","endl","height","i++","j++","procedural-programming","size"],"topics":[]},{"id":"chunk-20","heading":"4.6 «Автоматный распознаватель»","text":"Условие. Нужно перевести число из римской записи в арабскую и отследить некорректный ввод.","keywords":["файлы","4.6","procedural-programming","автоматный","арабскую","ввод.","графика","девять","дз","задач","записи","матрицы"],"topics":[]},{"id":"chunk-21","heading":"4.6 «Автоматный распознаватель»","text":"Как устроено решение. Функция cf сопоставляет римские символы их значениям, а основной цикл проходит по строке и складывает или вычитает значения в зависимости от соседних символов.","keywords":["файлы","4.6","cf","procedural-programming","автоматный","вычитает","графика","девять","дз","зависимости","задач","значения"],"topics":[]},{"id":"chunk-22","heading":"4.6 «Автоматный распознаватель»","text":"Что учесть. Проверка корректности здесь частичная: реализация демонстрирует общий принцип разбора, но не покрывает все правила записи римских чисел.","keywords":["файлы","чисел.","4.6","procedural-programming","автоматный","графика","девять","демонстрирует","дз","задач","записи","здесь"],"topics":[]},{"id":"chunk-23","heading":"4.6 «Автоматный распознаватель»","text":"```cpp title=\"resources/procedural programming/home work 4/4.6.cpp\" include include using namespace std; int cf(char x) { if (x == 'I') return(1); else if (x == 'X') return(10); else if (x == 'V') return(5); else if (x == 'IV') return(4); else if (x == 'L') return(50); else if (x == 'C') return(100); else if (x == 'D') return(500); else if (x == 'M') return(1000); else if (x == '0') return(0); else return(0)","keywords":["return","else","if","include","файлы","10","100","1000","4.6","4.6.cpp","50","500"],"topics":[]},{"id":"chunk-24","heading":"4.6 «Автоматный распознаватель»","text":"; } int main() { setlocale(0, \"\"); string ch; int s = 0; int i = 0; char v = 'I'; cout ch; if (ch.length() 2) { if (ch[0] == ch[1] and ch[0] == v and cf(ch[2]) 1) { cout << \"eror\"; return 0; } for (int i = 0; i < (ch.length() 3); i++) { if (ch[i] == ch[i + 1] and ch[i] == v and cf(ch[i + 2]) <= 1) { cout << \"Eror\"; return 0; } } }","keywords":["ch","int","cout","if","cf","ch.length","eror","return","файлы","4.6","char","i++"],"topics":["ai"]},{"id":"chunk-25","heading":"4.6 «Автоматный распознаватель»","text":"for (int i = 0; i < (ch.length()); i++) { if (cf(ch[i]) < cf(ch[i + 1]) and cf(ch[i]) != 0) { s = s + cf(ch[i + 1]) cf(ch[i]); ch[i + 1] = '0'; } else s = s + cf(ch[i]); } cout << s; } ```","keywords":["ch","cf","файлы","4.6","ch.length","cout","else","i++","if","int","procedural-programming","автоматный"],"topics":[]},{"id":"chunk-26","heading":"4.7 «Генератор случайных чисел»","text":"Условие. Нужно построить линейный конгруэнтный генератор по рекуррентной формуле.","keywords":["генератор","файлы","4.7","procedural-programming","графика","девять","дз","задач","конгруэнтный","линейный","матрицы","нужно"],"topics":[]},{"id":"chunk-27","heading":"4.7 «Генератор случайных чисел»","text":"Как устроено решение. Глобальная переменная s хранит текущее состояние, функция f() пересчитывает его по формуле (37 s + 3) % 64 , а затем main несколько раз вызывает генератор.","keywords":["файлы","37","4.7","64","main","procedural-programming","вызывает","генератор","генератор.","глобальная","графика","девять"],"topics":["ai"]},{"id":"chunk-28","heading":"4.7 «Генератор случайных чисел»","text":"cpp title=\"resources/procedural programming/home work 4/4.7.cpp\" include using namespace std; int s = 0; int f() { s = (37 s + 3) % 64; return s; } int main() { int n; cin n; for (int i = 1; i <= n + 1; ++i) { cout << f() << endl; } return 0; }","keywords":["int","return","файлы","++i","37","4.7","4.7.cpp","64","cin","cout","cpp","endl"],"topics":["ai"]},{"id":"chunk-29","heading":"4.8 «Умножение матриц»","text":"Условие. Нужно задать матрицы продаж и цен, получить произведение C = A x B и ответить на вопросы о выручке и комиссионных.","keywords":["матрицы","файлы","4.8","procedural-programming","вопросы","выручке","графика","девять","дз","задать","задач","комиссионных."],"topics":[]},{"id":"chunk-30","heading":"4.8 «Умножение матриц»","text":"Как устроено решение. Код вручную задаёт две константные матрицы, перемножает их, затем проходит по строкам результата и находит максимумы, минимумы и суммарные значения.","keywords":["матрицы","файлы","4.8","procedural-programming","вручную","графика","две","девять","дз","задает","задач","затем"],"topics":[]},{"id":"chunk-31","heading":"4.8 «Умножение матриц»","text":"```cpp title=\"resources/procedural programming/home work 4/4.8.cpp\" include include include include using namespace std; int main() { double maks = 90, mensh = 9999, maksk = 90, menshk = 990, obsh, obshk, obshd, x1, x2, x3, x4, x5 = 0, x6 = 0; double a[3][4] = { 5,2,0,10,3,5,2,5,20,0,0,0 }, b[4][2] = { 1.20,0.50,2.80,0.40,5,1,2,1.5 }, c[3][2] = {0}; for (int i = 0; i < 3; i++) { for (int j = 0; j < 4","keywords":["include","int","90","double","файлы","0.40","0.50","1.20","1.5","10","2.80","20"],"topics":["ai"]},{"id":"chunk-32","heading":"4.8 «Умножение матриц»","text":"; j++) { c[i][0] += a[i][j] b[j][0]; c[i][1] += a[i][j] b[j][1]; } }","keywords":["файлы","4.8","j++","procedural-programming","графика","девять","дз","задач","матриц","матрицы","подпрограммы","преобразование"],"topics":[]},{"id":"chunk-33","heading":"4.8 «Умножение матриц»","text":"for (int i = 0; i c[i][0]) { mensh = c[i][0]; x2 = i; } if (maksk c[i][1]) { menshk = c[i][1]; x4 = i; } x5 += c[i][0]; x6 += c[i][1];","keywords":["файлы","4.8","if","int","maksk","mensh","menshk","procedural-programming","x2","x4","x5","x6"],"topics":[]},{"id":"chunk-34","heading":"4.8 «Умножение матриц»","text":"for (int j = 0; j < 2; j++) { cout << c[i][j] << \" \"; } cout << endl; } cout << \"1) \" << x1 + 1 << \" \" << x2 + 1 << endl; cout << \"2) \" << x3 + 1 << \" \" << x4 + 1 << endl; cout << \"3) \" << x5 << endl; cout << \"4) \" << x6 << endl; cout << \"5) \" << x5 + x6 << endl; } ```","keywords":["cout","endl","x5","x6","файлы","4.8","int","j++","procedural-programming","x1","x2","x3"],"topics":[]},{"id":"chunk-35","heading":"4.9 «Системы счисления»","text":"Как устроено решение. Сначала символы исходной записи переводятся в десятичное значение через функцию cf , затем число делится на новое основание, а остатки собираются в ответ.","keywords":["системы","счисления","файлы","4.9","cf","procedural-programming","графика","девять","делится","десятичное","дз","задач"],"topics":[]},{"id":"chunk-36","heading":"4.9 «Системы счисления»","text":"Что учесть. Сама схема перевода правильная: сначала перевод в десятичную систему, потом в целевую. Это главная идея, которую стоит помнить.","keywords":["системы","счисления","файлы","4.9","procedural-programming","главная","графика","девять","десятичную","дз","задач","идея"],"topics":[]},{"id":"chunk-37","heading":"4.9 «Системы счисления»","text":"cpp title=\"resources/procedural programming/home work 4/4.9.cpp\" include include include using namespace std; int cf(char x) { if (x == '1') return(1); else if (x == '2') return(2); else if (x == '3') return(3); else if (x == '4') return(4); else if (x == '5') return(5); else if (x == '6') return(6); else if (x == '7') return(7); else if (x == '8') return(8); else if (x == '9') return(9); else if (x == '0') return(0)","keywords":["if","return","else","include","системы","счисления","файлы","4.9","4.9.cpp","cf","char","cpp"],"topics":[]},{"id":"chunk-38","heading":"4.9 «Системы счисления»","text":"; else if (x == 'A') return(10); else if (x == 'B') return(11); else if (x == 'C') return(12); else if (x == 'D') return(13); else if (x == 'E') return(14); else if (x == 'F') return(15); else if (x == 'G') return(16); else if (x == 'H') return(17); else if (x == 'I') return(18); else if (x == 'J') return(19); else if (x == 'K') return(20); else if (x == 'L') return(21); else if (x == 'M') return(22)","keywords":["else","if","return","системы","счисления","файлы","10","11","12","13","14","15"],"topics":[]},{"id":"chunk-39","heading":"4.9 «Системы счисления»","text":"; else if (x == 'N') return(23); else if (x == 'O') return(24); else if (x == 'P') return(25); else if (x == 'Q') return(26); else if (x == 'R') return(27); else if (x == 'S') return(28); else if (x == 'T') return(29); else if (x == 'U') return(30); else if (x == 'V') return(31); else if (x == 'W') return(32); else if (x == 'X') return(33); else if (x == 'Y') return(34); else if (x == 'Z') return(35); return 0","keywords":["return","else","if","системы","счисления","файлы","23","24","25","26","27","28"],"topics":[]},{"id":"chunk-40","heading":"4.9 «Системы счисления»","text":"; } int bk(char x) { if (x == 10) return('A'); else if (x == 11) return('B'); else if (x == 12) return('C'); else if (x == 13) return('D'); else if (x == 14) return('E'); else if (x == 15) return('F'); else if (x == 16) return('G'); else if (x == 17) return('H'); else if (x == 18) return('I'); else if (x == 19) return('J'); else if (x == 20) return('K'); else if (x == 'L') return(21); else if (x == 'M') return(22)","keywords":["if","return","else","системы","счисления","файлы","10","11","12","13","14","15"],"topics":[]},{"id":"chunk-41","heading":"4.9 «Системы счисления»","text":"; else if (x == 'N') return(23); else if (x == 'O') return(24); else if (x == 'P') return(25); else if (x == 'Q') return(26); else if (x == 'R') return(27); else if (x == 'S') return(28); else if (x == 'T') return(29); else if (x == 'U') return(30); else if (x == 'V') return(31); else if (x == 'W') return(32); else if (x == 'X') return(33); else if (x == 'Y') return(34); else if (x == 'Z') return(35); else return(0)","keywords":["else","return","if","системы","счисления","файлы","23","24","25","26","27","28"],"topics":[]},{"id":"chunk-42","heading":"4.9 «Системы счисления»","text":"; } int main() { setlocale(0, \"\"); string ch, otv = \"\"; string vr; long int s = 0, p = 4000, o, e, n, d = 0, pr; cout e; cout n; cout ch; if (e == n) { cout = e) { cout = n) { pr = d % n; d /= n; if (pr 9) { otv += bk(pr); } else { vr = to string(pr); otv += vr; } i++; } i++; if (d 9) { otv += bk(d); } else { vr = to string(d); otv += vr; } } reverse(otv.begin(), otv.end()); cout << otv; }","keywords":["cout","otv","pr","vr","string","if","bk","ch","else","i++","int","системы"],"topics":["ai"]}]},{"id":"procedural-programming/homework-05-number-theory-and-combinatorics","href":"/docs/procedural-programming/homework-05-number-theory-and-combinatorics","slug":["procedural-programming","homework-05-number-theory-and-combinatorics"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 5 — НОД, простые числа, индивидуальные варианты и шарики","description":"Пятое домашнее задание по теории чисел, файлам и перебору перестановок.","preview":"Что внутри В пятом домашнем задании есть как стандартные алгоритмы, так и индивидуальные варианты. По PDF видно, что пункты 5.3 , 5.4 и 5.5 должны были быть уникальными для каждого студента, поэтому в ресурсах есть готовый код только для части задач. 5.1 «Алго","keywords":["if","else","kol","num","5.3","int","balls","обработка","текстовых","файлов","include","шарики","5.6","cout","position","5.4","count","cpp"],"topics":["python","ai","oop","algorithms"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"В пятом домашнем задании есть как стандартные алгоритмы, так и индивидуальные варианты. По PDF видно, что пункты 5.3 , 5.4 и 5.5 должны были быть уникальными для каждого студента, поэтому в ресурсах есть готовый код только для части задач.","keywords":["индивидуальные","5.3","5.4","5.5","pdf","procedural-programming","алгоритмы","были","быть","варианты","варианты.","видно"],"topics":["algorithms"]},{"id":"chunk-1","heading":"5.1 «Алгоритм Евклида»","text":"Как устроено решение. Сохранённый код идёт не через классический цикл Евклида с остатком, а через перебор делителей от min(a, b) вниз до 1 . Как только находится общий делитель, программа печатает его и завершает работу.","keywords":["евклида","через","5.1","min","procedural-programming","алгоритм","варианты","вниз","делителей","делитель","дз","до"],"topics":["oop","algorithms"]},{"id":"chunk-2","heading":"5.1 «Алгоритм Евклида»","text":"cpp title=\"resources/procedural programming/home work 5/51 DONE.cpp\" include include using namespace std; int main(){ int a, b; cin a b; for (int i = min(a, b); i = 1; i ) { if (a % i == 0 and b % i == 0) { cout << i; return 0; } } }","keywords":["int","include","5.1","51","cin","cout","cpp","done.cpp","home","if","main","min"],"topics":["ai","algorithms"]},{"id":"chunk-3","heading":"5.2 «Решето Эратосфена»","text":"Как устроено решение. Файл из ресурсов проверяет каждое число j на делимость всеми меньшими числами и печатает его, если делителей нет.","keywords":["5.2","procedural-programming","варианты","всеми","делимость","делителей","дз","домашнее","его","если","задание","индивидуальные"],"topics":[]},{"id":"chunk-4","heading":"5.2 «Решето Эратосфена»","text":"Что учесть. Это решение реализует не решето Эратосфена в чистом виде, а прямую проверку на простоту. Кроме того, 2 здесь не выводится, потому что цикл начинается с 3 .","keywords":["решето","эратосфена","5.2","procedural-programming","варианты","виде","выводится","дз","домашнее","задание","здесь","индивидуальные"],"topics":[]},{"id":"chunk-5","heading":"5.2 «Решето Эратосфена»","text":"cpp title=\"resources/procedural programming/home work 5/52 DONE.cpp\" include include using namespace std; int main() { int a, b; cin a; for (int j = 3; j 1; i ) { if (j % i == 0) { b = 1; } } if (b == 0) { cout << j << \" \"; } } }","keywords":["int","if","include","5.2","52","cin","cout","cpp","done.cpp","home","main","namespace"],"topics":["ai"]},{"id":"chunk-6","heading":"5.3 «Обработка текстовых файлов»","text":"Условие. В PDF сказано, что это индивидуальный вариант. В ресурсах сохранён вариант, где программа читает файл и ищет наиболее часто встречающуюся согласную букву из заданного набора.","keywords":["5.3","pdf","procedural-programming","букву","вариант","вариант.","варианты","встречающуюся","дз","домашнее","задание","заданного"],"topics":[]},{"id":"chunk-7","heading":"5.3 «Обработка текстовых файлов»","text":"Как устроено решение. Код открывает файл 123.txt , считает частоты для заранее заданного массива букв, затем выбирает индекс максимального значения и печатает количество вхождений вместе с самой буквой.","keywords":["123.txt","5.3","procedural-programming","букв","буквой.","варианты","вместе","вхождений","выбирает","дз","домашнее","задание"],"topics":[]},{"id":"chunk-8","heading":"5.3 «Обработка текстовых файлов»","text":"```cpp title=\"resources/procedural programming/home work 5/5.3.cpp\" include include include using namespace std;","keywords":["include","5.3","5.3.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-9","heading":"5.3 «Обработка текстовых файлов»","text":"ifstream ifile; char bukvi[20]{ 'b', 'd', 'c', 'f', 'g', 'h', 'j', 'k', 'l', 'm', 'n', 'p', 'q', 'r', 's', 't', 'v', 'w', 'x', 'z' }; int kol[20]{};","keywords":["20","5.3","bukvi","char","ifile","ifstream","int","kol","procedural-programming","варианты","дз","домашнее"],"topics":[]},{"id":"chunk-10","heading":"5.3 «Обработка текстовых файлов»","text":"int main() { int max = 0, num; char c, a = 'a'; ifile.open(\"123.txt\"); if (ifile.is open()) { cout << \"okey...\"; getch();","keywords":["int","123.txt","5.3","char","cout","getch","if","ifile.is","ifile.open","main","max","num"],"topics":["ai"]},{"id":"chunk-11","heading":"5.3 «Обработка текстовых файлов»","text":"while (ifile.get(c)) { if (c == 'b') { kol[0] += 1; } else if (c == 'd') { kol[1] += 1; } else if (c == 'c') { kol[2] += 1; } else if (c == 'f') { kol[3] += 1; } else if (c == 'g') { kol[4] += 1; } else if (c == 'h') { kol[5] += 1; } else if (c == 'j') { kol[6] += 1; } else if (c == 'k') { kol[7] += 1; } else if (c == 'l') { kol[8] += 1; } else if (c == 'm') { kol[9] += 1; } else if (c == 'n') { kol[10] += 1","keywords":["if","kol","else","10","5.3","ifile.get","procedural-programming","while","варианты","дз","домашнее","задание"],"topics":[]},{"id":"chunk-12","heading":"5.3 «Обработка текстовых файлов»","text":"; } else if (c == 'p') { kol[11] += 1; } else if (c == 'q') { kol[12] += 1; } else if (c == 'r') { kol[13] += 1; } else if (c == 's') { kol[14] += 1; } else if (c == 't') { kol[15] += 1; } else if (c == 'v') { kol[16] += 1; } else if (c == 'w') { kol[17] += 1; } else if (c == 'x') { kol[18] += 1; } else if (c == 'z') { kol[19] += 1; } }","keywords":["else","if","kol","11","12","13","14","15","16","17","18","19"],"topics":[]},{"id":"chunk-13","heading":"5.3 «Обработка текстовых файлов»","text":"if (num == 0) { a = 'b'; } else if (num == 1) { a = 'd'; } else if (num == 2) { a = 'c'; } else if (num == 3) { a = 'f'; } else if (num == 4) { a = 'g'; } else if (num == 5) { a = 'h'; } else if (num == 6) { a = 'j'; } else if (num == 7) { a = 'k'; } else if (num == 8) { a = 'l'; } else if (num == 9) { a = 'm'; } else if (num == 10) { a = 'n'; } else if (num == 11) { a = 'p'; } else if (num == 12) { a = 'q'","keywords":["if","num","else","10","11","12","5.3","procedural-programming","варианты","дз","домашнее","задание"],"topics":[]},{"id":"chunk-14","heading":"5.3 «Обработка текстовых файлов»","text":"; } else if (num == 13) { a = 'r'; } else if (num == 14) { a = 's'; } else if (num == 15) { a = 't'; } else if (num == 16) { a = 'v'; } else if (num == 17) { a = 'w'; } else if (num == 18) { a = 'x'; } else if (num == 19) { a = 'z'; }","keywords":["else","if","num","13","14","15","16","17","18","19","5.3","procedural-programming"],"topics":[]},{"id":"chunk-15","heading":"5.3 «Обработка текстовых файлов»","text":"cout << \"\\n\" << max << \" [\" << a << \"]\"; } else { cout << \"ne okey...\"; getch(); exit(0); } } ```","keywords":["cout","5.3","else","exit","getch","max","ne","okey...","procedural-programming","варианты","дз","домашнее"],"topics":[]},{"id":"chunk-16","heading":"5.4 «Ряды»","text":"Что известно по PDF. В формулировке указано только то, что это индивидуальный вариант на человека.","keywords":["5.4","pdf.","procedural-programming","вариант","варианты","дз","домашнее","задание","известно","индивидуальные","индивидуальный","нод"],"topics":[]},{"id":"chunk-17","heading":"5.4 «Ряды»","text":"Что учесть. В resources/procedural programming нет отдельного условия или cpp файла для конкретного варианта, поэтому в вики нельзя восстановить точную постановку без исходного личного задания.","keywords":["5.4","cpp","procedural","procedural-programming","programming","resources","без","варианта","варианты","вики","восстановить","дз"],"topics":[]},{"id":"chunk-18","heading":"5.5 «Файлы»","text":"Что известно по PDF. Здесь ситуация такая же, как и в пункте 5.4 : в PDF сказано, что вариант индивидуальный.","keywords":["5.4","5.5","pdf","pdf.","procedural-programming","вариант","варианты","дз","домашнее","же","задание","здесь"],"topics":[]},{"id":"chunk-19","heading":"5.5 «Файлы»","text":"Что учесть. Без отдельного листа варианта или готового исходника точную задачу восстановить нельзя.","keywords":["5.5","procedural-programming","без","варианта","варианты","восстановить","готового","дз","домашнее","задание","задачу","индивидуальные"],"topics":[]},{"id":"chunk-20","heading":"5.6 «Шарики»","text":"Условие. Из урны последовательно достают пронумерованные шарики. Нужно посчитать число перестановок, в которых номер хотя бы одного шарика совпадает с номером шага.","keywords":["шарики","5.6","procedural-programming","бы","варианты","дз","домашнее","достают","задание","индивидуальные","которых","нод"],"topics":[]},{"id":"chunk-21","heading":"5.6 «Шарики»","text":"Как устроено решение. Ниже приведён аккуратный эталонный вариант, который соответствует алгоритму из PDF: строятся все перестановки, а на листе рекурсии проверяется наличие хотя бы одного совпадения balls[i] == i + 1 .","keywords":["шарики","5.6","balls","pdf","procedural-programming","аккуратный","алгоритму","бы","вариант","варианты","дз","домашнее"],"topics":["algorithms"]},{"id":"chunk-22","heading":"5.6 «Шарики»","text":"Что учесть. Это уже полноценная рекурсивная задача на перебор перестановок. Решение сохраняет идею из формулировки и работает для произвольного n , хотя на больших значениях факториальный рост быстро делает перебор тяжёлым.","keywords":["перебор","перестановок.","шарики","5.6","procedural-programming","больших","быстро","варианты","делает","дз","домашнее","задание"],"topics":[]},{"id":"chunk-23","heading":"5.6 «Шарики»","text":"```cpp title=\"resources/procedural programming/home work 5/5.6.cpp\" include include using namespace std;","keywords":["include","шарики","5.6","5.6.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std"],"topics":[]},{"id":"chunk-24","heading":"5.6 «Шарики»","text":"bool hasFixedPoint(const vector & balls) { for (int i = 0; i (balls.size()); ++i) { if (balls[i] == i + 1) { return true; } } return false; }","keywords":["balls","return","шарики","++i","5.6","balls.size","bool","const","false","hasfixedpoint","if","int"],"topics":[]},{"id":"chunk-25","heading":"5.6 «Шарики»","text":"void generatePermutations(int position, vector & balls, long long& count) { if (position == static cast (balls.size())) { if (hasFixedPoint(balls)) { ++count; } return; }","keywords":["balls","if","long","position","шарики","++count","5.6","balls.size","cast","count","generatepermutations","hasfixedpoint"],"topics":["python"]},{"id":"chunk-26","heading":"5.6 «Шарики»","text":"for (int i = position; i (balls.size()); ++i) { swap(balls[position], balls[i]); generatePermutations(position + 1, balls, count); swap(balls[position], balls[i]); } }","keywords":["balls","position","swap","шарики","++i","5.6","balls.size","count","generatepermutations","int","procedural-programming","варианты"],"topics":[]},{"id":"chunk-27","heading":"5.6 «Шарики»","text":"long long count = 0; generatePermutations(0, balls, count); cout << count; return 0; } ```","keywords":["count","long","шарики","5.6","balls","cout","generatepermutations","procedural-programming","return","варианты","дз","домашнее"],"topics":[]}]},{"id":"internet-of-things","href":"/docs/internet-of-things","slug":["internet-of-things"],"section":"internet-of-things","sectionTitle":"Интернет вещей","title":"Интернет вещей","description":"Материалы по IoT: устройства, протоколы обмена и интеграции.","preview":"О разделе Раздел Интернет вещей добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["вещей","интернет","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","iot","задач","интеграции.","кода","конспекты","материалы","можно","обмена"],"topics":[],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Интернет вещей добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["вещей","интернет","internet-of-things","iot","stackmirea","готов","добавлен","интеграции.","материалами.","материалы","наполнению","обмена"],"topics":[]}]},{"id":"configuration-management","href":"/docs/configuration-management","slug":["configuration-management"],"section":"configuration-management","sectionTitle":"Конфигурационное управление","title":"Конфигурационное управление","description":"Практики по управлению конфигурацией, версиям и средам разработки.","preview":"О разделе Раздел Конфигурационное управление добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["конфигурационное","управление","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","версиям","задач","кода","конспекты","конфигурацией","можно","практики","практические"],"topics":[],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Конфигурационное управление добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["конфигурационное","управление","configuration-management","stackmirea","версиям","готов","добавлен","конфигурацией","материалами.","наполнению","практики","раздел"],"topics":[]}]},{"id":"business-process-modeling","href":"/docs/business-process-modeling","slug":["business-process-modeling"],"section":"business-process-modeling","sectionTitle":"Моделирование бизнес процессов","title":"Моделирование бизнес процессов","description":"Подходы и инструменты для анализа и моделирования бизнес-процессов.","preview":"О разделе Раздел Моделирование бизнес процессов добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["бизнес","моделирование","процессов","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","анализа","бизнес-процессов.","задач","инструменты","кода","конспекты","моделирования"],"topics":[],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Моделирование бизнес процессов добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["бизнес","моделирование","процессов","business-process-modeling","stackmirea","анализа","бизнес-процессов.","готов","добавлен","инструменты","материалами.","моделирования"],"topics":[]}]},{"id":"object-oriented-programming","href":"/docs/object-oriented-programming","slug":["object-oriented-programming"],"section":"object-oriented-programming","sectionTitle":"Объектно ориентированное программирование","title":"Объектно ориентированное программирование","description":"Практики по ООП: классы, наследование, полиморфизм и архитектура.","preview":"О разделе Раздел Объектно ориентированное программирование добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["объектно","ориентированное","программирование","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","архитектура.","задач","классы","кода","конспекты","можно","наследование"],"topics":["oop"],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Объектно ориентированное программирование добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["объектно","ориентированное","программирование","object","oriented-programming","stackmirea","архитектура.","готов","добавлен","классы","материалами.","наполнению"],"topics":["oop"]}]},{"id":"bigdata/practice-01-introduction","href":"/docs/bigdata/practice-01-introduction","slug":["bigdata","practice-01-introduction"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 1. Введение в BigData","description":"Базовые операции и знакомство с форматом работы.","preview":"Wiki версия Практическая работа №1 1. Установить Python, если это не было сделано ранее. 2. Написать программу, которая вычисляет площадь фигуры, параметры которой подаются на вход. Фигуры, которые подаются на вход: треугольник, прямоугольник, круг. Результато","keywords":["non","null","dtype","column","count","значения","print","ok","чтение","..","none","входного","52.0","height","20640","operation","bhnqaptmp","float64"],"topics":["python","ai","pandas","numpy"],"chunks":[{"id":"chunk-0","heading":"Практическая работа №1","text":"2. Написать программу, которая вычисляет площадь фигуры, параметры которой подаются на вход. Фигуры, которые подаются на вход: треугольник, прямоугольник, круг. Результатом работы является словарь, где ключ – это название фигуры, а значение – это площадь.","keywords":["bigdata","фигуры","подаются","1.","2.","базовые","введение","вход","вход.","вычисляет","знакомство","значение"],"topics":[]},{"id":"chunk-1","heading":"Практическая работа №1","text":"def calculate areas(figures): \"\"\" figures — список словарей с параметрами фигур, пример: [ {'type': 'треугольник', 'base': 10, 'height': 5}, {'type': 'прямоугольник', 'width': 4, 'height': 6}, {'type': 'круг', 'radius': 3} ]","keywords":["bigdata","type","figures","height","1.","10","areas","base","calculate","def","radius","width"],"topics":[]},{"id":"chunk-2","heading":"Практическая работа №1","text":"if shape == 'треугольник': base = fig.get('base') height = fig.get('height') if base is not None and height is not None: area = 0.5 base height areas['треугольник'] = area","keywords":["base","height","bigdata","area","fig.get","if","none","not","треугольник","0.5","1.","areas"],"topics":[]},{"id":"chunk-3","heading":"Практическая работа №1","text":"elif shape == 'прямоугольник': width = fig.get('width') height = fig.get('height') if width is not None and height is not None: area = width height areas['прямоугольник'] = area","keywords":["height","width","bigdata","area","fig.get","none","not","прямоугольник","1.","areas","elif","if"],"topics":[]},{"id":"chunk-4","heading":"Практическая работа №1","text":"elif shape == 'круг': radius = fig.get('radius') if radius is not None: area = math.pi radius 2 areas['круг'] = area","keywords":["radius","bigdata","area","круг","1.","areas","elif","fig.get","if","math.pi","none","not"],"topics":[]},{"id":"chunk-5","heading":"Пример использования:","text":"figures = [ {'type': 'треугольник', 'base': 10, 'height': 5}, {'type': 'прямоугольник', 'width': 4, 'height': 6}, {'type': 'круг', 'radius': 3} ]","keywords":["bigdata","type","height","1.","10","base","figures","radius","width","базовые","введение","знакомство"],"topics":[]},{"id":"chunk-6","heading":"Вывод: {'треугольник': 25.0, 'прямоугольник': 24, 'круг': 28.274333882308138}","text":"3. Написать программу, которая на вход получает два числа и операцию, которую к ним нужно применить. Должны быть реализованы следующие операции: +, , /, //, abs – модуль, pow или – возведение в степень.","keywords":["bigdata","операции","1.","24","25.0","28.274333882308138","3.","abs","pow","базовые","быть","введение"],"topics":[]},{"id":"chunk-7","heading":"Вывод: {'треугольник': 25.0, 'прямоугольник': 24, 'круг': 28.274333882308138}","text":"```python def calculator(a, b, operation): if operation == '+': return a + b elif operation == ' ': return a b elif operation == '/': if b == 0: return \"Ошибка: деление на ноль\" return a / b elif operation == '//': if b == 0: return \"Ошибка: деление на ноль\" return a // b elif operation == 'abs':","keywords":["operation","return","elif","bigdata","if","деление","ноль","ошибка","1.","24","25.0","28.274333882308138"],"topics":["python"]},{"id":"chunk-8","heading":"Игнорируем b, возвращаем модуль a","text":"return abs(a) elif operation == 'pow' or operation == ' ': return a b else: return \"Ошибка: неизвестная операция\"","keywords":["bigdata","return","operation","1.","abs","elif","else","pow","базовые","введение","возвращаем","знакомство"],"topics":[]},{"id":"chunk-9","heading":"Пример использования:","text":"a = float(input(\"Введите первое число: \")) b = float(input(\"Введите второе число: \")) operation = input(\"Введите операцию (+, , /, //, abs, pow, ): \")","keywords":["bigdata","input","введите","float","число","1.","abs","operation","pow","базовые","введение","второе"],"topics":["numpy"]},{"id":"chunk-10","heading":"Пример использования:","text":"text Введите первое число: 5 Введите второе число: 4 Введите операцию (+, , /, //, abs, pow, ): // Результат: 1.0","keywords":["bigdata","введите","число","1.","1.0","abs","pow","text","базовые","введение","второе","знакомство"],"topics":[]},{"id":"chunk-11","heading":"Пример использования:","text":"4. Напишите программу, которая считывает с консоли числа (по одному в строке) до тех пор, пока сумма введённых чисел не будет равна 0 и после этого выводит сумму квадратов всех считанных чисел.","keywords":["bigdata","1.","4.","базовые","будет","введение","введенных","всех","выводит","до","знакомство","использования"],"topics":[]},{"id":"chunk-12","heading":"Пример использования:","text":"while True: try: num = float(input(\"Введите число: \")) except ValueError: print(\"Ошибка: введите корректное число.\") continue","keywords":["bigdata","введите","1.","continue","except","float","input","num","print","true","try","valueerror"],"topics":["numpy"]},{"id":"chunk-13","heading":"Пример использования:","text":"sum of squares = sum(x 2 for x in numbers) print(\"Сумма квадратов введённых чисел:\", sum of squares)","keywords":["bigdata","sum","squares","1.","numbers","print","базовые","введение","введенных","знакомство","использования","квадратов"],"topics":[]},{"id":"chunk-14","heading":"Запуск функции","text":"5. Напишите программу, которая выводит последовательность чисел, длинною N, где каждое число повторяется столько раз, чему оно равно. На вход программе передаётся неотрицательное целое число N. Например, если N = 7, то программа должна вывести 1 2 2 3 3 3 4. Вывод элементов списка через пробел – print( list).","keywords":["bigdata","число","1.","4.","5.","list","n.","print","базовые","введение","вход","вывести"],"topics":[]},{"id":"chunk-15","heading":"Запуск функции","text":"```python def generate sequence(N): result = [] num = 1 while len(result) < N: result.extend([num] num) num += 1","keywords":["num","bigdata","result","1.","def","generate","len","python","result.extend","sequence","while","базовые"],"topics":["python"]},{"id":"chunk-16","heading":"Чтение входного значения","text":"Создать словарь, в котором ключи – это содержимое списка В, а значения для ключей словаря – это сумма всех элементов списка А в соответствии с буквой, содержащийся на той же позиции в списке В.","keywords":["bigdata","значения","списка","1.","базовые","буквой","в.","введение","всех","входного","же","знакомство"],"topics":[]},{"id":"chunk-17","heading":"Чтение входного значения","text":"```python A = [1, 2, 3, 4, 2, 1, 3, 4, 5, 6, 5, 4, 3, 2] B = ['a', 'b', 'c', 'c', 'c', 'b', 'a', 'c', 'a', 'a', 'b', 'c', 'b', 'a']","keywords":["bigdata","1.","python","базовые","введение","входного","знакомство","значения","операции","практика","работы.","форматом"],"topics":["python"]},{"id":"chunk-18","heading":"Чтение входного значения","text":"result = {} for key, value in zip(B, A): result[key] = result.get(key, 0) + value","keywords":["bigdata","key","result","value","1.","result.get","zip","базовые","введение","входного","знакомство","значения"],"topics":[]},{"id":"chunk-19","heading":"Чтение входного значения","text":"Скачать и загрузить данные о стоимости домов в калифорнии, используя библиотеку sklearn.","keywords":["bigdata","1.","sklearn.","базовые","библиотеку","введение","входного","данные","домов","загрузить","знакомство","значения"],"topics":[]},{"id":"chunk-20","heading":"Чтение входного значения","text":"10. Вывести записи, где средний возраст домов в районе более 50 лет и население более 2500 человек, используя метод loc().","keywords":["bigdata","более","1.","10.","2500","50","loc","базовые","введение","возраст","входного","вывести"],"topics":[]},{"id":"chunk-21","heading":"Чтение входного значения","text":"12. Используя метод apply(), вывести на экран название признака и его среднее значение.","keywords":["bigdata","1.","12.","apply","базовые","введение","входного","вывести","его","знакомство","значение.","значения"],"topics":[]},{"id":"chunk-22","heading":"Чтение входного значения","text":"13. Составить отчет о проделанной работе. В отчете должен быть представлен код и результаты его выполнения с выводами.","keywords":["bigdata","1.","13.","базовые","быть","введение","входного","выводами.","выполнения","должен","его","знакомство"],"topics":[]},{"id":"chunk-23","heading":"Чтение входного значения","text":"Данные о стоимости домов в Калифорнии успешно загружены с использованием sklearn.datasets.fetch california housing с параметром as frame=True для удобной работы с pandas DataFrame.","keywords":["bigdata","1.","california","dataframe.","frame","housing","pandas","sklearn.datasets.fetch","true","базовые","введение","входного"],"topics":["pandas"]},{"id":"chunk-24","heading":"Чтение входного значения","text":"Метод info() предоставил сведения о структуре данных: количество строк (20640), типы столбцов и отсутствие пропущенных значений.","keywords":["bigdata","1.","20640","info","базовые","введение","входного","данных","знакомство","значений.","значения","количество"],"topics":[]},{"id":"chunk-25","heading":"Чтение входного значения","text":"Проверка на пропущенные значения с помощью isna().sum() подтвердила, что данные полные, без отсутствующих значений.","keywords":["bigdata","значения",".sum","1.","isna","базовые","без","введение","входного","данные","знакомство","значений."],"topics":[]},{"id":"chunk-26","heading":"Чтение входного значения","text":"Отфильтрованы записи районов с домами старше 50 лет и населением свыше 2500 человек, что позволяет выделить специфические участки для дополнительного анализа.","keywords":["bigdata","1.","2500","50","анализа.","базовые","введение","входного","выделить","домами","дополнительного","записи"],"topics":[]},{"id":"chunk-27","heading":"Чтение входного значения","text":"Найдены максимальное и минимальное значение целевой переменной — медианной стоимости дома, что дает ориентировку по разбросу цен.","keywords":["bigdata","1.","базовые","введение","входного","дает","дома","знакомство","значение","значения","максимальное","медианной"],"topics":[]},{"id":"chunk-28","heading":"Чтение входного значения","text":"Использование apply() помогло быстро вывести средние значения всех признаков для общего понимания их распределения.","keywords":["bigdata","значения","1.","apply","базовые","быстро","введение","всех","входного","вывести","знакомство","использование"],"topics":[]},{"id":"chunk-29","heading":"Чтение входного значения","text":"Эти шаги создают прочную основу для дальнейшего анализа с использованием методов машинного обучения или углубленной статистики.","keywords":["bigdata","1.","анализа","базовые","введение","входного","дальнейшего","знакомство","значения","использованием","машинного","методов"],"topics":[]},{"id":"chunk-30","heading":"Загрузка данных с признаком as frame=True для получения DataFrame сразу","text":"housing = fetch california housing(as frame=True) df = housing.frame pandas DataFrame с признаками и целевой переменной","keywords":["bigdata","dataframe","frame","housing","true","1.","california","df","fetch","housing.frame","pandas","базовые"],"topics":["pandas"]},{"id":"chunk-31","heading":"9. Узнать, есть ли пропущенные значения (NaN)","text":"print(\"Сумма пропущенных значений по столбцам:\") print(df.isna().sum()) print(\"\\n\")","keywords":["bigdata","print",".sum","1.","9.","df.isna","nan","базовые","введение","знакомство","значений","значения"],"topics":[]},{"id":"chunk-32","heading":"10. Вывести записи, где средний возраст домов 50 лет и население 2500 человек","text":"print(\"Записи с HouseAge 50 и Population 2500:\") filtered = df.loc[(df['HouseAge'] 50) & (df['Population'] 2500)] print(filtered) print(\"\\n\")","keywords":["2500","50","bigdata","print","df","filtered","houseage","population","записи","1.","10.","df.loc"],"topics":[]},{"id":"chunk-33","heading":"11. Максимальное и минимальное значение медианной стоимости дома","text":"max price = df['MedHouseVal'].max() min price = df['MedHouseVal'].min() print(f\"Максимальная медианная стоимость дома: {max price}\") print(f\"Минимальная медианная стоимость дома: {min price}\") print(\"\\n\")","keywords":["price","bigdata","print","дома","df","max","medhouseval","min","медианная","стоимость",".max",".min"],"topics":[]},{"id":"chunk-34","heading":"12. С использованием apply() вывести название признака и его среднее значение","text":"print(\"Средние значения признаков:\") def print mean(column): print(f\"{column.name}: {column.mean():.3f}\")","keywords":["bigdata","print",".3f","1.","12.","apply","column","column.mean","column.name","def","mean","базовые"],"topics":[]},{"id":"chunk-35","heading":"12. С использованием apply() вывести название признака и его среднее значение","text":"```text Информация о DataFrame: RangeIndex: 20640 entries, 0 to 20639 Data columns (total 9 columns):","keywords":["bigdata","columns","1.","12.","20639","20640","apply","data","dataframe","entries","rangeindex","text"],"topics":["pandas"]},{"id":"chunk-36","heading":"Column Non Null Count Dtype","text":"0 MedInc 20640 non null float64 1 HouseAge 20640 non null float64 2 AveRooms 20640 non null float64 3 AveBedrms 20640 non null float64 4 Population 20640 non null float64 5 AveOccup 20640 non null float64 6 Latitude 20640 non null float64 7 Longitude 20640 non null float64 8 MedHouseVal 20640 non null float64 dtypes: float64(9) memory usage: 1.4 MB","keywords":["float64","non","null","20640","bigdata","1.","1.4","avebedrms","aveoccup","averooms","column","count"],"topics":[]},{"id":"chunk-37","heading":"Column Non Null Count Dtype","text":"Сумма пропущенных значений по столбцам: MedInc 0 HouseAge 0 AveRooms 0 AveBedrms 0 Population 0 AveOccup 0 Latitude 0 Longitude 0 MedHouseVal 0 dtype: int64","keywords":["bigdata","dtype","1.","avebedrms","aveoccup","averooms","column","count","houseage","int64","latitude","longitude"],"topics":[]},{"id":"chunk-38","heading":"Column Non Null Count Dtype","text":"Записи с HouseAge 50 и Population 2500: MedInc HouseAge AveRooms AveBedrms Population AveOccup \\ 460 1.4012 52.0 3.105714 1.060000 3337.0 9.534286 4131 3.5349 52.0 4.646119 1.047945 2589.0 5.910959 4440 2.6806 52.0 4.806283 1.057592 3062.0 4.007853 5986 1.8750 52.0 4.500000 1.206349 2688.0 21.333333 7369 3.1901 52.0 4.730942 1.017937 3731.0 4.182735 8227 2.3305 52.0 3.488860 1.170380 3018.0 3.955439 13034 6.1359 52.0","keywords":["52.0","bigdata","houseage","population","1.","1.017937","1.047945","1.057592","1.060000","1.170380","1.206349","1.4012"],"topics":[]},{"id":"chunk-39","heading":"Column Non Null Count Dtype","text":"8.275862 1.517241 6675.0 230.172414 15634 1.8295 52.0 2.628169 1.053521 2957.0 4.164789 15652 0.9000 52.0 2.237474 1.053535 3260.0 2.237474 15657 2.5166 52.0 2.839075 1.184049 3436.0 1.621520 15659 1.7240 52.0 2.278566 1.082348 4518.0 1.780142 15795 2.5755 52.0 3.402576 1.058776 2619.0 2.108696 15868 2.8135 52.0 4.584329 1.041169 2987.0 3.966799","keywords":["52.0","bigdata","2.237474","0.9000","1.","1.041169","1.053521","1.053535","1.058776","1.082348","1.184049","1.517241"],"topics":[]},{"id":"chunk-40","heading":"Column Non Null Count Dtype","text":"Latitude Longitude MedHouseVal 460 37.87 122.26 1.75000 4131 34.13 118.20 1.93600 4440 34.08 118.21 1.53000 5986 34.10 117.71 2.12500 7369 33.97 118.21 1.67600 8227 33.78 118.20 1.62500 13034 38.69 121.15 2.25000 15634 37.80 122.41 2.43800 15652 37.80 122.41 5.00001 15657 37.79 122.41 2.75000 15659 37.79 122.41 2.25000 15795 37.77 122.42 3.25000 15868 37.76 122.41 2.60300","keywords":["122.41","bigdata","118.20","118.21","2.25000","37.79","37.80","1.","1.53000","1.62500","1.67600","1.75000"],"topics":[]},{"id":"chunk-41","heading":"Column Non Null Count Dtype","text":"Максимальная медианная стоимость дома: 5.00001 Минимальная медианная стоимость дома: 0.14999","keywords":["bigdata","дома","медианная","стоимость","0.14999","1.","5.00001","column","count","dtype","non","null"],"topics":[]},{"id":"chunk-42","heading":"Column Non Null Count Dtype","text":"Средние значения признаков: MedInc: 3.871 HouseAge: 28.639 AveRooms: 5.429 AveBedrms: 1.097 Population: 1425.477 AveOccup: 3.071 Latitude: 35.632 Longitude: 119.570 MedHouseVal: 2.069","keywords":["bigdata","1.","1.097","119.570","1425.477","2.069","28.639","3.071","3.871","35.632","5.429","avebedrms"],"topics":[]},{"id":"chunk-43","heading":"Column Non Null Count Dtype","text":"MedInc None HouseAge None AveRooms None AveBedrms None Population None AveOccup None Latitude None Longitude None MedHouseVal None dtype: object ```","keywords":["none","bigdata","dtype","1.","avebedrms","aveoccup","averooms","column","count","houseage","latitude","longitude"],"topics":[]},{"id":"chunk-44","heading":"Column Non Null Count Dtype","text":"1. Дан текст на английском языке. Необходимо закодировать его с помощью азбуки Морзе, где каждой букве соответствует последовательность точек и тире. Например, буква «g» превратится в строку « .». В переменной morze для удобства хранится словарь соответствия латинских букв коду Морзе.","keywords":["bigdata","1.","column","count","dtype","morze","non","null","азбуки","английском","базовые","букв"],"topics":[]},{"id":"chunk-45","heading":"Column Non Null Count Dtype","text":"morze = {'a': '. ', 'b': ' …', 'c': ' . .', 'd': ' ..', 'e': '.', 'f': '.. .', 'g': ' .', 'h': '….', 'i': '..', 'j': '. ', 'k': ' . ', 'l': '. ..', 'm': ' ', 'n': ' .', 'o': ' ', 'p': '. .', 'q': ' . ', 'r': '. .', 's': '…', 't': ' ', 'u': '.. ', 'v': '… ', 'w': '. ', 'x': ' .. ', 'y': ' . ', 'z': ' ..'}","keywords":["..","bigdata","1.","column","count","dtype","morze","non","null","базовые","введение","знакомство"],"topics":[]},{"id":"chunk-46","heading":"Column Non Null Count Dtype","text":"На входе: В одной строке вам дан текст, который состоит из латинских букв и пробелов.","keywords":["bigdata","1.","column","count","dtype","non","null","базовые","букв","вам","введение","входе"],"topics":[]},{"id":"chunk-47","heading":"Column Non Null Count Dtype","text":"Выведите каждое слово исходного текста, закодированное азбукой Морзе. Количество строк в ответе должно совпадать с количеством слов в исходном тексте. Между закодированными буквами ставится ровно один пробел. Например, слово «Help» превратится в «.... . . .. . .» . Строчные и заглавные буквы кодируются одинаково.","keywords":["bigdata","слово","..","....","1.","column","count","dtype","help","non","null","азбукой"],"topics":[]},{"id":"chunk-48","heading":"Column Non Null Count Dtype","text":"```python morze = { 'a': '. ', 'b': ' ...', 'c': ' . .', 'd': ' ..', 'e': '.', 'f': '.. .', 'g': ' .', 'h': '....', 'i': '..', 'j': '. ', 'k': ' . ', 'l': '. ..', 'm': ' ', 'n': ' .', 'o': ' ', 'p': '. .', 'q': ' . ', 'r': '. .', 's': '...', 't': ' ', 'u': '.. ', 'v': '... ', 'w': '. ', 'x': ' .. ', 'y': ' . ', 'z': ' ..' }","keywords":["..","...","bigdata","....","1.","column","count","dtype","morze","non","null","python"],"topics":["python"]},{"id":"chunk-49","heading":"Column Non Null Count Dtype","text":"for word in words: encoded letters = [morze[char.lower()] for char in word if char.lower() in morze] print(' '.join(encoded letters)) ```","keywords":["bigdata","char.lower","encoded","letters","morze","word",".join","1.","char","column","count","dtype"],"topics":[]},{"id":"chunk-50","heading":"Column Non Null Count Dtype","text":"Регистрация должна работать следующим образом: если новый пользователь хочет зарегистрироваться на сайте, то он должен послать системе запрос name со своим именем. Система должна определить, существует ли уже такое имя в базе данных. Если такого имени не существует, то оно заносится в базу данных системы и пользователю возвращается ответ \"ОК\", подтверждающий успешную регистрацию","keywords":["bigdata","должна","если","существует","то","1.","column","count","dtype","name","non","null"],"topics":[]},{"id":"chunk-51","heading":"Column Non Null Count Dtype","text":". А если пользователь с таким именем уже существует, то система должна сформировать новое имя и выдать его пользователю в качестве подсказки, при этом сама подсказка также добавляется в базу данных. Новое имя формируется следующим образом: к name последовательно приписываются числа, начиная с 1 (name1, name2 и так далее), и среди них находят такое наименьшее i, что namei еще не содержится в системе.","keywords":["bigdata","имя","новое","1.","column","count","dtype","name","name1","name2","namei","non"],"topics":[]},{"id":"chunk-52","heading":"Column Non Null Count Dtype","text":"Следующие n строк содержат запросы к системе. Каждый запрос представляет собой непустую строку длиной не более 32 символов, состоящую только из строчных букв латинского алфавита.","keywords":["bigdata","1.","32","column","count","dtype","non","null","алфавита.","базовые","более","букв"],"topics":[]},{"id":"chunk-53","heading":"Column Non Null Count Dtype","text":"В выходных данных должно содержаться n строк – ответы системы на запросы: \"OK\" в случае успешной регистрации, или подсказка с новым именем, если запрашиваемое уже занято.","keywords":["bigdata","1.","column","count","dtype","non","null","ok","базовые","введение","выходных","данных"],"topics":[]},{"id":"chunk-54","heading":"Column Non Null Count Dtype","text":"Вход: Выход: 3 b b b OK b1 b2 10 bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp OK bhnqaptmp1 bhnqaptmp2 bhnqaptmp3 bhnqaptmp4 bhnqaptmp5 bhnqaptmp6 bhnqaptmp7 bhnqaptmp8 bhnqaptmp9 10 fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh jmvplnrmba fpqhfouqdldravpjttarh jmvplnrmba","keywords":["bhnqaptmp","fpqhfouqdldravpjttarh","bigdata","10","jmvplnrmba","ok","1.","b1","b2","bhnqaptmp1","bhnqaptmp2","bhnqaptmp3"],"topics":[]},{"id":"chunk-55","heading":"Column Non Null Count Dtype","text":"fpqhfouqdldravpjttarh OK fpqhfouqdldravpjttarh1 fpqhfouqdldravpjttarh2 fpqhfouqdldravpjttarh3 fpqhfouqdldravpjttarh4 fpqhfouqdldravpjttarh5 OK fpqhfouqdldravpjttarh6 jmvplnrmba1 fpqhfouqdldravpjttarh7","keywords":["bigdata","ok","1.","column","count","dtype","fpqhfouqdldravpjttarh","fpqhfouqdldravpjttarh1","fpqhfouqdldravpjttarh2","fpqhfouqdldravpjttarh3","fpqhfouqdldravpjttarh4","fpqhfouqdldravpjttarh5"],"topics":[]},{"id":"chunk-56","heading":"Далее убеждаемся, что new name тоже уникально","text":"while new name in database: database[name] += 1 new name = name + str(database[name]) database[new name] = 0 print(new name)","keywords":["name","new","database","bigdata","1.","print","str","while","базовые","введение","далее","знакомство"],"topics":[]},{"id":"chunk-57","heading":"Вызов функции","text":"Необходимо создать программу обработки запросов пользователей к файловой системе компьютера. Над каждым файлом можно производить следующие действия: запись – w (\"write\") , чтение – r (\"read\") , запуск – x (\"execute\") .","keywords":["bigdata","1.","execute","read","write","базовые","введение","вызов","действия","запись","запросов","запуск"],"topics":[]},{"id":"chunk-58","heading":"Вызов функции","text":"На вход программе подаются следующие параметры: число n – количество файлов в файловой системе. В следующих n строках содержится информация с именами файлов и допустимыми действиями (w, x, r), разделенных пробелами. Далее идет число m – количество запросов к файлам вида «операция файл» (обозначение операции: \"write\" , \"read\" , \"execute\" ).","keywords":["bigdata","количество","операции","файлов","число","1.","execute","read","write","базовые","введение","вида"],"topics":[]},{"id":"chunk-59","heading":"Вызов функции","text":"Для каждого допустимого запроса программа должна возвращать OK, для недопустимого – Access denied .","keywords":["bigdata","1.","access","denied","ok","базовые","введение","возвращать","вызов","должна","допустимого","запроса"],"topics":[]},{"id":"chunk-60","heading":"Вызов функции","text":"Вход: Выход: 3 python.exe x book.txt r w notebook.exe r w x 5 read python.exe read book.txt write notebook.exe execute notebook.exe write book.txt Access denied OK OK OK OK 3 root.html r w x main.py x login.txt w r 4 read root.html write main.py execute main.py execute login.txt OK Access denied OK Access denied 2 1.txt 2.txt 2 write 1.txt execute 2.txt Access denied Access denied","keywords":["ok","access","denied","execute","write","bigdata","book.txt","main.py","notebook.exe","read","1.txt","2.txt"],"topics":["python","ai"]},{"id":"chunk-61","heading":"Чтение информации о файлах и их правах доступа","text":"for in range(n): line = input().strip().split() filename = line[0] allowed = set(line[1:]) if len(line) 1 else set() permissions[filename] = allowed","keywords":["line","bigdata","allowed","filename","set",".split",".strip","1.","else","if","input","len"],"topics":["numpy"]},{"id":"chunk-62","heading":"Обработка запросов","text":"for in range(m): operation, filename = input().split() op code = op map.get(operation) if op code and filename in permissions and op code in permissions[filename]: print(\"OK\") else: print(\"Access denied\")","keywords":["op","bigdata","code","filename","operation","permissions","print",".split","1.","access","denied","else"],"topics":["numpy"]},{"id":"chunk-63","heading":"Обработка запросов","text":"text 3 python.exe x book.txt r w notebook.exe r w x 5 read python.exe Access denied read book.txt OK write notebook.exe OK execute notebook.exe OK write book.txt OK","keywords":["ok","bigdata","book.txt","notebook.exe","python.exe","read","write","1.","access","denied","execute","text"],"topics":["python"]}]},{"id":"bigdata/practice-02-data-analysis","href":"/docs/bigdata/practice-02-data-analysis","slug":["bigdata","practice-02-data-analysis"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 2. Аналитика данных","description":"Обработка данных и исследовательский анализ в Jupyter Notebook.","preview":"Wiki версия Минькин Александр Дмитривеич ИКБО 25 22 Практическая работа №2 Визуализация в языке программирования Python Цель: ознакомится с различными библиотеками визуализации данных ( matplotlib , plotly , TSNE , UMAP ) и особенностями работы с ними в среде","keywords":["non","null","quality","1599","float64","dict","count","df","sne","0m","dtype","umap","use","column","dist","fontsize","grouped","data"],"topics":["python","ai","pandas","numpy","testing","algorithms"],"chunks":[{"id":"chunk-0","heading":"Визуализация в языке программирования Python","text":"Цель: ознакомится с различными библиотеками визуализации данных ( matplotlib , plotly , TSNE , UMAP ) и особенностями работы с ними в среде программирования Python.","keywords":["данных","bigdata","программирования","2.","jupyter","matplotlib","notebook.","plotly","python","python.","tsne","umap"],"topics":["python"]},{"id":"chunk-1","heading":"1. Задание","text":"Найти и выгрузить многомерные данные (с большим количеством признаков – столбцов) с использованием библиотеки pandas. В отчёте описать найденные данные.","keywords":["bigdata","данных","1.","2.","jupyter","notebook.","pandas.","анализ","аналитика","библиотеки","большим","выгрузить"],"topics":["pandas"]},{"id":"chunk-2","heading":"Датасет доступен на UCI","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\"","keywords":["bigdata","данных","2.","archive.ics.uci.edu","databases","https","jupyter","learning","machine","ml","notebook.","quality"],"topics":["ai"]},{"id":"chunk-3","heading":"Общая информация","text":"```text fixed acidity volatile acidity citric acid residual sugar chlorides \\ 0 7.4 0.70 0.00 1.9 0.076 1 7.8 0.88 0.00 2.6 0.098 2 7.8 0.76 0.04 2.3 0.092 3 11.2 0.28 0.56 1.9 0.075 4 7.4 0.70 0.00 1.9 0.076","keywords":["0.00","1.9","0.076","0.70","7.4","7.8","acidity","bigdata","данных","0.04","0.075","0.092"],"topics":[]},{"id":"chunk-4","heading":"Общая информация","text":"free sulfur dioxide total sulfur dioxide density pH sulphates \\ 0 11.0 34.0 0.9978 3.51 0.56 1 25.0 67.0 0.9968 3.20 0.68 2 15.0 54.0 0.9970 3.26 0.65 3 17.0 60.0 0.9980 3.16 0.58 4 11.0 34.0 0.9978 3.51 0.56","keywords":["0.56","0.9978","11.0","3.51","34.0","bigdata","dioxide","sulfur","данных","0.58","0.65","0.68"],"topics":[]},{"id":"chunk-5","heading":"Общая информация","text":"alcohol quality 0 9.4 5 1 9.8 5 2 9.8 5 3 9.8 6 4 9.4 5 Размерность: (1599, 12) RangeIndex: 1599 entries, 0 to 1598 Data columns (total 12 columns):","keywords":["9.8","12","1599","9.4","bigdata","columns","данных","1598","2.","alcohol","data","entries"],"topics":[]},{"id":"chunk-6","heading":"Column Non Null Count Dtype","text":"0 fixed acidity 1599 non null float64 1 volatile acidity 1599 non null float64 2 citric acid 1599 non null float64 3 residual sugar 1599 non null float64 4 chlorides 1599 non null float64 5 free sulfur dioxide 1599 non null float64 6 total sulfur dioxide 1599 non null float64 7 density 1599 non null float64 8 pH 1599 non null float64 9 sulphates 1599 non null float64 10 alcohol 1599 non null float64 11 quality 1599","keywords":["1599","non","null","float64","acidity","bigdata","dioxide","sulfur","данных","10","11","2."],"topics":[]},{"id":"chunk-7","heading":"Column Non Null Count Dtype","text":"non null int64 dtypes: float64(11), int64(1) memory usage: 150.0 KB None ```","keywords":["bigdata","int64","non","null","данных","11","150.0","2.","column","count","dtype","dtypes"],"topics":[]},{"id":"chunk-8","heading":"2. Вывести информацию о данных при помощи методов .info() , .head() .","text":"Проверить данные на наличие пустых значений. В случае их наличия удалить данные строки или интерполировать пропущенные значения. При необходимости дополнительно предобработать данные для дальнейшей работы с ними.","keywords":["данные","данных","2.","bigdata","при",".head",".info","jupyter","notebook.","анализ","аналитика","вывести"],"topics":[]},{"id":"chunk-9","heading":"Загружаем датасет (красное вино)","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-10","heading":"либо интерполировать (заполнить средним значением):","text":"df = df.interpolate() print(\"\\nПропуски были, выполнена интерполяция.\") else: print(\"\\nПропусков не найдено.\")","keywords":["bigdata","print","данных","2.","df","df.interpolate","else","jupyter","notebook.","nпропуски","nпропусков","анализ"],"topics":[]},{"id":"chunk-11","heading":"Проверим дубликаты","text":"duplicates = df.duplicated().sum() print(f\"\\nКоличество дубликатов: {duplicates}\")","keywords":["bigdata","duplicates","данных",".sum","2.","df.duplicated","jupyter","notebook.","nколичество","print","анализ","аналитика"],"topics":[]},{"id":"chunk-12","heading":"Финальные размеры набора данных","text":"```text === Информация о датасете === RangeIndex: 1599 entries, 0 to 1598 Data columns (total 12 columns):","keywords":["данных","bigdata","columns","12","1598","1599","2.","data","entries","jupyter","notebook.","rangeindex"],"topics":[]},{"id":"chunk-13","heading":"Column Non Null Count Dtype","text":"0 fixed acidity 1599 non null float64 1 volatile acidity 1599 non null float64 2 citric acid 1599 non null float64 3 residual sugar 1599 non null float64 4 chlorides 1599 non null float64 5 free sulfur dioxide 1599 non null float64 6 total sulfur dioxide 1599 non null float64 7 density 1599 non null float64 8 pH 1599 non null float64 9 sulphates 1599 non null float64 10 alcohol 1599 non null float64 11 quality 1599","keywords":["1599","non","null","float64","acidity","bigdata","dioxide","sulfur","данных","10","11","2."],"topics":[]},{"id":"chunk-14","heading":"Column Non Null Count Dtype","text":"non null int64 dtypes: float64(11), int64(1) memory usage: 150.0 KB None","keywords":["bigdata","int64","non","null","данных","11","150.0","2.","column","count","dtype","dtypes"],"topics":[]},{"id":"chunk-15","heading":"Column Non Null Count Dtype","text":"=== Первые 5 строк === fixed acidity volatile acidity citric acid residual sugar chlorides \\ 0 7.4 0.70 0.00 1.9 0.076 1 7.8 0.88 0.00 2.6 0.098 2 7.8 0.76 0.04 2.3 0.092 3 11.2 0.28 0.56 1.9 0.075 4 7.4 0.70 0.00 1.9 0.076","keywords":["0.00","1.9","0.076","0.70","7.4","7.8","acidity","bigdata","данных","0.04","0.075","0.092"],"topics":[]},{"id":"chunk-16","heading":"Column Non Null Count Dtype","text":"free sulfur dioxide total sulfur dioxide density pH sulphates \\ 0 11.0 34.0 0.9978 3.51 0.56 1 25.0 67.0 0.9968 3.20 0.68 2 15.0 54.0 0.9970 3.26 0.65 3 17.0 60.0 0.9980 3.16 0.58 4 11.0 34.0 0.9978 3.51 0.56","keywords":["0.56","0.9978","11.0","3.51","34.0","bigdata","dioxide","sulfur","данных","0.58","0.65","0.68"],"topics":[]},{"id":"chunk-17","heading":"Column Non Null Count Dtype","text":"=== Проверка на пропущенные значения === fixed acidity 0 volatile acidity 0 citric acid 0 residual sugar 0 chlorides 0 free sulfur dioxide 0 total sulfur dioxide 0 density 0 pH 0 sulphates 0 alcohol 0 quality 0 dtype: int64","keywords":["acidity","bigdata","dioxide","dtype","sulfur","данных","2.","acid","alcohol","chlorides","citric","column"],"topics":[]},{"id":"chunk-18","heading":"Column Non Null Count Dtype","text":"3. Построить столбчатую диаграмму (.bar) с использованием модуля graph objs из библиотеки Plotly со следующими параметрами:","keywords":["bigdata","данных",".bar","2.","3.","column","count","dtype","graph","jupyter","non","notebook."],"topics":[]},{"id":"chunk-19","heading":"Column Non Null Count Dtype","text":"3.1. По оси Х указать дату или название, по оси У указать количественный показатель.","keywords":["bigdata","данных","оси","указать","2.","3.1.","column","count","dtype","jupyter","non","notebook."],"topics":[]},{"id":"chunk-20","heading":"Column Non Null Count Dtype","text":"3.2. Сделать так, чтобы столбец принимал цвет в зависимости от значения показателя ( marker=dict(color=признак, coloraxis=\"coloraxis\") ).","keywords":["bigdata","coloraxis","данных","2.","3.2.","color","column","count","dict","dtype","jupyter","marker"],"topics":[]},{"id":"chunk-21","heading":"Column Non Null Count Dtype","text":"3.3. Сделать так, чтобы границы каждого столбца были выделены чёрной линией с толщиной равной 2 .","keywords":["bigdata","данных","2.","3.3.","column","count","dtype","jupyter","non","notebook.","null","анализ"],"topics":[]},{"id":"chunk-22","heading":"Column Non Null Count Dtype","text":"3.4. Отобразить заголовок диаграммы, разместив его по центру сверху, с 20 размером текста.","keywords":["bigdata","данных","2.","20","3.4.","column","count","dtype","jupyter","non","notebook.","null"],"topics":[]},{"id":"chunk-23","heading":"Column Non Null Count Dtype","text":"3.5. Добавить подписи для осей X и Y с размером текста, равным 16 . Для оси абсцисс развернуть метки так, чтобы они читались под углом, равным 315 .","keywords":["bigdata","данных","равным","16","2.","3.5.","315","column","count","dtype","jupyter","non"],"topics":[]},{"id":"chunk-24","heading":"Column Non Null Count Dtype","text":"3.7. Расположить график во всю ширину рабочей области и присвоить высоту, равную 700 пикселей.","keywords":["bigdata","данных","2.","3.7.","700","column","count","dtype","jupyter","non","notebook.","null"],"topics":[]},{"id":"chunk-25","heading":"Column Non Null Count Dtype","text":"3.8. Добавить сетку на график, сделать её цвет 'ivory' и толщину равную 2 . (Можно сделать это при настройке осей с помощью gridwidth=2 , gridcolor='ivory' )","keywords":["bigdata","ivory","данных","сделать","2.","3.8.","column","count","dtype","gridcolor","gridwidth","jupyter"],"topics":[]},{"id":"chunk-26","heading":"Загружаем датасет","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-27","heading":"по оси Y – среднее качество (quality)","text":"data grouped = df.groupby(df[\"alcohol\"].round())[\"quality\"].mean().reset index()","keywords":["bigdata","quality","данных",".mean",".reset",".round","2.","alcohol","data","df","df.groupby","grouped"],"topics":[]},{"id":"chunk-28","heading":"Создаём диаграмму","text":"fig = go.Figure( data=[ go.Bar( x=data grouped[\"alcohol\"], y=data grouped[\"quality\"], marker=dict( color=data grouped[\"quality\"], цвет в зависимости от значения coloraxis=\"coloraxis\", line=dict(color=\"black\", width=2) границы столбцов ) ) ] )","keywords":["data","grouped","bigdata","color","coloraxis","dict","quality","данных","2.","alcohol","black","fig"],"topics":[]},{"id":"chunk-29","heading":"Настройки оформления","text":"fig.update layout( title=dict( text=\"Среднее качество вина в зависимости от содержания алкоголя\", x=0.5, по центру xanchor=\"center\", yanchor=\"top\", font=dict(size=20) ), xaxis=dict( title=dict(text=\"Содержание алкоголя (%)\", font=dict(size=16)), tickangle=315, tickfont=dict(size=14), showgrid=True, gridwidth=2, gridcolor=\"ivory\" ), yaxis=dict( title=dict(text=\"Качество вина (среднее)\", font=dict(size=16)),","keywords":["dict","size","font","text","title","16","bigdata","алкоголя","вина","данных","качество","среднее"],"topics":[]},{"id":"chunk-30","heading":"Настройки оформления","text":"tickfont=dict(size=14), showgrid=True, gridwidth=2, gridcolor=\"ivory\" ), coloraxis=dict(colorscale=\"Viridis\"), height=700, autosize=True, margin=dict(l=0, r=0, t=50, b=0) убираем лишние отступы )","keywords":["dict","bigdata","true","данных","14","2.","50","700","autosize","coloraxis","colorscale","gridcolor"],"topics":[]},{"id":"chunk-31","heading":"Отображаем график","text":"4. Построить круговую диаграмму ( go.Pie ), использовав данные и стиль оформления из предыдущего графика. Сделать так, чтобы границы каждой доли были выделены чёрной линией с толщиной, равной 2 и категории круговой диаграммы были читаемы (к примеру, объединить часть объектов)","keywords":["bigdata","были","данных","2.","4.","go.pie","jupyter","notebook.","анализ","аналитика","выделены","границы"],"topics":[]},{"id":"chunk-32","heading":"Загружаем датасет","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-33","heading":"Считаем количество вин каждого качества","text":"quality counts = df[\"quality\"].value counts().reset index() quality counts.columns = [\"quality\", \"count\"]","keywords":["quality","bigdata","counts","данных",".reset",".value","2.","count","counts.columns","df","index","jupyter"],"topics":[]},{"id":"chunk-34","heading":"Объединим редкие категории (менее 50 объектов) в \"Other\"","text":"quality counts[\"quality\"] = quality counts[\"quality\"].astype(str) quality counts.loc[quality counts[\"count\"] < 50, \"quality\"] = \"Other\"","keywords":["quality","counts","50","bigdata","other","данных",".astype","2.","count","counts.loc","jupyter","notebook."],"topics":["python"]},{"id":"chunk-35","heading":"Создаём круговую диаграмму","text":"fig = go.Figure( data=[ go.Pie( labels=quality counts[\"quality\"], values=quality counts[\"count\"], marker=dict( line=dict(color=\"black\", width=2) границы долей ), textinfo=\"label+percent\", показываем категории и проценты insidetextorientation=\"radial\" ) ] )","keywords":["quality","bigdata","counts","dict","данных","2.","black","color","count","data","fig","go.figure"],"topics":[]},{"id":"chunk-36","heading":"Настройки оформления (стиль как у предыдущего графика)","text":"fig.update layout( title=dict( text=\"Распределение качества вин\", x=0.5, xanchor=\"center\", yanchor=\"top\", font=dict(size=20) ), height=700, autosize=True, margin=dict(l=0, r=0, t=50, b=0) убираем лишние отступы )","keywords":["dict","bigdata","данных","0.5","2.","20","50","700","autosize","center","fig.update","font"],"topics":[]},{"id":"chunk-37","heading":"Отображаем","text":"5. Построить линейные графики, взять один из параметров и определить зависимость между другими несколькими (от 2 до 5) показателями с использованием библиотеки matplotlib . Сделать вывод.","keywords":["bigdata","данных","2.","5.","jupyter","matplotlib","notebook.","анализ","аналитика","библиотеки","взять","вывод."],"topics":[]},{"id":"chunk-38","heading":"Загружаем датасет","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-39","heading":"Строим линейные графики","text":"plt.plot(df grouped[\"alcohol rounded\"], df grouped[\"quality\"], label=\"Качество\", marker=\"o\") plt.plot(df grouped[\"alcohol rounded\"], df grouped[\"sulphates\"], label=\"Сульфаты\", marker=\"s\") plt.plot(df grouped[\"alcohol rounded\"], df grouped[\"volatile acidity\"], label=\"Летучая кислотность\", marker=\"^\") plt.plot(df grouped[\"alcohol rounded\"], df grouped[\"density\"], label=\"Плотность\", marker=\"d\")","keywords":["df","grouped","alcohol","label","marker","plt.plot","rounded","bigdata","данных","2.","acidity","density"],"topics":[]},{"id":"chunk-40","heading":"Настройки графика","text":"plt.title(\"Зависимость показателей вина от содержания алкоголя\", fontsize=18) plt.xlabel(\"Содержание алкоголя (%)\", fontsize=14) plt.ylabel(\"Средние значения показателей\", fontsize=14) plt.xticks(rotation=45, fontsize=12) plt.yticks(fontsize=12) plt.grid(True, linestyle=\" \", alpha=0.7) plt.legend(fontsize=12) plt.tight layout()","keywords":["fontsize","12","14","bigdata","алкоголя","данных","показателей","0.7","18","2.","45","alpha"],"topics":[]},{"id":"chunk-41","heading":"Показ графика","text":"1. Качество вина в среднем растёт с увеличением содержания алкоголя — вина с высоким алкоголем чаще получают более высокие оценки.","keywords":["bigdata","вина","данных","1.","2.","jupyter","notebook.","алкоголем","алкоголя","анализ","аналитика","более"],"topics":[]},{"id":"chunk-42","heading":"Показ графика","text":"2. Сульфаты также постепенно увеличиваются вместе с алкоголем, что может указывать на технологические особенности производства.","keywords":["2.","bigdata","данных","jupyter","notebook.","алкоголем","анализ","аналитика","вместе","графика","исследовательский","может"],"topics":[]},{"id":"chunk-43","heading":"Показ графика","text":"3. Летучая кислотность наоборот снижается при увеличении алкоголя — вина с низкой кислотностью воспринимаются как более качественные.","keywords":["bigdata","данных","2.","3.","jupyter","notebook.","алкоголя","анализ","аналитика","более","вина","воспринимаются"],"topics":[]},{"id":"chunk-44","heading":"Показ графика","text":"4. Плотность имеет обратную зависимость: чем больше алкоголя, тем ниже плотность вина.","keywords":["bigdata","данных","плотность","2.","4.","jupyter","notebook.","алкоголя","анализ","аналитика","больше","вина."],"topics":[]},{"id":"chunk-45","heading":"Показ графика","text":"Таким образом, можно сделать вывод: алкогольное содержание оказывает значительное влияние на качество вина и связано с изменением химических свойств напитка (кислотность, плотность, уровень сульфатов).","keywords":["bigdata","данных","2.","jupyter","notebook.","алкогольное","анализ","аналитика","вина","влияние","вывод","графика"],"topics":[]},{"id":"chunk-46","heading":"Показ графика","text":"5.1. Сделать график с линиями и маркерами, цвет линии 'crimson' , цвет точек 'white' , цвет границ точек 'black' , толщина границ точек равна 2 .","keywords":["точек","цвет","bigdata","границ","данных","2.","5.1.","black","crimson","jupyter","notebook.","white"],"topics":[]},{"id":"chunk-47","heading":"Показ графика","text":"5.2. Добавить сетку на график, сделать её цвет 'mistyrose' и толщину равную 2 . (Можно сделать это при настройке осей с помощью linewidth=2 , color='mistyrose' ).","keywords":["bigdata","mistyrose","данных","сделать","2.","5.2.","color","jupyter","linewidth","notebook.","анализ","аналитика"],"topics":[]},{"id":"chunk-48","heading":"Загружаем датасет","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-49","heading":"Строим график","text":"plt.plot( df grouped[\"alcohol rounded\"], df grouped[\"quality\"], color=\"crimson\", цвет линии marker=\"o\", маркер markerfacecolor=\"white\", цвет точки (заливка) markeredgecolor=\"black\", цвет границы точки markeredgewidth=2, толщина границы точки linewidth=2, толщина линии label=\"Качество вина\" )","keywords":["точки","цвет","bigdata","df","grouped","границы","данных","линии","толщина","2.","alcohol","black"],"topics":[]},{"id":"chunk-50","heading":"Настройки графика","text":"plt.title(\"Зависимость качества вина от содержания алкоголя\", fontsize=18) plt.xlabel(\"Содержание алкоголя (%)\", fontsize=14) plt.ylabel(\"Среднее качество вина\", fontsize=14) plt.xticks(fontsize=12) plt.yticks(fontsize=12)","keywords":["fontsize","12","14","bigdata","алкоголя","вина","данных","18","2.","jupyter","notebook.","plt.title"],"topics":[]},{"id":"chunk-51","heading":"Показ графика","text":"6. Выполнить визуализацию многомерных данных, используя t SNE . Необходимо использовать набор данных MNIST или fashion MNIST (можно использовать и другие готовые наборы данных, где можно наблюдать разделение объектов по кластерам).","keywords":["данных","bigdata","mnist","использовать","можно","2.","6.","fashion","jupyter","notebook.","sne","анализ"],"topics":[]},{"id":"chunk-52","heading":"load dataset (fallback to digits)","text":"try: from tensorflow.keras.datasets import fashion mnist as fm (x train, y train), (x test, y test) = fm.load data() X = np.vstack([x train, x test]) y = np.hstack([y train, y test]) dataset name = \"Fashion MNIST (28x28 grayscale)\" X = X.reshape((X.shape[0], 1)).astype(np.float32) / 255.0 except Exception: from sklearn.datasets import load digits digits = load digits() X = digits.data.astype(np.float32) y =","keywords":["digits","test","train","load","bigdata","dataset","fashion","import","mnist","np.float32","данных",".astype"],"topics":["python","ai","numpy","testing"]},{"id":"chunk-53","heading":"load dataset (fallback to digits)","text":"digits.target dataset name = \"sklearn digits (8x8 grayscale) fallback\"","keywords":["bigdata","dataset","digits","fallback","данных","2.","8x8","digits.target","grayscale","jupyter","load","name"],"topics":[]},{"id":"chunk-54","heading":"load dataset (fallback to digits)","text":"print(\"Dataset:\", dataset name) print(\"Samples:\", X.shape[0], \"Features:\", X.shape[1])","keywords":["dataset","bigdata","print","x.shape","данных","2.","digits","fallback","features","jupyter","load","name"],"topics":[]},{"id":"chunk-55","heading":"use a small subset for reliability","text":"max samples = 300 if X.shape[0] max samples: rng = np.random.default rng(42) idx = rng.choice(X.shape[0], size=max samples, replace=False) X sub = X[idx] y sub = y[idx] else: X sub = X.copy() y sub = y.copy()","keywords":["sub","idx","max","samples","bigdata","rng","x.shape","данных","2.","300","42","else"],"topics":["numpy"]},{"id":"chunk-56","heading":"use a small subset for reliability","text":"from sklearn.decomposition import PCA n pca = min(20, X sub.shape[0] 1, X sub.shape[1]) pca = PCA(n components=n pca, random state=42) X pca = pca.fit transform(X sub) print(\"PCA reduced to\", X pca.shape[1], \"components\")","keywords":["pca","bigdata","components","sub.shape","данных","2.","20","42","import","jupyter","min","notebook."],"topics":[]},{"id":"chunk-57","heading":"use a small subset for reliability","text":"from sklearn.manifold import TSNE perplexities = [5, 30] compare two perplexities tsne results = {}","keywords":["bigdata","perplexities","tsne","данных","2.","30","compare","import","jupyter","notebook.","reliability","results"],"topics":[]},{"id":"chunk-58","heading":"use a small subset for reliability","text":"for perp in perplexities: print(f\"Running t SNE with perplexity={perp} ...\") tsne = TSNE(n components=2, perplexity=perp, init='pca', random state=42, learning rate='auto', n iter=300) X emb = tsne.fit transform(X pca) tsne results[perp] = X emb print(f\"Done perplexity={perp}\")","keywords":["perp","perplexity","tsne","bigdata","emb","pca","print","данных","...","2.","300","42"],"topics":[]},{"id":"chunk-59","heading":"use a small subset for reliability","text":"for perp, X emb in tsne results.items(): plt.figure(figsize=(10,8)) sc = plt.scatter(X emb[:,0], X emb[:,1], c=y sub, cmap='tab10', s=20, alpha=0.9) plt.title(f\"t SNE (perplexity={perp}) — {dataset name}\", fontsize=16) plt.xlabel(\"t SNE 1\", fontsize=12) plt.ylabel(\"t SNE 2\", fontsize=12) plt.grid(True, linewidth=1, color='lightgray', linestyle=' ') cbar = plt.colorbar(sc, ticks=np.unique(y sub)) cbar.ax.tick","keywords":["emb","fontsize","sne","12","bigdata","perp","sc","sub","данных","0.9","10","16"],"topics":["numpy"]},{"id":"chunk-60","heading":"use a small subset for reliability","text":"params(labelsize=10) plt.tight layout() plt.show()","keywords":["bigdata","данных","10","2.","jupyter","labelsize","layout","notebook.","params","plt.show","plt.tight","reliability"],"topics":[]},{"id":"chunk-61","heading":"use a small subset for reliability","text":"```text Downloading data from https://storage.googleapis.com/tensorflow/tf keras datasets/train labels idx1 ubyte.gz \u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf keras datasets/train images idx3 ubyte.gz \u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step Downloading data from","keywords":["0m","data","downloading","0us","32m","37m","bigdata","datasets","https","keras","step","storage.googleapis.com"],"topics":["ai"]},{"id":"chunk-62","heading":"use a small subset for reliability","text":"https://storage.googleapis.com/tensorflow/tf keras datasets/t10k labels idx1 ubyte.gz \u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf keras datasets/t10k images idx3 ubyte.gz \u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step Dataset: Fashion MNIST (28x28 grayscale) Samples: 70000 Features:","keywords":["0m","0us","32m","37m","bigdata","datasets","https","keras","step","storage.googleapis.com","t10k","tensorflow"],"topics":[]},{"id":"chunk-63","heading":"use a small subset for reliability","text":"784 PCA reduced to 20 components Running t SNE with perplexity=5 ...","keywords":["bigdata","данных","...","2.","20","784","components","jupyter","notebook.","pca","perplexity","reduced"],"topics":[]},{"id":"chunk-64","heading":"use a small subset for reliability","text":"/usr/local/lib/python3.12/dist packages/sklearn/manifold/ t sne.py:1164: FutureWarning:","keywords":["bigdata","данных","1164","2.","dist","futurewarning","jupyter","lib","local","manifold","notebook.","packages"],"topics":["python"]},{"id":"chunk-65","heading":"use a small subset for reliability","text":"/usr/local/lib/python3.12/dist packages/sklearn/manifold/ t sne.py:1164: FutureWarning:","keywords":["bigdata","данных","1164","2.","dist","futurewarning","jupyter","lib","local","manifold","notebook.","packages"],"topics":["python"]},{"id":"chunk-66","heading":"use a small subset for reliability","text":"7. Выполнить визуализацию многомерных данных, используя UMAP с различными параметрами n neighbors и min dist . Рассчитать время работы алгоритма с помощью библиотеки time и сравнить его с временем работы t SNE .","keywords":["данных","bigdata","работы","2.","7.","dist","jupyter","min","neighbors","notebook.","reliability","small"],"topics":["algorithms"]},{"id":"chunk-67","heading":"use a small subset for reliability","text":"```python from sklearn.datasets import load digits from sklearn.manifold import TSNE","keywords":["bigdata","import","данных","2.","digits","jupyter","load","notebook.","python","reliability","sklearn.datasets","sklearn.manifold"],"topics":["python"]},{"id":"chunk-68","heading":"Список параметров для UMAP","text":"params = [ {\"n neighbors\": 5, \"min dist\": 0.1}, {\"n neighbors\": 15, \"min dist\": 0.1}, {\"n neighbors\": 30, \"min dist\": 0.3}, ]","keywords":["dist","min","neighbors","0.1","bigdata","данных","0.3","15","2.","30","jupyter","notebook."],"topics":[]},{"id":"chunk-69","heading":"UMAP с разными параметрами","text":"for i, p in enumerate(params, 1): start = time.time() reducer = umap.UMAP(n neighbors=p[\"n neighbors\"], min dist=p[\"min dist\"], random state=42) X umap = reducer.fit transform(X) duration = time.time() start results[f\"UMAP {p}\"] = duration","keywords":["umap","bigdata","dist","duration","min","neighbors","start","time.time","данных","2.","42","enumerate"],"topics":[]},{"id":"chunk-70","heading":"UMAP с разными параметрами","text":"plt.figure(figsize=(5,4)) plt.scatter(X umap[:,0], X umap[:,1], c=y, cmap=\"Spectral\", s=5) plt.title(f\"UMAP (n neighbors={p['n neighbors']}, min dist={p['min dist']})\\nВремя: {duration:.2f} сек\") plt.show()","keywords":["umap","bigdata","dist","min","neighbors","данных",".2f","2.","cmap","duration","figsize","jupyter"],"topics":[]},{"id":"chunk-71","heading":"t SNE","text":"start = time.time() X tsne = TSNE(n components=2, random state=42, init=\"pca\", perplexity=30).fit transform(X) duration = time.time() start results[\"t SNE\"] = duration","keywords":["bigdata","duration","sne","start","time.time","tsne","данных",".fit","2.","30","42","components"],"topics":[]},{"id":"chunk-72","heading":"t SNE","text":"plt.figure(figsize=(5,4)) plt.scatter(X tsne[:,0], X tsne[:,1], c=y, cmap=\"Spectral\", s=5) plt.title(f\"t SNE\\nВремя: {duration:.2f} сек\") plt.show()","keywords":["bigdata","sne","tsne","данных",".2f","2.","cmap","duration","figsize","jupyter","notebook.","nвремя"],"topics":[]},{"id":"chunk-73","heading":"t SNE","text":"```text /usr/local/lib/python3.12/dist packages/umap/umap .py:1952: UserWarning:","keywords":["bigdata","umap","данных",".py","1952","2.","dist","jupyter","lib","local","notebook.","packages"],"topics":["python"]},{"id":"chunk-74","heading":"t SNE","text":"n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism.","keywords":["bigdata","данных","2.","jobs","jupyter","no","notebook.","overridden","parallelism.","random","seed","setting"],"topics":[]},{"id":"chunk-75","heading":"t SNE","text":"n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism.","keywords":["bigdata","данных","2.","jobs","jupyter","no","notebook.","overridden","parallelism.","random","seed","setting"],"topics":[]},{"id":"chunk-76","heading":"t SNE","text":"n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism.","keywords":["bigdata","данных","2.","jobs","jupyter","no","notebook.","overridden","parallelism.","random","seed","setting"],"topics":[]},{"id":"chunk-77","heading":"t SNE","text":"{\"UMAP {'n neighbors': 5, 'min dist': 0.1}\": 14.975495338439941, \"UMAP {'n neighbors': 15, 'min dist': 0.1}\": 7.100525617599487, \"UMAP {'n neighbors': 30, 'min dist': 0.3}\": 7.385646104812622, 't SNE': 21.6993350982666} ```","keywords":["dist","min","neighbors","umap","0.1","bigdata","sne","данных","0.3","14.975495338439941","15","2."],"topics":[]}]},{"id":"bigdata/practice-03-regression-and-datasets","href":"/docs/bigdata/practice-03-regression-and-datasets","slug":["bigdata","practice-03-regression-and-datasets"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 3. Регрессия и наборы данных","description":"Работа с несколькими датасетами и построение моделей.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитривеич ИКБО 25 22 Практическая работа №3 Статистика на Python Генеральная совокупность – это множество абсолютно всех объектов, которые испол","keywords":["print","df","nan","4.","bmi","col","cols","3.",".2f","charges","mean","true","95","test","построение","0.000000","вывод","количество"],"topics":["python","pandas","numpy","testing","regression"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","3.","wiki","версия","данных","датасетами","документацию.","исходный","материал","моделей.","наборы","несколькими"],"topics":[]},{"id":"chunk-1","heading":"Статистика на Python","text":"Генеральная совокупность – это множество абсолютно всех объектов, которые используются для исследования. Выборка","keywords":["bigdata","3.","python","абсолютно","всех","выборка","генеральная","данных","датасетами","используются","исследования.","которые"],"topics":["python"]},{"id":"chunk-2","heading":"Статистика на Python","text":"1. Загрузить данные из файла “insurance.csv”. 2. С помощью метода describe() посмотреть статистику по данным. Сделать выводы.","keywords":["bigdata","1.","2.","3.","describe","insurance.csv","python","выводы.","данные","данным.","данных","датасетами"],"topics":["python"]},{"id":"chunk-3","heading":"4. Статистическое описание","text":"print(\"Статистическое описание данных:\") print(df.describe(include='all'), \"\\n\") ```","keywords":["bigdata","print","данных","описание","статистическое","3.","4.","all","df.describe","include","датасетами","моделей."],"topics":[]},{"id":"chunk-4","heading":"4. Статистическое описание","text":"```text Первые 5 строк датасета: age sex bmi children smoker region charges 0 19 female 27.900 0 yes southwest 16884.92400 1 18 male 33.770 1 no southeast 1725.55230 2 28 male 33.000 3 no southeast 4449.46200 3 33 male 22.705 0 no northwest 21984.47061 4 32 male 28.880 0 no northwest 3866.85520","keywords":["male","no","bigdata","northwest","southeast","16884.92400","1725.55230","18","19","21984.47061","22.705","27.900"],"topics":["python"]},{"id":"chunk-5","heading":"4. Статистическое описание","text":"Информация о данных: RangeIndex: 1338 entries, 0 to 1337 Data columns (total 7 columns):","keywords":["bigdata","columns","данных","1337","1338","3.","4.","data","entries","rangeindex","total","датасетами"],"topics":[]},{"id":"chunk-6","heading":"Column Non Null Count Dtype","text":"0 age 1338 non null int64 1 sex 1338 non null object 2 bmi 1338 non null float64 3 children 1338 non null int64 4 smoker 1338 non null object 5 region 1338 non null object 6 charges 1338 non null float64 dtypes: float64(2), int64(2), object(3) memory usage: 73.3+ KB None","keywords":["non","null","1338","object","float64","int64","bigdata","3.","73.3+","age","bmi","charges"],"topics":[]},{"id":"chunk-7","heading":"Column Non Null Count Dtype","text":"Статистическое описание данных: age sex bmi children smoker region \\ count 1338.000000 1338 1338.000000 1338.000000 1338 1338 unique NaN 2 NaN NaN 2 4 top NaN male NaN NaN no southeast freq NaN 676 NaN NaN 1064 364 mean 39.207025 NaN 30.663397 1.094918 NaN NaN std 14.049960 NaN 6.098187 1.205493 NaN NaN min 18.000000 NaN 15.960000 0.000000 NaN NaN 25% 27.000000 NaN 26.296250 0.000000 NaN NaN 50% 39.000000 NaN","keywords":["nan","1338","1338.000000","0.000000","bigdata","count","данных","1.094918","1.205493","1064","14.049960","15.960000"],"topics":["python"]},{"id":"chunk-8","heading":"Column Non Null Count Dtype","text":"30.400000 1.000000 NaN NaN 75% 51.000000 NaN 34.693750 2.000000 NaN NaN max 64.000000 NaN 53.130000 5.000000 NaN NaN","keywords":["nan","bigdata","1.000000","2.000000","3.","30.400000","34.693750","5.000000","51.000000","53.130000","64.000000","75"],"topics":[]},{"id":"chunk-9","heading":"Column Non Null Count Dtype","text":"charges count 1338.000000 unique NaN top NaN freq NaN mean 13270.422265 std 12110.011237 min 1121.873900 25% 4740.287150 50% 9382.033000 75% 16639.912515 max 63770.428010 ```","keywords":["nan","bigdata","count","1121.873900","12110.011237","13270.422265","1338.000000","16639.912515","25","3.","4740.287150","50"],"topics":[]},{"id":"chunk-10","heading":"Column Non Null Count Dtype","text":"Выводы: Возраст: от 18 до 64 лет, средний — около 39 лет. Пол: примерно поровну мужчин и женщин. Средний BMI 30.7, что указывает на избыточный вес. Среднее количество детей — 1. Курильщиков 20%. Средние страховые выплаты около 13 270, но разброс большой (до 63 770).","keywords":["bigdata","до","около","средний","1.","13","18","20","270","3.","30.7","39"],"topics":[]},{"id":"chunk-11","heading":"2. Просмотр числовых столбцов","text":"numeric cols = df.select dtypes(include=['int64', 'float64']).columns print(\"Числовые признаки:\", list(numeric cols), \"\\n\")","keywords":["bigdata","cols","numeric",".columns","2.","3.","df.select","dtypes","float64","include","int64","list"],"topics":[]},{"id":"chunk-12","heading":"3. Построение гистограмм","text":"df[numeric cols].hist(bins=20, figsize=(12, 8), color='skyblue', edgecolor='black') plt.suptitle(\"Гистограммы числовых признаков страховых данных\", fontsize=16) plt.show() ```","keywords":["3.","bigdata","данных","построение",".hist","12","16","20","bins","black","color","cols"],"topics":[]},{"id":"chunk-13","heading":"3. Построение гистограмм","text":"Выводы: Возраст (age): распределение равномерное от 18 до 64 лет. Индекс массы тела (bmi): большинство людей имеют BMI в диапазоне 25–35 (избыточный вес). Количество детей (children): большинство клиентов не имеют детей или имеют 1–2 ребёнка. Страховые выплаты (charges): распределение сильно скошено вправо — у большинства низкие выплаты, у меньшинства очень высокие.","keywords":["имеют","3.","bigdata","bmi","большинство","выплаты","детей","построение","распределение","18","25","35"],"topics":[]},{"id":"chunk-14","heading":"3. Построение гистограмм","text":"4. Найти меры центральной тенденции и меры разброса для индекса массы тела (bmi) и расходов (charges). Отобразить результаты в виде текста и на гистограммах (3 вертикальные линии). Добавить легенду на графики.","keywords":["3.","bigdata","меры","построение","4.","bmi","charges","вертикальные","виде","гистограмм","гистограммах","графики."],"topics":[]},{"id":"chunk-15","heading":"2. Расчёт статистических мер","text":"for col in ['bmi', 'charges']: mean = df[col].mean() median = df[col].median() mode = df[col].mode()[0] std = df[col].std() min val = df[col].min() max val = df[col].max()","keywords":["col","df","bigdata","val",".max",".mean",".median",".min",".mode",".std","2.","3."],"topics":[]},{"id":"chunk-16","heading":"3. Текстовый вывод","text":"print(f\"===== {col.upper()} =====\") print(f\"Среднее значение: {mean:.2f}\") print(f\"Медиана: {median:.2f}\") print(f\"Мода: {mode:.2f}\") print(f\"Стандартное отклонение: {std:.2f}\") print(f\"Минимум: {min val:.2f}\") print(f\"Максимум: {max val:.2f}\") print()","keywords":["print",".2f","3.","bigdata","val","col.upper","max","mean","median","min","mode","std"],"topics":[]},{"id":"chunk-17","heading":"4. Построение гистограммы с линиями","text":"plt.figure(figsize=(8, 5)) plt.hist(df[col], bins=30, color='skyblue', edgecolor='black') plt.axvline(mean, color='red', linestyle=' ', linewidth=2, label=f'Среднее ({mean:.2f})') plt.axvline(median, color='green', linestyle=' ', linewidth=2, label=f'Медиана ({median:.2f})') plt.axvline(mode, color='orange', linestyle=' .', linewidth=2, label=f'Мода ({mode:.2f})') plt.title(f\"Распределение {col}\") plt.xlabel(col)","keywords":["color",".2f","col","label","linestyle","linewidth","plt.axvline","bigdata","mean","median","mode","построение"],"topics":[]},{"id":"chunk-18","heading":"4. Построение гистограммы с линиями","text":"plt.ylabel(\"Частота\") plt.legend() plt.grid(True, linestyle=' ', alpha=0.6) plt.show() ```","keywords":["bigdata","построение","0.6","3.","4.","alpha","linestyle","plt.grid","plt.legend","plt.show","plt.ylabel","true"],"topics":[]},{"id":"chunk-19","heading":"4. Построение гистограммы с линиями","text":"```text ===== BMI ===== Среднее значение: 30.66 Медиана: 30.40 Мода: 32.30 Стандартное отклонение: 6.10 Минимум: 15.96 Максимум: 53.13","keywords":["bigdata","построение","15.96","3.","30.40","30.66","32.30","4.","53.13","6.10","bmi","text"],"topics":[]},{"id":"chunk-20","heading":"4. Построение гистограммы с линиями","text":"===== CHARGES ===== Среднее значение: 13270.42 Медиана: 9382.03 Мода: 1639.56 Стандартное отклонение: 12110.01 Минимум: 1121.87 Максимум: 63770.43","keywords":["bigdata","построение","1121.87","12110.01","13270.42","1639.56","3.","4.","63770.43","9382.03","charges","гистограммы"],"topics":[]},{"id":"chunk-21","heading":"4. Построение гистограммы с линиями","text":"5. Построить box plot для числовых показателей. Названия графиков должны соответствовать названиям признаков. Сделать выводы.","keywords":["bigdata","построение","3.","4.","5.","box","plot","выводы.","гистограммы","графиков","данных","датасетами"],"topics":[]},{"id":"chunk-22","heading":"3. Построим boxplot для каждого числового признака","text":"for i, col in enumerate(numeric cols, 1): plt.subplot(2, 2, i) 2 строки, 2 столбца plt.boxplot(df[col], patch artist=True, boxprops=dict(facecolor='skyblue')) plt.title(col) plt.ylabel(\"Значение\") plt.grid(True, linestyle=' ', alpha=0.6)","keywords":["col","3.","bigdata","true","0.6","alpha","artist","boxplot","boxprops","cols","df","dict"],"topics":[]},{"id":"chunk-23","heading":"3. Построим boxplot для каждого числового признака","text":"plt.suptitle(\"Box plot для числовых признаков\", fontsize=16) plt.tight layout(rect=[0, 0, 1, 0.96]) plt.show() ```","keywords":["3.","bigdata","0.96","16","box","boxplot","fontsize","layout","plot","plt.show","plt.suptitle","plt.tight"],"topics":[]},{"id":"chunk-24","heading":"3. Построим boxplot для каждого числового признака","text":"6. Используя признак charges или imb, проверить, выполняется ли центральная предельная теорема. Использовать различные длины выборок n. Количество выборок = 300. Вывести результат в виде гистограмм. Найти стандартное отклонение и среднее для полученных распределений. Сделать выводы.","keywords":["3.","bigdata","выборок","300.","6.","boxplot","charges","imb","n.","виде","вывести","выводы."],"topics":[]},{"id":"chunk-25","heading":"создаем 300 выборок размера n и считаем среднее каждой","text":"for in range(num samples): sample = np.random.choice(data, size=n, replace=True) sample means.append(np.mean(sample))","keywords":["sample","bigdata","3.","300","data","means.append","np.mean","np.random.choice","num","range","replace","samples"],"topics":["numpy"]},{"id":"chunk-26","heading":"5. Строим гистограммы распределений средних","text":"plt.subplot(1, 3, i) plt.hist(sample means, bins=20, color='skyblue', edgecolor='black') plt.title(f'n = {n}\\nСреднее: {sample means.mean():.2f}, σ: {sample means.std():.2f}') plt.xlabel(\"Среднее значение выборки\") plt.ylabel(\"Частота\") plt.grid(True, linestyle=' ', alpha=0.6)","keywords":["sample",".2f","bigdata","0.6","20","3.","5.","alpha","bins","black","color","edgecolor"],"topics":[]},{"id":"chunk-27","heading":"5. Строим гистограммы распределений средних","text":"plt.suptitle(\"Проверка Центральной предельной теоремы (ЦПТ) для признака 'charges'\", fontsize=14) plt.tight layout(rect=[0, 0, 1, 0.95]) plt.show() ```","keywords":["bigdata","0.95","14","3.","5.","charges","fontsize","layout","plt.show","plt.suptitle","plt.tight","rect"],"topics":[]},{"id":"chunk-28","heading":"5. Строим гистограммы распределений средних","text":"7. Построить 95% и 99% доверительный интервал для среднего значения расходов и среднего значения индекса массы тела.","keywords":["bigdata","значения","среднего","3.","5.","7.","95","99","гистограммы","данных","датасетами","доверительный"],"topics":[]},{"id":"chunk-29","heading":"2. Функция для вычисления доверительного интервала","text":"def confidence interval(data, confidence=0.95): n = len(data) mean = np.mean(data) std err = st.sem(data) стандартная ошибка среднего h = std err st.t.ppf((1 + confidence) / 2, n 1) return mean, mean h, mean + h","keywords":["data","mean","confidence","bigdata","err","std","0.95","2.","3.","def","interval","len"],"topics":["numpy"]},{"id":"chunk-30","heading":"3. Расчёт интервалов для 'charges' и 'bmi'","text":"for col in ['charges', 'bmi']: mean 95, lower 95, upper 95 = confidence interval(df[col], 0.95) mean 99, lower 99, upper 99 = confidence interval(df[col], 0.99)","keywords":["95","99","col","3.","bigdata","bmi","charges","confidence","df","interval","lower","mean"],"topics":[]},{"id":"chunk-31","heading":"3. Расчёт интервалов для 'charges' и 'bmi'","text":"print(f\"===== {col.upper()} =====\") print(f\"Среднее значение: {mean 95:.2f}\") print(f\"95% доверительный интервал: [{lower 95:.2f}, {upper 95:.2f}]\") print(f\"99% доверительный интервал: [{lower 99:.2f}, {upper 99:.2f}]\") print()","keywords":[".2f","print","95","99","3.","bigdata","lower","upper","доверительный","интервал","bmi","charges"],"topics":[]},{"id":"chunk-32","heading":"4. (Дополнительно) Визуализация интервалов","text":"cols = ['charges', 'bmi'] conf 95 = [confidence interval(df[c], 0.95) for c in cols] conf 99 = [confidence interval(df[c], 0.99) for c in cols]","keywords":["cols","bigdata","conf","confidence","df","interval","0.95","0.99","3.","4.","95","99"],"topics":[]},{"id":"chunk-33","heading":"Построение графика","text":"plt.errorbar(x, means, yerr=err 95, fmt='o', color='blue', capsize=5, label='95% ДИ') plt.errorbar(x, means, yerr=err 99, fmt='o', color='red', capsize=8, label='99% ДИ') plt.xticks(x, cols) plt.ylabel(\"Среднее значение\") plt.title(\"Доверительные интервалы для средних значений (charges и bmi)\") plt.legend() plt.grid(True, linestyle=' ', alpha=0.6) plt.show() ```","keywords":["95","99","bigdata","capsize","color","err","fmt","label","means","plt.errorbar","yerr","ди"],"topics":[]},{"id":"chunk-34","heading":"Построение графика","text":"```text ===== CHARGES ===== Среднее значение: 13270.42 95% доверительный интервал: [12620.95, 13919.89] 99% доверительный интервал: [12416.43, 14124.41]","keywords":["bigdata","доверительный","интервал","построение","12416.43","12620.95","13270.42","13919.89","14124.41","3.","95","99"],"topics":[]},{"id":"chunk-35","heading":"Построение графика","text":"===== BMI ===== Среднее значение: 30.66 95% доверительный интервал: [30.34, 30.99] 99% доверительный интервал: [30.23, 31.09]","keywords":["bigdata","доверительный","интервал","построение","3.","30.23","30.34","30.66","30.99","31.09","95","99"],"topics":[]},{"id":"chunk-36","heading":"Построение графика","text":"8. Проверить распределения следующих признаков на нормальность: индекс массы тела, расходы. Сформулировать нулевую и альтернативную гипотезы. Для каждого признака использовать KS тест и q q plot. Сделать выводы на основе полученных p значений.","keywords":["bigdata","построение","3.","8.","ks","plot.","альтернативную","выводы","гипотезы.","графика","данных","датасетами"],"topics":[]},{"id":"chunk-37","heading":"KS тест","text":"print(f\"===== Признак: {col.upper()} =====\") print(\"Нулевая гипотеза H₀: данные распределены нормально.\") print(\"Альтернативная гипотеза H₁: данные НЕ распределены нормально.\") print(f\"Статистика теста: {ks stat:.4f}\") print(f\"p значение: {p value:.4f}\")","keywords":["print",".4f","bigdata","ks","гипотеза","данные","нормально.","распределены","3.","col.upper","h₀","h₁"],"topics":[]},{"id":"chunk-38","heading":"KS тест","text":"if p value < 0.05: print(\"→ Отклоняем H₀: распределение статистически отличается от нормального.\\n\") else: print(\"→ Не отклоняем H₀: нет оснований считать распределение ненормальным.\\n\")","keywords":["bigdata","h₀","print","отклоняем","распределение","0.05","3.","else","if","ks","value","данных"],"topics":[]},{"id":"chunk-39","heading":"Q Q plot","text":"plt.figure(figsize=(6, 5)) sm.qqplot(data, line='s', markerfacecolor='blue', alpha=0.5) plt.title(f\"Q Q Plot для признака '{col}'\") plt.grid(True, linestyle=' ', alpha=0.6) plt.show() ```","keywords":["alpha","bigdata","plot","0.5","0.6","3.","blue","col","data","figsize","line","linestyle"],"topics":[]},{"id":"chunk-40","heading":"Q Q plot","text":"```text ===== Признак: BMI ===== Нулевая гипотеза H₀: данные распределены нормально. Альтернативная гипотеза H₁: данные НЕ распределены нормально. Статистика теста: 0.0261 p значение: 0.3145 → Не отклоняем H₀: нет оснований считать распределение ненормальным.","keywords":["bigdata","h₀","гипотеза","данные","нормально.","распределены","0.0261","0.3145","3.","bmi","h₁","plot"],"topics":[]},{"id":"chunk-41","heading":"Q Q plot","text":"===== Признак: CHARGES ===== Нулевая гипотеза H₀: данные распределены нормально. Альтернативная гипотеза H₁: данные НЕ распределены нормально. Статистика теста: 0.1885 p значение: 0.0000 → Отклоняем H₀: распределение статистически отличается от нормального.","keywords":["bigdata","h₀","гипотеза","данные","нормально.","распределены","0.0000","0.1885","3.","charges","h₁","plot"],"topics":[]},{"id":"chunk-42","heading":"Q Q plot","text":"10. Проверить в данных наличие пропущенных значений. Вывести количество пропущенных значений в процентах. Удалить два признака, в которых больше всех пропущенных значений. Для оставшихся признаков обработать пропуски: для категориального признака использовать заполнение значением по умолчанию (например, «other»), для числового признака использовать заполнение медианным значением","keywords":["признака","пропущенных","bigdata","данных","заполнение","значением","значений.","использовать","10.","3.","other","plot"],"topics":[]},{"id":"chunk-43","heading":"Q Q plot","text":". Показать, что пропусков больше в данных нет.","keywords":["bigdata","данных","3.","plot","больше","датасетами","моделей.","наборы","несколькими","нет.","показать","построение"],"topics":[]},{"id":"chunk-44","heading":"1. Проверяем наличие пропущенных значений","text":"missing counts = df.isna().sum() missing percent = (missing counts / len(df)) 100 print(\"Пропущенные значения в процентах:\\n\", missing percent)","keywords":["missing","bigdata","counts","percent",".sum","1.","100","3.","df","df.isna","len","print"],"topics":[]},{"id":"chunk-45","heading":"2. Находим два признака с наибольшим количеством пропусков и удаляем их","text":"top2 missing cols = missing counts.sort values(ascending=False).head(2).index df = df.drop(columns=top2 missing cols) print(\"\\nУдалены признаки с наибольшим количеством пропусков:\", list(top2 missing cols))","keywords":["missing","cols","top2","bigdata","количеством","наибольшим","пропусков",".head",".index","2.","3.","ascending"],"topics":[]},{"id":"chunk-46","heading":"Для категориальных признаков заполняем значением \"other\"","text":"categorical cols = df.select dtypes(include=['object']).columns df[categorical cols] = df[categorical cols].fillna(\"other\")","keywords":["categorical","cols","bigdata","df","other",".columns",".fillna","3.","df.select","dtypes","include","object"],"topics":[]},{"id":"chunk-47","heading":"Для числовых признаков заполняем медианой","text":"numeric cols = df.select dtypes(include=['number']).columns df[numeric cols] = df[numeric cols].fillna(df[numeric cols].median())","keywords":["cols","numeric","df","bigdata",".columns",".fillna",".median","3.","df.select","dtypes","include","number"],"topics":[]},{"id":"chunk-48","heading":"4. Проверяем, что пропусков больше нет","text":"print(\"\\nКоличество пропущенных значений после обработки:\\n\", df.isna().sum()) ```","keywords":["bigdata",".sum","3.","4.","df.isna","nколичество","print","больше","данных","датасетами","значений","моделей."],"topics":[]},{"id":"chunk-49","heading":"4. Проверяем, что пропусков больше нет","text":"```text Пропущенные значения в процентах: dateRep 0.000000 day 0.000000 month 0.000000 year 0.000000 cases 0.000000 deaths 0.000000 countriesAndTerritories 0.000000 geoId 0.444236 countryterritoryCode 0.198695 popData2019 0.198695 continentExp 0.000000 Cumulative number for 14 days of COVID 19 cases per 100000 4.650750 dtype: float64","keywords":["0.000000","0.198695","bigdata","cases","0.444236","100000","14","19","3.","4.","4.650750","continentexp"],"topics":[]},{"id":"chunk-50","heading":"4. Проверяем, что пропусков больше нет","text":"Удалены признаки с наибольшим количеством пропусков: ['Cumulative number for 14 days of COVID 19 cases per 100000', 'geoId']","keywords":["bigdata","пропусков","100000","14","19","3.","4.","cases","covid","cumulative","days","geoid"],"topics":[]},{"id":"chunk-51","heading":"4. Проверяем, что пропусков больше нет","text":"Количество пропущенных значений после обработки: dateRep 0 day 0 month 0 year 0 cases 0 deaths 0 countriesAndTerritories 0 countryterritoryCode 0 popData2019 0 continentExp 0 dtype: int64 ```","keywords":["bigdata","3.","4.","cases","continentexp","countriesandterritories","countryterritorycode","daterep","day","deaths","dtype","int64"],"topics":[]},{"id":"chunk-52","heading":"4. Проверяем, что пропусков больше нет","text":"11. Посмотреть статистику по данным, используя describe(). Сделать выводы о том, какие признаки содержат выбросы. Посмотреть, для каких стран количество смертей в день превысило 3000 и сколько таких дней было.","keywords":["bigdata","посмотреть","11.","3.","3000","4.","describe","больше","было.","выбросы.","выводы","данным"],"topics":[]},{"id":"chunk-53","heading":"Обычно выбросы проявляются как экстремальные значения в 'max' и 'min'","text":"numeric cols = df.select dtypes(include=['number']).columns for col in numeric cols: q1 = df[col].quantile(0.25) q3 = df[col].quantile(0.75) iqr = q3 q1 lower bound = q1 1.5 iqr upper bound = q3 + 1.5 iqr outliers = df[(df[col] upper bound)] if not outliers.empty: print(f\"\\nПризнак '{col}' содержит выбросы.\") print(f\"Количество выбросов: {len(outliers)}\")","keywords":["col","df","bound","iqr","q1","q3",".quantile","1.5","bigdata","cols","numeric","outliers"],"topics":[]},{"id":"chunk-54","heading":"3. Смотрим, для каких стран количество смертей в день превышало 3000","text":"high deaths = df[df['deaths'] 3000] print(\"\\nДни с количеством смертей 3000:\") print(high deaths[['countriesAndTerritories', 'dateRep', 'deaths']])","keywords":["deaths","3000","3.","bigdata","df","high","print","смертей","countriesandterritories","daterep","nдни","данных"],"topics":[]},{"id":"chunk-55","heading":"4. Сколько таких дней было","text":"num high death days = len(high deaths) print(f\"\\nКоличество дней с количеством смертей 3000: {num high death days}\") ```","keywords":["high","bigdata","days","death","num","дней","3.","3000","4.","deaths","len","nколичество"],"topics":[]},{"id":"chunk-56","heading":"4. Сколько таких дней было","text":"day month year cases deaths \\ count 61904.000000 61904.000000 61904.000000 61904.000000 61904.000000 mean 15.629232 7.067104 2019.998918 1155.079026 26.053987 std 8.841624 2.954816 0.032881 6779.010824 131.222948 min 1.000000 1.000000 2019.000000 8261.000000 1918.000000 25% 8.000000 5.000000 2020.000000 0.000000 0.000000 50% 15.000000 7.000000 2020.000000 15.000000 0.000000 75% 23.000000 10.000000 2020.000000","keywords":["61904.000000","0.000000","2020.000000","1.000000","15.000000","bigdata","0.032881","10.000000","1155.079026","131.222948","15.629232","1918.000000"],"topics":[]},{"id":"chunk-57","heading":"4. Сколько таких дней было","text":"273.000000 4.000000 max 31.000000 12.000000 2020.000000 234633.000000 4928.000000","keywords":["bigdata","12.000000","2020.000000","234633.000000","273.000000","3.","31.000000","4.","4.000000","4928.000000","max","было"],"topics":[]},{"id":"chunk-58","heading":"4. Сколько таких дней было","text":"popData2019 \\ count 6.178100e+04 mean 4.098628e+07 std 1.531246e+08 min 8.150000e+02 25% 1.293120e+06 50% 7.169456e+06 75% 2.851583e+07 max 1.433784e+09","keywords":["bigdata","1.293120e+06","1.433784e+09","1.531246e+08","2.851583e+07","25","3.","4.","4.098628e+07","50","6.178100e+04","7.169456e+06"],"topics":[]},{"id":"chunk-59","heading":"4. Сколько таких дней было","text":"Cumulative number for 14 days of COVID 19 cases per 100000 count 59025.000000 mean 66.316369 std 162.324550 min 147.419587 25% 0.757526 50% 6.724045 75% 52.561206 max 1900.836210","keywords":["bigdata","0.757526","100000","14","147.419587","162.324550","19","1900.836210","25","3.","4.","50"],"topics":[]},{"id":"chunk-60","heading":"4. Сколько таких дней было","text":"Признак 'Cumulative number for 14 days of COVID 19 cases per 100000' содержит выбросы. Количество выбросов: 8073","keywords":["bigdata","100000","14","19","3.","4.","8073","cases","covid","cumulative","days","number"],"topics":[]},{"id":"chunk-61","heading":"4. Сколько таких дней было","text":"Дни с количеством смертей 3000: countriesAndTerritories dateRep deaths 2118 Argentina 02/10/2020 3351 16908 Ecuador 07/09/2020 3800 37038 Mexico 09/10/2020 3013 44888 Peru 14/08/2020 3935 44909 Peru 24/07/2020 3887 59007 United States of America 12/12/2020 3343 59009 United States of America 10/12/2020 3124 59016 United States of America 03/12/2020 3190 59239 United States of America 24/04/2020 3179 59245 United","keywords":["2020","united","12","america","states","10","07","09","24","bigdata","peru","02"],"topics":["testing"]},{"id":"chunk-62","heading":"4. Сколько таких дней было","text":"States of America 18/04/2020 3770 59247 United States of America 16/04/2020 4928","keywords":["04","2020","america","bigdata","states","16","18","3.","3770","4.","4928","59247"],"topics":["testing"]},{"id":"chunk-63","heading":"1. Проверяем наличие дубликатов","text":"num duplicates = df.duplicated().sum() print(f\"Количество дубликатов: {num duplicates}\")","keywords":["bigdata","duplicates","num","дубликатов",".sum","1.","3.","df.duplicated","print","данных","датасетами","количество"],"topics":[]},{"id":"chunk-64","heading":"2. Удаляем дубликаты","text":"df = df.drop duplicates() print(f\"Количество строк после удаления дубликатов: {len(df)}\") ```","keywords":["bigdata","df","2.","3.","df.drop","duplicates","len","print","данных","датасетами","дубликатов","дубликаты"],"topics":[]},{"id":"chunk-65","heading":"2. Удаляем дубликаты","text":"Взять оттуда две выборки. Одна выборка – это индекс массы тела людей c региона northwest, вторая выборка – это индекс массы тела людей с региона southwest. Сравнить средние значения этих выборок, используя t критерий Стьюдента. Предварительно проверить выборки на нормальность (критерий Шопиро Уилка) и на гомогенность дисперсии (критерий Бартлетта).","keywords":["критерий","bigdata","выборка","индекс","людей","массы","региона","тела","2.","3.","northwest","southwest."],"topics":[]},{"id":"chunk-66","heading":"2. Создаем две выборки по регионам","text":"bmi nw = df[df['region'] == 'northwest']['bmi'] bmi sw = df[df['region'] == 'southwest']['bmi']","keywords":["bmi","df","bigdata","region","2.","3.","northwest","nw","southwest","sw","выборки","данных"],"topics":[]},{"id":"chunk-67","heading":"3. Проверка на нормальность (Shapiro Wilk test)","text":"print(\"Shapiro Wilk test для northwest:\", shapiro nw) print(\"Shapiro Wilk test для southwest:\", shapiro sw)","keywords":["shapiro","test","wilk","3.","bigdata","print","northwest","nw","southwest","sw","данных","датасетами"],"topics":["testing"]},{"id":"chunk-68","heading":"4. Проверка на гомогенность дисперсий (Bartlett test)","text":"bartlett test = bartlett(bmi nw, bmi sw) print(\"\\nBartlett test:\", bartlett test)","keywords":["bartlett","test","bigdata","bmi","3.","4.","nbartlett","nw","print","sw","гомогенность","данных"],"topics":["testing"]},{"id":"chunk-69","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"equal var = bartlett test.pvalue 0.05 t test = ttest ind(bmi nw, bmi sw, equal var=equal var)","keywords":["equal","var","bigdata","bmi","0.05","3.","bartlett","false","ind","nw","sw","test"],"topics":["testing"]},{"id":"chunk-70","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"print(\"\\nT test Стьюдента:\", t test) print(\"\\nСредние значения BMI по выборкам:\") print(\"Northwest:\", bmi nw.mean()) print(\"Southwest:\", bmi sw.mean()) ```","keywords":["print","bmi","bigdata","test","3.","equal","false","northwest","nt","nw.mean","nсредние","southwest"],"topics":["testing"]},{"id":"chunk-71","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"```text Shapiro Wilk test для northwest: ShapiroResult(statistic=np.float64(0.995464981663833), pvalue=np.float64(0.4655897798883668)) Shapiro Wilk test для southwest: ShapiroResult(statistic=np.float64(0.9949269360950754), pvalue=np.float64(0.36296471144790743))","keywords":["np.float64","bigdata","pvalue","shapiro","shapiroresult","statistic","test","wilk","0.36296471144790743","0.4655897798883668","0.9949269360950754","0.995464981663833"],"topics":["numpy","testing"]},{"id":"chunk-72","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"Bartlett test: BartlettResult(statistic=np.float64(3.4000745256459286), pvalue=np.float64(0.06519347353581818))","keywords":["bigdata","np.float64","0.06519347353581818","3.","3.4000745256459286","bartlett","bartlettresult","equal","false","pvalue","statistic","test"],"topics":["numpy","testing"]},{"id":"chunk-73","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"T test Стьюдента: TtestResult(statistic=np.float64( 3.2844171500398582), pvalue=np.float64(0.001076958496307695), df=np.float64(648.0))","keywords":["np.float64","bigdata","0.001076958496307695","3.","3.2844171500398582","648.0","df","equal","false","pvalue","statistic","test"],"topics":["numpy","testing"]},{"id":"chunk-74","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"Средние значения BMI по выборкам: Northwest: 29.199784615384615 Southwest: 30.59661538461538 ```","keywords":["bigdata","29.199784615384615","3.","30.59661538461538","bmi","equal","false","northwest","southwest","true","var","выборкам"],"topics":[]},{"id":"chunk-75","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"14. Кубик бросили 600 раз, получили следующие результаты: N Количество выпадений 1 97 2 98 3 109 4 95 5 97 6 104","keywords":["97","bigdata","104","109","14.","3.","600","95","98","equal","false","true"],"topics":[]},{"id":"chunk-76","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"С помощью критерия Хи квадрат проверить, является ли полученное распределение равномерным. scipy.stats.chisquare().","keywords":["bigdata","3.","equal","false","scipy.stats.chisquare","true","var","гомогенные","данных","датасетами","дисперсии","если"],"topics":[]},{"id":"chunk-77","heading":"2. Ожидаемое количество выпадений для равномерного распределения","text":"total rolls = sum(observed) expected = [total rolls / 6] 6 равные вероятности для 6 граней","keywords":["bigdata","rolls","total","2.","3.","expected","observed","sum","вероятности","выпадений","граней","данных"],"topics":[]},{"id":"chunk-78","heading":"4. Вывод о равномерности","text":"alpha = 0.05 if p value < alpha: print(\"Распределение не равномерное (отвергаем H0)\") else: print(\"Распределение можно считать равномерным (не отвергаем H0)\") ```","keywords":["alpha","bigdata","h0","print","отвергаем","распределение","0.05","3.","4.","else","if","value"],"topics":[]},{"id":"chunk-79","heading":"4. Вывод о равномерности","text":"text Chi square statistic: 1.44 p value: 0.9198882077437889 Распределение можно считать равномерным (не отвергаем H0)","keywords":["bigdata","0.9198882077437889","1.44","3.","4.","chi","h0","square","statistic","text","value","вывод"],"topics":[]},{"id":"chunk-80","heading":"4. Вывод о равномерности","text":"15. С помощью критерия Хи квадрат проверить, являются ли переменные зависимыми. Создать датафрейм, используя следующий код:","keywords":["bigdata","15.","3.","4.","вывод","данных","датасетами","датафрейм","зависимыми.","используя","квадрат","код"],"topics":["pandas"]},{"id":"chunk-81","heading":"4. Вывод о равномерности","text":"data = pd.DataFrame({'Женат': [89,17,11,43,22,1], 'Гражданский брак': [80,22,20,35,6,4], 'Не состоит в отношениях': [35,44,35,6,8,22]}) data.index = ['Полный рабочий день','Частичная занятость','Временно не работает','На домохозяйстве','На пенсии','Учёба']","keywords":["22","35","bigdata","11","17","20","3.","4.","43","44","80","89"],"topics":["pandas"]},{"id":"chunk-82","heading":"4. Вывод о равномерности","text":"Использовать функцию scipy.stats.chi2 contingency() . Влияет ли семейное положение на занятость?","keywords":["bigdata","3.","4.","contingency","scipy.stats.chi2","влияет","вывод","данных","датасетами","занятость","использовать","ли"],"topics":[]},{"id":"chunk-83","heading":"1. Создаем датафрейм","text":"data = pd.DataFrame({ 'Женат': [89, 17, 11, 43, 22, 1], 'Гражданский брак': [80, 22, 20, 35, 6, 4], 'Не состоит в отношениях': [35, 44, 35, 6, 8, 22] }) data.index = ['Полный рабочий день','Частичная занятость','Временно не работает', 'На домохозяйстве','На пенсии','Учёба']","keywords":["22","35","bigdata","1.","11","17","20","3.","43","44","80","89"],"topics":["pandas"]},{"id":"chunk-84","heading":"2. Применяем критерий Хи квадрат для таблицы сопряженности","text":"print(\"\\nChi square statistic:\", chi2 stat) print(\"p value:\", p value) print(\"Степени свободы:\", dof) print(\"\\nОжидаемые частоты:\\n\", pd.DataFrame(expected, index=data.index, columns=data.columns))","keywords":["print","bigdata","value","2.","3.","chi2","columns","data.columns","data.index","dof","expected","index"],"topics":["pandas"]},{"id":"chunk-85","heading":"3. Вывод о зависимости","text":"alpha = 0.05 if p value < alpha: print(\"\\nСемейное положение и занятость зависимы (отвергаем H0)\") else: print(\"\\nСемейное положение и занятость независимы (не отвергаем H0)\") ```","keywords":["3.","alpha","bigdata","h0","nсемейное","print","занятость","отвергаем","положение","0.05","else","if"],"topics":[]},{"id":"chunk-86","heading":"3. Вывод о зависимости","text":"Женат Гражданский брак Не состоит в отношениях Полный рабочий день 89 80 35 Частичная занятость 17 22 44 Временно не работает 11 20 35 На домохозяйстве 43 35 6 На пенсии 22 6 8 Учёба 1 4 22","keywords":["22","35","3.","bigdata","11","17","20","43","44","80","89","брак"],"topics":[]},{"id":"chunk-87","heading":"3. Вывод о зависимости","text":"Chi square statistic: 122.29654948595365 p value: 1.7291616900960234e 21 Степени свободы: 10","keywords":["3.","bigdata","1.7291616900960234e","10","122.29654948595365","21","chi","square","statistic","value","вывод","данных"],"topics":[]},{"id":"chunk-88","heading":"3. Вывод о зависимости","text":"Ожидаемые частоты: Женат Гражданский брак Не состоит в отношениях Полный рабочий день 74.664 68.136 61.2 Частичная занятость 30.378 27.722 24.9 Временно не работает 24.156 22.044 19.8 На домохозяйстве 30.744 28.056 25.2 На пенсии 13.176 12.024 10.8 Учёба 9.882 9.018 8.1","keywords":["3.","bigdata","10.8","12.024","13.176","19.8","22.044","24.156","24.9","25.2","27.722","28.056"],"topics":[]},{"id":"chunk-89","heading":"3. Вывод о зависимости","text":"В ходе выполнения заданий был использован комплекс технологий анализа данных: библиотеки pandas, scipy, matplotlib и seaborn.","keywords":["3.","bigdata","данных","matplotlib","pandas","scipy","seaborn.","анализа","библиотеки","был","вывод","выполнения"],"topics":["pandas"]},{"id":"chunk-90","heading":"3. Вывод о зависимости","text":"Получен опыт работы с реальными наборами данных — от первичной загрузки и проверки на пропуски и дубликаты до статистического анализа и визуализации: расчет описательных статистик, построение гистограмм и box plot, проверка нормальности и гомогенности дисперсий, применение t теста и χ² теста для проверки гипотез.","keywords":["3.","bigdata","данных","построение","проверки","теста","box","plot","χ²","анализа","визуализации","вывод"],"topics":[]},{"id":"chunk-91","heading":"3. Вывод о зависимости","text":"В заданиях столкнулся с обработкой медицинских и эпидемиологических данных, анализом распределений, проверкой равномерности событий и исследованием зависимостей между категориальными переменными, что позволило закрепить практические навыки статистического анализа и работы с данными.","keywords":["3.","bigdata","данных","анализа","анализом","вывод","данными.","датасетами","зависимостей","зависимости","заданиях","закрепить"],"topics":[]}]},{"id":"python/practice-4-oop","href":"/docs/python/practice-4-oop","slug":["python","practice-4-oop"],"section":"python","sectionTitle":"Python","title":"Практика 4 — OOP","description":"Классы, объекты и деревья выражений из рабочей тетради №4.","preview":"Источник: pr Python/pr Python/pract4.ipynb Выполнение рабочей тетради № 4 1. Некоторые операции с классами и объектами 1.1. (уровень сложности: простейший) Напишите код, который выведет на экране все имена полей объекта произвольного пользовательского класса,","keywords":["self","сложности","уровень","def","key","средний","return","value","dict","my","age","node","node.left.accept","node.right.accept","self.key","visit","простейший","3.3."],"topics":["python","ai","oop","testing","algorithms"],"chunks":[{"id":"chunk-0","heading":"1.1. (уровень сложности: простейший)","text":"Напишите код, который выведет на экране все имена полей объекта произвольного пользовательского класса, кроме служебных имен.","keywords":["python","1.1.","4.","oop","выведет","выражений","деревья","имен.","имена","класса","классы","код"],"topics":["oop"]},{"id":"chunk-1","heading":"1.1. (уровень сложности: простейший)","text":"````python class Body(object): def init (self,age, len): self.age = age self.len = len","keywords":["python","age","len","1.1.","4.","body","class","def","init","object","oop","self"],"topics":["python"]},{"id":"chunk-2","heading":"1.2. (уровень сложности: простейший)","text":"Напишите код, который по имени метода, заданному строкой, вызовет этот метод в объекте некоторого пользовательского класса.","keywords":["python","1.2.","4.","oop","вызовет","выражений","деревья","заданному","имени","класса.","классы","код"],"topics":["oop"]},{"id":"chunk-3","heading":"1.2. (уровень сложности: простейший)","text":"````python class Body(object): def init (self,age, len): self.age = age self.len = len","keywords":["python","age","len","1.2.","4.","body","class","def","init","object","oop","self"],"topics":["python"]},{"id":"chunk-4","heading":"1.3. (уровень сложности: простейший)","text":"С кодом ниже что то не так. Что именно неправильно и как это исправить? ``` class A: pass","keywords":["python","1.3.","4.","class","oop","pass","выражений","деревья","именно","исправить","классы","кодом"],"topics":[]},{"id":"chunk-5","heading":"1.4. (уровень сложности: низкий)","text":"Напишите функцию однострочник get inheritance для вывода строки, отражающей иерархию наследования для входного класса.","keywords":["python","1.4.","4.","get","inheritance","oop","входного","вывода","выражений","деревья","иерархию","класса."],"topics":["oop"]},{"id":"chunk-6","heading":"1.4. (уровень сложности: низкий)","text":"def getinheritance(classname): return f\"{classname. name } {', '.join([base. name for base in classname. bases ])}\"","keywords":["classname.","name","python",".join","1.4.","4.","base","base.","bases","classname","def","getinheritance"],"topics":[]},{"id":"chunk-7","heading":"2. Своя реализация структуры данных","text":"Примените тестирование на случайных данных с использованием assert и оригинального dict.","keywords":["python","данных","2.","4.","assert","dict.","oop","выражений","деревья","использованием","классы","объекты"],"topics":["testing","algorithms"]},{"id":"chunk-8","heading":"2.2. (уровень сложности: низкий)","text":"Реализуйте для методов своей хэш таблицы тот же интерфейс, что и в dict, включая перегрузку операций.","keywords":["python","2.2.","4.","dict","oop","включая","выражений","деревья","же","интерфейс","классы","методов"],"topics":[]},{"id":"chunk-9","heading":"2.3. (уровень сложности: средний)","text":"Реализуйте поддержку итератора для цикла for. Обязательно протестируйте код на примерах с вложенными циклами!","keywords":["python","2.3.","4.","for.","oop","вложенными","выражений","деревья","итератора","классы","код","объекты"],"topics":[]},{"id":"chunk-10","heading":"2.3. (уровень сложности: средний)","text":"def getitem (self, key): for k, v in self.key value pairs: if k == key: return v raise KeyError(f\"Key '{key}' not found\")","keywords":["key","python","2.3.","4.","def","found","getitem","if","keyerror","not","oop","pairs"],"topics":["ai"]},{"id":"chunk-11","heading":"2.3. (уровень сложности: средний)","text":"def setitem (self, key, value): for i, (k, v) in enumerate(self.key value pairs): if k == key: self.key value pairs[i] = (key, value) return self.key value pairs.append((key, value))","keywords":["value","key","self.key","pairs","python","2.3.","4.","def","enumerate","if","oop","pairs.append"],"topics":["ai"]},{"id":"chunk-12","heading":"2.3. (уровень сложности: средний)","text":"def delitem (self, key): for i, (k, v) in enumerate(self.key value pairs): if k == key: del self.key value pairs[i] return raise KeyError(f\"Key '{key}' not found\")","keywords":["key","pairs","python","self.key","value","2.3.","4.","def","del","delitem","enumerate","found"],"topics":["ai"]},{"id":"chunk-13","heading":"Тестирование пользовательской хэш таблицы","text":"my dict = MyHashTable() my dict[\"name\"] = \"Alice\" my dict[\"age\"] = 30 my dict[\"city\"] = \"Wonderland\"","keywords":["dict","my","python","30","4.","age","alice","city","myhashtable","name","oop","wonderland"],"topics":["testing"]},{"id":"chunk-14","heading":"Получение значений","text":"print(my dict[\"name\"]) Вывод: Alice print(my dict[\"age\"]) Вывод: 30 print(my dict[\"city\"]) Вывод: Wonderland","keywords":["dict","my","print","вывод","python","30","4.","age","alice","city","name","oop"],"topics":[]},{"id":"chunk-15","heading":"3. Деревья выражений","text":"Требуется реализовать операции для печати, вычисления арифметических выражений и компиляции выражений в код стековой машины.","keywords":["выражений","python","деревья","3.","4.","oop","арифметических","вычисления","классы","код","компиляции","машины."],"topics":[]},{"id":"chunk-16","heading":"3.1. (уровень сложности: низкий)","text":"Реализовать классы узлов дерева: Num, Add и Mul. Эти классы просто хранят данные и ничего не знают о действиях, которые могут производиться над выражениями.","keywords":["классы","python","3.1.","4.","add","mul.","num","oop","выражений","выражениями.","данные","действиях"],"topics":["oop"]},{"id":"chunk-17","heading":"3.1. (уровень сложности: низкий)","text":"Пример, который будет использоваться далее: ast = Add(Num(7), Mul(Num(3), Num(2)))","keywords":["num","python","3.1.","4.","add","ast","mul","oop","будет","выражений","далее","деревья"],"topics":["python"]},{"id":"chunk-18","heading":"3.2. (уровень сложности: средний)","text":"Реализовать класс посетитель PrintVisitor для печати выражения. Обойтись без перегрузки repr и str, а также без операторов ветвления!","keywords":["python","без","3.2.","4.","oop","printvisitor","repr","str","ветвления","выражений","выражения.","деревья"],"topics":["oop"]},{"id":"chunk-19","heading":"3.2. (уровень сложности: средний)","text":"def visit add(self, node): return f\"({node.left.accept(self)} + {node.right.accept(self)})\"","keywords":["self","python","3.2.","4.","add","def","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-20","heading":"3.2. (уровень сложности: средний)","text":"def visit mul(self, node): return f\"({node.left.accept(self)} {node.right.accept(self)})\"","keywords":["self","python","3.2.","4.","def","mul","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-21","heading":"3.3. (уровень сложности: средний)","text":"Реализовать класс посетитель CalcVisitor для вычисления выражения. Обойтись без eval с exec, а также без операторов ветвления!","keywords":["python","без","3.3.","4.","calcvisitor","eval","exec","oop","ветвления","выражений","выражения.","вычисления"],"topics":["oop"]},{"id":"chunk-22","heading":"3.3. (уровень сложности: средний)","text":"def visit add(self, node): return f\"({node.left.accept(self)} + {node.right.accept(self)})\"","keywords":["self","python","3.3.","4.","add","def","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-23","heading":"3.3. (уровень сложности: средний)","text":"def visit mul(self, node): return f\"({node.left.accept(self)} {node.right.accept(self)})\"","keywords":["self","python","3.3.","4.","def","mul","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-24","heading":"3.3. (уровень сложности: средний)","text":"def visit add(self, node): return node.left.accept(self) + node.right.accept(self)","keywords":["self","python","3.3.","4.","add","def","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-25","heading":"3.3. (уровень сложности: средний)","text":"def visit mul(self, node): return node.left.accept(self) node.right.accept(self)","keywords":["self","python","3.3.","4.","def","mul","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]}]},{"id":"bigdata/practice-04-insurance-analysis","href":"/docs/bigdata/practice-04-insurance-analysis","slug":["bigdata","practice-04-insurance-analysis"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 4. Анализ данных страхования","description":"Практика с датасетом по страхованию и подготовкой признаков.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитривеич ИКБО 25 22 Практическая работа №4 Корреляция, линейная регрессия и дисперсионный анализ 1. Определить два вектора, представляющие собо","keywords":["non","null","schools","136","136.000000","count","int64","bmi","df","girls","mean","boys","northeast","northwest","southeast","southwest","75","region"],"topics":["python","ai","numpy","testing","regression"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","практика","4.","wiki","анализ","версия","данных","датасетом","документацию.","исходный","материал","ноутбук"],"topics":[]},{"id":"chunk-1","heading":"Корреляция, линейная регрессия и дисперсионный анализ","text":"1. Определить два вектора, представляющие собой число автомобилей, припаркованных в течении 5 рабочих дней у бизнес центра на уличной стоянке и в подземном гараже.","keywords":["bigdata","анализ","практика","1.","4.","автомобилей","бизнес","вектора","гараже.","данных","датасетом","два"],"topics":["regression"]},{"id":"chunk-2","heading":"Корреляция, линейная регрессия и дисперсионный анализ","text":"День Улица Гараж Понедельник 80 100 Вторник 98 82 Среда 75 105 Четверг 91 89 Пятница 78 102","keywords":["bigdata","анализ","практика","100","102","105","4.","75","78","80","82","89"],"topics":["regression"]},{"id":"chunk-3","heading":"Выводим данные","text":"text Понедельник Улица: 80, Гараж: 100 Вторник Улица: 98, Гараж: 82 Среда Улица: 75, Гараж: 105 Четверг Улица: 91, Гараж: 89 Пятница Улица: 78, Гараж: 102","keywords":["гараж","улица","bigdata","практика","100","102","105","4.","75","78","80","82"],"topics":[]},{"id":"chunk-4","heading":"Выводим данные","text":"1.1. Найти и интерпретировать корреляцию между переменными «Улица» и «Гараж» (подсчитать корреляцию по Пирсону).","keywords":["bigdata","корреляцию","практика","1.1.","4.","анализ","выводим","гараж","данные","данных","датасетом","интерпретировать"],"topics":[]},{"id":"chunk-5","heading":"Данные","text":"улица = np.array([80, 98, 75, 91, 78]) гараж = np.array([100, 82, 105, 89, 102])","keywords":["bigdata","np.array","практика","100","102","105","4.","75","78","80","82","89"],"topics":["numpy"]},{"id":"chunk-6","heading":"Вычисляем корреляцию Пирсона","text":"print(f\"Коэффициент корреляции Пирсона между улицей и гаражом: {корреляция:.2f}\") ```","keywords":["bigdata","пирсона","практика",".2f","4.","print","анализ","вычисляем","гаражом","данных","датасетом","корреляции"],"topics":[]},{"id":"chunk-7","heading":"Данные","text":"улица = np.array([80, 98, 75, 91, 78]) гараж = np.array([100, 82, 105, 89, 102]) дни = [\"Понедельник\", \"Вторник\", \"Среда\", \"Четверг\", \"Пятница\"]","keywords":["bigdata","np.array","практика","100","102","105","4.","75","78","80","82","89"],"topics":["numpy"]},{"id":"chunk-8","heading":"Добавляем подписи к точкам","text":"for i, день in enumerate(дни): plt.text(улица[i]+0.5, гараж[i]+0.5, день, fontsize=9)","keywords":["+0.5","bigdata","день","практика","4.","enumerate","fontsize","plt.text","анализ","гараж","данных","датасетом"],"topics":[]},{"id":"chunk-9","heading":"Подписи осей и заголовок","text":"plt.xlabel(\"Улица\") plt.ylabel(\"Гараж\") plt.title(\"Диаграмма рассеяния: Улица vs Гараж\") plt.grid(True) plt.show() ```","keywords":["bigdata","гараж","практика","улица","4.","plt.grid","plt.show","plt.title","plt.xlabel","plt.ylabel","true","vs"],"topics":[]},{"id":"chunk-10","heading":"Подписи осей и заголовок","text":"2. Найти и выгрузить данные. Вывести, провести предобработку и описать признаки.","keywords":["bigdata","практика","2.","4.","анализ","вывести","выгрузить","данные.","данных","датасетом","заголовок","найти"],"topics":[]},{"id":"chunk-11","heading":"Подписи осей и заголовок","text":"2.1. Построить корреляционную матрицу по одной целевой переменной. Определить наиболее коррелирующую переменную, продолжить с ней работу в следующем пункте.","keywords":["bigdata","практика","2.1.","4.","анализ","данных","датасетом","заголовок","коррелирующую","корреляционную","матрицу","наиболее"],"topics":[]},{"id":"chunk-12","heading":"4. Описательная статистика","text":"```text Первые строки данных: sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","4.","bigdata","данных","практика","115635","161594","178958","2023","272506","27808"],"topics":["python"]},{"id":"chunk-13","heading":"4. Описательная статистика","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","4.","bigdata","практика","121821","15153","2.79","2.83","2017","211201","265676","29510"],"topics":[]},{"id":"chunk-14","heading":"4. Описательная статистика","text":"primary girls schools secondary boys schools secondary girls schools \\ 0 107 24 13 1 84 23 18 2 177 45 46 3 124 46 29 4 94 24 31","keywords":["schools","24","4.","46","bigdata","girls","secondary","практика","107","124","13","177"],"topics":[]},{"id":"chunk-15","heading":"4. Описательная статистика","text":"high boys schools high girls schools Intermediate boys schools \\ 0 29 10 2 1 27 17 1 2 32 21 6 3 27 11 4 4 18 18 2","keywords":["schools","18","27","4.","bigdata","boys","high","практика","10","11","17","21"],"topics":[]},{"id":"chunk-16","heading":"4. Описательная статистика","text":"Intermediate girls schools total boys schools total girls schools \\ 0 2 249 132 1 1 269 120 2 1 569 245 3 4 507 168 4 0 268 143","keywords":["schools","4.","bigdata","girls","total","практика","120","132","143","168","245","249"],"topics":[]},{"id":"chunk-17","heading":"4. Описательная статистика","text":"Информация о данных: RangeIndex: 136 entries, 0 to 135 Data columns (total 22 columns):","keywords":["4.","bigdata","columns","данных","практика","135","136","22","data","entries","rangeindex","total"],"topics":[]},{"id":"chunk-18","heading":"Column Non Null Count Dtype","text":"0 sr.no. 136 non null int64 1 province 136 non null object 2 division 136 non null object 3 district 136 non null object 4 households 136 non null int64 5 population 2023 136 non null int64 6 average household size 136 non null float64 7 population 2017 136 non null int64 8 growth rate 136 non null float64 9 area(km²) 136 non null int64 10 density 2023(people/km²) 136 non null float64 11 primary boys schools 136 non","keywords":["non","136","null","int64","float64","object","2023","bigdata","km²","population","практика","10"],"topics":[]},{"id":"chunk-19","heading":"Column Non Null Count Dtype","text":"null int64 12 primary girls schools 136 non null int64 13 secondary boys schools 136 non null int64 14 secondary girls schools 136 non null int64 15 high boys schools 136 non null int64 16 high girls schools 136 non null int64 17 Intermediate boys schools 136 non null int64 18 Intermediate girls schools 136 non null int64 19 total boys schools 136 non null int64 20 total girls schools 136 non null int64 21 total","keywords":["null","int64","non","136","schools","girls","boys","total","bigdata","high","intermediate","secondary"],"topics":[]},{"id":"chunk-20","heading":"Column Non Null Count Dtype","text":"schools 136 non null int64 dtypes: float64(3), int64(16), object(3) memory usage: 23.5+ KB None","keywords":["bigdata","int64","non","null","практика","136","16","23.5+","4.","column","count","dtype"],"topics":[]},{"id":"chunk-21","heading":"Column Non Null Count Dtype","text":"Описательные статистики: sr.no. households population 2023 average household size \\ count 136.000000 1.360000e+02 1.360000e+02 136.000000 mean 68.500000 2.819159e+05 1.775731e+06 6.451618 std 39.403892 2.789400e+05 1.791408e+06 0.944771 min 1.000000 1.640000e+04 1.275710e+05 4.180000 25% 34.750000 7.052575e+04 4.999090e+05 5.852500 50% 68.500000 2.187750e+05 1.370099e+06 6.390000 75% 102.250000 3.838998e+05","keywords":["1.360000e+02","136.000000","68.500000","bigdata","count","практика","0.944771","1.000000","1.275710e+05","1.370099e+06","1.640000e+04","1.775731e+06"],"topics":[]},{"id":"chunk-22","heading":"Column Non Null Count Dtype","text":"2.338289e+06 6.960000 max 136.000000 2.012526e+06 1.300414e+07 9.110000","keywords":["bigdata","практика","1.300414e+07","136.000000","2.012526e+06","2.338289e+06","4.","6.960000","9.110000","column","count","dtype"],"topics":[]},{"id":"chunk-23","heading":"Column Non Null Count Dtype","text":"population 2017 growth rate area(km²) density 2023(people/km²) \\ count 1.360000e+02 136.000000 136.000000 136.000000 mean 1.527093e+06 2.827574 6036.323529 1521.740000 std 1.540987e+06 1.542254 6802.861419 6036.440643 min 9.705200e+04 0.230000 69.000000 6.020000 25% 4.326818e+05 2.077500 2289.000000 122.947500 50% 1.216293e+06 2.460000 3709.500000 331.580000 75% 1.942269e+06 2.875000 7492.000000 772.712500 max","keywords":["136.000000","bigdata","count","km²","практика","0.230000","1.216293e+06","1.360000e+02","1.527093e+06","1.540987e+06","1.542254","1.942269e+06"],"topics":[]},{"id":"chunk-24","heading":"Column Non Null Count Dtype","text":"1.111998e+07 9.510000 44748.000000 55396.010000","keywords":["bigdata","практика","1.111998e+07","4.","44748.000000","55396.010000","9.510000","column","count","dtype","non","null"],"topics":[]},{"id":"chunk-25","heading":"Column Non Null Count Dtype","text":"primary boys schools primary girls schools secondary boys schools \\ count 136.000000 136.000000 136.000000 mean 579.588235 264.397059 51.860294 std 591.909008 207.461492 34.935321 min 0.000000 0.000000 0.000000 25% 239.250000 93.750000 24.750000 50% 378.000000 206.000000 46.000000 75% 641.750000 395.250000 72.500000 max 3375.000000 1068.000000 241.000000","keywords":["0.000000","136.000000","schools","bigdata","boys","count","primary","практика","1068.000000","206.000000","207.461492","239.250000"],"topics":[]},{"id":"chunk-26","heading":"Column Non Null Count Dtype","text":"secondary girls schools high boys schools high girls schools \\ count 136.000000 136.000000 136.000000 mean 50.786765 64.617647 44.029412 std 51.258879 53.103959 51.681781 min 0.000000 0.000000 0.000000 25% 16.750000 27.000000 10.000000 50% 31.000000 53.000000 23.500000 75% 72.500000 84.250000 55.250000 max 281.000000 345.000000 300.000000","keywords":["0.000000","136.000000","schools","bigdata","count","girls","high","практика","10.000000","16.750000","23.500000","25"],"topics":[]},{"id":"chunk-27","heading":"Column Non Null Count Dtype","text":"Intermediate boys schools Intermediate girls schools \\ count 136.000000 136.000000 mean 10.647059 6.389706 std 20.981660 8.071140 min 0.000000 0.000000 25% 3.000000 1.000000 50% 6.500000 3.000000 75% 12.000000 9.250000 max 233.000000 51.000000","keywords":["0.000000","136.000000","3.000000","bigdata","count","intermediate","schools","практика","1.000000","10.647059","12.000000","20.981660"],"topics":[]},{"id":"chunk-28","heading":"Column Non Null Count Dtype","text":"total boys schools total girls schools total schools count 136.000000 136.000000 136.000000 mean 706.713235 365.602941 1072.316176 std 621.938128 292.229090 753.412205 min 0.000000 0.000000 0.000000 25% 321.250000 120.000000 469.750000 50% 540.500000 268.000000 864.000000 75% 788.000000 582.750000 1439.250000 max 3684.000000 1367.000000 4269.000000 ```","keywords":["0.000000","136.000000","schools","total","bigdata","count","практика","1072.316176","120.000000","1367.000000","1439.250000","25"],"topics":[]},{"id":"chunk-29","heading":"Данные (например, наиболее коррелирующая переменная с целевой)","text":"X = np.array([80, 98, 75, 91, 78]) независимая переменная y = np.array([100, 82, 105, 89, 102]) целевая переменная","keywords":["переменная","bigdata","np.array","практика","100","102","105","4.","75","78","80","82"],"topics":["numpy"]},{"id":"chunk-30","heading":"Данные","text":"X = np.array([80, 98, 75, 91, 78]) независимая переменная y = np.array([100, 82, 105, 89, 102]) целевая переменная","keywords":["bigdata","np.array","переменная","практика","100","102","105","4.","75","78","80","82"],"topics":["numpy"]},{"id":"chunk-31","heading":"Линейная регрессия вручную","text":"X mean = np.mean(X) y mean = np.mean(y) w = np.sum((X X mean) (y y mean)) / np.sum((X X mean) 2) b = y mean w X mean","keywords":["mean","bigdata","np.mean","np.sum","практика","4.","анализ","вручную","данных","датасетом","линейная","подготовкой"],"topics":["numpy","regression"]},{"id":"chunk-32","heading":"Построение графика","text":"plt.figure(figsize=(8,5)) plt.scatter(X, y, color='blue', label='Данные') plt.plot(X, y pred, color='red', label=f'Регрессия: y = {w:.2f}x + {b:.2f}') plt.xlabel(\"X (наиболее коррелирующая переменная)\") plt.ylabel(\"y (Целевая переменная)\") plt.title(\"Линейная регрессия вручную\") plt.legend() plt.grid(True) plt.show() ```","keywords":[".2f","bigdata","color","label","переменная","практика","регрессия","4.","blue","figsize","plt.figure","plt.grid"],"topics":["regression"]},{"id":"chunk-33","heading":"Построение графика","text":"3. Загрузить данные: 'insurance.csv'. Вывести и провести предобработку. Вывести список уникальных регионов.","keywords":["bigdata","вывести","практика","3.","4.","insurance.csv","анализ","графика","данные","данных","датасетом","загрузить"],"topics":[]},{"id":"chunk-34","heading":"Построение графика","text":"3.1. Выполнить однофакторный ANOVA тест, чтобы проверить влияние региона на индекс массы тела (BMI), используя первый способ, через библиотеку Scipy.","keywords":["bigdata","практика","3.1.","4.","anova","bmi","scipy.","анализ","библиотеку","влияние","выполнить","графика"],"topics":[]},{"id":"chunk-35","heading":"Группируем данные по регионам и извлекаем BMI для каждой группы","text":"groups = [df[df['region'] == region]['bmi'] for region in df['region'].unique()]","keywords":["region","df","bigdata","bmi","практика",".unique","4.","groups","анализ","группируем","группы","данные"],"topics":[]},{"id":"chunk-36","heading":"Интерпретация","text":"if p value < 0.05: print(\"Результат значимый: средние значения BMI различаются между регионами.\") else: print(\"Результат не значимый: средние значения BMI не различаются между регионами.\") ```","keywords":["bigdata","bmi","print","значения","значимый","между","практика","различаются","регионами.","результат","средние","0.05"],"topics":[]},{"id":"chunk-37","heading":"Интерпретация","text":"text Уникальные регионы: ['southwest' 'southeast' 'northwest' 'northeast'] F статистика: 39.495 P значение: 0.000 Результат значимый: средние значения BMI различаются между регионами.","keywords":["bigdata","практика","0.000","39.495","4.","bmi","northeast","northwest","southeast","southwest","text","анализ"],"topics":["python"]},{"id":"chunk-38","heading":"Интерпретация","text":"3.2. Выполнить однофакторный ANOVA тест, чтобы проверить влияние региона на индекс массы тела (BMI), используя второй способ, с помощью функции anova lm() из библиотеки statsmodels.","keywords":["anova","bigdata","практика","3.2.","4.","bmi","lm","statsmodels.","анализ","библиотеки","влияние","второй"],"topics":[]},{"id":"chunk-39","heading":"Интерпретация","text":"```python from statsmodels.formula.api import ols from statsmodels.stats.anova import anova lm","keywords":["bigdata","import","практика","4.","anova","lm","ols","python","statsmodels.formula.api","statsmodels.stats.anova","анализ","данных"],"topics":["python"]},{"id":"chunk-40","heading":"Вывод результатов","text":"text Уникальные регионы: ['southwest' 'southeast' 'northwest' 'northeast'] df sum sq mean sq F PR( F) C(region) 3.0 4055.880631 1351.960210 39.495057 1.881839e 24 Residual 1334.0 45664.319755 34.231124 NaN NaN","keywords":["bigdata","nan","sq","практика","1.881839e","1334.0","1351.960210","24","3.0","34.231124","39.495057","4."],"topics":["python"]},{"id":"chunk-41","heading":"Вывод результатов","text":"3.3. С помощью t критерия Стьюдента перебрать все пары. Определить поправку Бонферрони. Сделать выводы.","keywords":["bigdata","практика","3.3.","4.","анализ","бонферрони.","вывод","выводы.","данных","датасетом","критерия","определить"],"topics":[]},{"id":"chunk-42","heading":"Выполняем t тест для каждой пары","text":"for r1, r2 in pairs: group1 = df[df['region'] == r1]['bmi'] group2 = df[df['region'] == r2]['bmi'] t stat, p value = ttest ind(group1, group2, equal var=False) Welch t test significant = \"Да\" if p value < bonf alpha else \"Нет\" print(f\"{r1} vs {r2}: t = {t stat:.3f}, p = {p value:.4f}, значимо после Bonferroni? {significant}\") ```","keywords":["df","r1","r2","value","bigdata","bmi","group1","group2","region","significant","stat","практика"],"topics":["ai","testing"]},{"id":"chunk-43","heading":"Выполняем t тест для каждой пары","text":"text Уникальные регионы: ['southwest' 'southeast' 'northwest' 'northeast'] Поправка Бонферрони: 0.0083 southwest vs southeast: t = 5.952, p = 0.0000, значимо после Bonferroni? Да southwest vs northwest: t = 3.284, p = 0.0011, значимо после Bonferroni? Да southwest vs northeast: t = 3.117, p = 0.0019, значимо после Bonferroni? Да southeast vs northwest: t = 9.377, p = 0.0000, значимо после Bonferroni","keywords":["bonferroni","southwest","vs","значимо","после","northwest","southeast","да","0.0000","bigdata","northeast","практика"],"topics":["python"]},{"id":"chunk-44","heading":"Выполняем t тест для каждой пары","text":"? Да southeast vs northeast: t = 8.835, p = 0.0000, значимо после Bonferroni? Да northwest vs northeast: t = 0.060, p = 0.9519, значимо после Bonferroni? Нет","keywords":["bigdata","bonferroni","northeast","vs","да","значимо","после","практика","0.0000","0.060","0.9519","4."],"topics":["python"]},{"id":"chunk-45","heading":"Пост хок тест Тьюки","text":"tukey = pairwise tukeyhsd(endog=df['bmi'], зависимая переменная groups=df['region'], группирующая переменная alpha=0.05)","keywords":["bigdata","df","переменная","практика","0.05","4.","alpha","bmi","endog","groups","pairwise","region"],"topics":["ai"]},{"id":"chunk-46","heading":"Визуализация результатов","text":"tukey.plot simultaneous(comparison name=df['region'].unique()[0]) выбираем первый регион для базовой линии plt.title(\"Пост хок тест Тьюки для BMI по регионам\") plt.xlabel(\"Разница средних BMI\") plt.show() ```","keywords":["bigdata","bmi","практика",".unique","4.","comparison","df","name","plt.show","plt.title","plt.xlabel","region"],"topics":[]},{"id":"chunk-47","heading":"Визуализация результатов","text":"```text Multiple Comparison of Means Tukey HSD, FWER=0.05 ========================================================== group1 group2 meandiff p adj lower upper reject northeast northwest 0.0263 0.9999 1.1552 1.2078 False northeast southeast 4.1825 0.0 3.033 5.332 True northeast southwest 1.4231 0.0107 0.2416 2.6046 True northwest southeast 4.1562 0.0 3.0077 5.3047 True northwest southwest 1.3968 0.0127 0.2162 2.5774","keywords":["northeast","northwest","true","0.0","bigdata","southeast","southwest","практика","0.0107","0.0127","0.0263","0.05"],"topics":["python"]},{"id":"chunk-48","heading":"Визуализация результатов","text":"True southeast southwest 2.7594 0.0 3.9079 1.6108 True","keywords":["bigdata","true","практика","0.0","1.6108","2.7594","3.9079","4.","southeast","southwest","анализ","визуализация"],"topics":["python"]},{"id":"chunk-49","heading":"Визуализация результатов","text":"3.5. Выполнить двухфакторный ANOVA тест, чтобы проверить влияние региона и пола на индекс массы тела (BMI), используя функцию anova lm() из библиотеки statsmodels.","keywords":["anova","bigdata","практика","3.5.","4.","bmi","lm","statsmodels.","анализ","библиотеки","визуализация","влияние"],"topics":[]},{"id":"chunk-50","heading":"Визуализация результатов","text":"```python from statsmodels.formula.api import ols from statsmodels.stats.anova import anova lm","keywords":["bigdata","import","практика","4.","anova","lm","ols","python","statsmodels.formula.api","statsmodels.stats.anova","анализ","визуализация"],"topics":["python"]},{"id":"chunk-51","heading":"Проверим уникальные значения регионов и пола","text":"print(\"Уникальные регионы:\", df['region'].unique()) print(\"Уникальные значения пола:\", df['sex'].unique())","keywords":["уникальные",".unique","bigdata","df","print","значения","пола","практика","4.","region","sex","анализ"],"topics":[]},{"id":"chunk-52","heading":"Вывод результатов","text":"text Уникальные регионы: ['southwest' 'southeast' 'northwest' 'northeast'] Уникальные значения пола: ['female' 'male'] df sum sq mean sq F PR( F) C(region) 3.0 4055.880631 1351.960210 39.539923 1.773031e 24 C(sex) 1.0 86.007035 86.007035 2.515393 1.129767e 01 Residual 1333.0 45578.312720 34.192283 NaN NaN","keywords":["86.007035","bigdata","nan","sq","практика","уникальные","01","1.0","1.129767e","1.773031e","1333.0","1351.960210"],"topics":["python"]},{"id":"chunk-53","heading":"Вывод результатов","text":"```python from statsmodels.stats.multicomp import pairwise tukeyhsd from statsmodels.formula.api import ols","keywords":["bigdata","import","практика","4.","ols","pairwise","python","statsmodels.formula.api","statsmodels.stats.multicomp","tukeyhsd","анализ","вывод"],"topics":["python","ai"]},{"id":"chunk-54","heading":"Визуализация","text":"tukey.plot simultaneous(comparison name=df['region'].unique()[0]) базовый регион plt.title(\"Пост хок тест Тьюки для BMI по регионам\") plt.xlabel(\"Разница средних BMI\") plt.show() ```","keywords":["bigdata","bmi","практика",".unique","4.","comparison","df","name","plt.show","plt.title","plt.xlabel","region"],"topics":[]},{"id":"chunk-55","heading":"Визуализация","text":"```text Multiple Comparison of Means Tukey HSD, FWER=0.05 ========================================================== group1 group2 meandiff p adj lower upper reject northeast northwest 0.0263 0.9999 1.1552 1.2078 False northeast southeast 4.1825 0.0 3.033 5.332 True northeast southwest 1.4231 0.0107 0.2416 2.6046 True northwest southeast 4.1562 0.0 3.0077 5.3047 True northwest southwest 1.3968 0.0127 0.2162 2.5774","keywords":["northeast","northwest","true","0.0","bigdata","southeast","southwest","практика","0.0107","0.0127","0.0263","0.05"],"topics":["python"]},{"id":"chunk-56","heading":"Визуализация","text":"True southeast southwest 2.7594 0.0 3.9079 1.6108 True","keywords":["bigdata","true","практика","0.0","1.6108","2.7594","3.9079","4.","southeast","southwest","анализ","визуализация"],"topics":["python"]},{"id":"chunk-57","heading":"Визуализация","text":"В ходе работы были проведены статистические анализы данных о здоровье и парковках: выполнена загрузка и предобработка наборов данных, рассчитаны корреляции Пирсона, построены диаграммы рассеяния и реализована линейная регрессия вручную с оценкой наклона, сдвига и MSE .","keywords":["данных","bigdata","практика","4.","mse","анализ","анализы","были","визуализация","вручную","выполнена","датасетом"],"topics":["regression"]},{"id":"chunk-58","heading":"Визуализация","text":"Для данных insurance.csv проведен однофакторный и двухфакторный ANOVA анализ с использованием библиотек Scipy и Statsmodels , выполнены пост хок тесты Тьюки, применена поправка Бонферрони и визуализированы результаты различий между группами. Работа показала применение методов статистики и Python для анализа, визуализации и интерпретации данных.","keywords":["bigdata","анализ","данных","практика","4.","anova","insurance.csv","python","scipy","statsmodels","анализа","библиотек"],"topics":["python"]}]},{"id":"python/practice-5-test-automation","href":"/docs/python/practice-5-test-automation","slug":["python","practice-5-test-automation"],"section":"python","sectionTitle":"Python","title":"Практика 5 — Тестовая автоматизация","description":"Практикум по pytest, coverage, мутационному и property-based тестированию.","preview":"Источник: pr Python/pr Python/pract5.ipynb Практическое занятие №5 Многие задачи из этого блокнота решать удобнее в парном составе: один студент занимается написанием кода программы и пытается внести в код такие ошибки, которые будет сложно выявить с помощью т","keywords":["test","def","number","сложности","уровень","return","assert","средний","account","bytes","balance","self.account","программы","amount","rle","self","тестирование","divide"],"topics":["python","ai","numpy","oop","sql","testing"],"chunks":[{"id":"chunk-0","heading":"Практическое занятие №5","text":"Многие задачи из этого блокнота решать удобнее в парном составе: один студент занимается написанием кода программы и пытается внести в код такие ошибки, которые будет сложно выявить с помощью тестов другому студенту.","keywords":["python","coverage","property-based","pytest","автоматизация","блокнота","будет","внести","выявить","другому","задачи","занимается"],"topics":[]},{"id":"chunk-1","heading":"Практическое занятие №5","text":"Далее потребуется несколько программ, содержащих ошибки. Если в задаче явно не указана программа для тестирования, то используется одна из следующих программ:","keywords":["python","программ","coverage","property-based","pytest","автоматизация","далее","если","задаче","занятие","используется","мутационному"],"topics":[]},{"id":"chunk-2","heading":"2. Бинарный поиск.","text":"python def binary search(arr, x): left = 0 right = len(arr) while left <= right: mid = round((left + right) / 2) if arr[mid] == x: return mid if arr[mid] < x: left = mid + 1 else: right = mid return 1","keywords":["mid","arr","left","right","python","if","return","2.","binary","coverage","def","else"],"topics":["python"]},{"id":"chunk-3","heading":"4. Определение типа треугольника.","text":"python def triangle type(x1, y1, x2, y2, x3, y3): a = distance(x1, y1, x2, y2) b = distance(x2, y2, x3, y3) c = distance(x3, y3, x1, y1) if a == b == c: return \"равнобедренный\" elif a == b or a == c or b == c: return \"равносторонний\" elif a != b != c: return \"разносторонний\"","keywords":["distance","python","return","x1","x2","x3","y1","y2","y3","elif","4.","coverage"],"topics":["python"]},{"id":"chunk-4","heading":"5. Сжатие и расжатие данных по методу RLE.","text":"````python def encode rle(data): encoded = bytes() count = 0 last char = data[ 1] for i in range(1, len(data) + 1): if data[i] == last char: count += 1 else: encoded.append(data[i]) encoded.append(count) count = 1 last char = data[i] encoded.append(count) encoded.append(last char) return bytes(encoded)","keywords":["data","count","char","encoded.append","last","python","bytes","encoded","5.","coverage","def","else"],"topics":["python"]},{"id":"chunk-5","heading":"5. Сжатие и расжатие данных по методу RLE.","text":"def decode rle(data): decoded = bytes() i = 1 while i < len(data): count = data[i 1] char = data[i] decoded.extend([char] count) i += 1 return bytes(decoded) ````","keywords":["data","bytes","char","count","decoded","python","5.","coverage","decode","decoded.extend","def","len"],"topics":[]},{"id":"chunk-6","heading":"6. Банковский счет.","text":"````python class BankAccount: def init (self, account number, balance=0): self.account number = account number self.balance = balance","keywords":["number","python","account","balance","6.","bankaccount","class","coverage","def","init","property-based","pytest"],"topics":["python"]},{"id":"chunk-7","heading":"6. Банковский счет.","text":"def deposit(self, amount): self.balance += amount return f\"{amount} средств успешно зачислены на счет {self.account number}\"","keywords":["amount","python","6.","coverage","def","deposit","number","property-based","pytest","return","self","self.account"],"topics":[]},{"id":"chunk-8","heading":"6. Банковский счет.","text":"def withdraw(self, amount): self.balance = amount return f\"{amount} средств успешно сняты с счета {self.account number}\"","keywords":["amount","python","6.","coverage","def","number","property-based","pytest","return","self","self.account","self.balance"],"topics":[]},{"id":"chunk-9","heading":"6. Банковский счет.","text":"def check balance(self): return f\"Баланс счета {self.account number}: {self.balance}\" ````","keywords":["python","6.","balance","check","coverage","def","number","property-based","pytest","return","self","self.account"],"topics":[]},{"id":"chunk-10","heading":"7. Банковский счет с использованием базы данных.","text":"class BankAccount: def init (self, account number): self.account number = account number self.conn = sqlite3.connect('bank.db') self.cursor = self.conn.cursor() self.cursor.execute( \"CREATE TABLE IF NOT EXISTS accounts (account number INTEGER PRIMARY KEY, balance REAL)\") self.conn.commit()","keywords":["number","account","python","7.","accounts","balance","bank.db","bankaccount","class","coverage","create","def"],"topics":["sql"]},{"id":"chunk-11","heading":"7. Банковский счет с использованием базы данных.","text":"def deposit(self, amount): self.cursor.execute( \"UPDATE accounts SET balance = balance + ? WHERE account number = ?\", (amount, self.account number)) self.conn.commit() return f\"{amount} средств успешно зачислены на счет {self.account number}\"","keywords":["amount","number","balance","python","self.account","счет","7.","account","accounts","coverage","def","deposit"],"topics":["sql"]},{"id":"chunk-12","heading":"7. Банковский счет с использованием базы данных.","text":"def withdraw(self, amount): self.cursor.execute( \"SELECT balance FROM accounts WHERE account number = ?\", (self.account number,)) balance = self.cursor.fetchone()[0] self.cursor.execute( \"UPDATE accounts SET balance = balance ? WHERE account number = ?\", (amount, self.account number)) self.conn.commit() return f\"{amount} средств успешно сняты с счета {self.account number}\"","keywords":["number","balance","amount","self.account","account","accounts","python","self.cursor.execute","where","7.","coverage","def"],"topics":["sql"]},{"id":"chunk-13","heading":"7. Банковский счет с использованием базы данных.","text":"def check balance(self): self.cursor.execute( \"SELECT balance FROM accounts WHERE account number = ?\", (self.account number,)) balance = self.cursor.fetchone()[0] return f\"Баланс счета {self.account number}: {balance}\"","keywords":["balance","number","python","self.account","7.","account","accounts","check","coverage","def","property-based","pytest"],"topics":["sql"]},{"id":"chunk-14","heading":"7. Банковский счет с использованием базы данных.","text":"def close account(self): self.cursor.execute( \"DELETE FROM accounts WHERE account number = ?\", (self.account number,)) self.conn.commit() return f\"Счет {self.account number} закрыт\"","keywords":["number","account","python","self.account","счет","7.","accounts","close","coverage","def","delete","property-based"],"topics":["sql"]},{"id":"chunk-15","heading":"7. Банковский счет с использованием базы данных.","text":"def create account(self, balance): self.cursor.execute( \"INSERT INTO accounts (account number, balance) VALUES (?, ?)\", (self.account number, balance)) self.conn.commit() return f\"Счет {self.account number} успешно создан\" ````","keywords":["balance","number","account","python","self.account","счет","7.","accounts","coverage","create","def","insert"],"topics":["sql"]},{"id":"chunk-16","heading":"1.1. (уровень сложности: низкий)","text":"Исправьте функцию distance. Добавьте документацию к функции в виде docstring строки. Укажите примеры в формате doctest. Примеры должны охватывать граничные случаи. Протестируйте программу с помощью вызова модуля doctest. Перенесите примеры в отдельный файл и снова протестируйте программу.","keywords":["примеры","doctest.","python","протестируйте","1.1.","coverage","distance.","docstring","property-based","pytest","автоматизация","виде"],"topics":["testing"]},{"id":"chunk-17","heading":"1.1. (уровень сложности: низкий)","text":"````python def distance(x1, y1, x2, y2): \"\"\" Calculate the Euclidean distance between two points in a 2D plane.","keywords":["python","distance","1.1.","2d","between","calculate","coverage","def","euclidean","plane.","points","property-based"],"topics":["python"]},{"id":"chunk-18","heading":"1.1. (уровень сложности: низкий)","text":"Arguments: x1 x coordinate of the first point y1 y coordinate of the first point x2 x coordinate of the second point y2 y coordinate of the second point","keywords":["coordinate","point","first","python","second","1.1.","arguments","coverage","property-based","pytest","x1","x2"],"topics":[]},{"id":"chunk-19","heading":"1.1. (уровень сложности: низкий)","text":"Example: distance(0, 0, 3, 4) 3 4 5 right triangle 5.0 distance(0, 0, 1, 1) distance is sqrt(2) 1.4142135623730951 distance(0, 0, 0, 0) distance between the same point 0.0 \"\"\" return ((x2 x1) 2 + (y2 y1) 2) 0.5","keywords":["distance","python","0.0","0.5","1.1.","1.4142135623730951","5.0","between","coverage","example","point","property-based"],"topics":[]},{"id":"chunk-20","heading":"1.2.(уровень сложности: высокий)","text":"Добавьте к функции сортировки тестирование на случайных данных. Исправьте ошибки в функции.","keywords":["python","1.2.","coverage","property-based","pytest","автоматизация","высокий","данных.","добавьте","исправьте","мутационному","ошибки"],"topics":["testing"]},{"id":"chunk-21","heading":"1.2.(уровень сложности: высокий)","text":"Напишите к функции сортировки отдельную функцию спецификацию в виде набора assert'ов. Спецификация должна исчерпывающим образом описывать задачу сортировки (без привлечения готовых функций сортировки), иными словами – для общего случая нельзя придумать такое искажение кода сортировки, которое будет принято спецификацией.","keywords":["сортировки","python","1.2.","assert","coverage","property-based","pytest","автоматизация","без","будет","виде","высокий"],"topics":[]},{"id":"chunk-22","heading":"Проверяем кодирование","text":"assert encode rle(bytes([1, 1, 1, 2, 2, 3, 3, 3, 3])) == bytes([1, 3, 2, 2, 3, 4])","keywords":["bytes","python","assert","coverage","encode","property-based","pytest","rle","автоматизация","кодирование","мутационному","практика"],"topics":[]},{"id":"chunk-23","heading":"Проверяем декодирование","text":"assert decode rle(bytes([1, 3, 2, 2, 3, 4])) == bytes([1, 1, 1, 2, 2, 3, 3, 3, 3])","keywords":["bytes","python","assert","coverage","decode","property-based","pytest","rle","автоматизация","декодирование","мутационному","практика"],"topics":[]},{"id":"chunk-24","heading":"Проверяем крайний случай с одним элементом","text":"assert encode rle(bytes([5])) == bytes([5, 1]) assert decode rle(bytes([5, 1])) == bytes([5])","keywords":["bytes","assert","python","rle","coverage","decode","encode","property-based","pytest","автоматизация","крайний","мутационному"],"topics":[]},{"id":"chunk-25","heading":"Проверяем пустую последовательность","text":"assert encode rle(bytes([])) == bytes([]) assert decode rle(bytes([])) == bytes([])","keywords":["bytes","assert","python","rle","coverage","decode","encode","property-based","pytest","автоматизация","мутационному","последовательность"],"topics":[]},{"id":"chunk-26","heading":"Проверяем последовательность с одним повторяющимся элементом","text":"assert encode rle(bytes([1, 1, 1, 1, 1])) == bytes([1, 5]) assert decode rle(bytes([1, 5])) == bytes([1, 1, 1, 1, 1])","keywords":["bytes","assert","python","rle","coverage","decode","encode","property-based","pytest","автоматизация","мутационному","одним"],"topics":[]},{"id":"chunk-27","heading":"Проверяем последовательность с различными элементами","text":"assert encode rle(bytes([4, 4, 1, 1, 2, 2, 2, 5, 6, 6, 6, 3, 3])) == bytes([4, 2, 1, 2, 2, 3, 5, 6, 3, 3]) assert decode rle(bytes([4, 2, 1, 2, 2, 3, 5, 6, 3, 3])) == bytes([4, 4, 1, 1, 2, 2, 2, 5, 6, 6, 6, 3, 3]) ````","keywords":["bytes","assert","python","rle","coverage","decode","encode","property-based","pytest","автоматизация","мутационному","последовательность"],"topics":[]},{"id":"chunk-28","heading":"1.3. (уровень сложности: средний)","text":"Реализуйте конструкцию raises с помощью менеджера контекста в духе таковой из pytest.","keywords":["python","1.3.","coverage","property-based","pytest","pytest.","raises","автоматизация","духе","конструкцию","контекста","менеджера"],"topics":["python","ai","testing"]},{"id":"chunk-29","heading":"1.3. (уровень сложности: средний)","text":"def exit (self, exc type, exc value, traceback): if exc type is None: raise AssertionError(f\"{self.exception. name } was not raised\") if issubclass(exc type, self.exception): return True","keywords":["exc","type","if","python","1.3.","assertionerror","coverage","def","exit","issubclass","name","none"],"topics":["ai"]},{"id":"chunk-30","heading":"1.3. (уровень сложности: средний)","text":"raise AssertionError(f\"Expected {self.exception. name }, but got {exc type. name }\")","keywords":["name","python","1.3.","assertionerror","but","coverage","exc","expected","got","property-based","pytest","raise"],"topics":["ai"]},{"id":"chunk-31","heading":"1.3. (уровень сложности: средний)","text":"````python with raises(MealyError) as e: raise MealyError(\"This error was explicitly raised\")","keywords":["python","mealyerror","1.3.","coverage","error","explicitly","property-based","pytest","raise","raised","raises","this"],"topics":["python","ai"]},{"id":"chunk-32","heading":"2.1. (уровень сложности: средний)","text":"Научитесь работать с модулем pytest. Выберите одну из программ, содержащих ошибки. Создайте отдельный файл для тестирования, в который поместите тестирующие функции (не менее двух). Упростите код с помощью добавления fixture функций. Добавьте параметризацию.","keywords":["python","2.1.","coverage","fixture","property-based","pytest","pytest.","автоматизация","выберите","двух","добавления","добавьте"],"topics":["python","testing"]},{"id":"chunk-33","heading":"Тестирование программы с помощью pytest (test program.py)","text":"def test divide numbers(numbers): a, b = numbers assert divide numbers(a, b) == 5","keywords":["numbers","divide","pytest","python","test","assert","coverage","def","program.py","property-based","автоматизация","мутационному"],"topics":["python","testing"]},{"id":"chunk-34","heading":"Тестирование программы с помощью pytest (test program.py)","text":"@pytest.mark.parametrize(\"test input, expected\", [(\"hello\", \"HELLO\"), (\"world\", \"WORLD\")]) def test uppercase string(test input, expected): assert uppercase string(test input) == expected ````","keywords":["test","expected","input","hello","pytest","python","string","uppercase","world","assert","coverage","def"],"topics":["python","numpy","testing"]},{"id":"chunk-35","heading":"2.2. (уровень сложности: средний)","text":"Выберите одну из программ, содержащих ошибки. Добавьте туда ввод со стороны пользователя. Добавьте макетный код для тестирования, с учетом такого ввода.","keywords":["python","добавьте","2.2.","coverage","property-based","pytest","автоматизация","ввод","ввода.","выберите","код","макетный"],"topics":[]},{"id":"chunk-36","heading":"Программа с ошибкой и вводом от пользователя (program with error.py)","text":"def divide numbers(): try: a = float(input(\"Enter the first number: \")) b = float(input(\"Enter the second number: \")) result = a / b return result except ZeroDivisionError: return \"Error: Division by zero\" except ValueError: return \"Error: Invalid input\"","keywords":["input","return","enter","error","except","float","number","python","result","coverage","def","divide"],"topics":["numpy"]},{"id":"chunk-37","heading":"Программа с ошибкой и вводом от пользователя (program with error.py)","text":"Теперь давайте создадим файл для тестирования с использованием pytest, учитывая ввод от пользователя:","keywords":["pytest","python","от","пользователя","coverage","error.py","program","property-based","автоматизация","ввод","вводом","давайте"],"topics":["python","testing"]},{"id":"chunk-38","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"from program with error import divide numbers, uppercase string from io import StringIO","keywords":["import","program","python","coverage","divide","error","error.py","io","numbers","property-based","pytest","string"],"topics":["testing"]},{"id":"chunk-39","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"@pytest.fixture def numbers(monkeypatch): monkeypatch.setattr('sys.stdin', StringIO(\"10\\n2\\n\")) return 10, 2","keywords":["10","python","coverage","def","error.py","monkeypatch","monkeypatch.setattr","n2","numbers","program","property-based","pytest"],"topics":["python","testing"]},{"id":"chunk-40","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"@pytest.mark.parametrize(\"test input, expected\", [(\"hello\\n\", \"HELLO\"), (\"world\\n\", \"WORLD\")]) def test uppercase string(test input, expected, monkeypatch): monkeypatch.setattr('sys.stdin', StringIO(test input)) assert uppercase string() == expected ````","keywords":["test","expected","input","hello","python","string","uppercase","world","assert","coverage","def","error.py"],"topics":["python","numpy","testing"]},{"id":"chunk-41","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"В этом файле мы также использовали @pytest.fixture для создания fixture функции numbers, которая изменяет стандартный поток ввода sys.stdin на заданные значения. Это позволяет нам имитировать ввод от пользователя в тестах.","keywords":["python","от","пользователя","coverage","error.py","fixture","numbers","program","property-based","pytest","pytest.fixture","sys.stdin"],"topics":["python","testing"]},{"id":"chunk-42","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"Тестирующие функции test divide numbers и test uppercase string теперь также учитывают ввод от пользователя, благодаря использованию pytest.mark.parametrize и изменению потока ввода для каждого теста.","keywords":["test","python","от","пользователя","coverage","divide","error.py","numbers","program","property-based","pytest","pytest.mark.parametrize"],"topics":["python","testing"]},{"id":"chunk-43","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"Для запуска тестов также используйте команду pytest в терминале, указав файл для тестирования:","keywords":["pytest","python","coverage","error.py","program","property-based","test","автоматизация","вводом","запуска","используйте","команду"],"topics":["python","testing"]},{"id":"chunk-44","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"Теперь программа с ошибкой и вводом от пользователя готова для тестирования с помощью pytest.","keywords":["python","вводом","от","ошибкой","пользователя","coverage","error.py","program","property-based","pytest","pytest.","test"],"topics":["python","testing"]},{"id":"chunk-45","heading":"2.2. (уровень сложности: средний)","text":"Научитесь работать с модулем coverage. Выберите одну из программ, содержащих ошибки. Получите статистику по покрытию операторов. Получите статистику по покрытию ветвей. Найдите случай, когда покрытие ветвей отличается от покрытия операторов.","keywords":["python","операторов.","покрытию","получите","статистику","2.2.","coverage","coverage.","property-based","pytest","автоматизация","ветвей"],"topics":[]},{"id":"chunk-46","heading":"2.2. (уровень сложности: средний)","text":"Постарайтесь изменить код исходной программы так, чтобы затруднить получение 100% покрытия. Найдите простой пример ошибки в выбранной программе, при полученном 100% покрытии.","keywords":["100","python","2.2.","coverage","property-based","pytest","автоматизация","выбранной","затруднить","изменить","исходной","код"],"topics":[]},{"id":"chunk-47","heading":"2.2. (уровень сложности: средний)","text":"Реализуйте вывод статистики о покрытии в HTML представлении с демонстрацией покрытия по строкам программы.","keywords":["python","2.2.","coverage","html","property-based","pytest","автоматизация","вывод","демонстрацией","мутационному","покрытии","покрытия"],"topics":["ai"]},{"id":"chunk-48","heading":"2.2. (уровень сложности: средний)","text":"Для примера, давайте выберем программу с ошибкой \"programwitherror.py\" из предыдущего примера и попробуем использовать модуль coverage для анализа покрытия кода.","keywords":["coverage","python","примера","2.2.","programwitherror.py","property-based","pytest","автоматизация","анализа","выберем","давайте","использовать"],"topics":[]},{"id":"chunk-49","heading":"2.2. (уровень сложности: средний)","text":"Сначала установим модуль coverage, если он еще не установлен, с помощью команды: pip install coverage","keywords":["coverage","python","2.2.","install","pip","property-based","pytest","автоматизация","если","еще","команды","модуль"],"topics":[]},{"id":"chunk-50","heading":"2.2. (уровень сложности: средний)","text":"Теперь создадим скрипт, который запускает тесты с использованием coverage и собирает статистику покрытия: coverage run m pytest test program with error.py","keywords":["coverage","pytest","python","2.2.","error.py","program","property-based","run","test","автоматизация","запускает","использованием"],"topics":["python","testing"]},{"id":"chunk-51","heading":"2.2. (уровень сложности: средний)","text":"После того как тесты успешно выполнены, мы можем получить статистику по покрытию операторов: coverage report m","keywords":["coverage","python","2.2.","property-based","pytest","report","автоматизация","выполнены","можем","мутационному","мы","операторов"],"topics":[]},{"id":"chunk-52","heading":"2.2. (уровень сложности: средний)","text":"Это покажет покрытие по каждому файлу, каждой функции и оператору внутри функции.","keywords":["python","2.2.","coverage","property-based","pytest","автоматизация","внутри","каждой","каждому","мутационному","оператору","покажет"],"topics":[]},{"id":"chunk-53","heading":"2.2. (уровень сложности: средний)","text":"Далее, для анализа покрытия ветвей, воспользуемся командой: coverage report m include=program with error.py","keywords":["coverage","python","2.2.","error.py","include","program","property-based","pytest","report","автоматизация","анализа","ветвей"],"topics":[]},{"id":"chunk-54","heading":"2.2. (уровень сложности: средний)","text":"Теперь давайте внесем изменения в программу, чтобы создать ситуацию, когда покрытие ветвей отличается от покрытия операторов:","keywords":["python","2.2.","coverage","property-based","pytest","автоматизация","ветвей","внесем","давайте","изменения","когда","мутационному"],"topics":[]},{"id":"chunk-55","heading":"Изменим программу, чтобы создать ошибку при 100% покрытии","text":"def divide numbers(a, b): if b == 0: return \"Error: Division by zero\" result = a / b return result","keywords":["python","result","return","100","coverage","def","divide","division","error","if","numbers","property-based"],"topics":[]},{"id":"chunk-56","heading":"Изменим программу, чтобы создать ошибку при 100% покрытии","text":"def uppercase string(s): if not s: return \"Error: Empty input\" return s.upper() ````","keywords":["python","return","100","coverage","def","empty","error","if","input","not","property-based","pytest"],"topics":["numpy"]},{"id":"chunk-57","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"def test divide numbers(): assert divide numbers(10, 0) == \"Error: Division by zero\" assert divide numbers(10, 2) == 5","keywords":["divide","numbers","10","assert","error","python","test","coverage","def","division","program","property-based"],"topics":["testing"]},{"id":"chunk-58","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"def test uppercase string(): assert uppercase string(\"\") == \"Error: Empty input\" assert uppercase string(\"hello\") == \"HELLO\" ````","keywords":["string","uppercase","assert","error","hello","python","test","coverage","def","empty","input","program"],"topics":["numpy","testing"]},{"id":"chunk-59","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"И, наконец, выведем статистику покрытия в HTML представлении с помощью команды: coverage html","keywords":["coverage","html","python","error","program","property-based","pytest","test","v2.py","автоматизация","версии","выведем"],"topics":["ai","testing"]},{"id":"chunk-60","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"После выполнения этой команды, вы найдете папку \"htmlcov\" в текущей директории, в которой содержится детальная информация о покрытии операторов и ветвей, включая подсвеченный исходный код с указанием покрытых и непокрытых строк.","keywords":["python","coverage","error","htmlcov","program","property-based","pytest","test","v2.py","автоматизация","версии","ветвей"],"topics":["ai","testing"]},{"id":"chunk-61","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"Теперь вы можете исследовать различия между покрытием операторов и ветвей, а также обнаружить пример ошибки, проявляющейся при 100% покрытии.","keywords":["python","100","coverage","error","program","property-based","pytest","test","v2.py","автоматизация","версии","ветвей"],"topics":["testing"]},{"id":"chunk-62","heading":"3. Мутационное тестирование","text":"Прототип системы мутационного тестирования приведен ниже. Попробуйте разобраться в том, как работает этот код. Вам поможет документация к модулю ast.","keywords":["python","3.","ast.","coverage","property-based","pytest","автоматизация","вам","документация","код.","модулю","мутационного"],"topics":["python","testing"]},{"id":"chunk-63","heading":"3. Мутационное тестирование","text":"Функция mut test принимает на вход тестируемую функцию и функцию, осуществляющую тестирование с помощью assert.","keywords":["python","тестирование","функцию","3.","assert.","coverage","mut","property-based","pytest","test","автоматизация","вход"],"topics":["testing"]},{"id":"chunk-64","heading":"TODO","text":"def mutate code(src): tree = ast.parse(src) Mutator().visit(tree) return ast.unparse(tree)","keywords":["tree","python","src",".visit","ast.parse","ast.unparse","code","coverage","def","mutate","mutator","property-based"],"topics":["python","numpy"]},{"id":"chunk-65","heading":"TODO","text":"def make mutants(func, size): mutant = src = ast.unparse(ast.parse(inspect.getsource(func))) mutants = [src] while len(mutants) < size + 1: while mutant in mutants: mutant = mutate code(src) mutants.append(mutant) return mutants[1:]","keywords":["mutants","mutant","src","func","python","size","while","ast.parse","ast.unparse","code","coverage","def"],"topics":["python","numpy"]},{"id":"chunk-66","heading":"TODO","text":"def mut test(func, test, size=20): survived = [] mutants = make mutants(func, size) for mutant in mutants: try: exec(mutant, globals()) test() survived.append(mutant) except: pass return survived ````","keywords":["mutant","mutants","test","func","python","size","survived","20","coverage","def","except","exec"],"topics":["testing"]},{"id":"chunk-67","heading":"3.1. (уровень сложности: средний)","text":"Выберите одну из программ, содержащих ошибки. Доработайте код мутационного тестирования так, чтобы генерировались программы мутанты со случайными константами. Покажите, что при 100% покрытии тестами мутационное тестирование в состоянии находить ошибки.","keywords":["python","ошибки.","100","3.1.","coverage","property-based","pytest","автоматизация","выберите","генерировались","доработайте","код"],"topics":["testing"]},{"id":"chunk-68","heading":"3.1. (уровень сложности: средний)","text":"class Mutator(ast.NodeTransformer): def visit Constant(self, node): if isinstance(node.value, int) or isinstance(node.value, float): if random.random() < 0.5: 50% вероятность мутации константы mutated value = random.randint( 100, 100) случайное целое число от 100 до 100 return ast.copy location(ast.Constant(value=mutated value), node) return node","keywords":["100","node","value","if","isinstance","mutated","node.value","python","return","0.5","3.1.","50"],"topics":["python"]},{"id":"chunk-69","heading":"3.1. (уровень сложности: средний)","text":"def test mutant divide by two(): assert divide by two(42) == 21 Неверное ожидаемое значение для успешного тестирования","keywords":["divide","python","two","21","3.1.","42","assert","coverage","def","mutant","property-based","pytest"],"topics":["testing"]},{"id":"chunk-70","heading":"3.1. (уровень сложности: средний)","text":"def main(): survivors = mut test(divide by two, test divide by two) print(\"Survived mutants:\", len(survivors))","keywords":["divide","python","survivors","test","two","3.1.","coverage","def","len","main","mut","mutants"],"topics":["ai","testing"]},{"id":"chunk-71","heading":"3.2. (уровень сложности: средний)","text":"Добавьте к системе мутационного тестирования генерацию случайных бинарных операций. Проверьте результат на сортировке. Постарайтесь генерировать программы мутанты, которые «выживают» после большинства assert'ов.","keywords":["python","3.2.","assert","coverage","property-based","pytest","автоматизация","бинарных","большинства","выживают","генерацию","генерировать"],"topics":[]},{"id":"chunk-72","heading":"3.2. (уровень сложности: средний)","text":"class Mutator(ast.NodeTransformer): def visit BinOp(self, node): if random.random() < 0.5: 50% вероятность мутации бинарной операции operators = [ast.Add(), ast.Sub(), ast.Mult(), ast.Div()] возможные операторы mutated op = random.choice(operators) return ast.copy location(ast.BinOp(left=node.left, op=mutated op, right=node.right), node) return node","keywords":["node","op","mutated","operators","python","return","0.5","3.2.","50","ast.add","ast.binop","ast.copy"],"topics":["python"]},{"id":"chunk-73","heading":"3.2. (уровень сложности: средний)","text":"def bubble sort(arr): n = len(arr) for i in range(n): for j in range(0, n i 1): if arr[j] arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] return arr","keywords":["arr","j+1","python","range","3.2.","bubble","coverage","def","if","len","property-based","pytest"],"topics":[]},{"id":"chunk-74","heading":"3.2. (уровень сложности: средний)","text":"def test mutant bubble sort(): assert bubble sort([42, 3, 15]) == [3, 15, 42] Неверное ожидаемое значение для успешного тестирования","keywords":["15","42","bubble","python","sort","3.2.","assert","coverage","def","mutant","property-based","pytest"],"topics":["testing"]},{"id":"chunk-75","heading":"3.2. (уровень сложности: средний)","text":"def main(): survivors = mut test(bubble sort, test bubble sort) print(\"Survived mutants:\", len(survivors))","keywords":["bubble","python","sort","survivors","test","3.2.","coverage","def","len","main","mut","mutants"],"topics":["ai","testing"]},{"id":"chunk-76","heading":"4.1. (уровень сложности: средний)","text":"Изучите работу с модулем deal. Для тестирования контрактов используйте pytest. Выберите одну из программ, содержащих ошибки. Добавьте к программе контракты pre, post, ensure, raises, reason, has.","keywords":["python","4.1.","coverage","deal.","ensure","has.","post","pre","property-based","pytest","pytest.","raises"],"topics":["python","ai","testing"]},{"id":"chunk-77","heading":"pip install deal pytest","text":"def test divide(): assert divide(10, 2) == 5 assert divide(5, 0) == 0 Ошибка: деление на ноль","keywords":["divide","assert","pytest","python","10","coverage","deal","def","install","pip","property-based","test"],"topics":["python","testing"]},{"id":"chunk-78","heading":"","text":"@deal.pre(lambda a, b: b != 0, \"Делитель не должен быть равен нулю\") @deal.post(lambda , , result: result != float('inf'), \"Результат не должен быть бесконечностью\") @deal.ensure(lambda , , result, old result: old result b == a, \"Корректность деления\") @deal.raises(ZeroDivisionError) def divide(a, b): return a / b","keywords":["result","lambda","old","python","быть","должен","coverage","deal.ensure","deal.post","deal.pre","deal.raises","def"],"topics":["ai"]},{"id":"chunk-79","heading":"4.2. (уровень сложности: средний)","text":"Перепишите класс банковского счета (6) с использованием контрактного программирования и, в частности, инвариантов класса. Продемонстрируйте, что реализованные инварианты класса действительно позволяют выявлять ошибки.","keywords":["python","4.2.","coverage","property-based","pytest","автоматизация","банковского","выявлять","действительно","инвариантов","инварианты","использованием"],"topics":["oop"]},{"id":"chunk-80","heading":"pip install deal","text":"@deal.inv(lambda self: self.balance = 0, \"Баланс счета не может быть отрицательным\") class BankAccount: def init (self, account number, balance=0): self.account number = account number self.balance = balance","keywords":["number","account","balance","python","self","self.balance","bankaccount","class","coverage","deal","deal.inv","def"],"topics":[]},{"id":"chunk-81","heading":"pip install deal","text":"@deal.post(lambda , result: result 0, \"Сумма депозита должна быть положительной\") def deposit(self, amount): self.balance += amount return f\"{amount} средств успешно зачислены на счет {self.account number}\"","keywords":["amount","python","result","coverage","deal","deal.post","def","deposit","install","lambda","number","pip"],"topics":[]},{"id":"chunk-82","heading":"pip install deal","text":"@deal.post(lambda , result: result 0, \"Сумма списания должна быть положительной\") def withdraw(self, amount): self.balance = amount return f\"{amount} средств успешно сняты с счета {self.account number}\"","keywords":["amount","python","result","coverage","deal","deal.post","def","install","lambda","number","pip","property-based"],"topics":[]},{"id":"chunk-83","heading":"pip install deal","text":"def check balance(self): return f\"Баланс счета {self.account number}: {self.balance}\"","keywords":["python","balance","check","coverage","deal","def","install","number","pip","property-based","pytest","return"],"topics":[]},{"id":"chunk-84","heading":"pip install deal","text":"account = BankAccount(12345, 100) print(account.withdraw(150)) Пытаемся снять больше, чем на счету","keywords":["python","100","12345","150","account","account.withdraw","bankaccount","coverage","deal","install","pip","print"],"topics":[]},{"id":"chunk-85","heading":"4.3. (уровень сложности: высокий)","text":"Реализуйте контракт, выполнение которого deal проверяет статически. Какие ограничения имеют статически проверяемые контракты?","keywords":["python","4.3.","coverage","deal","property-based","pytest","автоматизация","выполнение","высокий","имеют","какие","контракт"],"topics":[]},{"id":"chunk-86","heading":"4.3. (уровень сложности: высокий)","text":"@deal.pure def check positive number(number: int) int: if number <= 0: raise ValueError(\"Число должно быть положительным\") return number","keywords":["number","int","python","4.3.","check","coverage","deal.pure","def","if","positive","property-based","pytest"],"topics":["ai"]},{"id":"chunk-87","heading":"4.3. (уровень сложности: высокий)","text":"number = 10 checked number = check positive number(number) print(checked number) ````","keywords":["number","checked","python","10","4.3.","check","coverage","positive","print","property-based","pytest","автоматизация"],"topics":[]},{"id":"chunk-88","heading":"4.3. (уровень сложности: высокий)","text":"В этом примере мы объявляем функцию check positive number, которая принимает число типа int и возвращает также int. Мы добавляем контракт, который проверяет, что число положительное. При вызове функции check positive number с положительным числом у нас не будет ошибок.","keywords":["check","number","positive","python","мы","число","4.3.","coverage","int","int.","property-based","pytest"],"topics":[]},{"id":"chunk-89","heading":"4.3. (уровень сложности: высокий)","text":"1. Ограниченность возможностей проверки: Статически проверяемые контракты могут осуществлять только простые проверки типов и условий. Они не могут осуществлять сложные или динамические проверки.","keywords":["python","могут","осуществлять","проверки","1.","4.3.","coverage","property-based","pytest","автоматизация","возможностей","высокий"],"topics":[]},{"id":"chunk-90","heading":"4.3. (уровень сложности: высокий)","text":"2. Ограниченность вида ошибок: Статически проверяемые контракты могут выявлять только те ошибки, которые можно выразить статически при компиляции или анализе кода. Они могут не покрывать все возможные сценарии ошибок.","keywords":["python","могут","статически","2.","4.3.","coverage","property-based","pytest","автоматизация","анализе","вида","возможные"],"topics":[]},{"id":"chunk-91","heading":"4.3. (уровень сложности: высокий)","text":"3. Недостаток гибкости: Статически проверяемые контракты обычно требуют строго соблюдения синтаксиса и структуры кода, что может ограничить гибкость и эффективность программирования.","keywords":["python","3.","4.3.","coverage","property-based","pytest","автоматизация","высокий","гибкости","гибкость","кода","контракты"],"topics":[]},{"id":"chunk-92","heading":"4.3. (уровень сложности: высокий)","text":"Несмотря на ограничения, статически проверяемые контракты могут быть полезными для раннего выявления ошибок и повышения надежности программного обеспечения. Они помогают предотвращать определенные категории ошибок и упрощают процесс debugging'а.","keywords":["python","ошибок","4.3.","coverage","debugging","property-based","pytest","а.","автоматизация","быть","высокий","выявления"],"topics":[]},{"id":"chunk-93","heading":"Задаем стратегию генерации данных для тестирования","text":"@given(st.floats(), st.floats(), st.floats(), st.floats()) def test distance(x1, y1, x2, y2): result = distance(x1, y1, x2, y2) assert isinstance(result, float) assert result = 0","keywords":["st.floats","result","assert","distance","python","x1","x2","y1","y2","coverage","def","float"],"topics":["testing"]},{"id":"chunk-94","heading":"Тестирование функции encode rle","text":"def test encode rle(): assert encode rle(b'AAAABBBCCDAA') == b'\\x04A\\x03B\\x02C\\x01D\\x02A'","keywords":["encode","rle","python","aaaabbbccdaa","assert","coverage","def","property-based","pytest","test","x01d","x02a"],"topics":["testing"]},{"id":"chunk-95","heading":"Тестирование функции decode rle","text":"def test decode rle(): assert decode rle(b'\\x04A\\x03B\\x02C\\x01D\\x02A') == b'AAAABBBCCDAA'","keywords":["decode","rle","python","aaaabbbccdaa","assert","coverage","def","property-based","pytest","test","x01d","x02a"],"topics":["testing"]},{"id":"chunk-96","heading":"5.3. (уровень сложности: высокий)","text":"Реализуйте тестирование для деревьев выражений из предыдущей практики, для одного из «посетителей».","keywords":["python","5.3.","coverage","property-based","pytest","автоматизация","выражений","высокий","деревьев","мутационному","одного","посетителей"],"topics":["testing"]},{"id":"chunk-97","heading":"5.4. (уровень сложности: высокий)","text":"Используйте тестирование по модели для проверки реализации банковского счета (7).","keywords":["python","5.4.","coverage","property-based","pytest","автоматизация","банковского","высокий","используйте","модели","мутационному","практика"],"topics":["testing"]},{"id":"chunk-98","heading":"файл test bank account.py с тестами","text":"@pytest.fixture def bank account(): account = BankAccount(12345) account.create account(1000) return account","keywords":["account","bank","python","1000","12345","account.create","account.py","bankaccount","coverage","def","property-based","pytest"],"topics":["python","testing"]},{"id":"chunk-99","heading":"файл test bank account.py с тестами","text":"def test deposit(bank account): assert bank account.deposit(500) == \"500 средств успешно зачислены на счет 12345\"","keywords":["bank","500","python","test","12345","account","account.deposit","account.py","assert","coverage","def","deposit"],"topics":["testing"]},{"id":"chunk-100","heading":"файл test bank account.py с тестами","text":"def test withdraw(bank account): assert bank account.withdraw(200) == \"200 средств успешно сняты с счета 12345\" assert bank account.withdraw(900) == \"Недостаточно средств на счете\"","keywords":["bank","200","account.withdraw","assert","python","test","средств","12345","900","account","account.py","coverage"],"topics":["testing"]},{"id":"chunk-101","heading":"файл test bank account.py с тестами","text":"def test check balance(bank account): assert bank account.check balance() == \"Баланс счета 12345: 800\"","keywords":["bank","balance","python","test","12345","800","account","account.check","account.py","assert","check","coverage"],"topics":["testing"]},{"id":"chunk-102","heading":"файл test bank account.py с тестами","text":"def test close account(bank account): assert bank account.close account() == \"Счет 12345 закрыт\"","keywords":["account","bank","python","test","12345","account.close","account.py","assert","close","coverage","def","property-based"],"topics":["testing"]}]},{"id":"bigdata/practice-05-classification","href":"/docs/bigdata/practice-05-classification","slug":["bigdata","practice-05-classification"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 5. Классификация","description":"Построение и оценка моделей классификации.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитривеич ИКБО 25 22 Практическая работа №5 Классификация 1. Найти данные для классификации. Данные в группе повторяться не должны. Предобработа","keywords":["test","train","print","import","accuracy","name","класс","knn","svm","preds","0.71","encoded","iter","logistic","regression","split","встречается","выводы"],"topics":["python","ai","knn","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","5.","wiki","версия","документацию.","исходный","классификации.","классификация","материал","моделей","ноутбук","оценка"],"topics":[]},{"id":"chunk-1","heading":"Классификация","text":"1. Найти данные для классификации. Данные в группе повторяться не должны. Предобработать данные, если это необходимо.","keywords":["данные","bigdata","классификации.","классификация","1.","5.","группе","должны.","если","моделей","найти","необходимо."],"topics":["oop"]},{"id":"chunk-2","heading":"Строим гистограмму","text":"plt.figure(figsize=(8, 5)) counts.plot(kind='bar') plt.title(f'Баланс классов по признаку \"{class column}\"') plt.xlabel('Класс') plt.ylabel('Количество объектов') plt.grid(axis='y', linestyle=' ', alpha=0.5) plt.tight layout() plt.show()","keywords":["bigdata","0.5","5.","alpha","axis","bar","class","column","counts.plot","figsize","kind","layout"],"topics":["oop"]},{"id":"chunk-3","heading":"Выводы","text":"print(\"=== Выводы ===\") print(counts) print(\"\\nСамый редкий класс:\", counts.idxmin()) print(\"Самый частый класс:\", counts.idxmax()) ```","keywords":["print","bigdata","выводы","класс","5.","counts","counts.idxmax","counts.idxmin","nсамый","классификации.","классификация","моделей"],"topics":["oop"]},{"id":"chunk-4","heading":"Выводы","text":"=== Выводы === province Punjab 36 KP 35 Balochistan 34 Sindh 30 Capital territory 1 Name: count, dtype: int64","keywords":["bigdata","выводы","30","34","35","36","5.","balochistan","capital","count","dtype","int64"],"topics":[]},{"id":"chunk-5","heading":"Выводы","text":"3. Разбить выборку на тренировочную и тестовую. Тренировочная для обучения модели, тестовая для проверки ее качества.","keywords":["bigdata","3.","5.","выборку","выводы","ее","качества.","классификации.","классификация","моделей","модели","обучения"],"topics":[]},{"id":"chunk-6","heading":"Выводы","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import LabelEncoder","keywords":["bigdata","import","5.","labelencoder","python","selection","sklearn.model","sklearn.preprocessing","split","test","train","выводы"],"topics":["python","ai","testing"]},{"id":"chunk-7","heading":"удаляем класс, который встречается только один раз","text":"y = df filtered['province'] X = df filtered.select dtypes(include=['int64', 'float64'])","keywords":["bigdata","df","5.","dtypes","filtered","filtered.select","float64","include","int64","province","встречается","класс"],"topics":["oop"]},{"id":"chunk-8","heading":"удаляем класс, который встречается только один раз","text":"X train, X test, y train, y test = train test split( X, y encoded, test size=0.2, random state=42, stratify=y encoded )","keywords":["test","train","bigdata","encoded","0.2","42","5.","random","size","split","state","stratify"],"topics":["ai","oop","testing"]},{"id":"chunk-9","heading":"удаляем класс, который встречается только один раз","text":"print(\"Train:\", X train.shape) print(\"Test:\", X test.shape) print(\"Классы:\", le.classes ) ```","keywords":["print","bigdata","5.","le.classes","test","test.shape","train","train.shape","встречается","класс","классификации.","классификация"],"topics":["ai","oop","testing"]},{"id":"chunk-10","heading":"удаляем класс, который встречается только один раз","text":"text Train: (108, 19) Test: (27, 19) Классы: ['Balochistan' 'KP' 'Punjab' 'Sindh']","keywords":["19","bigdata","108","27","5.","balochistan","kp","punjab","sindh","test","text","train"],"topics":["ai","oop","testing"]},{"id":"chunk-11","heading":"удаляем класс, который встречается только один раз","text":"4. Применить алгоритмы классификации: логистическая регрессия, SVM, KNN. Построить матрицу ошибок по результатам работы моделей (использовать confusion matrix из sklearn.metrics).","keywords":["bigdata","моделей","4.","5.","confusion","knn.","matrix","sklearn.metrics","svm","алгоритмы","встречается","использовать"],"topics":["knn","oop","algorithms","regression"]},{"id":"chunk-12","heading":"удаляем класс, который встречается только один раз","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import LabelEncoder from sklearn.metrics import confusion matrix from sklearn.linear model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier","keywords":["import","bigdata","5.","confusion","kneighborsclassifier","labelencoder","logisticregression","matrix","model","python","selection","sklearn.linear"],"topics":["python","ai","oop","testing","regression"]},{"id":"chunk-13","heading":"","text":"X train, X test, y train, y test = train test split( X, y encoded, test size=0.2, random state=42, stratify=y encoded )","keywords":["test","train","bigdata","encoded","0.2","42","5.","random","size","split","state","stratify"],"topics":["ai","testing"]},{"id":"chunk-14","heading":"Логистическая регрессия","text":"log reg = LogisticRegression(max iter=500) log reg.fit(X train, y train) pred log = log reg.predict(X test)","keywords":["log","bigdata","train","5.","500","iter","logisticregression","max","pred","reg","reg.fit","reg.predict"],"topics":["ai","testing","regression"]},{"id":"chunk-15","heading":"KNN","text":"knn = KNeighborsClassifier(n neighbors=5) knn.fit(X train, y train) pred knn = knn.predict(X test)","keywords":["knn","bigdata","train","5.","kneighborsclassifier","knn.fit","knn.predict","neighbors","pred","test","классификации.","классификация"],"topics":["ai","knn","testing"]},{"id":"chunk-16","heading":"","text":"for name, preds in models.items(): cm = confusion matrix(y test, preds) plt.figure(figsize=(6, 4)) sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=le.classes , yticklabels=le.classes ) plt.title(f\"Confusion Matrix — {name}\") plt.xlabel(\"Predicted\") plt.ylabel(\"Actual\") plt.show()","keywords":["bigdata","cm","confusion","le.classes","matrix","name","preds","5.","actual","annot","blues","cmap"],"topics":["testing"]},{"id":"chunk-17","heading":"","text":"print(\"=== Accuracy ===\") print(\"Logistic Regression:\", accuracy score(y test, pred log)) print(\"SVM:\", accuracy score(y test, pred svm)) print(\"KNN:\", accuracy score(y test, pred knn)) ```","keywords":["accuracy","print","pred","score","test","bigdata","knn","svm","5.","log","logistic","regression"],"topics":["knn","testing","regression"]},{"id":"chunk-18","heading":"","text":"```text /usr/local/lib/python3.12/dist packages/sklearn/linear model/ logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.","keywords":["bigdata","465","5.","converge","convergencewarning","dist","failed","iterations","lbfgs","lib","limit.","linear"],"topics":["python","ai"]},{"id":"chunk-19","heading":"","text":"Increase the number of iterations (max iter) or scale the data as shown in: https://scikit learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit learn.org/stable/modules/linear model.html logistic regression n iter i = check optimize result(","keywords":["bigdata","https","iter","learn.org","modules","scikit","stable","5.","also","alternative","check","data"],"topics":["ai","regression"]},{"id":"chunk-20","heading":"","text":"=== Accuracy === Logistic Regression: 0.7407407407407407 SVM: 0.5555555555555556 KNN: 0.5555555555555556 ```","keywords":["0.5555555555555556","bigdata","0.7407407407407407","5.","accuracy","knn","logistic","regression","svm","классификации.","классификация","моделей"],"topics":["knn","regression"]},{"id":"chunk-21","heading":"","text":"5. Сравнить результаты классификации, используя accuracy, precision, recall и f1 меру (можно использовать classification report из sklearn.metrics). Сделать выводы.","keywords":["5.","bigdata","accuracy","classification","f1","precision","recall","report","sklearn.metrics","выводы.","использовать","используя"],"topics":["oop"]},{"id":"chunk-22","heading":"","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import LabelEncoder from sklearn.metrics import classification report, accuracy score from sklearn.linear model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier","keywords":["import","bigdata","5.","accuracy","classification","kneighborsclassifier","labelencoder","logisticregression","model","python","report","score"],"topics":["python","ai","testing","regression"]},{"id":"chunk-23","heading":"","text":"X train, X test, y train, y test = train test split( X, y encoded, test size=0.2, random state=42, stratify=y encoded )","keywords":["test","train","bigdata","encoded","0.2","42","5.","random","size","split","state","stratify"],"topics":["ai","testing"]},{"id":"chunk-24","heading":"","text":"models = { \"Logistic Regression\": LogisticRegression(max iter=500), \"SVM\": SVC(), \"KNN\": KNeighborsClassifier(n neighbors=5) }","keywords":["bigdata","5.","500","iter","kneighborsclassifier","knn","logistic","logisticregression","max","models","neighbors","regression"],"topics":["knn","regression"]},{"id":"chunk-25","heading":"","text":"for name, model in models.items(): model.fit(X train, y train) preds = model.predict(X test) predictions[name] = preds reports[name] = classification report(y test, preds, target names=le.classes ) print(f\"\\n===== {name} =====\") print(reports[name])","keywords":["name","preds","bigdata","print","reports","test","train","5.","classification","le.classes","model","model.fit"],"topics":["ai","testing"]},{"id":"chunk-26","heading":"","text":"print(\"\\n=== Accuracy сравнение ===\") for name, preds in predictions.items(): print(f\"{name}: {accuracy score(y test, preds)}\") ```","keywords":["accuracy","bigdata","name","preds","print","5.","predictions.items","score","test","классификации.","классификация","моделей"],"topics":["testing"]},{"id":"chunk-27","heading":"","text":"Balochistan 0.75 0.86 0.80 7 KP 0.57 0.57 0.57 7 Punjab 0.71 0.71 0.71 7 Sindh 1.00 0.83 0.91 6","keywords":["0.57","0.71","bigdata","0.75","0.80","0.83","0.86","0.91","1.00","5.","balochistan","kp"],"topics":[]},{"id":"chunk-28","heading":"","text":"Balochistan 0.60 0.86 0.71 7 KP 0.40 0.29 0.33 7 Punjab 0.62 0.71 0.67 7 Sindh 0.50 0.33 0.40 6","keywords":["0.33","0.40","0.71","bigdata","0.29","0.50","0.60","0.62","0.67","0.86","5.","balochistan"],"topics":[]},{"id":"chunk-29","heading":"","text":"Balochistan 0.75 0.86 0.80 7 KP 0.33 0.43 0.38 7 Punjab 0.62 0.71 0.67 7 Sindh 0.50 0.17 0.25 6","keywords":["bigdata","0.17","0.25","0.33","0.38","0.43","0.50","0.62","0.67","0.71","0.75","0.80"],"topics":[]},{"id":"chunk-30","heading":"","text":"=== Accuracy сравнение === Logistic Regression: 0.7407407407407407 SVM: 0.5555555555555556 KNN: 0.5555555555555556","keywords":["0.5555555555555556","bigdata","0.7407407407407407","5.","accuracy","knn","logistic","regression","svm","классификации.","классификация","моделей"],"topics":["knn","regression"]},{"id":"chunk-31","heading":"","text":"/usr/local/lib/python3.12/dist packages/sklearn/linear model/ logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.","keywords":["bigdata","465","5.","converge","convergencewarning","dist","failed","iterations","lbfgs","lib","limit.","linear"],"topics":["python","ai"]},{"id":"chunk-32","heading":"","text":"Increase the number of iterations (max iter) or scale the data as shown in: https://scikit learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit learn.org/stable/modules/linear model.html logistic regression n iter i = check optimize result( ```","keywords":["bigdata","https","iter","learn.org","modules","scikit","stable","5.","also","alternative","check","data"],"topics":["ai","regression"]},{"id":"chunk-33","heading":"","text":"В ходе выполнения работы был проведён полный цикл решения задачи классификации: выполнена загрузка и предобработка данных, построена гистограмма, продемонстрировавшая заметный дисбаланс классов из за наличия редкой категории, в связи с чем было принято решение исключить класс с единственным объектом","keywords":["bigdata","5.","был","было","выполнена","выполнения","гистограмма","данных","дисбаланс","единственным","за","загрузка"],"topics":["oop"]},{"id":"chunk-34","heading":"","text":". После этого данные были корректно разделены на тренировочную и тестовую выборки, а затем обучены три модели классификации: логистическая регрессия, SVM и KNN. Сравнение результатов с использованием accuracy, precision, recall и F1 меры показало, что наибольшее качество демонстрирует модель SVM, наиболее устойчиво справляясь с разделением классов и обеспечивая лучшие значения ключевых метрик","keywords":["bigdata","svm","5.","accuracy","f1","knn.","precision","recall","были","выборки","данные","демонстрирует"],"topics":["knn","oop","regression"]},{"id":"chunk-35","heading":"","text":". Логистическая регрессия выступила надёжной базовой моделью со стабильными, но более скромными результатами, в то время как KNN оказался менее эффективным из за чувствительности к структуре и масштабу признаков. Таким образом, наиболее подходящей моделью для классификации на основании данного набора данных является SVM, обеспечивающая оптимальный баланс качества и устойчивости.","keywords":["bigdata","моделью","5.","knn","svm","базовой","баланс","более","время","выступила","данного","данных"],"topics":["knn","oop","regression"]}]},{"id":"python/practice-6-regex-combinators","href":"/docs/python/practice-6-regex-combinators","slug":["python","practice-6-regex-combinators"],"section":"python","sectionTitle":"Python","title":"Практика 6 — Комбинаторы и функциональный стиль","description":"Функции высшего порядка, декораторы и функциональные структуры данных.","preview":"Источник: pr Python/pr Python/pract6.ipynb 1.1. (уровень сложности: низкий) Напишите функцию deriv для приближенного вычисления производной в заданной точке. Пример работы: deriv(lambda x: x 3)(5) 75.00014999664018 python def deriv(f, h=0.0001): def fprime(x):","keywords":["node","использования","current","list","def","lambda","value","примеры","return","my","pair","print","age","name","none","p1","if","next"],"topics":["python","ai","numpy","oop","algorithms"],"chunks":[{"id":"chunk-0","heading":"","text":"Напишите функцию deriv для приближенного вычисления производной в заданной точке.","keywords":["python","deriv","высшего","вычисления","данных.","декораторы","заданной","комбинаторы","напишите","порядка","практика","приближенного"],"topics":[]},{"id":"chunk-1","heading":"","text":"````python def deriv(f, h=0.0001): def fprime(x): return (f(x + h) f(x)) / h return fprime","keywords":["python","def","fprime","return","0.0001","deriv","высшего","данных.","декораторы","комбинаторы","порядка","практика"],"topics":["python"]},{"id":"chunk-2","heading":"","text":"Создайте вариант именованного кортежа с помощью ФВП. Классы и готовые структуры данных (словари, кортежи и так далее) использовать нельзя.","keywords":["python","структуры","вариант","высшего","готовые","далее","данных","данных.","декораторы","именованного","использовать","классы"],"topics":["oop","algorithms"]},{"id":"chunk-3","heading":"","text":"p1 = person(name='Иван', age=20) p2 = replace(replace(p1, 'name', 'Алексей'), 'age', 21) get(p1, 'name'), get(p1, 'age') ('Иван', 20) get(p2, 'name'), get(p2, 'age') ('Алексей', 21)","keywords":["age","get","name","p1","p2","20","21","python","replace","алексей","иван","person"],"topics":[]},{"id":"chunk-4","heading":"","text":"````python def person( kwargs): def get(obj, key): return obj[\"data\"][key] if key in obj[\"data\"] else None","keywords":["key","obj","python","data","def","else","get","if","kwargs","none","person","return"],"topics":["python"]},{"id":"chunk-5","heading":"Примеры использования","text":"p1 = person(name=\"Иван\", age=20) p2 = person(replace(p1, 'name', 'Алексей'), 'age', 21)","keywords":["age","name","p1","person","python","20","21","p2","replace","алексей","высшего","данных."],"topics":[]},{"id":"chunk-6","heading":"Примеры использования","text":"print(person.get(p1, \"name\"), person.get(p1, \"age\")) print(person.get(p2, \"name\"), person.get(p2, \"age\")) ````","keywords":["person.get","age","name","p1","p2","print","python","высшего","данных.","декораторы","использования","комбинаторы"],"topics":[]},{"id":"chunk-7","heading":"Примеры использования","text":"Реализуйте рекурсивное вычисление факториала в виде выражения. Необходимо это сделать без использования именованных функций, переменных (в том числе без имени факториала) и присваиваний.","keywords":["python","без","использования","факториала","виде","выражения.","высшего","вычисление","данных.","декораторы","имени","именованных"],"topics":[]},{"id":"chunk-8","heading":"Примеры использования","text":"python (lambda f: (lambda x: f(lambda k: x(x)(k)))(lambda x: f(lambda k: x(x)(k)))(lambda f: lambda n: 1 if n == 0 else n f(f)(n 1))(10)","keywords":["lambda","python","10","else","if","высшего","данных.","декораторы","использования","комбинаторы","порядка","практика"],"topics":["python"]},{"id":"chunk-9","heading":"Примеры использования","text":"Создайте декоратор io, который задает функции для получения входных аргументов и вовращения результата.","keywords":["python","функции","io","аргументов","вовращения","входных","высшего","данных.","декоратор","декораторы","задает","использования"],"topics":[]},{"id":"chunk-10","heading":"Примеры использования","text":"f1() one two three onetwothree @io(lambda: random.random(), lambda: random.random(), lambda x: x) def f2(x, y): return x y","keywords":["lambda","python","random.random","def","f1","f2","io","one","onetwothree","return","three","two"],"topics":[]},{"id":"chunk-11","heading":"Примеры использования","text":"def io( inputs and output): def decorator(func): def wrapper( args): input values = [input func() for input func in inputs and output[: 1]] result = func( input values) output func = inputs and output[ 1] output func(result) return wrapper return decorator","keywords":["func","output","input","def","inputs","decorator","python","result","return","values","wrapper","args"],"topics":["numpy"]},{"id":"chunk-12","heading":"Примеры использования","text":"@io(lambda: random.random(), lambda: random.random(), lambda x: x) def f2(x, y): return x y ````","keywords":["lambda","python","random.random","def","f2","io","return","высшего","данных.","декораторы","использования","комбинаторы"],"topics":[]},{"id":"chunk-13","heading":"Примеры использования","text":"Создайте декоратор класса @collect, который собирает все создаваемые объекты класса в единый список. К классу добавляется метод get objects, который выдает этот список.","keywords":["python","класса","который","список.","collect","get","objects","выдает","высшего","данных.","декоратор","декораторы"],"topics":["oop"]},{"id":"chunk-14","heading":"Примеры использования","text":"def wrapper( args, kwargs): obj = cls. new (cls) obj. init ( args, kwargs) cls. objects.append(obj) return obj","keywords":["obj","args","cls.","kwargs","python","cls","def","init","new","obj.","objects.append","return"],"topics":[]},{"id":"chunk-15","heading":"Примеры использования","text":"2. Работа со списками в функциональном стиле Создайте тип данных односвязный список с помощью ФВП. При создании списка нельзя использовать классы, готовые списки, кортежи и так далее.","keywords":["python","2.","высшего","готовые","далее.","данных","данных.","декораторы","использования","использовать","классы","комбинаторы"],"topics":["oop"]},{"id":"chunk-16","heading":"Примеры использования","text":"def prepend(node, value): new node = create node(value) new node[\"next\"] = node return new node","keywords":["node","new","python","value","create","def","next","prepend","return","высшего","данных.","декораторы"],"topics":[]},{"id":"chunk-17","heading":"Примеры использования","text":"def find(node, value): current = node while current is not None: if current[\"value\"] == value: return current current = current[\"next\"] return None","keywords":["current","value","node","none","python","return","def","find","if","next","not","while"],"topics":[]},{"id":"chunk-18","heading":"Примеры использования","text":"def print list(node): current = node while current is not None: print(current[\"value\"], end=\" \") current = current[\"next\"] print()","keywords":["current","print","node","python","def","end","list","next","none","not","value","while"],"topics":[]},{"id":"chunk-19","heading":"Пример использования","text":"my list = None my list = prepend(my list, 3) my list = prepend(my list, 2) my list = append(my list, 4) my list = append(my list, 5) print list(my list)","keywords":["list","my","append","prepend","python","none","print","высшего","данных.","декораторы","использования","комбинаторы"],"topics":[]},{"id":"chunk-20","heading":"Поиск элемента","text":"element = find(my list, 4) if element: print(\"Found:\", element[\"value\"]) else: print(\"Not Found\") ````","keywords":["element","found","print","python","else","find","if","list","my","not","value","высшего"],"topics":[]},{"id":"chunk-21","heading":"Поиск элемента","text":"Создайте функцию pair(head, tail), которая порождает элемент списка. Не используйте ветвления. Создайте также функции head(lst) (возвращает значение головы списка) и tail(lst) (возвращает хвост списка).","keywords":["head","lst","python","tail","возвращает","создайте","списка","функции","pair","ветвления.","высшего","головы"],"topics":["ai"]},{"id":"chunk-22","heading":"Функция pair создает элемент списка","text":"def pair(value, next node=None): return lambda selector: value if selector == \"value\" else next node","keywords":["value","next","node","pair","python","selector","def","else","if","lambda","none","return"],"topics":["ai"]},{"id":"chunk-23","heading":"Пример использования","text":"list node = pair(1, pair(2, pair(3, pair(4)))) print(\"Head node value:\", head(list node))","keywords":["pair","node","head","list","python","print","value","высшего","данных.","декораторы","использования","комбинаторы"],"topics":["ai"]},{"id":"chunk-24","heading":"Пример использования","text":"def pair(value, next node=None): return lambda selector: value if selector == \"value\" else next node","keywords":["value","next","node","python","selector","def","else","if","lambda","none","pair","return"],"topics":["ai"]},{"id":"chunk-25","heading":"Пример использования","text":"for arg in reversed(args): if list node is None: list node = pair(arg) current node = list node else: new node = pair(arg, current node) current node = new node","keywords":["node","arg","current","list","new","pair","python","args","else","if","none","reversed"],"topics":["ai"]},{"id":"chunk-26","heading":"Пример использования","text":"while current node: print(current node(\"value\")) current node = current node(\"tail\") ````","keywords":["current","node","python","print","tail","value","while","высшего","данных.","декораторы","использования","комбинаторы"],"topics":["ai"]}]},{"id":"bigdata/practice-06-clustering","href":"/docs/bigdata/practice-06-clustering","slug":["bigdata","practice-06-clustering"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 6. Кластеризация","description":"Неподконтрольное обучение и анализ кластеров.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитриевич ИКБО 25 22 Практическая работа №6 Задача кластеризации 1. Найти данные для кластеризации. Данные в группе не должны повторяться. Если","keywords":["schools","кластерами","первых","просмотр","строк","...","kalat","balochistan","cluster","dbscan","clusters","boys","girls","method","import","umap","кластеров","kmeans"],"topics":["python","ai","numpy","algorithms","clustering"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","6.","wiki","анализ","версия","документацию.","исходный","кластеризация","кластеров.","материал","неподконтрольное","ноутбук"],"topics":[]},{"id":"chunk-1","heading":"Задача кластеризации","text":"1. Найти данные для кластеризации. Данные в группе не должны повторяться. Если признаки в данных имеют очень сильно разные масштабы, то необходимо данные предварительно нормализовать.","keywords":["данные","bigdata","1.","6.","анализ","группе","данных","должны","если","задача","имеют","кластеризации"],"topics":[]},{"id":"chunk-2","heading":"Задача кластеризации","text":"2. Провести кластеризацию данных с помощью алгоритма k means. Использовать «правило локтя» и коэффициент силуэта для поиска оптимального количества кластеров.","keywords":["bigdata","кластеров.","2.","6.","means.","алгоритма","анализ","данных","задача","использовать","кластеризации","кластеризацию"],"topics":["algorithms"]},{"id":"chunk-3","heading":"Импортируем необходимые библиотеки","text":"from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler from sklearn.metrics import silhouette score","keywords":["import","bigdata","6.","kmeans","score","silhouette","sklearn.cluster","sklearn.metrics","sklearn.preprocessing","standardscaler","анализ","библиотеки"],"topics":["clustering"]},{"id":"chunk-4","heading":"Используем все числовые столбцы для кластеризации","text":"numerical cols = data.select dtypes(include=['float64', 'int64']).columns X = data[numerical cols]","keywords":["bigdata","cols","numerical",".columns","6.","data","data.select","dtypes","float64","include","int64","анализ"],"topics":[]},{"id":"chunk-5","heading":"Поиск оптимального числа кластеров с помощью \"правила локтя\"","text":"inertia = [] K range = range(2, 11) Проверим от 2 до 10 кластеров for k in K range: kmeans = KMeans(n clusters=k, random state=42) kmeans.fit(X scaled) inertia.append(kmeans.inertia )","keywords":["range","bigdata","kmeans","кластеров","10","11","42","6.","clusters","inertia","inertia.append","kmeans.fit"],"topics":["clustering"]},{"id":"chunk-6","heading":"Визуализация правила локтя","text":"plt.figure(figsize=(8,5)) plt.plot(K range, inertia, marker='o') plt.title(\"Elbow Method\") plt.xlabel(\"Number of clusters\") plt.ylabel(\"Inertia (Sum of Squared Distances)\") plt.show()","keywords":["bigdata","inertia","6.","clusters","distances","elbow","figsize","marker","method","number","plt.figure","plt.plot"],"topics":["clustering"]},{"id":"chunk-7","heading":"Расчет коэффициента силуэта для каждого числа кластеров","text":"silhouette scores = [] for k in K range: kmeans = KMeans(n clusters=k, random state=42) labels = kmeans.fit predict(X scaled) score = silhouette score(X scaled, labels) silhouette scores.append(score)","keywords":["score","silhouette","bigdata","kmeans","labels","scaled","42","6.","clusters","kmeans.fit","predict","random"],"topics":["clustering"]},{"id":"chunk-8","heading":"Визуализация коэффициента силуэта","text":"plt.figure(figsize=(8,5)) plt.plot(K range, silhouette scores, marker='o', color='orange') plt.title(\"Silhouette Score\") plt.xlabel(\"Number of clusters\") plt.ylabel(\"Silhouette Score\") plt.show()","keywords":["silhouette","bigdata","score","6.","clusters","color","figsize","marker","number","orange","plt.figure","plt.plot"],"topics":["clustering"]},{"id":"chunk-9","heading":"Выбираем оптимальное число кластеров по максимальному силуэту","text":"optimal k = K range[silhouette scores.index(max(silhouette scores))] print(f\"Оптимальное число кластеров: {optimal k}\")","keywords":["bigdata","optimal","silhouette","кластеров","оптимальное","число","6.","max","print","range","scores","scores.index"],"topics":[]},{"id":"chunk-10","heading":"Финальная кластеризация с оптимальным числом кластеров","text":"final kmeans = KMeans(n clusters=optimal k, random state=42) data['cluster'] = final kmeans.fit predict(X scaled)","keywords":["bigdata","final","kmeans","кластеризация","42","6.","cluster","clusters","data","kmeans.fit","optimal","predict"],"topics":["clustering"]},{"id":"chunk-11","heading":"Просмотр первых строк с кластерами","text":"sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","115635","161594","178958","2023","272506","27808","313271","33415","43736"],"topics":["python"]},{"id":"chunk-12","heading":"Просмотр первых строк с кластерами","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","121821","15153","2.79","2.83","2017","211201","265676","29510","3.78","35380"],"topics":[]},{"id":"chunk-13","heading":"Просмотр первых строк с кластерами","text":"secondary boys schools secondary girls schools high boys schools \\ 0 24 13 29 1 23 18 27 2 45 46 32 3 46 29 27 4 24 31 18","keywords":["schools","18","24","27","29","46","bigdata","boys","secondary","13","23","31"],"topics":[]},{"id":"chunk-14","heading":"Просмотр первых строк с кластерами","text":"high girls schools Intermediate boys schools Intermediate girls schools \\ 0 10 2 2 1 17 1 1 2 21 6 1 3 11 4 4 4 18 2 0","keywords":["schools","bigdata","girls","intermediate","10","11","17","18","21","6.","boys","high"],"topics":[]},{"id":"chunk-15","heading":"Просмотр первых строк с кластерами","text":"total boys schools total girls schools total schools cluster 0 249 132 381 0 1 269 120 389 0 2 569 245 814 0 3 507 168 675 0 4 268 143 411 0","keywords":["schools","total","bigdata","120","132","143","168","245","249","268","269","381"],"topics":["clustering"]},{"id":"chunk-16","heading":"Просмотр первых строк с кластерами","text":"3. Провести кластеризацию данных с помощью алгоритма иерархической кластеризации.","keywords":["bigdata","3.","6.","алгоритма","анализ","данных","иерархической","кластерами","кластеризации.","кластеризацию","кластеризация","кластеров."],"topics":["algorithms"]},{"id":"chunk-17","heading":"Импортируем библиотеки","text":"from sklearn.preprocessing import StandardScaler from scipy.cluster.hierarchy import dendrogram, linkage, fcluster","keywords":["bigdata","import","6.","dendrogram","fcluster","linkage","scipy.cluster.hierarchy","sklearn.preprocessing","standardscaler","анализ","библиотеки","импортируем"],"topics":["clustering"]},{"id":"chunk-18","heading":"Используем числовые столбцы","text":"numerical cols = data.select dtypes(include=['float64', 'int64']).columns X = data[numerical cols]","keywords":["bigdata","cols","numerical",".columns","6.","data","data.select","dtypes","float64","include","int64","анализ"],"topics":[]},{"id":"chunk-19","heading":"Визуализация дендрограммы","text":"plt.figure(figsize=(10, 6)) dendrogram(linked, orientation='top', distance sort='ascending', show leaf counts=True) plt.title(\"Дендрограмма иерархической кластеризации\") plt.xlabel(\"Объекты\") plt.ylabel(\"Расстояние\") plt.show()","keywords":["bigdata","10","6.","ascending","counts","dendrogram","distance","figsize","leaf","linked","orientation","plt.figure"],"topics":[]},{"id":"chunk-20","heading":"Здесь можно задать число кластеров, например 3","text":"num clusters = 3 clusters = fcluster(linked, num clusters, criterion='maxclust')","keywords":["clusters","bigdata","num","6.","criterion","fcluster","linked","maxclust","анализ","задать","здесь","кластеризация"],"topics":["clustering"]},{"id":"chunk-21","heading":"Просмотр первых строк с кластерами","text":"sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","115635","161594","178958","2023","272506","27808","313271","33415","43736"],"topics":["python"]},{"id":"chunk-22","heading":"Просмотр первых строк с кластерами","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","121821","15153","2.79","2.83","2017","211201","265676","29510","3.78","35380"],"topics":[]},{"id":"chunk-23","heading":"Просмотр первых строк с кластерами","text":"secondary boys schools secondary girls schools high boys schools \\ 0 24 13 29 1 23 18 27 2 45 46 32 3 46 29 27 4 24 31 18","keywords":["schools","18","24","27","29","46","bigdata","boys","secondary","13","23","31"],"topics":[]},{"id":"chunk-24","heading":"Просмотр первых строк с кластерами","text":"high girls schools Intermediate boys schools Intermediate girls schools \\ 0 10 2 2 1 17 1 1 2 21 6 1 3 11 4 4 4 18 2 0","keywords":["schools","bigdata","girls","intermediate","10","11","17","18","21","6.","boys","high"],"topics":[]},{"id":"chunk-25","heading":"Просмотр первых строк с кластерами","text":"total boys schools total girls schools total schools \\ 0 249 132 381 1 269 120 389 2 569 245 814 3 507 168 675 4 268 143 411","keywords":["schools","total","bigdata","120","132","143","168","245","249","268","269","381"],"topics":[]},{"id":"chunk-26","heading":"Импортируем библиотеки","text":"from sklearn.preprocessing import StandardScaler from sklearn.cluster import DBSCAN from sklearn.neighbors import NearestNeighbors","keywords":["import","bigdata","6.","dbscan","nearestneighbors","sklearn.cluster","sklearn.neighbors","sklearn.preprocessing","standardscaler","анализ","библиотеки","импортируем"],"topics":["clustering"]},{"id":"chunk-27","heading":"Выбираем числовые столбцы","text":"numerical cols = data.select dtypes(include=['float64', 'int64']).columns X = data[numerical cols]","keywords":["bigdata","cols","numerical",".columns","6.","data","data.select","dtypes","float64","include","int64","анализ"],"topics":[]},{"id":"chunk-28","heading":"Подбор eps с помощью графика расстояний до k ближайшего соседа","text":"neighbors = NearestNeighbors(n neighbors=5) k = min samples 1, обычно 4 neighbors fit = neighbors.fit(X scaled) distances, indices = neighbors fit.kneighbors(X scaled) distances = np.sort(distances[:, 4]) 4 й сосед plt.figure(figsize=(8,5)) plt.plot(distances) plt.title(\"k distance график для подбора eps\") plt.xlabel(\"Объекты, отсортированные по расстоянию\") plt.ylabel(\"Расстояние до 5 го ближайшего соседа\")","keywords":["distances","neighbors","bigdata","eps","scaled","ближайшего","до","соседа","6.","distance","figsize","fit"],"topics":["numpy"]},{"id":"chunk-29","heading":"Подбор eps с помощью графика расстояний до k ближайшего соседа","text":"plt.show()","keywords":["bigdata","6.","eps","plt.show","анализ","ближайшего","графика","до","кластеризация","кластеров.","неподконтрольное","обучение"],"topics":[]},{"id":"chunk-30","heading":"eps нужно подобрать визуально из графика k distance","text":"dbscan = DBSCAN(eps=1.5, min samples=5) примерные значения dbscan labels = dbscan.fit predict(X scaled)","keywords":["dbscan","bigdata","eps","1.5","6.","dbscan.fit","distance","labels","min","predict","samples","scaled"],"topics":[]},{"id":"chunk-31","heading":"Выводим количество кластеров и количество шумовых точек","text":"n clusters = len(set(dbscan labels)) (1 if 1 in dbscan labels else 0) n noise = list(dbscan labels).count( 1) print(f\"Количество кластеров: {n clusters}\") print(f\"Количество шумовых точек: {n noise}\")","keywords":["количество","dbscan","labels","bigdata","clusters","noise","print","кластеров","точек","шумовых",".count","6."],"topics":["clustering"]},{"id":"chunk-32","heading":"Просмотр первых строк с кластерами","text":"sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","115635","161594","178958","2023","272506","27808","313271","33415","43736"],"topics":["python"]},{"id":"chunk-33","heading":"Просмотр первых строк с кластерами","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","121821","15153","2.79","2.83","2017","211201","265676","29510","3.78","35380"],"topics":[]},{"id":"chunk-34","heading":"Просмотр первых строк с кластерами","text":"secondary boys schools secondary girls schools high boys schools \\ 0 24 13 29 1 23 18 27 2 45 46 32 3 46 29 27 4 24 31 18","keywords":["schools","18","24","27","29","46","bigdata","boys","secondary","13","23","31"],"topics":[]},{"id":"chunk-35","heading":"Просмотр первых строк с кластерами","text":"high girls schools Intermediate boys schools Intermediate girls schools \\ 0 10 2 2 1 17 1 1 2 21 6 1 3 11 4 4 4 18 2 0","keywords":["schools","bigdata","girls","intermediate","10","11","17","18","21","6.","boys","high"],"topics":[]},{"id":"chunk-36","heading":"Просмотр первых строк с кластерами","text":"total boys schools total girls schools total schools dbscan cluster 0 249 132 381 1 1 269 120 389 0 2 569 245 814 1 3 507 168 675 0 4 268 143 411 0","keywords":["schools","total","bigdata","120","132","143","168","245","249","268","269","381"],"topics":["clustering"]},{"id":"chunk-37","heading":"Просмотр первых строк с кластерами","text":"5. Визуализировать кластеризованные данные с помощью t SNE или UMAP, если необходимо. Если данные трехмерные, то можно использовать трехмерный точечный график.","keywords":["bigdata","данные","если","5.","6.","sne","umap","анализ","визуализировать","график.","использовать","кластерами"],"topics":[]},{"id":"chunk-38","heading":"Импорт библиотек","text":"from sklearn.preprocessing import StandardScaler from sklearn.cluster import KMeans, DBSCAN from sklearn.metrics import silhouette score from scipy.cluster.hierarchy import linkage, fcluster from sklearn.manifold import TSNE from mpl toolkits.mplot3d import Axes3D","keywords":["import","bigdata","6.","axes3d","dbscan","fcluster","kmeans","linkage","mpl","scipy.cluster.hierarchy","score","silhouette"],"topics":["clustering"]},{"id":"chunk-39","heading":"1. Загрузка данных","text":"data = pd.read csv(\"data population2023.csv\") numerical cols = data.select dtypes(include=['float64', 'int64']).columns X = data[numerical cols]","keywords":["data","bigdata","cols","numerical",".columns","1.","6.","csv","data.select","dtypes","float64","include"],"topics":[]},{"id":"chunk-40","heading":"2. K means кластеризация","text":"optimal k = 3 пример, можно изменить после анализа локтя/силуэта kmeans = KMeans(n clusters=optimal k, random state=42) data['kmeans cluster'] = kmeans.fit predict(X scaled)","keywords":["kmeans","bigdata","optimal","кластеризация","2.","42","6.","cluster","clusters","data","kmeans.fit","means"],"topics":["clustering"]},{"id":"chunk-41","heading":"3. Иерархическая кластеризация","text":"linked = linkage(X scaled, method='ward') num clusters = 3 пример, можно подбирать по дендрограмме data['hierarchical cluster'] = fcluster(linked, num clusters, criterion='maxclust')","keywords":["bigdata","clusters","linked","num","кластеризация","3.","6.","cluster","criterion","data","fcluster","hierarchical"],"topics":["clustering"]},{"id":"chunk-42","heading":"4. DBSCAN","text":"dbscan = DBSCAN(eps=1.5, min samples=5) eps можно подбирать через k distance график data['dbscan cluster'] = dbscan.fit predict(X scaled)","keywords":["dbscan","bigdata","eps","1.5","4.","6.","cluster","data","dbscan.fit","distance","min","predict"],"topics":["clustering"]},{"id":"chunk-43","heading":"5. Визуализация кластеров через t SNE и UMAP","text":"def visualize clusters(X scaled, df, cluster col, method='t SNE', dims=2): if method == 't SNE': reducer = TSNE(n components=dims, random state=42) elif method == 'UMAP': reducer = umap.UMAP(n components=dims, random state=42) else: raise ValueError(\"method must be 't SNE' or 'UMAP'\")","keywords":["method","sne","dims","umap","42","bigdata","components","random","reducer","state","5.","6."],"topics":["ai","clustering"]},{"id":"chunk-44","heading":"5. Визуализация кластеров через t SNE и UMAP","text":"if dims == 2: plt.figure(figsize=(8,6)) plt.scatter(X reduced[:,0], X reduced[:,1], c=df[cluster col], cmap='tab10', s=50) plt.title(f\"{method} визуализация ({cluster col})\") plt.xlabel(f\"{method} 1\") plt.ylabel(f\"{method} 2\") plt.colorbar(label='Cluster') plt.show() elif dims == 3: fig = plt.figure(figsize=(8,6)) ax = fig.add subplot(111, projection='3d') scatter = ax.scatter(X reduced[:,0], X reduced[:,1], X","keywords":["reduced","cluster","method","bigdata","col","dims","figsize","plt.figure","визуализация","111","3d","5."],"topics":["clustering"]},{"id":"chunk-45","heading":"5. Визуализация кластеров через t SNE и UMAP","text":"reduced[:,2], c=df[cluster col], cmap='tab10', s=50) ax.set title(f\"3D {method} визуализация ({cluster col})\") ax.set xlabel(f\"{method} 1\") ax.set ylabel(f\"{method} 2\") ax.set zlabel(f\"{method} 3\") fig.colorbar(scatter, ax=ax, label='Cluster') plt.show()","keywords":["ax.set","method","cluster","ax","bigdata","col","визуализация","3d","5.","50","6.","cmap"],"topics":["clustering"]},{"id":"chunk-46","heading":"Визуализируем все кластеризации (2D)","text":"for col in ['kmeans cluster', 'hierarchical cluster', 'dbscan cluster']: visualize clusters(X scaled, data, col, method='t SNE', dims=2) visualize clusters(X scaled, data, col, method='UMAP', dims=2)","keywords":["cluster","col","bigdata","clusters","data","dims","method","scaled","visualize","2d","6.","dbscan"],"topics":["clustering"]},{"id":"chunk-47","heading":"Просмотр первых строк с кластерами","text":"/usr/local/lib/python3.12/dist packages/umap/umap .py:1952: UserWarning: n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism. warn(","keywords":["bigdata","umap",".py","1952","6.","dist","jobs","lib","local","no","overridden","packages"],"topics":["python"]},{"id":"chunk-48","heading":"Просмотр первых строк с кластерами","text":"/usr/local/lib/python3.12/dist packages/umap/umap .py:1952: UserWarning: n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism. warn(","keywords":["bigdata","umap",".py","1952","6.","dist","jobs","lib","local","no","overridden","packages"],"topics":["python"]},{"id":"chunk-49","heading":"Просмотр первых строк с кластерами","text":"/usr/local/lib/python3.12/dist packages/umap/umap .py:1952: UserWarning: n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism. warn(","keywords":["bigdata","umap",".py","1952","6.","dist","jobs","lib","local","no","overridden","packages"],"topics":["python"]},{"id":"chunk-50","heading":"Просмотр первых строк с кластерами","text":"sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","115635","161594","178958","2023","272506","27808","313271","33415","43736"],"topics":["python"]},{"id":"chunk-51","heading":"Просмотр первых строк с кластерами","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","121821","15153","2.79","2.83","2017","211201","265676","29510","3.78","35380"],"topics":[]},{"id":"chunk-52","heading":"Просмотр первых строк с кластерами","text":"high boys schools high girls schools Intermediate boys schools \\ 0 29 10 2 1 27 17 1 2 32 21 6 3 27 11 4 4 18 18 2","keywords":["schools","18","27","bigdata","boys","high","10","11","17","21","29","32"],"topics":[]},{"id":"chunk-53","heading":"Просмотр первых строк с кластерами","text":"Intermediate girls schools total boys schools total girls schools \\ 0 2 249 132 1 1 269 120 2 1 569 245 3 4 507 168 4 0 268 143","keywords":["schools","bigdata","girls","total","120","132","143","168","245","249","268","269"],"topics":[]},{"id":"chunk-54","heading":"Просмотр первых строк с кластерами","text":"total schools kmeans cluster hierarchical cluster dbscan cluster 0 381 2 1 1 1 389 2 1 0 2 814 2 1 1 3 675 2 1 0 4 411 2 1 0","keywords":["cluster","bigdata","381","389","411","6.","675","814","dbscan","hierarchical","kmeans","schools"],"topics":["clustering"]},{"id":"chunk-55","heading":"Просмотр первых строк с кластерами","text":"В ходе выполнения заданий 2–5 была проведена комплексная кластеризация данных с использованием различных алгоритмов: K means, иерархической кластеризации и DBSCAN, с последующим анализом качества кластеризации через правило локтя, коэффициент силуэта и визуальный подбор параметров","keywords":["bigdata","кластеризации","кластеризация","6.","dbscan","means","алгоритмов","анализ","анализом","была","визуальный","выполнения"],"topics":["algorithms","clustering"]},{"id":"chunk-56","heading":"Просмотр первых строк с кластерами","text":". K means позволил выявить оптимальное число кластеров на основе максимального силуэта, и объекты были распределены по четко различимым группам. Иерархическая кластеризация обеспечила наглядное представление структуры данных через дендрограмму и позволила формировать кластеры на разных уровнях разбиения. DBSCAN выявил как плотные кластеры, так и шумовые точки, что особенно полезно при наличии выбросов","keywords":["bigdata","кластеризация","кластеры","6.","dbscan","means","анализ","были","выбросов","выявил","выявить","группам."],"topics":["algorithms","clustering"]},{"id":"chunk-57","heading":"Просмотр первых строк с кластерами","text":". Для визуализации результатов была применена методика снижения размерности (t SNE и UMAP), которая позволила на двумерной или трехмерной плоскости наглядно оценить распределение кластеров и взаимное расстояние между ними, подтвердив внутреннюю структуру и качество выделенных групп.","keywords":["bigdata","6.","sne","umap","анализ","была","взаимное","визуализации","внутреннюю","выделенных","групп.","двумерной"],"topics":[]}]},{"id":"python/practice-6-2-regex-combinators","href":"/docs/python/practice-6-2-regex-combinators","slug":["python","practice-6-2-regex-combinators"],"section":"python","sectionTitle":"Python","title":"Практика 6.2 — Расширенные комбинаторы regex","description":"Дополнительные задания по комбинаторному и функциональному подходу.","preview":"Источник: pr Python/pr Python/pract6 2.ipynb 4. Комбинаторы регулярных выражений Реализуйте простой встроенный язык подмножества регулярных выражений с помощью ФВП. Комбинаторы имеют следующий общий вид: def comb(param, ...): def parse(text, ...): ... return n","keywords":["pos","text","hex","return","digit","def","sym","parse","сложности","уровень","высокий","seq","none","if","new","range","color","parsers"],"topics":["python","ai"],"chunks":[{"id":"chunk-0","heading":"4. Комбинаторы регулярных выражений","text":"Реализуйте простой встроенный язык подмножества регулярных выражений с помощью ФВП. Комбинаторы имеют следующий общий вид: def comb(param, ...): def parse(text, ...): ... return new text, ... return parse","keywords":["...","комбинаторы","def","parse","python","return","text","выражений","регулярных","4.","6.2","comb"],"topics":["python"]},{"id":"chunk-1","heading":"4. Комбинаторы регулярных выражений","text":"````python def char(c): def parse(text, pos): if pos = len(text) or text[pos] != c: return text, pos return text, pos + 1 return parse","keywords":["pos","text","python","return","def","parse","комбинаторы","4.","6.2","char","if","len"],"topics":["python"]},{"id":"chunk-2","heading":"4. Комбинаторы регулярных выражений","text":"def seq( parsers): def parse(text, pos): for p in parsers: text, pos = p(text, pos) if pos == 1: return text, pos return text, pos return parse","keywords":["pos","text","return","def","parse","parsers","python","комбинаторы","4.","6.2","if","regex"],"topics":["python"]},{"id":"chunk-3","heading":"4. Комбинаторы регулярных выражений","text":"def alt( parsers): def parse(text, pos): for p in parsers: new text, new pos = p(text, pos) if new pos != 1: return new text, new pos return text, 1 return parse","keywords":["new","pos","text","return","def","parse","parsers","python","комбинаторы","4.","6.2","alt"],"topics":["python"]},{"id":"chunk-4","heading":"4. Комбинаторы регулярных выражений","text":"def rep(p): def parse(text, pos): while pos < len(text): text, pos = p(text, pos) return text, pos return parse","keywords":["pos","text","def","parse","python","return","комбинаторы","4.","6.2","len","regex","rep"],"topics":["python"]},{"id":"chunk-5","heading":"4. Комбинаторы регулярных выражений","text":"def opt(p): def parse(text, pos): new text, new pos = p(text, pos) return new text, new pos if new pos != 1 else pos return parse","keywords":["pos","new","text","def","parse","python","return","комбинаторы","4.","6.2","else","if"],"topics":["python"]},{"id":"chunk-6","heading":"4. Комбинаторы регулярных выражений","text":"result = parser(\"aaabbaa\", 0) if result[1] == 1: print(\"Failed to parse\") else: print(\"Parsed successfully\") ````","keywords":["print","python","result","комбинаторы","4.","6.2","aaabbaa","else","failed","if","parse","parsed"],"topics":["python","ai"]},{"id":"chunk-7","heading":"4.1. (уровень сложности: высокий)","text":"Реализуйте комбинаторы sym (разобрать символ) и seq (разобрать последовательность).","keywords":["python","комбинаторы","разобрать","4.1.","6.2","regex","seq","sym","высокий","дополнительные","задания","комбинаторному"],"topics":["python"]},{"id":"chunk-8","heading":"4.1. (уровень сложности: высокий)","text":"``` text = 'abc' re1 = seq(sym('a'), sym('b'), sym('c')) re2 = seq(sym('a'), sym('z'), sym('c'))","keywords":["sym","python","seq","4.1.","6.2","abc","re1","re2","regex","text","высокий","дополнительные"],"topics":[]},{"id":"chunk-9","heading":"4.1. (уровень сложности: высокий)","text":"````python def sym(c): def parse(text, pos): if pos < len(text) and text[pos] == c: return pos + 1 return None return parse","keywords":["pos","python","return","text","def","parse","4.1.","6.2","if","len","none","regex"],"topics":["python"]},{"id":"chunk-10","heading":"4.1. (уровень сложности: высокий)","text":"def seq( parsers): def parse(text, pos): for p in parsers: pos = p(text, pos) if pos is None: return None return pos return parse","keywords":["pos","return","def","none","parse","parsers","python","text","4.1.","6.2","if","regex"],"topics":[]},{"id":"chunk-11","heading":"4.1. (уровень сложности: высокий)","text":"text = 'abc' re1 = seq(sym('a'), sym('b'), sym('c')) re2 = seq(sym('a'), sym('z'), sym('c'))","keywords":["sym","python","seq","4.1.","6.2","abc","re1","re2","regex","text","высокий","дополнительные"],"topics":[]},{"id":"chunk-12","heading":"4.2. (уровень сложности: средний)","text":"````python def range of(start, end): def parse(text, pos): if pos < len(text) and start <= text[pos] <= end: return pos + 1 return None return parse","keywords":["pos","python","return","text","def","end","parse","start","4.2.","6.2","if","len"],"topics":["python"]},{"id":"chunk-13","heading":"4.2. (уровень сложности: средний)","text":"def seq( parsers): def parse(text, pos): for p in parsers: pos = p(text, pos) if pos is None: return None return pos return parse","keywords":["pos","return","def","none","parse","parsers","python","text","4.2.","6.2","if","regex"],"topics":[]},{"id":"chunk-14","heading":"4.3. (уровень сложности: высокий)","text":"``` digit = range of('0', '9') hex digit = alt(digit, range of('a', 'f'), range of('A', 'F')) space = alt(sym(' '), sym('\\n'), sym('\\t')) hex color = seq(sym(' '), hex digit, hex digit, hex digit, hex digit, hex digit, hex digit)","keywords":["digit","hex","sym","range","alt","python","4.3.","6.2","color","regex","seq","space"],"topics":[]},{"id":"chunk-15","heading":"4.3. (уровень сложности: высокий)","text":"print(hex color(' ffaa43') is not None) print(hex color(' xxxxxx') is not None) ```","keywords":["color","hex","none","not","print","python","4.3.","6.2","ffaa43","regex","xxxxxx","высокий"],"topics":[]},{"id":"chunk-16","heading":"4.3. (уровень сложности: высокий)","text":"````python def alt( parsers): def parse(text, pos): for p in parsers: new pos = p(text, pos) if new pos is not None: return new pos return None return parse","keywords":["pos","new","python","return","def","none","parse","parsers","text","4.3.","6.2","alt"],"topics":["python"]},{"id":"chunk-17","heading":"4.3. (уровень сложности: высокий)","text":"def range of(start, end): def parse(text, pos): if pos < len(text) and start <= text[pos] <= end: return pos + 1 return None return parse","keywords":["pos","return","text","def","end","parse","python","start","4.3.","6.2","if","len"],"topics":[]},{"id":"chunk-18","heading":"4.3. (уровень сложности: высокий)","text":"def sym(char): def parse(text, pos): if pos < len(text) and text[pos] == char: return pos + 1 return None return parse","keywords":["pos","return","text","char","def","parse","python","4.3.","6.2","if","len","none"],"topics":[]},{"id":"chunk-19","heading":"4.3. (уровень сложности: высокий)","text":"def seq( parsers): def parse(text, pos): for p in parsers: pos = p(text, pos) if pos is None: return None return pos return parse","keywords":["pos","return","def","none","parse","parsers","python","text","4.3.","6.2","if","regex"],"topics":[]},{"id":"chunk-20","heading":"4.3. (уровень сложности: высокий)","text":"digit = range of('0', '9') hex digit = alt(digit, range of('a', 'f'), range of('A', 'F')) space = alt(sym(' '), sym('\\n'), sym('\\t')) hex color = seq(sym(' '), hex digit, hex digit, hex digit, hex digit, hex digit, hex digit)","keywords":["digit","hex","sym","range","alt","python","4.3.","6.2","color","regex","seq","space"],"topics":[]},{"id":"chunk-21","heading":"4.3. (уровень сложности: высокий)","text":"print(hex color(' ffaa43') is not None) print(hex color(' xxxxxx') is not None) ````","keywords":["color","hex","none","not","print","python","4.3.","6.2","ffaa43","regex","xxxxxx","высокий"],"topics":[]},{"id":"chunk-22","heading":"4.4. (уровень сложности: высокий)","text":"Реализуйте комбинатор group (группировка) для извлечения разобранных фрагментов.","keywords":["python","4.4.","6.2","group","regex","высокий","группировка","дополнительные","задания","извлечения","комбинатор","комбинаторному"],"topics":["python"]},{"id":"chunk-23","heading":"4.4. (уровень сложности: высокий)","text":"``` digit = range of('0', '9') hex digit = alt(digit, range of('a', 'f'), range of('A', 'F')) space = alt(sym(' '), sym('\\n'), sym('\\t')) hex color = seq(sym(' '), group(seq(hex digit, hex digit, hex digit, hex digit, hex digit, hex digit))) hex colors = seq(hex color, sym(' '), hex color)","keywords":["hex","digit","sym","color","range","seq","alt","python","4.4.","6.2","colors","group"],"topics":[]},{"id":"chunk-24","heading":"4.4. (уровень сложности: высокий)","text":"````python def group(rule): def inner(text, start): result, end = rule(text, start) if result: return ([result], end) return (result, end) return inner","keywords":["result","end","python","return","def","inner","rule","start","text","4.4.","6.2","group"],"topics":["python"]},{"id":"chunk-25","heading":"4.4. (уровень сложности: высокий)","text":"digit = range of('0', '9') hex digit = alt(digit, range of('a', 'f'), range of('A', 'F')) space = alt(sym(' '), sym('\\n'), sym('\\t')) hex color = seq(sym(' '), group(seq(hex digit, hex digit, hex digit, hex digit, hex digit, hex digit))) hex colors = seq(hex color, sym(' '), hex color)","keywords":["hex","digit","sym","color","range","seq","alt","python","4.4.","6.2","colors","group"],"topics":[]},{"id":"chunk-26","heading":"4.4. (уровень сложности: высокий)","text":"def parse hex colors(text, start): result, end = hex colors(text, start) return ('', result)","keywords":["colors","hex","python","result","start","text","4.4.","6.2","def","end","parse","regex"],"topics":[]}]},{"id":"bigdata/practice-07-ensemble-learning","href":"/docs/bigdata/practice-07-ensemble-learning","slug":["bigdata","practice-07-ensemble-learning"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 7. Ансамблевое обучение","description":"Комбинирование моделей для повышения качества предсказаний.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитриевич ИКБО 25 22 Практическая работа №7 Ансамблевое обучение // Stacking // Bagging // Boosting 1. Найти данные для кластеризации. Данные в","keywords":["boosting","bagging","ансамблевое","обучение","stacking","schools","test","girls","random","train","mse","boys","rf","gbr","r2","...","forest","import"],"topics":["python","ai","pandas","testing","algorithms"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","7.","wiki","ансамблевое","версия","документацию.","исходный","качества","комбинирование","материал","моделей","ноутбук"],"topics":[]},{"id":"chunk-1","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"1. Найти данные для кластеризации. Данные в группе не должны повторяться. Если признаки в данных имеют очень сильно разные масштабы, то необходимо данные предварительно нормализовать.","keywords":["данные","bigdata","ансамблевое","обучение","1.","7.","bagging","boosting","stacking","группе","данных","должны"],"topics":[]},{"id":"chunk-2","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```python from sklearn.model selection import train test split from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean squared error, r2 score from sklearn.tree import plot tree","keywords":["import","bigdata","ансамблевое","обучение","7.","bagging","boosting","error","mean","plot","python","r2"],"topics":["python","ai","testing"]},{"id":"chunk-3","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"features = ['households', 'average household size', 'population 2017', 'growth rate', 'area(km²)', 'density 2023(people/km²)', 'primary boys schools', 'primary girls schools', 'secondary boys schools', 'secondary girls schools', 'high boys schools', 'high girls schools', 'Intermediate boys schools', 'Intermediate girls schools', 'total boys schools', 'total girls schools', 'total schools']","keywords":["schools","boys","girls","total","bigdata","high","intermediate","km²","primary","secondary","ансамблевое","обучение"],"topics":[]},{"id":"chunk-4","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"X train, X test, y train, y test = train test split(X, y, test size=0.2, random state=42)","keywords":["test","train","bigdata","ансамблевое","обучение","0.2","42","7.","bagging","boosting","random","size"],"topics":["ai","testing"]},{"id":"chunk-5","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"rf model = RandomForestRegressor( n estimators=100, max depth=None, min samples leaf=1, random state=42 )","keywords":["bigdata","ансамблевое","обучение","100","42","7.","bagging","boosting","depth","estimators","leaf","max"],"topics":[]},{"id":"chunk-6","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(f'\\nMean Squared Error (MSE): {mse:.2f}') print(f'Коэффициент детерминации (R²): {r2:.2f}')","keywords":[".2f","bigdata","mse","print","ансамблевое","обучение","7.","bagging","boosting","error","nmean","r2"],"topics":[]},{"id":"chunk-7","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"feature importances = pd.Series(rf model.feature importances , index=features).sort values(ascending=False)","keywords":["bigdata","importances","ансамблевое","обучение",".sort","7.","ascending","bagging","boosting","false","feature","features"],"topics":["pandas"]},{"id":"chunk-8","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"plt.figure(figsize=(12,6)) sns.barplot(x=feature importances.values, y=feature importances.index) plt.title('Важность признаков (Feature Importance) в Random Forest') plt.show()","keywords":["feature","bigdata","ансамблевое","обучение","12","7.","bagging","boosting","figsize","forest","importance","importances.index"],"topics":[]},{"id":"chunk-9","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"plt.figure(figsize=(20,10)) plot tree(tree, feature names=features, filled=True, rounded=True, fontsize=10) plt.title(\"Пример одного дерева из Random Forest\") plt.show() ```","keywords":["10","bigdata","tree","true","ансамблевое","обучение","20","7.","bagging","boosting","feature","features"],"topics":[]},{"id":"chunk-10","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```text Первые 5 строк датасета: sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","ансамблевое","обучение","115635","161594","178958","2023","272506","27808","313271"],"topics":["python"]},{"id":"chunk-11","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","ансамблевое","обучение","121821","15153","2.79","2.83","2017","211201","265676","29510"],"topics":[]},{"id":"chunk-12","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"primary girls schools secondary boys schools secondary girls schools \\ 0 107 24 13 1 84 23 18 2 177 45 46 3 124 46 29 4 94 24 31","keywords":["schools","24","46","bigdata","girls","secondary","ансамблевое","обучение","107","124","13","177"],"topics":[]},{"id":"chunk-13","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"high boys schools high girls schools Intermediate boys schools \\ 0 29 10 2 1 27 17 1 2 32 21 6 3 27 11 4 4 18 18 2","keywords":["schools","18","27","bigdata","boys","high","ансамблевое","обучение","10","11","17","21"],"topics":[]},{"id":"chunk-14","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Intermediate girls schools total boys schools total girls schools \\ 0 2 249 132 1 1 269 120 2 1 569 245 3 4 507 168 4 0 268 143","keywords":["schools","bigdata","girls","total","ансамблевое","обучение","120","132","143","168","245","249"],"topics":[]},{"id":"chunk-15","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.metrics import mean squared error, r2 score","keywords":["import","bigdata","ансамблевое","обучение","7.","bagging","boosting","error","gradientboostingregressor","mean","python","r2"],"topics":["python","ai","testing"]},{"id":"chunk-16","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"features = data.drop(columns=['sr.no.', 'population 2023', 'province', 'division', 'district']) target = data['population 2023']","keywords":["2023","bigdata","population","ансамблевое","обучение","7.","bagging","boosting","columns","data","data.drop","district"],"topics":[]},{"id":"chunk-17","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"X train, X test, y train, y test = train test split(features, target, test size=0.2, random state=42)","keywords":["test","train","bigdata","ансамблевое","обучение","0.2","42","7.","bagging","boosting","features","random"],"topics":["ai","testing"]},{"id":"chunk-18","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"scaler = StandardScaler() X train scaled = scaler.fit transform(X train) X test scaled = scaler.transform(X test) rf = RandomForestRegressor( n estimators=100, max depth=10, min samples leaf=5, random state=42 )","keywords":["bigdata","scaled","test","train","ансамблевое","обучение","10","100","42","7.","bagging","boosting"],"topics":["ai","testing"]},{"id":"chunk-19","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(\"Random Forest R^2:\", r2 score(y test, y pred rf)) print(\"Random Forest MSE:\", mean squared error(y test, y pred rf))","keywords":["bigdata","forest","pred","print","random","rf","test","ансамблевое","обучение","7.","bagging","boosting"],"topics":["testing"]},{"id":"chunk-20","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"gbr = GradientBoostingRegressor( n estimators=200, learning rate=0.1, max depth=3, min samples leaf=5, random state=42 )","keywords":["bigdata","ансамблевое","обучение","0.1","200","42","7.","bagging","boosting","depth","estimators","gbr"],"topics":[]},{"id":"chunk-21","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(\"Gradient Boosting R^2:\", r2 score(y test, y pred gbr)) print(\"Gradient Boosting MSE:\", mean squared error(y test, y pred gbr)) print(\"R^2 Random Forest:\", r2 score(y test, y pred rf)) print(\"R^2 Gradient Boosting:\", r2 score(y test, y pred gbr)) ```","keywords":["boosting","pred","print","test","gbr","gradient","r2","score","bigdata","ансамблевое","обучение","7."],"topics":["testing"]},{"id":"chunk-22","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"text Random Forest R^2: 0.9902127230594779 Random Forest MSE: 8824200825.067791 Gradient Boosting R^2: 0.9680482164967752 Gradient Boosting MSE: 28807701678.92102 R^2 Random Forest: 0.9902127230594779 R^2 Gradient Boosting: 0.9680482164967752","keywords":["boosting","forest","gradient","random","0.9680482164967752","0.9902127230594779","bigdata","mse","ансамблевое","обучение","28807701678.92102","7."],"topics":[]},{"id":"chunk-23","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"4) Сравнить результаты работы алгоритмов (время работы и качество моделей). Сделать выводы.","keywords":["bigdata","ансамблевое","моделей","обучение","работы","7.","bagging","boosting","stacking","алгоритмов","время","выводы."],"topics":["algorithms"]},{"id":"chunk-24","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.metrics import mean squared error, r2 score","keywords":["import","bigdata","ансамблевое","обучение","7.","bagging","boosting","error","gradientboostingregressor","mean","python","r2"],"topics":["python","ai","testing"]},{"id":"chunk-25","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"features = data.drop(columns=['sr.no.', 'population 2023', 'province', 'division', 'district']) target = data['population 2023']","keywords":["2023","bigdata","population","ансамблевое","обучение","7.","bagging","boosting","columns","data","data.drop","district"],"topics":[]},{"id":"chunk-26","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"X train, X test, y train, y test = train test split(features, target, test size=0.2, random state=42)","keywords":["test","train","bigdata","ансамблевое","обучение","0.2","42","7.","bagging","boosting","features","random"],"topics":["ai","testing"]},{"id":"chunk-27","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"scaler = StandardScaler() X train scaled = scaler.fit transform(X train) X test scaled = scaler.transform(X test)","keywords":["bigdata","scaled","test","train","ансамблевое","обучение","7.","bagging","boosting","scaler","scaler.fit","scaler.transform"],"topics":["ai","testing"]},{"id":"chunk-28","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"rf = RandomForestRegressor( n estimators=100, max depth=10, min samples leaf=5, random state=42 )","keywords":["bigdata","ансамблевое","обучение","10","100","42","7.","bagging","boosting","depth","estimators","leaf"],"topics":[]},{"id":"chunk-29","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"start time = time.time() rf.fit(X train, y train) rf time = time.time() start time","keywords":["time","bigdata","start","time.time","train","ансамблевое","обучение","7.","bagging","boosting","rf","rf.fit"],"topics":["ai"]},{"id":"chunk-30","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"rf r2 = r2 score(y test, y pred rf) rf mse = mean squared error(y test, y pred rf)","keywords":["rf","bigdata","pred","r2","test","ансамблевое","обучение","7.","bagging","boosting","error","mean"],"topics":["testing"]},{"id":"chunk-31","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(f\"Random Forest: R^2 = {rf r2:.4f}, MSE = {rf mse:.2f}, Время = {rf time:.2f} сек\")","keywords":["rf",".2f","bigdata","mse","ансамблевое","обучение",".4f","7.","bagging","boosting","forest","print"],"topics":[]},{"id":"chunk-32","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"gbr = GradientBoostingRegressor( n estimators=200, learning rate=0.1, max depth=3, min samples leaf=5, random state=42 )","keywords":["bigdata","ансамблевое","обучение","0.1","200","42","7.","bagging","boosting","depth","estimators","gbr"],"topics":[]},{"id":"chunk-33","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"start time = time.time() gbr.fit(X train scaled, y train) gbr time = time.time() start time","keywords":["time","bigdata","start","time.time","train","ансамблевое","обучение","7.","bagging","boosting","gbr","gbr.fit"],"topics":["ai"]},{"id":"chunk-34","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"gbr r2 = r2 score(y test, y pred gbr) gbr mse = mean squared error(y test, y pred gbr)","keywords":["gbr","bigdata","pred","r2","test","ансамблевое","обучение","7.","bagging","boosting","error","mean"],"topics":["testing"]},{"id":"chunk-35","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(f\"Gradient Boosting: R^2 = {gbr r2:.4f}, MSE = {gbr mse:.2f}, Время = {gbr time:.2f} сек\")","keywords":["gbr",".2f","bigdata","boosting","mse","ансамблевое","обучение",".4f","7.","bagging","gradient","print"],"topics":[]},{"id":"chunk-36","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"comparison = pd.DataFrame({ 'Модель': ['Random Forest (Bagging)', 'Gradient Boosting'], 'R^2': [rf r2, gbr r2], 'MSE': [rf mse, gbr mse], 'Время обучения (сек)': [rf time, gbr time] })","keywords":["gbr","mse","rf","bagging","bigdata","boosting","r2","time","ансамблевое","обучение","7.","comparison"],"topics":["pandas"]},{"id":"chunk-37","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```text sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","ансамблевое","обучение","115635","161594","178958","2023","272506","27808","313271"],"topics":["python"]},{"id":"chunk-38","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","ансамблевое","обучение","121821","15153","2.79","2.83","2017","211201","265676","29510"],"topics":[]},{"id":"chunk-39","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"primary girls schools secondary boys schools secondary girls schools \\ 0 107 24 13 1 84 23 18 2 177 45 46 3 124 46 29 4 94 24 31","keywords":["schools","24","46","bigdata","girls","secondary","ансамблевое","обучение","107","124","13","177"],"topics":[]},{"id":"chunk-40","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"high boys schools high girls schools Intermediate boys schools \\ 0 29 10 2 1 27 17 1 2 32 21 6 3 27 11 4 4 18 18 2","keywords":["schools","18","27","bigdata","boys","high","ансамблевое","обучение","10","11","17","21"],"topics":[]},{"id":"chunk-41","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Intermediate girls schools total boys schools total girls schools \\ 0 2 249 132 1 1 269 120 2 1 569 245 3 4 507 168 4 0 268 143","keywords":["schools","bigdata","girls","total","ансамблевое","обучение","120","132","143","168","245","249"],"topics":[]},{"id":"chunk-42","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"[5 rows x 22 columns] Index(['sr.no.', 'province', 'division', 'district', 'households', 'population 2023', 'average household size', 'population 2017', 'growth rate', 'area(km²)', 'density 2023(people/km²)', 'primary boys schools', 'primary girls schools', 'secondary boys schools', 'secondary girls schools', 'high boys schools', 'high girls schools', 'Intermediate boys schools', 'Intermediate girls schools', 'total","keywords":["schools","boys","girls","2023","bigdata","high","intermediate","km²","population","primary","secondary","ансамблевое"],"topics":[]},{"id":"chunk-43","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"boys schools', 'total girls schools', 'total schools'], dtype='object') Random Forest: R^2 = 0.9902, MSE = 8824200825.07, Время = 0.34 сек Gradient Boosting: R^2 = 0.9680, MSE = 28807701678.92, Время = 0.38 сек Модель R^2 MSE Время обучения (сек) 0 Random Forest (Bagging) 0.990213 8.824201e+09 0.341938 1 Gradient Boosting 0.968048 2.880770e+10 0.379149 ```","keywords":["boosting","mse","schools","время","сек","bagging","bigdata","forest","gradient","random","total","ансамблевое"],"topics":[]},{"id":"chunk-44","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"В ходе практической работы была выполнена задача регрессии на данных по населению и школам провинций.","keywords":["bigdata","ансамблевое","обучение","7.","bagging","boosting","stacking","была","выполнена","данных","задача","качества"],"topics":[]},{"id":"chunk-45","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Сначала реализован бэггинг с использованием случайного леса, который показал высокую стабильность предсказаний и относительно быстрое обучение.","keywords":["bigdata","ансамблевое","обучение","7.","bagging","boosting","stacking","быстрое","бэггинг","высокую","использованием","качества"],"topics":[]},{"id":"chunk-46","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Затем на тех же данных применён градиентный бустинг, который продемонстрировал чуть более высокое качество прогнозов (выше R² и ниже MSE), но требовал большего времени на обучение.","keywords":["bigdata","ансамблевое","обучение","7.","bagging","boosting","mse","r²","stacking","более","большего","бустинг"],"topics":[]},{"id":"chunk-47","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Сравнение моделей показало, что случайный лес удобен для быстрой и устойчивой оценки, а бустинг эффективен для повышения точности предсказаний.","keywords":["bigdata","ансамблевое","моделей","обучение","повышения","предсказаний.","7.","bagging","boosting","stacking","бустинг","быстрой"],"topics":[]},{"id":"chunk-48","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Результаты подтверждают преимущества ансамблевых методов в уменьшении дисперсии и смещения моделей.","keywords":["bigdata","ансамблевое","обучение","7.","bagging","boosting","stacking","ансамблевых","дисперсии","качества","комбинирование","методов"],"topics":[]}]},{"id":"bigdata/practice-08-final-report","href":"/docs/bigdata/practice-08-final-report","slug":["bigdata","practice-08-final-report"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 8. Итоговая работа","description":"Финальный отчет и итоговые материалы по модулю BigData.","preview":"Wiki версия требуется доработка 1. Настроить виртуальную машину Ubuntu Server 22.04.5 LTS в системе виртуализации операционных систем, а также настроить Docker контейнеры PostgreSQL, Arenadata Cluster Manager совместно с бандлами для кластера и мониторинга (Pr","keywords":["требуется","wiki","версия","доработка","заказов","данных","категории","количество","нужно","уникальных","доставки","каждой","оплаты","товарных","postgresql","виде","каждого","настроить"],"topics":["sql","clustering"],"chunks":[{"id":"chunk-0","heading":"Wiki версия требуется доработка","text":"1. Настроить виртуальную машину Ubuntu Server 22.04.5 LTS в системе виртуализации операционных систем, а также настроить Docker контейнеры PostgreSQL, Arenadata Cluster Manager совместно с бандлами для кластера и мониторинга (Prometheus, Grafana). (1 балл)","keywords":["bigdata","настроить","1.","22.04.5","8.","arenadata","bigdata.","cluster","docker","grafana","lts","manager"],"topics":["sql","clustering"]},{"id":"chunk-1","heading":"Wiki версия требуется доработка","text":"В отчет прикрепить скриншот с интерфейса ADCM на странице Cluster Overview где показано, что все сервисы подняты и работают в штатном режиме.","keywords":["bigdata","отчет","8.","adcm","bigdata.","cluster","overview","wiki","версия","доработка","интерфейса","итоговая"],"topics":["clustering"]},{"id":"chunk-2","heading":"Wiki версия требуется доработка","text":"2. Создать базу данных ecommerce и развернуть public схему таблиц, на основе приложенного скрипта, для хранения данных транзакций в PostgreSQL посредством postgresql client (DBeaver или любой другой админ баз данных). Для каждой таблицы сделать импорт данных посредством импорта из CSV файла в следующем порядке (1 балл):","keywords":["данных","bigdata","postgresql","посредством","2.","8.","bigdata.","client","csv","dbeaver","ecommerce","public"],"topics":["sql"]},{"id":"chunk-3","heading":"Wiki версия требуется доработка","text":"В отчет прикрепить визуализацию схемы таблиц, полученную в консоли командной строки или в виде диаграммы в админе баз данных (например DBeaver).","keywords":["bigdata","отчет","8.","bigdata.","dbeaver","wiki","админе","баз","версия","виде","визуализацию","данных"],"topics":[]},{"id":"chunk-4","heading":"Wiki версия требуется доработка","text":"3. Написать запросы на языке SQL для базы данных ecommerce для получения данных по следующим заданиям:","keywords":["bigdata","данных","3.","8.","bigdata.","ecommerce","sql","wiki","базы","версия","доработка","заданиям"],"topics":["sql"]},{"id":"chunk-5","heading":"Wiki версия требуется доработка","text":"Нужно определить десять товарных категорий с наибольшим оборотом продаж. Для каждой категории требуется посчитать сумму цен всех проданных позиций (это и есть выручка/GMV категории), количество уникальных заказов, в которых встречались товары этой категории, и общее число товарных позиций. Дополнительно к исходному названию категории нужно вывести её англоязычный перевод из словаря переводов","keywords":["категории","bigdata","нужно","товарных","требуется","8.","bigdata.","gmv","wiki","англоязычный","версия","всех"],"topics":[]},{"id":"chunk-6","heading":"Wiki версия требуется доработка","text":". Итоговый набор данных должен быть отсортирован по выручке по убыванию и ограничен первыми десятью строками.","keywords":["bigdata","8.","bigdata.","wiki","быть","версия","выручке","данных","десятью","должен","доработка","итоговая"],"topics":[]},{"id":"chunk-7","heading":"Wiki версия требуется доработка","text":"Нужно сравнить используемые способы оплаты. По каждому типу оплаты требуется вывести количество зарегистрированных платежей, среднее число рассрочек (если применимо), средний размер платежа и долю заказов, в которых встречается данный способ оплаты.","keywords":["bigdata","оплаты.","требуется","8.","bigdata.","wiki","версия","встречается","вывести","данный","долю","доработка"],"topics":[]},{"id":"chunk-8","heading":"Wiki версия требуется доработка","text":"Под долей заказов понимается отношение числа уникальных заказов, где зафиксирован хотя бы один платеж данного типа, к общему числу уникальных заказов, по которым известны записи об оплате.","keywords":["заказов","bigdata","уникальных","8.","bigdata.","wiki","бы","версия","данного","долей","доработка","записи"],"topics":[]},{"id":"chunk-9","heading":"Wiki версия требуется доработка","text":"Следует учитывать, что у одного заказа может быть несколько платежей. Доля считается на уровне «есть/нет» для типа оплаты в заказе.","keywords":["bigdata","8.","bigdata.","wiki","быть","версия","доля","доработка","заказа","заказе.","итоговая","итоговые"],"topics":[]},{"id":"chunk-10","heading":"Wiki версия требуется доработка","text":"Нужно изучить, как длительность доставки коррелирует с оценками в отзывах. Для каждого заказа с отзывом и известной фактической датой доставки рассчитывается длительность доставки в днях как разница между датой доставки клиенту и датой покупки.","keywords":["доставки","датой","bigdata","длительность","8.","bigdata.","wiki","версия","днях","доработка","заказа","известной"],"topics":[]},{"id":"chunk-11","heading":"Wiki версия требуется доработка","text":"Для каждого интервала требуется посчитать количество заказов и среднюю оценку из отзывов. Итог должен содержать три строки — по одному агрегату на интервал — в логическом порядке от самого быстрого интервала к самому длительному.","keywords":["bigdata","интервала","требуется","8.","bigdata.","wiki","агрегату","быстрого","версия","длительному.","должен","доработка"],"topics":[]},{"id":"chunk-12","heading":"Wiki версия требуется доработка","text":"Нужно получить рейтинг продавцов по активности и базовым бизнес показателям. Для каждого продавца считается количество уникальных заказов, в которых он участвовал, число уникальных клиентов, которым он продал хотя бы одну позицию, общее число проданных товарных позиций, совокупный оборот по этим позициям (сумма цен) и средняя стоимость доставки на позицию (среднее от значений стоимости фрахта по позициям)","keywords":["bigdata","он","позицию","позициям","уникальных","число","8.","bigdata.","wiki","активности","базовым","бизнес"],"topics":[]},{"id":"chunk-13","heading":"Wiki версия требуется доработка","text":". 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Notebook 3 — Метрики и KNN расстояния между объектами и классификация ближайших соседей. Notebook 4 — Регрессия линейные модели, аппроксимация и оценка качества. Notebook 5 — Деревья решений деревья решений и работа с классификаторами","keywords":["notebook","ai","деревья","решений","knn","mdx.","numpy","pandas","python","аппроксимация","базовая","ближайших"],"topics":["python","knn","pandas","numpy","oop","regression"]},{"id":"chunk-3","heading":"Ноутбуки","text":". Notebook 6 — Генетические и эволюционные методы оптимизация, генетические алгоритмы и отжиг. Notebook 7 — Нейронные сети персептрон, MLP и основы обучения сети. Notebook 8 — Кластеризация методы группировки данных без учителя.","keywords":["ai","notebook","генетические","методы","mdx.","mlp","алгоритмы","без","группировки","данных","интеллекту","искусственному"],"topics":["ai","algorithms","clustering"]},{"id":"chunk-4","heading":"Как читать раздел","text":"Начните с Notebook 1 и Notebook 2 , если нужно выровнять базу по Python и обработке данных. Для классического ML переходите к Notebook 3 , Notebook 4 и Notebook 5 . Темы оптимизации и более продвинутых подходов собраны в Notebook 6 , Notebook 7 и Notebook 8 .","keywords":["notebook","ai","mdx.","ml","python","базу","более","выровнять","данных.","если","интеллекту","искусственному"],"topics":["python","ai","oop"]}]},{"id":"ai/notebook-01-python-basics","href":"/docs/ai/notebook-01-python-basics","slug":["ai","notebook-01-python-basics"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 1 — Основа Python","description":"Типы данных, условия, циклы и старт работы с NumPy.","preview":"Основа Python. Библиотеки. Дата 25.02.2023 1.1. Теоретический материал – Типы данных Типы данных Все типы данных в Python относятся к одной из 2 х категорий: изменяемые (mutable) и неизменяемые (immutable). Неизменяемые объекты: исловые данные (int, float), bo","keywords":["print","python","type","задача","10","elif","dev","от","plt.plot","range","данных","apple","df","if","import","rng","trapz","массива"],"topics":["python","ai","knn","numpy"],"chunks":[{"id":"chunk-0","heading":"","text":"Типы данных Все типы данных в Python относятся к одной из 2 х категорий: изменяемые (mutable) и неизменяемые (immutable). Неизменяемые объекты: исловые данные (int, float), bool, None, символьные строки (class 'str'), кортежи (tuple). Изменяемые объекты: списки (list), множества (set), словари (dict).","keywords":["ai","данных","типы","python","изменяемые","неизменяемые","объекты","bool","class","dict","float","immutable"],"topics":["python"]},{"id":"chunk-1","heading":"","text":"python x= 3+5.2 7 y= None z= 'a',5,12.345, (2,'b') df= [['Антонова Антонина',34,'ж'],['Борисов Борис',26,'м']] A={1,'title',2,'content'} print(x,' ',type(x),'\\n',y,' ',type(y),'\\n',df,' ',type(df),'\\n',A,' ',type(A),'\\n')","keywords":["type","ai","df","python","12.345","26","3+5.2","34","content","none","notebook","numpy."],"topics":["python"]},{"id":"chunk-2","heading":"","text":"code 39.4 None [['Антонова Антонина', 34, 'ж'], ['Борисов Борис', 26, 'м']] {'content', 1, 2, 'title'}","keywords":["ai","26","34","39.4","code","content","none","notebook","numpy.","python","title","антонина"],"topics":[]},{"id":"chunk-3","heading":"","text":"python x=5 =2 A={ 1,3,7,8} B={2,4,5,10,'apple'} C=A&B df= 'Антонова Антонина',34,'ж' z='type' D=[1,'title',2,'connect'] print(x,' ',type(x),'\\n',A,' ',type(A),'\\n',B,' ',type(B),'\\n',C,' ',type(C),'\\n',df,' ',type(df),'\\n',z,' ',type(z),'\\n',D,' ',type(D),'\\n')","keywords":["type","ai","df","python","10","34","apple","connect","notebook","numpy.","print","title"],"topics":["python"]},{"id":"chunk-4","heading":"","text":"text True {8, 1, 3, 7} {2, 'apple', 4, 5, 10} set() ('Антонова Антонина', 34, 'ж') type [1, 'title', 2, 'connect']","keywords":["ai","10","34","apple","connect","notebook","numpy.","python","set","text","title","true"],"topics":[]},{"id":"chunk-5","heading":"","text":"В коде часто приходится проверять выполнимость или невыполнимость каких то условий. Синтаксис:","keywords":["ai","notebook","numpy.","python","выполнимость","данных","каких","коде","невыполнимость","основа","приходится","проверять"],"topics":[]},{"id":"chunk-6","heading":"","text":"Обратите внимание, что код, который должен выполняться внутри каждого условия, записывается с отступом в 4 пробела от уровня if, elif и else: в питоне области видимости переменных обозначаются отступами.","keywords":["ai","условия","elif","else","if","notebook","numpy.","python","видимости","внимание","внутри","выполняться"],"topics":["python"]},{"id":"chunk-7","heading":"","text":"То есть, отступы позволяют понять, где начинается код, который должен выполняться при выполнении условия в if, и где заканчивается.","keywords":["ai","условия","if","notebook","numpy.","python","выполнении","выполняться","данных","должен","заканчивается.","код"],"topics":[]},{"id":"chunk-8","heading":"","text":"Задача:Вывести на экран является ли переменная х положительной, отрицательной или равна нулю.","keywords":["ai","notebook","numpy.","python","вывести","данных","задача","ли","нулю.","основа","отрицательной","переменная"],"topics":[]},{"id":"chunk-9","heading":"","text":"python x=125 if x<0: print('x отрицательный') elif x==0: print('x равен 0') else: print('x положительный')","keywords":["ai","print","python","125","elif","else","if","notebook","numpy.","данных","основа","отрицательный"],"topics":["python"]},{"id":"chunk-10","heading":"","text":"Задача: Напишите код. Задается х, напечатать какому из интервалов принадлежит: ( infinity, 5), [ 5, 5] или от (5, +infinity)","keywords":["ai","+infinity","infinity","notebook","numpy.","python","данных","задается","задача","интервалов","какому","код."],"topics":[]},{"id":"chunk-11","heading":"","text":"python x=int(input()) if x < 5: print('x пренадлежит интервалу от бесконечности до 5') elif 5<x<5: print('x пренадлежит интервалу от 5 до 5') else: print('x пренадлежит интервалу от 5 до +бесконечности')","keywords":["ai","print","до","интервалу","от","пренадлежит","python","+бесконечности","elif","else","if","input"],"topics":["python","numpy"]},{"id":"chunk-12","heading":"","text":"Цикл while проверяет истинность условия, и если условие истинно, то выполняет инструкции цикла.","keywords":["ai","условия","notebook","numpy.","python","while","выполняет","данных","если","инструкции","истинно","истинность"],"topics":[]},{"id":"chunk-13","heading":"","text":"Инструкции, которые относятся к циклу while, располагаются на последующих строках и должны иметь отступ от начала ключевого слова while.","keywords":["ai","notebook","numpy.","python","while","while.","данных","должны","иметь","инструкции","ключевого","которые"],"topics":[]},{"id":"chunk-14","heading":"","text":"Цикл for проходит по набору значений, помещает каждое значение в переменную с которой в цикле можно производить различные действия.","keywords":["ai","notebook","numpy.","python","данных","действия.","значение","значений","каждое","которой","можно","набору"],"topics":[]},{"id":"chunk-15","heading":"","text":"После ключевого слова for идет название переменной, в которую будут помещаться значения. После оператора in указывается набор значений и двоеточие. Инструкции, которые относятся к циклу, располагаются на последующих строках и должны иметь отступ. При создании цикла удобно пользоваться функцией range(a, b) , которая создает последовательность чисел от a до b 1 .","keywords":["ai","после","notebook","numpy.","python","range","будут","данных","двоеточие.","до","должны","значений"],"topics":[]},{"id":"chunk-16","heading":"","text":"python models=['KNN','decision tree','lineral models'] for model in models: print (model)","keywords":["ai","models","model","python","decision","knn","lineral","notebook","numpy.","print","tree","данных"],"topics":["python","knn"]},{"id":"chunk-17","heading":"","text":"Задача:Срез. Необходимо получить каждый k й элемент массива a, начиная с элемента с индексом i (включительно) и заканчивая элементом с индексом j (не включительно), c шагом 2. Переставить все элементы в обратном порядке.","keywords":["ai","включительно","индексом","2.","notebook","numpy.","python","данных","задача","заканчивая","каждый","массива"],"topics":[]},{"id":"chunk-18","heading":"","text":"3.3.1 Задание Задача:Выведите числа из примера на while (3.2.1) в обратном порядке.","keywords":["ai","3.2.1","3.3.1","notebook","numpy.","python","while","выведите","данных","задание","задача","обратном"],"topics":[]},{"id":"chunk-19","heading":"","text":"Задача: При решении задач машинного обучения часто в качестве объектов исследования выступает человек. Создайте список значимых характеристик (признаков), идентифицирующих человека. Выведите список на экран.","keywords":["ai","список","notebook","numpy.","python","выведите","выступает","данных","задач","задача","значимых","идентифицирующих"],"topics":[]},{"id":"chunk-20","heading":"","text":"python id=['Фамилия','Имя', 'Отчетсво',Int Возраст,'Город'] for i in id: print(i)","keywords":["ai","id","python","int","notebook","numpy.","print","возраст","город","данных","имя","основа"],"topics":["python"]},{"id":"chunk-21","heading":"","text":"Задача: Выведите числа из примера на for c функцией range() (3.2.4) в обратном порядке.","keywords":["ai","3.2.4","notebook","numpy.","python","range","выведите","данных","задача","обратном","основа","порядке."],"topics":[]},{"id":"chunk-22","heading":"","text":"Задача: Срез. Напишите код, который все элементы массива x с четными индексами переставит в обратном порядке. Т.е. если x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], то код должен сформировать [8, 1, 6, 3, 4, 5, 2, 7, 0, 9].","keywords":["ai","код","notebook","numpy.","python","данных","должен","если","задача","индексами","который","массива"],"topics":[]},{"id":"chunk-23","heading":"","text":"Машинное обучение опирается на фундаментальные понятия и законы математики и информатики. Библиотека математических функций (math) применяется при решении большинства прикладных задач анализа данных. Также, и некоторые другие библиотеки Python содержат математические функции (например, NumPy)","keywords":["ai","python","math","notebook","numpy","numpy.","анализа","библиотека","библиотеки","большинства","данных","данных."],"topics":["python","ai","numpy"]},{"id":"chunk-24","heading":"","text":". Важную роль играет библиотека matplotlib, которая позволяет производить визуализацию расчетов в удобной для восприятия человеком форме: графики, диаграммы, гистограммы.","keywords":["ai","matplotlib","notebook","numpy.","python","библиотека","важную","визуализацию","восприятия","гистограммы.","графики","данных"],"topics":[]},{"id":"chunk-25","heading":"","text":"Библиотеки в python содержат не только функции, позволяющие решать различные задачи, но и некоторые популярные константы, например число Эйлера.","keywords":["ai","python","notebook","numpy.","библиотеки","данных","задачи","константы","например","некоторые","основа","позволяющие"],"topics":["python"]},{"id":"chunk-26","heading":"","text":"Задача: Для функции sin⁡〖(xe^cos⁡〖(x)〗 )〗 на интервале [0;10] с шагом 0,05 построить график функции, закрасить площадь под ним и вычислить значение этой площади. Д ля вычисления площади воспользуйтесь функцией trapz(), вычисляющей интеграл по правилу трапеции. Для ее корректной работы необходимо подключить следующие библиотеки:","keywords":["ai","работы","функции","05","10","cos","notebook","numpy.","python","sin","trapz","xe"],"topics":[]},{"id":"chunk-27","heading":"","text":"````python from scipy.integrate import simps from numpy import trapz x=np.arange(0.0,10,0.1) y=np.abs(np.sin(x np.exp(np.cos(x)))) plt.grid() plt.plot(x,y,c='r') plt.fill between(x,y)","keywords":["ai","import","python","0.0","0.1","10","between","notebook","np.abs","np.arange","np.cos","np.exp"],"topics":["python","numpy"]},{"id":"chunk-28","heading":"","text":"Задача:Дано некоторое распределение оценок в группе за экзамен. Нарисовать круговую и точечную диаграммы распределения.","keywords":["ai","notebook","numpy.","python","группе","данных","дано","диаграммы","за","задача","круговую","нарисовать"],"topics":[]},{"id":"chunk-29","heading":"","text":"data = [3,7,8,4] fig= plt.figure(figsize=(10,7)) plt.pie(data, labels=marks) plt.show() plt.grid() plt.scatter (marks,data) ````","keywords":["ai","data","marks","10","fig","figsize","labels","notebook","numpy.","plt.figure","plt.grid","plt.pie"],"topics":[]},{"id":"chunk-30","heading":"","text":"Задача: Задайте массив случайных значений из интервала (0; 1). Рассчитайте средние и медианные значения для массива, сравните результаты, какие выводы можно сделать о значениях? Постройте точечную диаграмму рассеяния полученного ряда.","keywords":["ai","notebook","numpy.","python","выводы","данных","диаграмму","задайте","задача","значений","значения","значениях"],"topics":[]},{"id":"chunk-31","heading":"","text":"python size = 10 x = range(0, size) random = np.random.random sample(size = size) average = np.average(random) median = np.median(random) print(random, average, median) if average == median: print(\"Средние и медианные значения массива равны\") elif average median: print(\"Среднее значение больше медианного\") else: print(\"Медианное значение больше среднего\") fig, ax = plt.subplots(figsize=(10, 6)) ax.scatter(x, random)","keywords":["random","average","median","print","size","ai","10","python","больше","значение","ax","ax.scatter"],"topics":["python","numpy"]},{"id":"chunk-32","heading":"","text":"plt.show()","keywords":["ai","notebook","numpy.","plt.show","python","данных","основа","работы","старт","типы","условия","циклы"],"topics":[]},{"id":"chunk-33","heading":"","text":"Дана функция image.png Создать массив из 10 значений функции (x, например, изменяется от 1 до 10). Выделить срез первой половины массива и построить графики для основного массива, используя plt.plot(), для среза – точечный (plt.scatter()).","keywords":["ai","10","массива","image.png","notebook","numpy.","plt.plot","plt.scatter","python","выделить","графики","дана"],"topics":[]},{"id":"chunk-34","heading":"","text":"python max=[] midium=[] n=11 fig, ax = plt.subplots() ax.set facecolor('black') for x in range(1, n): max.append(((1+np.e x (0.5)+np.cos(x 2)) 0.5/ abs(1 np.sin(x) 3))+np.log10(abs(x 2))) print(max) plt.plot(max) plt.show() for i in range (5): midium.append(max[i]) print(midium) plt.scatter(midium, midium, c = 'black') plt.show()","keywords":["max","midium","ai","0.5","abs","black","plt.show","print","python","range","+np.cos","+np.log10"],"topics":["python","numpy"]},{"id":"chunk-35","heading":"","text":"Задача: Дана функция cos⁡(xe^((cos⁡(x)+ln⁡(x+1) ) ) ) Построить график на интервале (0, 10) с шагом 1 с заливкой площади и найти эту площадь под ним. Для вычисления площади воспользуйтесь функцией trapz(), вычисляющей интеграл по правилу трапеции. Для ее корректной работы необходимо подключить следующие библиотеки: from scipy.integrate import simps from numpy import trapz","keywords":["ai","cos","import","trapz","площади","работы","+ln","10","notebook","numpy","numpy.","python"],"topics":["numpy"]},{"id":"chunk-36","heading":"","text":"python from scipy.integrate import simps from numpy import trapz y=[] x=np.arange(0.0, 10, 1) for i in range(1, 11): y.append((np.cos(i np.e ((np.cos(i)+np.log10(i+1)))) 2) 0.5) print(max) plt.grid() plt.plot(x, y, c=\"r\") plt.fill between(x, y) area=trapz(y) print (area)","keywords":["ai","area","import","np.cos","print","python","trapz","+np.log10","0.0","0.5","10","11"],"topics":["python","numpy"]},{"id":"chunk-37","heading":"","text":"Задача: Откройте статистические данные по стоимости акций Apple, Microsoft, Google за 12 месяцев 2021 г. Постройте 3 графика на плоскости и оцените их динамику. Подумайте, как можно улучшить визуализацию результатов. Для начальных данных допустимо использовать следующий ресурс: https://finance.yahoo.com","keywords":["ai","данных","12","2021","apple","finance.yahoo.com","google","https","microsoft","notebook","numpy.","python"],"topics":[]},{"id":"chunk-38","heading":"","text":"python Apple = [167.30, 154.73, 170.09, 157.28, 137.13, 147.04, 165.35, 150.7, 140.09, 142.16, 129.62,132.62] Microsoft = [252.75, 295.2, 294.95, 309.42, 284.47, 272.42, 259.58, 278.01, 260.04, 240.74, 228.11, 154.69] Google = [89.70, 135.3, 134.17, 140.7, 117.16, 114.14, 109.08, 115.48, 110.55, 99.3, 90.5, 101.28] rng = numpy.arange(12) fig, ax = plt.subplots(figsize=(10, 5)) plt.plot(1 + rng, Microsoft, c = 'red',","keywords":["ai","microsoft","python","rng","10","101.28","109.08","110.55","114.14","115.48","117.16","12"],"topics":["python","numpy"]},{"id":"chunk-39","heading":"","text":"label = 'Microsoft') plt.plot(1 + rng, Apple, c = 'gray', label = \"Apple\") plt.plot(1 + rng, Google, c = 'blue', label = 'Google') ax.set xlim(xmin=1 + rng[0], xmax=1 + rng[ 1]) ax.legend(loc='upper left') plt.grid() plt.show()","keywords":["rng","ai","label","apple","google","plt.plot","ax.legend","ax.set","blue","gray","left","loc"],"topics":[]},{"id":"chunk-40","heading":"","text":"Задача: Создайте простейший калькулятор, включающий основные действия для двух переменных '+',' ','∙','÷', а также вычисление следующих функций: e^(x+y),sin⁡〖(x+y)〗,cos⁡〖(x+y)〗,x^y.","keywords":["ai","x+y","cos","notebook","numpy.","python","sin","y.","включающий","вычисление","данных","двух"],"topics":[]},{"id":"chunk-41","heading":"","text":"for dev in range(0, 8): if dev == 0: print(x + y) elif dev == 1: print(x y) elif dev == 2: print(x y) elif dev == 3: print(x / y) elif dev == 4: print(math.e (x + y)) elif dev == 5: print(math.sin(x + y)) elif dev == 6: print(math.cos(x + y)) else: print(x y) ````","keywords":["dev","print","elif","ai","else","if","math.cos","math.e","math.sin","notebook","numpy.","python"],"topics":[]},{"id":"chunk-42","heading":"","text":"code 17 7 60 0.4166666666666667 24154952.753575277 0.9613974918795568 0.27516333805159693 244140625","keywords":["ai","0.27516333805159693","0.4166666666666667","0.9613974918795568","17","24154952.753575277","244140625","60","code","notebook","numpy.","python"],"topics":[]}]},{"id":"ai/notebook-02-numpy-pandas","href":"/docs/ai/notebook-02-numpy-pandas","slug":["ai","notebook-02-numpy-pandas"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 2 — NumPy и pandas","description":"Базовые операции с массивами и таблицами, масштабирование данных.","preview":"NumPy и pandas Дата 25.02.2023 1.1. Теоретический материал – Библиотека NumPy NumPy (NumericalPython) это библиотека Python с открытым исходным кодом, которая используется практически во всех областях науки и техники. Это универсальный стандарт для работы с чи","keywords":["0.","1.","print","dataframe","python","данных","url","10","задача","cm","создать","np.nan","numpy","https","можно","...","0.2","code"],"topics":["python","ai","pandas","numpy","oop","testing","algorithms"],"chunks":[{"id":"chunk-0","heading":"","text":"NumPy (NumericalPython) это библиотека Python с открытым исходным кодом, которая используется практически во всех областях науки и техники. Это универсальный стандарт для работы с числовыми данными в Python.","keywords":["ai","numpy","notebook","numericalpython","pandas","python","python.","базовые","библиотека","всех","данными","данных."],"topics":["python","numpy"]},{"id":"chunk-1","heading":"","text":"Если у вас уже есть Python, вы можете установить NumPy с помощью командной строки:","keywords":["ai","numpy","notebook","pandas","python","базовые","вас","вы","данных.","если","командной","массивами"],"topics":["python","numpy"]},{"id":"chunk-2","heading":"","text":"Чтобы начать использовать NumPy необходимо импортировать соответствующую библиотеку:","keywords":["ai","numpy","notebook","pandas","базовые","библиотеку","данных.","импортировать","использовать","массивами","масштабирование","начать"],"topics":["numpy"]},{"id":"chunk-3","heading":"","text":"Основным объектом NumPy является однородный многомерный массив (в numpy называется numpy.ndarray). Это многомерный массив элементов (обычно чисел), одного типа.","keywords":["ai","numpy","массив","многомерный","notebook","numpy.ndarray","pandas","базовые","данных.","массивами","масштабирование","называется"],"topics":["numpy"]},{"id":"chunk-4","heading":"","text":"ndarray.shape размеры массива, его форма. Это кортеж натуральных чисел, показывающий длину массива по каждой оси. Для матрицы из n строк и m столбов, shape будет (n,m). Число элементов кортежа shape равно ndim.","keywords":["ai","shape","массива","ndarray.shape","ndim.","notebook","numpy","pandas","базовые","будет","данных.","длину"],"topics":[]},{"id":"chunk-5","heading":"","text":"ndarray.size количество элементов массива. Очевидно, равно произведению всех элементов атрибута shape.","keywords":["ai","элементов","ndarray.size","notebook","numpy","pandas","shape.","атрибута","базовые","всех","данных.","количество"],"topics":[]},{"id":"chunk-6","heading":"","text":"ndarray.dtype объект, описывающий тип элементов массива. Можно определить dtype, используя стандартные типы данных Python . NumPy здесь предоставляет целый букет возможностей, как встроенных, например: bool , character , int8 , int16 , int32 , int64 , float8 , float16 , float32 , float64 , complex64 , object , так и возможность определить собственные типы данных, в том числе и составные.","keywords":["ai","numpy","данных","определить","типы","bool","character","complex64","dtype","float16","float32","float64"],"topics":["python","numpy"]},{"id":"chunk-7","heading":"","text":"Обычно не нужно использовать этот атрибут, так как обращаться к элементам массива проще всего с помощью индексов.","keywords":["ai","notebook","numpy","pandas","атрибут","базовые","всего","данных.","индексов.","использовать","массива","массивами"],"topics":[]},{"id":"chunk-8","heading":"","text":"Подробнее о массивах в NumPy можно найти в официальной документации https://numpy.org/doc/stable/user/absolute beginners.html","keywords":["ai","numpy","absolute","beginners.html","doc","https","notebook","numpy.org","pandas","stable","user","базовые"],"topics":["ai","numpy"]},{"id":"chunk-9","heading":"","text":"Задача: Создать массив 5x2. Вывести все значения массива, значение элемента с индексом (3,1) и второй столбец. Индексация начинается с нуля.","keywords":["ai","5x2.","notebook","numpy","pandas","базовые","второй","вывести","данных.","задача","значение","значения"],"topics":[]},{"id":"chunk-10","heading":"","text":"python x=np.array([[1,2],[3,4],[5,6],[7,8],[9,10]]) print(x) print(x[3][1]) print(x[1])","keywords":["ai","print","10","notebook","np.array","numpy","pandas","python","базовые","данных.","массивами","масштабирование"],"topics":["python","numpy"]},{"id":"chunk-11","heading":"","text":"Задача: Пример. Выполнить следующее: 1. Создать вектор (одномерный массив) размера 10, заполненный нулями. 2. Создать вектор размера 10, заполненный единицами. 3. Создать вектор размера 10, заполненный заданным числом. 4. Создать вектор со значениями от 10 до 19.","keywords":["10","вектор","создать","ai","заполненный","размера","1.","19.","2.","3.","4.","notebook"],"topics":[]},{"id":"chunk-12","heading":"","text":"python a = np.zeros(10) b = np.ones(10) c = np.full(10, 5) d = np.arange(10, 20) print(a,\"\\n\", b,\"\\n\", c,\"\\n\", d,\"\\n\")","keywords":["10","ai","20","notebook","np.arange","np.full","np.ones","np.zeros","numpy","pandas","print","python"],"topics":["python","numpy"]},{"id":"chunk-13","heading":"","text":"code [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] [5 5 5 5 5 5 5 5 5 5] [10 11 12 13 14 15 16 17 18 19]","keywords":["0.","1.","ai","10","11","12","13","14","15","16","17","18"],"topics":[]},{"id":"chunk-14","heading":"","text":"Задача: Создать массив 10x10 со случайными значениями, найти минимум, максимум и среднее значение.","keywords":["ai","10x10","notebook","numpy","pandas","базовые","данных.","задача","значение.","значениями","максимум","массив"],"topics":[]},{"id":"chunk-15","heading":"","text":"python Z = np.random.random((10,10)) Zmin,Zmax,Zmean = Z.min(),Z.max(),Z.mean() print(Zmin,Zmax,Zmean)","keywords":["ai","10","zmax","zmean","zmin","notebook","np.random.random","numpy","pandas","print","python","z.max"],"topics":["python","numpy"]},{"id":"chunk-16","heading":"","text":"code [[ 5 6 7 8 9] [ 0 1 2 3 4] [10 11 12 13 14] [15 16 17 18 19] [20 21 22 23 24]]","keywords":["ai","10","11","12","13","14","15","16","17","18","19","20"],"topics":[]},{"id":"chunk-17","heading":"","text":"Задача: Выяснить результат следующих выражений: 0 np.nan np.nan == np.nan np.inf np.nan np.nan np.nan 0.3 == 3 0.1","keywords":["np.nan","ai","0.1","0.3","notebook","np.inf","numpy","pandas","базовые","выражений","выяснить","данных."],"topics":["numpy"]},{"id":"chunk-18","heading":"","text":"python print(0 np.nan) print(np.nan == np.nan) print(np.inf np.nan) print(np.nan np.nan) print(0.3 == 3 0.1)","keywords":["np.nan","print","ai","0.1","0.3","notebook","np.inf","numpy","pandas","python","базовые","данных."],"topics":["python","numpy"]},{"id":"chunk-19","heading":"","text":"Задача: Создать 8x8 матрицу и заполнить её в шахматном порядке нулями и единицами.","keywords":["ai","8x8","notebook","numpy","pandas","базовые","данных.","единицами.","ее","задача","заполнить","массивами"],"topics":[]},{"id":"chunk-20","heading":"","text":"code [[0. 1. 0. 1. 0. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0.] [0. 1. 0. 1. 0. 1. 0. 1.] [1. 0. 1. 0. 1. 0. 1. 0.]]","keywords":["0.","1.","ai","code","notebook","numpy","pandas","базовые","данных.","массивами","масштабирование","операции"],"topics":[]},{"id":"chunk-21","heading":"","text":"Задача: Создать 5x5 матрицу со значениями в строках от 0 до 4. Для создания необходимо использовать функцию arrange.","keywords":["ai","4.","5x5","arrange.","notebook","numpy","pandas","базовые","данных.","до","задача","значениями"],"topics":[]},{"id":"chunk-22","heading":"","text":"code [[0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.]]","keywords":["0.","1.","2.","3.","4.","ai","code","notebook","numpy","pandas","базовые","данных."],"topics":[]},{"id":"chunk-23","heading":"","text":"````code [[[0.12103427 0.58612308 0.97128317] [0.85425347 0.05382162 0.24655898] [0.62425414 0.42687911 0.88727021]]","keywords":["ai","0.05382162","0.12103427","0.24655898","0.42687911","0.58612308","0.62425414","0.85425347","0.88727021","0.97128317","code","notebook"],"topics":[]},{"id":"chunk-24","heading":"","text":"[[0.89520018 0.6148935 0.34365111] [0.4170647 0.74841712 0.53984955] [0.44451711 0.42021953 0.81843469]]","keywords":["ai","0.34365111","0.4170647","0.42021953","0.44451711","0.53984955","0.6148935","0.74841712","0.81843469","0.89520018","notebook","numpy"],"topics":[]},{"id":"chunk-25","heading":"","text":"[[0.52503207 0.5691061 0.32632757] [0.63784264 0.08092314 0.28269851] [0.36876011 0.05424346 0.48220954]]] ````","keywords":["ai","0.05424346","0.08092314","0.28269851","0.32632757","0.36876011","0.48220954","0.52503207","0.5691061","0.63784264","notebook","numpy"],"topics":[]},{"id":"chunk-26","heading":"","text":"code [[1. 1. 1. 1. 1. 1. 1. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 0. 0. 0. 0. 0. 0. 1.] [1. 1. 1. 1. 1. 1. 1. 1.]]","keywords":["0.","1.","ai","code","notebook","numpy","pandas","базовые","данных.","массивами","масштабирование","операции"],"topics":[]},{"id":"chunk-27","heading":"","text":"python arr = np.array([2,1,5,3,7,4,6,8]) arr=np.sort(arr) arr=arr[:: 1] print(arr)","keywords":["arr","ai","notebook","np.array","np.sort","numpy","pandas","print","python","базовые","данных.","массивами"],"topics":["python","numpy"]},{"id":"chunk-28","heading":"","text":"python x = random.randint(1,10) y = random.randint(1,10) A = np.zeros((x, y), int) print(A) if x == y: print('Форма матрицы: квадратная матрица') else: print('Форма матрицы: прямоугольная матрица') print(\"Размер: \",x+y) print('Размерность: ', x y)","keywords":["print","ai","10","random.randint","матрица","матрицы","форма","else","if","int","notebook","np.zeros"],"topics":["python","numpy"]},{"id":"chunk-29","heading":"","text":"code [[0 0 0 0 0] [0 0 0 0 0] [0 0 0 0 0] [0 0 0 0 0]] Форма матрицы: прямоугольная матрица Размер: 9 Размерность: 20","keywords":["ai","20","code","notebook","numpy","pandas","базовые","данных.","массивами","масштабирование","матрица","матрицы"],"topics":[]},{"id":"chunk-30","heading":"","text":"Первым шагом в любом начинании в области машинного обучения является введение исходных данных в систему. Исходные данные могут вводиться вручную, содержаться в файле или храниться в интернете в каком либо формате. Кроме того, часто требуется получить данные из нескольких источников.","keywords":["ai","данные","notebook","numpy","pandas","базовые","введение","вводиться","вручную","данных","данных.","интернете"],"topics":[]},{"id":"chunk-31","heading":"","text":"Библиотека pandas – это удобный и быстрый инструмент для работы с данными, обладающий большим функционалом. Если очень кратко, тоpandas – это библиотека, которая предоставляет очень удобные с точки зрения использования инструменты для хранения данных и работе с ними. Библиотека pandas присутствует в стандартной поставке Anaconda. Если же ее там нет, то его можно установить отдельно","keywords":["ai","pandas","библиотека","если","очень","anaconda.","notebook","numpy","базовые","большим","быстрый","данными"],"topics":["pandas"]},{"id":"chunk-32","heading":"","text":". Для этого введите командной строке:","keywords":["ai","notebook","numpy","pandas","базовые","введите","данных.","командной","массивами","масштабирование","операции","строке"],"topics":[]},{"id":"chunk-33","heading":"","text":"Библиотека pandas предоставляет две ключевые структуры данных: Series и DataFrame .","keywords":["ai","pandas","dataframe","notebook","numpy","series","базовые","библиотека","данных","данных.","две","ключевые"],"topics":["pandas","algorithms"]},{"id":"chunk-34","heading":"","text":"Series – это одномерная структура данных, ее можно представить, как таблицу с одной строкой. С Series можно работать как с обычным массивом (обращаться по номеру индекса), и как с ассоциированным массивом, когда можно использовать ключ для доступа к элементам данных.","keywords":["ai","можно","series","данных.","массивом","notebook","numpy","pandas","ассоциированным","базовые","данных","доступа"],"topics":["pandas"]},{"id":"chunk-35","heading":"","text":"DataFrame – это двумерная структура. Идейно она очень похожа на обычную таблицу, что выражается в способе ее создания и работе с ее элементами.","keywords":["ai","ее","dataframe","notebook","numpy","pandas","базовые","выражается","данных.","двумерная","идейно","массивами"],"topics":["pandas"]},{"id":"chunk-36","heading":"","text":"Задача: Создать Series из списка Python, словаря Python, и массива Numpy (установить буквенные метки для последнего).","keywords":["ai","numpy","python","notebook","pandas","series","базовые","буквенные","данных.","задача","массива","массивами"],"topics":["python","pandas","numpy"]},{"id":"chunk-37","heading":"","text":"````python lst = [1,2,3,4,5] d={'a':1,'b':2,'c':3} ndarr = np.array([1,2,3,4,5])","keywords":["ai","lst","ndarr","notebook","np.array","numpy","pandas","python","базовые","данных.","массивами","масштабирование"],"topics":["python","numpy"]},{"id":"chunk-38","heading":"","text":"code 0 1 1 2 2 3 3 4 4 5 dtype: int64 a 1 b 2 c 3 dtype: int64 a 1 b 2 c 3 d 4 e 5 dtype: int64","keywords":["ai","dtype","int64","code","notebook","numpy","pandas","базовые","данных.","массивами","масштабирование","операции"],"topics":[]},{"id":"chunk-39","heading":"","text":"Задача: Дано два Series. Напечатать их первые элементы и все элементы после третьего (во втором фрейме).","keywords":["ai","элементы","notebook","numpy","pandas","series.","базовые","втором","данных.","дано","два","задача"],"topics":["pandas"]},{"id":"chunk-40","heading":"","text":"python s1=pd.Series([1,2,3,4,5],['a','b','c','d','e']) s2=pd.Series([5,4,3,2,1]) print(s1['a']) print(s2[0]) print(s1[3:])","keywords":["ai","print","s1","pd.series","s2","notebook","numpy","pandas","python","базовые","данных.","массивами"],"topics":["python","pandas"]},{"id":"chunk-41","heading":"","text":"python dataframe = pd.DataFrame() dataframe['Имя']=['Джеки Джексон','Стивен Стивенсон'] dataframe['Возраст']=[38,25] dataframe['Водитель']=[True,False] dataframe","keywords":["dataframe","ai","25","38","false","notebook","numpy","pandas","pd.dataframe","python","true","базовые"],"topics":["python","pandas"]},{"id":"chunk-42","heading":"","text":"Задача: Загрузите фрейм данных по ссылке: https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv","keywords":["ai","chrisalbon","datasets","https","master","notebook","numpy","pandas","raw.githubusercontent.com","simulated","titanic.csv","базовые"],"topics":["python"]},{"id":"chunk-43","heading":"","text":"python Создать url адрес url = 'https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv' Загрузить данные dataframe = pd.read csv(url) показать 5 строк dataframe.head(5)","keywords":["ai","url","chrisalbon","csv","dataframe","dataframe.head","datasets","https","master","notebook","numpy","pandas"],"topics":["python","pandas"]},{"id":"chunk-44","heading":"","text":"Одна из самых простых вещей, которые мы можем сделать после загрузки данных, – это взглянуть на первые несколько строк с помощью метода head. На последние строки можно посмотреть с помощью функции tail. Мы также можем взглянуть на количество строк и столбцов: dataframe.shape. Кроме того, используя метод describe, мы можем получить описательную статистику для любых числовых столбцов.","keywords":["ai","можем","мы","взглянуть","помощью","строк","dataframe.shape.","describe","head.","notebook","numpy","pandas"],"topics":["ai","pandas"]},{"id":"chunk-45","heading":"","text":"Более подробно с возможностями работы с фреймами данных можно узнать по ссылке ниже:","keywords":["ai","notebook","numpy","pandas","базовые","более","возможностями","данных","данных.","массивами","масштабирование","можно"],"topics":[]},{"id":"chunk-46","heading":"","text":"url = 'https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv' dataframe = pd.read csv(url)","keywords":["ai","url","chrisalbon","csv","dataframe","datasets","https","master","notebook","numpy","pandas","pd.read"],"topics":["python","pandas"]},{"id":"chunk-47","heading":"","text":"print('\\n',dataframe.head(2)) print() print('\\n',dataframe.tail(3)) print('\\n',dataframe.shape) print('\\n',dataframe.describe()) ````","keywords":["print","ai","dataframe.describe","dataframe.head","dataframe.shape","dataframe.tail","notebook","numpy","pandas","базовые","данных.","массивами"],"topics":["ai","pandas"]},{"id":"chunk-48","heading":"","text":"Для выбора одной или нескольких строк, либо значений, можно использовать методы 1ос или iloc.","keywords":["ai","1ос","iloc.","notebook","numpy","pandas","базовые","выбора","данных.","значений","использовать","либо"],"topics":[]},{"id":"chunk-49","heading":"","text":"url = 'https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv' dataframe = pd.read csv(url)","keywords":["ai","url","chrisalbon","csv","dataframe","datasets","https","master","notebook","numpy","pandas","pd.read"],"topics":["python","pandas"]},{"id":"chunk-50","heading":"","text":"Задача: Требуется отобрать строки фрейма данных на основе некоторого условия. Необходимо сформировать новый фрейм данных из пассажиров первого класса.","keywords":["ai","данных","notebook","numpy","pandas","базовые","данных.","задача","класса.","массивами","масштабирование","некоторого"],"topics":["oop"]},{"id":"chunk-51","heading":"","text":"url = 'https://raw.githubusercontent.com/chrisalbon/simulated datasets/master/titanic.csv' dataframe = pd.read csv(url)","keywords":["ai","url","chrisalbon","csv","dataframe","datasets","https","master","notebook","numpy","pandas","pd.read"],"topics":["python","pandas"]},{"id":"chunk-52","heading":"","text":"Задача: Найдите евклидово расстояние между двумя Series (точками) a и b, не используя встроенную формулу.","keywords":["ai","notebook","numpy","pandas","series","базовые","встроенную","данных.","двумя","евклидово","задача","используя"],"topics":["pandas"]},{"id":"chunk-53","heading":"","text":"Задача: Найдите в Интернете ссылку на любой csv файл и сформируйте из него фрейм данных (например, коллекцию фреймов данных можно найти здесь: https://github.com/akmand/datasets).","keywords":["ai","данных","akmand","csv","datasets","github.com","https","notebook","numpy","pandas","базовые","данных."],"topics":[]},{"id":"chunk-54","heading":"","text":"python url = 'https://raw.githubusercontent.com/akmand/datasets/main/anneal.csv' dataframe = pd.read csv(url) dataframe.head(5)","keywords":["ai","url","akmand","anneal.csv","csv","dataframe","dataframe.head","datasets","https","main","notebook","numpy"],"topics":["python","ai","pandas"]},{"id":"chunk-55","heading":"","text":"Задача: Проделайте с получившемся из предыдущего задания фреймом данных те же действия, что и в примерах 2.2.5 2.2.7.","keywords":["ai","2.2.5","2.2.7.","notebook","numpy","pandas","базовые","данных","данных.","действия","же","задания"],"topics":[]},{"id":"chunk-56","heading":"","text":"url='https://raw.githubusercontent.com/akmand/datasets/main/anneal.csv' dataframe=pd.read csv(url)","keywords":["ai","url","akmand","anneal.csv","csv","dataframe","datasets","https","main","notebook","numpy","pandas"],"topics":["ai","pandas"]},{"id":"chunk-57","heading":"","text":"print(dataframe.head(2)) print(dataframe.tail(3)) print(dataframe.shape) print(dataframe.describe()) print(dataframe.iloc[1:4]) ````","keywords":["print","ai","dataframe.describe","dataframe.head","dataframe.iloc","dataframe.shape","dataframe.tail","notebook","numpy","pandas","базовые","данных."],"topics":["ai","pandas"]},{"id":"chunk-58","heading":"","text":"Количественные данные что то измеряют – будь то размер класса, ежемесячные продажи или оценки учащихся. Естественным способом представления этих величин является численным (например, 150 студентов, $529 392 продаж).","keywords":["ai","то","150","392","529","notebook","numpy","pandas","базовые","будь","величин","данные"],"topics":["oop"]},{"id":"chunk-59","heading":"","text":"Нормализация данных — это общепринятая задача предобработки в машинном обучении. Многие алгоритмы предполагают, что все признаки находятся в единой шкале, как правило, от 0 до 1 или от 1 до 1.","keywords":["ai","до","от","1.","notebook","numpy","pandas","алгоритмы","базовые","данных","данных.","единой"],"topics":["algorithms"]},{"id":"chunk-60","heading":"","text":"Существует множество способов нормализации значений признаков, чтобы масштабировать их к единому диапазону и использовать в различных моделях машинного обучения. В зависимости от используемой функции, их можно разделить на 2 большие группы: линейные и нелинейные. При нелинейной нормализации в расчетных соотношениях используются функции логистической сигмоиды или гиперболического тангенса","keywords":["ai","их","нормализации","функции","notebook","numpy","pandas","базовые","большие","гиперболического","группы","данных."],"topics":[]},{"id":"chunk-61","heading":"","text":". В линейной нормализации изменение переменных осуществляется пропорционально, по линейному закону.","keywords":["ai","notebook","numpy","pandas","базовые","данных.","закону.","изменение","линейной","линейному","массивами","масштабирование"],"topics":[]},{"id":"chunk-62","heading":"","text":"Мин и макс – линейное преобразование данных в диапазоне [0..1], где минимальное и максимальное масштабируемые значения соответствуют 0 и 1 соответственно;","keywords":["ai","0..1","notebook","numpy","pandas","базовые","данных","данных.","диапазоне","значения","линейное","макс"],"topics":[]},{"id":"chunk-63","heading":"","text":"Z масштабирование данных на основе среднего значения и стандартного отклонения: производят деление разницы между переменной и средним значением на стандартное отклонение.","keywords":["ai","масштабирование","notebook","numpy","pandas","базовые","данных","данных.","деление","значением","значения","массивами"],"topics":[]},{"id":"chunk-64","heading":"","text":"При масштабировании данных мы будем использовать одну из популярных библиотек машинного обучения Scikit learn. Библиотека содержит пакет sklearn.preprocessing, который предоставляет широкиевозможности для нормализации данных. Следует отметить, что в целом алгоритмы обучения выигрывают от стандартизации набора данных.","keywords":["ai","данных.","обучения","learn.","notebook","numpy","pandas","scikit","sklearn.preprocessing","алгоритмы","базовые","библиотек"],"topics":["algorithms"]},{"id":"chunk-65","heading":"","text":"Создать признак feature = np.array([[ 500.5],[ 100.1],[0],[100.1],[900.9]]) Создать шкалировщик minmax scale = preprocessing.MinMaxScaler(feature range = (0,1))","keywords":["ai","100.1","feature","создать","500.5","900.9","minmax","notebook","np.array","numpy","pandas","preprocessing.minmaxscaler"],"topics":["numpy"]},{"id":"chunk-66","heading":"","text":"Задача: Преобразуйте признак, чтобы он имел среднее значение 0 и стандартное отклонение 1.","keywords":["ai","1.","notebook","numpy","pandas","базовые","данных.","задача","значение","имел","массивами","масштабирование"],"topics":[]},{"id":"chunk-67","heading":"","text":"````python x=np.array([[ 1000.1],[ 200.2],[500.5],[600.6],[9000.9]]) Создать шкалировщик scaler = preprocessing.StandardScaler() Преобразовать признак standardized = scaler.fit transform(x) Показать признак standardized","keywords":["ai","standardized","признак","1000.1","200.2","500.5","600.6","9000.9","notebook","np.array","numpy","pandas"],"topics":["python","numpy"]},{"id":"chunk-68","heading":"","text":"Мы можем увидеть эффект стандартизации, обратившись к среднему значению и стандартному отклонению результата нашего решения:","keywords":["ai","notebook","numpy","pandas","базовые","данных.","значению","массивами","масштабирование","можем","мы","нашего"],"topics":[]},{"id":"chunk-69","heading":"","text":"print(\"Среднее:\",round(standardized.mean())) print(\"Стандартное отклонение:\", standardized.std()) ````","keywords":["ai","print","notebook","numpy","pandas","round","standardized.mean","standardized.std","базовые","данных.","массивами","масштабирование"],"topics":[]},{"id":"chunk-70","heading":"","text":"````python from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() dfTest = pd.DataFrame({'A':[14.00,90.20,90.95,96.27,91.21], 'B':[103.02,107.26,110.35,114.23,114.68], 'C':['big','small','big','small','small']})","keywords":["ai","small","big","minmaxscaler","103.02","107.26","110.35","114.23","114.68","14.00","90.20","90.95"],"topics":["python","pandas","testing"]},{"id":"chunk-71","heading":"","text":"code A B C 0 0.000000 0.000000 big 1 0.926219 0.363636 small 2 0.935335 0.628645 big 3 1.000000 0.961407 small 4 0.938495 1.000000 small","keywords":["ai","small","0.000000","1.000000","big","0.363636","0.628645","0.926219","0.935335","0.938495","0.961407","code"],"topics":[]},{"id":"chunk-72","heading":"","text":"Необходимо выполнить нормализацию первого числового признака (sepal length cm) с использованием минимаксного преобразования, а второго (sepal width cm) с задействованием z масштабирования.","keywords":["ai","cm","sepal","length","notebook","numpy","pandas","width","базовые","второго","выполнить","данных."],"topics":[]},{"id":"chunk-73","heading":"","text":"````python from sklearn import preprocessing from sklearn.preprocessing import MinMaxScaler","keywords":["ai","import","minmaxscaler","notebook","numpy","pandas","preprocessing","python","sklearn","sklearn.preprocessing","базовые","данных."],"topics":["python"]},{"id":"chunk-74","heading":"","text":"url='https://raw.githubusercontent.com/akmand/datasets/master/iris.csv' dataframe=pd.read csv(url) scaler = MinMaxScaler() dataframe[['sepal length cm']] = scaler.fit transform(dataframe[['sepal length cm']]) dataframe ````","keywords":["dataframe","ai","cm","length","sepal","url","akmand","csv","datasets","https","iris.csv","master"],"topics":["python","pandas"]},{"id":"chunk-75","heading":"","text":"````code sepal length cm sepal width cm petal length cm petal width cm \\ 0 0.222222 3.5 1.4 0.2 1 0.166667 3.0 1.4 0.2 2 0.111111 3.2 1.3 0.2 3 0.083333 3.1 1.5 0.2 4 0.194444 3.6 1.4 0.2 .. ... ... ... ... 145 0.666667 3.0 5.2 2.3 146 0.555556 2.5 5.0 1.9 147 0.611111 3.0 5.2 2.0 148 0.527778 3.4 5.4 2.3 149 0.444444 3.0 5.1 1.8","keywords":["0.2","...","3.0","cm","1.4","ai","2.3","5.2","length","petal","sepal","width"],"topics":[]},{"id":"chunk-76","heading":"","text":"species 0 setosa 1 setosa 2 setosa 3 setosa 4 setosa .. ... 145 virginica 146 virginica 147 virginica 148 virginica 149 virginica","keywords":["setosa","virginica","ai","..","...","145","146","147","148","149","notebook","numpy"],"topics":[]},{"id":"chunk-77","heading":"","text":"````python from sklearn import preprocessing from sklearn.preprocessing import StandardScaler","keywords":["ai","import","notebook","numpy","pandas","preprocessing","python","sklearn","sklearn.preprocessing","standardscaler","базовые","данных."],"topics":["python"]},{"id":"chunk-78","heading":"","text":"url='https://raw.githubusercontent.com/akmand/datasets/master/iris.csv' dataframe=pd.read csv(url) scaler= StandardScaler() dataframe[['sepal width cm']] = scaler.fit transform(dataframe[['sepal width cm']]) dataframe ````","keywords":["dataframe","ai","cm","sepal","url","width","akmand","csv","datasets","https","iris.csv","master"],"topics":["python","pandas"]},{"id":"chunk-79","heading":"","text":"````text sepal length cm sepal width cm petal length cm petal width cm \\ 0 5.1 1.032057 1.4 0.2 1 4.9 0.124958 1.4 0.2 2 4.7 0.337848 1.3 0.2 3 4.6 0.106445 1.5 0.2 4 5.0 1.263460 1.4 0.2 .. ... ... ... ... 145 6.7 0.124958 5.2 2.3 146 6.3 1.281972 5.0 1.9 147 6.5 0.124958 5.2 2.0 148 6.2 0.800654 5.4 2.3 149 5.9 0.124958 5.1 1.8","keywords":["0.2","...","0.124958","cm","1.4","ai","2.3","5.0","5.1","5.2","length","petal"],"topics":[]},{"id":"chunk-80","heading":"","text":"species 0 setosa 1 setosa 2 setosa 3 setosa 4 setosa .. ... 145 virginica 146 virginica 147 virginica 148 virginica 149 virginica","keywords":["setosa","virginica","ai","..","...","145","146","147","148","149","notebook","numpy"],"topics":[]}]},{"id":"ai/notebook-03-metrics-and-knn","href":"/docs/ai/notebook-03-metrics-and-knn","slug":["ai","notebook-03-metrics-and-knn"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 3 — Метрики и KNN","description":"Метрики качества, расстояния и классификация K ближайших соседей.","preview":"1.1. Теоретический материал – Функции Python Перед тем как рассматривать задачи классификации вспомним понятие функции (метода) в Python. Функция в python объект, принимающий аргументы и возвращающий значение. Обычно функция определяется с помощью инструкции d","keywords":["данных","0.","python","между","print","test","расстояния","np.array","1.","2.","расстояние","iris","задача","соседей","функции","функция","ближайших","от"],"topics":["python","ai","knn","pandas","numpy","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"","text":"Перед тем как рассматривать задачи классификации вспомним понятие функции (метода) в Python. Функция в python объект, принимающий аргументы и возвращающий значение. Обычно функция определяется с помощью инструкции def .","keywords":["ai","метрики","функция","def","knn","notebook","python","python.","аргументы","ближайших","возвращающий","вспомним"],"topics":["python","oop"]},{"id":"chunk-1","heading":"","text":"Инструкция return говорит, что нужно вернуть значение. В нашем случае функция возвращает сумму x и y. Теперь мы ее можем вызвать:","keywords":["ai","метрики","knn","notebook","return","y.","ближайших","вернуть","возвращает","вызвать","говорит","ее"],"topics":[]},{"id":"chunk-2","heading":"","text":"Функция может быть любой сложности и возвращать любые объекты (списки, кортежи, и даже функции):","keywords":["ai","метрики","knn","notebook","ближайших","быть","возвращать","даже","качества","классификация","кортежи","любой"],"topics":[]},{"id":"chunk-3","heading":"","text":"Функция может и не заканчиваться инструкцией return, при этом функция вернет значение None:","keywords":["ai","метрики","функция","knn","none","notebook","return","ближайших","вернет","заканчиваться","значение","инструкцией"],"topics":[]},{"id":"chunk-4","heading":"","text":"Функция может принимать произвольное количество аргументов или не принимать их вовсе. Также распространены функции с произвольным числом аргументов, функции с позиционными и именованными аргументами, обязательными и необязательными.","keywords":["ai","аргументов","метрики","принимать","функции","knn","notebook","аргументами","ближайших","вовсе.","именованными","их"],"topics":[]},{"id":"chunk-5","heading":"","text":"Как видно из примера, args это кортеж из всех переданных аргументов функции, и с переменной можно работать также, как и с кортежем.","keywords":["ai","метрики","args","knn","notebook","аргументов","ближайших","видно","всех","качества","классификация","кортеж"],"topics":[]},{"id":"chunk-6","heading":"","text":"Функция может принимать и произвольное число именованных аргументов, тогда перед именем ставится :","keywords":["ai","метрики","knn","notebook","аргументов","ближайших","именем","именованных","качества","классификация","может","перед"],"topics":[]},{"id":"chunk-7","heading":"","text":"Задача: Напишите функцию sum range(start, end), которая суммирует все целые числа от значения «start» до величины «end» включительно. Если пользователь задаст первое число большее чем второе, просто поменяйте их местами.","keywords":["ai","end","start","метрики","knn","notebook","range","sum","ближайших","большее","величины","включительно."],"topics":[]},{"id":"chunk-8","heading":"","text":"````python def sum range(start,end): if start end: end,start = start,end return sum(range(start,end+1))","keywords":["start","end","ai","range","sum","метрики","def","end+1","if","knn","notebook","python"],"topics":["python"]},{"id":"chunk-9","heading":"","text":"python def fact(num): if num ==0: return 1 По договоренности факторивл 0 равен 1 else: return num fact(num 1) возвращает результат произведения num и результата возвращенного функцией fact(num 1)","keywords":["num","ai","fact","return","метрики","def","else","if","knn","notebook","python","ближайших"],"topics":["python"]},{"id":"chunk-10","heading":"","text":"Сходство или различие между объектами классификации устанавливается в зависимости от выбранного метрического расстояния между ними. Если каждый объект описывается n свойствами (признаками), то он может быть представлен как точка в n мерном пространстве, и сходство с другими объектами будет определяться как соответствующее расстояние. При классификации используются различные меры расстояния между объектами.","keywords":["ai","между","расстояния","классификации","метрики","объектами","сходство","knn","notebook","ближайших","будет","быть"],"topics":["oop"]},{"id":"chunk-11","heading":"","text":"Это, пожалуй, наиболее часто используемая мера расстояния. Она является геометрическим расстоянием в многомерном пространстве и вычисляется следующим образом:","keywords":["ai","метрики","knn","notebook","ближайших","вычисляется","геометрическим","используемая","качества","классификация","мера","многомерном"],"topics":[]},{"id":"chunk-12","heading":"","text":"P – расстояние между объектами A и B; Ai – значение i свойства объекта A; Bi – значение i свойства объекта B.","keywords":["ai","значение","метрики","объекта","свойства","b.","bi","knn","notebook","ближайших","качества","классификация"],"topics":["ai"]},{"id":"chunk-13","heading":"","text":"Данная мера расстояния используется в тех случаях, когда требуется придать больше значение более отдаленным друг от друга объектам. Это расстояние вычисляется следующим образом:","keywords":["ai","метрики","расстояния","knn","notebook","ближайших","более","больше","вычисляется","данная","друг","друга"],"topics":[]},{"id":"chunk-14","heading":"","text":"Применяется в тех случаях, когда каждому i свойству удается приписать некоторый «вес» wi, пропорционально степени важности признака в задаче классификации:","keywords":["ai","метрики","knn","notebook","wi","ближайших","важности","вес","задаче","каждому","качества","классификации"],"topics":["oop"]},{"id":"chunk-15","heading":"","text":"Также называется манхэттенским, сити блок расстоянием или расстоянием городских кварталов. Это расстояние является разностью по координатам. Хеммингово расстояние вычисляется по формуле:","keywords":["ai","метрики","расстояние","расстоянием","knn","notebook","ближайших","блок","вычисляется","городских","качества","кварталов."],"topics":[]},{"id":"chunk-16","heading":"","text":"Принимает значение наибольшего модуля разности между значениями соответствующих свойств (признаков) объектов:","keywords":["ai","метрики","knn","notebook","ближайших","значение","значениями","качества","классификация","между","модуля","наибольшего"],"topics":[]},{"id":"chunk-17","heading":"","text":"Выбор меры расстояния и весов для классифицирующих свойств – очень важный этап, так как от этих процедур зависят состав и количество формируемых классов, а также степень сходства объектов внутри классов.","keywords":["ai","метрики","расстояния","knn","notebook","ближайших","важный","весов","внутри","выбор","зависят","качества"],"topics":["oop"]},{"id":"chunk-18","heading":"","text":"Задача: Напишите функции в Python, которая вычисляет Евклидово расстояние между двумя массивами NumPy.","keywords":["ai","метрики","knn","notebook","numpy.","python","ближайших","вычисляет","двумя","евклидово","задача","качества"],"topics":["python","numpy"]},{"id":"chunk-19","heading":"","text":"````python def euclidean distance(v1,v2): return sum((x y) 2 for x,y,in zip(v1,v2)) 0.5","keywords":["ai","v1","v2","метрики","0.5","def","distance","euclidean","knn","notebook","python","return"],"topics":["python"]},{"id":"chunk-20","heading":"","text":"Задача: Напишите 4 функции в Python, которые рассчитывают квадрат Евклидова расстояния, взвешенное евклидово расстояние, Хеммингово расстояние и расстояние Чебышева между двумя массивами NumPy.","keywords":["ai","расстояние","метрики","расстояния","knn","notebook","numpy.","python","ближайших","взвешенное","двумя","евклидова"],"topics":["python","numpy"]},{"id":"chunk-21","heading":"","text":"def weighted euclidean distance(v1,v2,w): return sum((x y) 2 s for x,y,s in zip(v1,v2,w)) 0.5","keywords":["ai","v1","v2","метрики","0.5","def","distance","euclidean","knn","notebook","return","sum"],"topics":[]},{"id":"chunk-22","heading":"","text":"x= np.array([0,0,0]) y= np.array([3,3,3]) w= np.array([0,0,1]) print(sqr euclidean distance(x,y)) print(weighted euclidean distance(x,y,w)) print(manhattan distance(x,y)) print(chebyshev distnce(x,y)) ````","keywords":["print","ai","distance","np.array","euclidean","метрики","chebyshev","distnce","knn","manhattan","notebook","sqr"],"topics":["numpy"]},{"id":"chunk-23","heading":"","text":"Задача: В Python есть встроенные функции для вычисления расстояний между векторами. Мы будем использовать NumPy для расчета расстояния для двух точек, поскольку ранее рассмотренные структуры данных могут быть переведены в Numpy массив с помошью специальных функциий. Например, для серий это будет выглядеть следующим образом: seriesName.to numpy().","keywords":["ai","numpy","метрики","расстояния","knn","notebook","python","seriesname.to","ближайших","будем","будет","быть"],"topics":["python","pandas","numpy","algorithms"]},{"id":"chunk-24","heading":"","text":"Для удобства визуализации и анализа результатов в дальнейших расчетах будм использовать 2 точки в 3 х мерном пространстве:","keywords":["ai","метрики","knn","notebook","анализа","ближайших","будм","визуализации","дальнейших","использовать","качества","классификация"],"topics":[]},{"id":"chunk-25","heading":"","text":"Задача: Рассчитать расстояния между двумя точками с использованием методов определения расстояний, представленных выше.","keywords":["ai","метрики","расстояния","knn","notebook","ближайших","выше.","двумя","задача","использованием","качества","классификация"],"topics":[]},{"id":"chunk-26","heading":"","text":"python x= np.array([0,0,0]) y= np.array([3,3,3]) w= np.array([0,0,1]) Расстояние Евклида print(np.linalg.norm(x y)) Квадрат Евклидова расстояния print(np.linalg.norm(x y) 2) Раcстояние Чебышева print(np.linalg.norm(x y, ord = np.inf)) Расстояние Хемминга print(np.linalg.norm(x y, ord = 1))","keywords":["np.linalg.norm","print","ai","np.array","ord","метрики","расстояние","расстояния","knn","notebook","np.inf","python"],"topics":["python","numpy"]},{"id":"chunk-27","heading":"","text":"Задача: Задайте 4 точки в трехмерном пространстве, рассчитайте между ними расстояния по описанным в примере выше метрикам. Отобразите точки в трехмерном пространстве.","keywords":["ai","метрики","расстояния","точки","трехмерном","knn","notebook","ближайших","выше","задайте","задача","качества"],"topics":[]},{"id":"chunk-28","heading":"","text":"t1 = np.array([1,1,1]) t2 = np.array([5,3,1]) t3 = np.array([4,5,6]) t4 = np.array([2,2,8])","keywords":["np.array","ai","метрики","knn","notebook","t1","t2","t3","t4","ближайших","качества","классификация"],"topics":["numpy"]},{"id":"chunk-29","heading":"","text":"ax.scatter(t1[0], t1[1], t1[2]) ax.scatter(t2[0], t2[1], t2[2]) ax.scatter(t3[0], t3[1], t3[2]) ax.scatter(t4[0], t4[1], t4[2])","keywords":["ax.scatter","ai","t1","t2","t3","t4","метрики","knn","notebook","ближайших","качества","классификация"],"topics":[]},{"id":"chunk-30","heading":"","text":"print(f'Вектор AB = {np.linalg.norm(t1 t2)}') print(f'Вектор BC = {np.linalg.norm(t2 t3)}') print(f'Вектор CD = {np.linalg.norm(t3 t4)}') print(f'Вектор DA = {np.linalg.norm(t4 t1)}') ````","keywords":["np.linalg.norm","print","вектор","ai","t1","t2","t3","t4","метрики","ab","bc","cd"],"topics":["numpy"]},{"id":"chunk-31","heading":"","text":"Задача: Создать 5x5 матрицу со значениями в строках от 0 до 4. Для создания необходимо использовать функцию arange.","keywords":["ai","метрики","4.","5x5","arange.","knn","notebook","ближайших","до","задача","значениями","использовать"],"topics":[]},{"id":"chunk-32","heading":"","text":"code [[0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.] [0. 1. 2. 3. 4.]]","keywords":["0.","1.","2.","3.","4.","ai","метрики","code","knn","notebook","ближайших","качества"],"topics":[]},{"id":"chunk-33","heading":"","text":"Решение задачи классификации методом k ближайших соседей Метод k ближайших соседей используется для решения задачи классификации. Он относит объекты к классу, которому принадлежит большинство из k его ближайших соседей в многомерном пространстве признаков. Это один из простейших алгоритмов обучения классификационных моделей","keywords":["ближайших","ai","соседей","задачи","метрики","knn","notebook","алгоритмов","большинство","его","используется","качества"],"topics":["knn","oop","algorithms"]},{"id":"chunk-34","heading":"","text":". Число k – это количество соседних объектов в пространстве признаков, которые сравниваются с классифицируемым объектом. Иными словами, если k=10, то каждый объект сравнивается с 10 ю соседями. В процессе обучения алгоритм просто запоминает все векторы признаков и соответствующие им метки классов. При работе с реальными данными, т.е","keywords":["ai","10","метрики","признаков","knn","notebook","алгоритм","ближайших","векторы","данными","если","запоминает"],"topics":["oop","algorithms"]},{"id":"chunk-35","heading":"","text":". наблюдениями, метки класса которых неизвестны, вычисляется расстояние между вектором нового наблюдения и ранее запомненными. Затем выбирается k ближайших к нему векторов, и новый объект относится к классу, которому принадлежит большинство из них. Приведем алгоритм метода. 1. Выберите значение K соседей (скажем, k = 5) 2","keywords":["ai","ближайших","метрики","1.","knn","notebook","алгоритм","большинство","векторов","вектором","выберите","выбирается"],"topics":["oop","algorithms"]},{"id":"chunk-36","heading":"","text":". Найдите ближайшую точку данных K (5) для нашей новой точки данных на основе евклидова расстояния (которое мы обсудим позже) 3. Среди этих K точек данных подсчитайте точки данных в каждой категории. 4. Назначьте новую точку данных категории, которая имеет наибольшее количество соседей с новой точкой данных","keywords":["данных","ai","метрики","новой","расстояния","точки","точку","3.","4.","knn","notebook","ближайших"],"topics":[]},{"id":"chunk-37","heading":"","text":"Модуль библиотеки sklearn sklearn.neighbors предоставляет функциональные возможности для контролируемого обучения на основе соседей. Обучение на основе контролируемых соседей бывает двух видов: классификация данных с дискретными метками и регрессия для данных с непрерывными метками. В данном разделе рассмотрим несколько примеров с использованием названного метода.","keywords":["ai","данных","классификация","метрики","основе","соседей.","knn","notebook","sklearn","sklearn.neighbors","библиотеки","ближайших"],"topics":["oop","regression"]},{"id":"chunk-38","heading":"","text":"Задача: В примере показано создание 2d массива со значениями x и y. Список target содержит возможные выходные классы (часто называемые метками). Далее происходит обучене классификатора k ближайших соседей по исходным данным. Далее производится прогноз принадлежности к классам для двух точек данных","keywords":["ai","ближайших","далее","метрики","2d","knn","notebook","target","y.","возможные","выходные","данным."],"topics":["knn","oop"]},{"id":"chunk-39","heading":"","text":"данные X = np.array([[ 1, 1],[ 2, 1],[ 3, 2],[1,1],[2,1],[3,2]]) target = [0,0,0,1,1,1]","keywords":["ai","метрики","knn","notebook","np.array","target","ближайших","данные","качества","классификация","расстояния","соседей."],"topics":["numpy"]},{"id":"chunk-40","heading":"","text":"обучаем модель k ближайших соседей к данным K=3 model = KNeighborsClassifier(n neighbors = K) model.fit(X,target) print(model)","keywords":["ai","model","ближайших","метрики","kneighborsclassifier","knn","model.fit","neighbors","notebook","print","target","данным"],"topics":["knn"]},{"id":"chunk-41","heading":"","text":"code KNeighborsClassifier(n neighbors=3) ( 2, 2) is class [0] 91,3) is class [1]","keywords":["ai","class","метрики","91","code","kneighborsclassifier","knn","neighbors","notebook","ближайших","качества","классификация"],"topics":[]},{"id":"chunk-42","heading":"","text":"Задача: Далее приведем более наглядный пример. Будет построена граница решения для каждого класса. В качестве данных будем использовать уже знакомый нам и встроенный в библиотеку sklearn набор данных ирисов Фишера. Этот набор данных стал уже классическим, и часто используется в литературе для иллюстрации работы различных статистических алгоритмов","keywords":["ai","данных","метрики","набор","уже","knn","notebook","sklearn","алгоритмов","библиотеку","ближайших","более"],"topics":["oop","algorithms"]},{"id":"chunk-43","heading":"","text":". Датасет содержит наблюдения за 150 разными цветками ирисов, данные по каждому цветку расположены в строках. В стобцах записаны длина и ширина чашелистика, длина и ширина лепестка, вид ириса.","keywords":["ai","длина","метрики","ширина","150","knn","notebook","ближайших","вид","данные","датасет","за"],"topics":[]},{"id":"chunk-44","heading":"","text":"python ьfrom sklearn.neighbors import KNeighborsClassifier iris = sns.load dataset('iris') iris","keywords":["ai","iris","метрики","dataset","import","kneighborsclassifier","knn","notebook","python","sklearn.neighbors","sns.load","ближайших"],"topics":["python"]},{"id":"chunk-45","heading":"","text":"````code sepal length sepal width petal length petal width species 0 5.1 3.5 1.4 0.2 setosa 1 4.9 3.0 1.4 0.2 setosa 2 4.7 3.2 1.3 0.2 setosa 3 4.6 3.1 1.5 0.2 setosa 4 5.0 3.6 1.4 0.2 setosa .. ... ... ... ... ... 145 6.7 3.0 5.2 2.3 virginica 146 6.3 2.5 5.0 1.9 virginica 147 6.5 3.0 5.2 2.0 virginica 148 6.2 3.4 5.4 2.3 virginica 149 5.9 3.0 5.1 1.8 virginica","keywords":["...","0.2","setosa","virginica","3.0","1.4","ai","2.3","5.0","5.1","5.2","length"],"topics":[]},{"id":"chunk-46","heading":"","text":"Задача: Покажем на графиках зависимости ширины лепестка от его длины, а также аналогичный график зависимость для длины и ширины чашелистика. Разные виды цветков отмечены разными цветами.","keywords":["ai","длины","метрики","ширины","knn","notebook","аналогичный","ближайших","виды","график","графиках","его"],"topics":[]},{"id":"chunk-47","heading":"","text":"plt.subplot(121) sns.scatterplot( data=iris, x = 'petal width', y='petal length', hue='species', s=70 )","keywords":["ai","petal","метрики","121","70","data","hue","iris","knn","length","notebook","plt.subplot"],"topics":[]},{"id":"chunk-48","heading":"","text":"plt.xlabel('Длинна лепестка, см') plt.ylabel('Ширина лепестка, см') plt.legend() plt.grid()","keywords":["ai","лепестка","метрики","см","knn","notebook","plt.grid","plt.legend","plt.xlabel","plt.ylabel","ближайших","длинна"],"topics":[]},{"id":"chunk-49","heading":"","text":"plt.subplot (122) sns.scatterplot(data=iris, x='sepal width', y='sepal length', hue='species', s=70) plt.xlabel('Длинна чашелепестка, см') plt.ylabel('Ширина чашелепестка, см') plt.legend() plt.grid(); ````","keywords":["ai","sepal","метрики","см","чашелепестка","122","70","data","hue","iris","knn","length"],"topics":[]},{"id":"chunk-50","heading":"","text":"Из графиков видно, что в первом случае классы визуально хорошо отделимы друг от друга, хотя два классе имеют небольшое пересечение. Во втором случае разделить два класса между собой уже намного труднее. Далее разделим датасет на обучающую и тестовую выборки в соотношении 80:20. Обучающая выборка (training sample) — выборка, по которой производится настройка (оптимизация параметров) модели зависимости","keywords":["ai","выборка","два","метрики","случае","20.","80","knn","notebook","sample","training","ближайших"],"topics":["ai","oop"]},{"id":"chunk-51","heading":"","text":". Тестовая (или контрольная) выборка (test sample) — выборка, по которой оценивается качество построенной модели.","keywords":["ai","выборка","метрики","knn","notebook","sample","test","ближайших","качества","качество","классификация","контрольная"],"topics":["testing"]},{"id":"chunk-52","heading":"","text":"````python from sklearn.model selection import train test split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy score","keywords":["ai","import","метрики","accuracy","kneighborsclassifier","knn","notebook","python","score","selection","sklearn.metrics","sklearn.model"],"topics":["python","ai","testing"]},{"id":"chunk-53","heading":"","text":"iris = sns.load dataset('iris') X train, X test, y train, y test = train test split( iris.iloc[:,: 1], iris.iloc[:, 1], test size = 0.20 )","keywords":["test","ai","train","iris","iris.iloc","метрики","0.20","dataset","knn","notebook","size","sns.load"],"topics":["ai","testing"]},{"id":"chunk-54","heading":"","text":"X train.shape, X test.shape, y train.shape, y test.shape X train.head() y train.head() ````","keywords":["ai","test.shape","train.head","train.shape","метрики","knn","notebook","ближайших","качества","классификация","расстояния","соседей."],"topics":["ai","testing"]},{"id":"chunk-55","heading":"","text":"code 70 versicolor 104 virginica 134 virginica 4 setosa 69 versicolor Name: species, dtype: object","keywords":["ai","versicolor","virginica","метрики","104","134","69","70","code","dtype","knn","name"],"topics":[]},{"id":"chunk-56","heading":"","text":"Для предыдущего примера поэкспериментируйте с параметрами классификатора: 1. Установите другое количество ближайших соседей (k = 1, 5, 10). 2. Установите размер тестовой выборки 15% от всего датасета. 3. Постройте графики и оцените качество моделей, проанализируйте результаты.","keywords":["ai","ближайших","метрики","установите","1.","10","15","2.","3.","knn","notebook","всего"],"topics":["knn","oop"]},{"id":"chunk-57","heading":"","text":"````python from sklearn.model selection import train test split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy score","keywords":["ai","import","метрики","accuracy","kneighborsclassifier","knn","notebook","python","score","selection","sklearn.metrics","sklearn.model"],"topics":["python","ai","testing"]},{"id":"chunk-58","heading":"","text":"iris = sns.load dataset('iris') X train, X test, y train, y test = train test split( iris.iloc[:,: 1], iris.iloc[:, 1], test size = 0.15 )","keywords":["test","ai","train","iris","iris.iloc","метрики","0.15","dataset","knn","notebook","size","sns.load"],"topics":["ai","testing"]},{"id":"chunk-59","heading":"","text":"plt.figure(figsize=(10,7)) sns.scatterplot(x = 'petal width', y = 'petal length', data=iris, hue='species', s = 70) plt.xlabel('Длинна лепестка, см') plt.ylabel('Ширина лепестка, см') plt.legend(loc=2) plt.grid","keywords":["ai","petal","лепестка","метрики","см","10","70","data","figsize","hue","iris","knn"],"topics":[]},{"id":"chunk-60","heading":"","text":"for i in range(len(y test)): if np.array(y test)[i] != y pred[i]: plt.scatter(X test.iloc[i, 3], X test.iloc[i, 2], color='r', s=150)","keywords":["ai","test","test.iloc","метрики","150","color","if","knn","len","notebook","np.array","plt.scatter"],"topics":["numpy","testing"]},{"id":"chunk-61","heading":"","text":"Часто бывает полезно разбивать объекты на категории не по количеству, а по качеству. Эта качественная информация нередко представляется как принадлежность наблюдения к отдельной категории, такой как пол, цвета или марка автомобиля. Однако не все категориальные данные одинаковые. Наборы категорий без внутреннего упорядочения называются номинальными. Примеры номинальных категорий включают:","keywords":["ai","категории","категорий","метрики","knn","notebook","автомобиля.","без","ближайших","бывает","включают","внутреннего"],"topics":[]},{"id":"chunk-62","heading":"","text":"С другой стороны, когда набор категорий имеет некое естественное упорядочение, мы называем его порядковым. Например:","keywords":["ai","метрики","knn","notebook","ближайших","другой","его","естественное","имеет","категорий","качества","классификация"],"topics":[]},{"id":"chunk-63","heading":"","text":"Более того, категориальная информация часто представлена в данных в виде вектора или столбца символьных значений (например, \"Москва\", \"Санкт Петербург\", \"Казань\"). Проблема в том, что большинство машинно\u0002обучающихся алгоритмов требуют ввода числовых значений.","keywords":["ai","метрики","knn","notebook","алгоритмов","ближайших","более","большинство","ввода","вектора","виде","данных"],"topics":["algorithms"]},{"id":"chunk-64","heading":"","text":"Алгоритм k ближайших соседей предоставляет простой пример. Одним из шагов в алгоритме является вычисление расстояний между наблюдениями — часто с использованием евклидова расстояния. Однако вычисление расстояния, очевидно, невозможно, если значение х, является строковым типом (например, \"Москва\")","keywords":["ai","ближайших","вычисление","метрики","расстояния","является","knn","notebook","алгоритм","алгоритме","евклидова","если"],"topics":["knn","algorithms"]},{"id":"chunk-65","heading":"","text":". Для того чтобы его можно было ввести в уравнение евклидова расстояния, нам нужно преобразовать это строковое значение в числовой формат. Наша цель — сделать преобразование, которое правильно передает информацию в категориях (упорядоченность, относительные интервалы между категориями и т. д.). Существует большое количество методов кодирования категориальных данных, среди которых нет универсальных","keywords":["ai","метрики","расстояния","knn","notebook","ближайших","большое","было","ввести","д.","данных","евклидова"],"topics":[]},{"id":"chunk-66","heading":"","text":". Выбирая метод, стоит отталкиваться от ваших данных, мощности множества категорий и алгоритма машинного обучения.","keywords":["ai","метрики","knn","notebook","алгоритма","ближайших","ваших","выбирая","данных","категорий","качества","классификация"],"topics":["algorithms"]},{"id":"chunk-67","heading":"","text":"Дан порядковый категориальный признак (например, высокий, средний, низкий). Выполнить его кодировку. Для решения задачи можно использовать метод replace фрейма данных pandas для преобразования строковых меток в числовые эквиваленты","keywords":["ai","метрики","knn","notebook","pandas","replace","ближайших","выполнить","высокий","дан","данных","его"],"topics":["pandas"]},{"id":"chunk-68","heading":"","text":"Создать признаки dataframe = pd.DataFrame({\"оценка\": [\"низкая\",\"низкая\",\"средняя\",\"средняя\",\"высокая\"]}) Создать словарь преобразования шкалы scale mapper = {\"низкая\":1,\"средняя\":2,\"высокая\":3}","keywords":["ai","низкая","средняя","высокая","метрики","создать","dataframe","knn","mapper","notebook","pd.dataframe","scale"],"topics":["pandas"]},{"id":"chunk-69","heading":"","text":"заменить значения признаков значениями словаря dataframe[\"оценка\"].replace(scale mapper) ````","keywords":["ai","метрики",".replace","dataframe","knn","mapper","notebook","scale","ближайших","заменить","значения","значениями"],"topics":["pandas"]},{"id":"chunk-70","heading":"","text":"Дан словарь, и требуется его конвертировать в матрицу признаков. Для решения задачи можно задействовать класс векторизатор словаря Dictvectorizer:","keywords":["ai","метрики","dictvectorizer","knn","notebook","ближайших","векторизатор","дан","его","задачи","задействовать","качества"],"topics":["oop"]},{"id":"chunk-71","heading":"","text":"python импортировать блок схему from sklearn.feature extraction import DictVectorizer Создать словарь data dict = [{\"карасный\":2,\"синий\":2}, {\"карасный\":4,\"синий\":3}, {\"карасный\":1,\"синий\":2}, {\"карасный\":2,\"синий\":2}] Создать векторизатор словаря dictvectorizer = DictVectorizer(sparse = False) Конвертировать словарь в матрицу признаков features = dictvectorizer.fit transform(data dict) features","keywords":["карасный","синий","ai","dictvectorizer","data","dict","features","метрики","словарь","создать","dictvectorizer.fit","extraction"],"topics":["python"]},{"id":"chunk-72","heading":"","text":"Определите набор признаков человека, по аналогии из РТ 1, – например, цвет глаз и конвертируйте его в матрицу признаков.","keywords":["ai","метрики","knn","notebook","аналогии","ближайших","глаз","его","качества","классификация","конвертируйте","матрицу"],"topics":[]},{"id":"chunk-73","heading":"","text":"data = [{\"глаз\":2, \"рука\":2}, {\"голова\":1, \"рука\":2}, {\"голова\":1, \"нога\":2}, {\"глаз\":1, \"нога\":2}, {\"нога\":1, \"рука\":3}]","keywords":["ai","нога","рука","глаз","голова","метрики","data","knn","notebook","ближайших","качества","классификация"],"topics":[]},{"id":"chunk-74","heading":"","text":"dictvectorizer = DictVectorizer(sparse = False) features = dictvectorizer.fit transform(data) features ````","keywords":["ai","dictvectorizer","features","метрики","data","dictvectorizer.fit","false","knn","notebook","sparse","transform","ближайших"],"topics":[]},{"id":"chunk-75","heading":"","text":"code array([[2., 0., 0., 2.], [0., 1., 0., 2.], [0., 1., 2., 0.], [1., 0., 2., 0.], [0., 0., 1., 3.]])","keywords":["0.","2.","1.","ai","метрики","3.","array","code","knn","notebook","ближайших","качества"],"topics":[]}]},{"id":"ai/notebook-04-regression","href":"/docs/ai/notebook-04-regression","slug":["ai","notebook-04-regression"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 4 — Регрессия","description":"Линейные модели, аппроксимация и визуализация регрессионных зависимостей.","preview":"Дата 22.04.2023 1.1. Теоретический материал – Линейные регрессионные модели Линейная регрессия Линейная регрессия (Linear regression) – модель зависимости переменной x от одной или нескольких других переменных (факторов, регрессоров, независимых переменных) с","keywords":["данные","зашумляем","beta","xdata","test","print","dataframe","opt","ydata","двух","01","списков","формируем","ax.set","train","lw","plt.show","dataset"],"topics":["python","ai","pandas","numpy","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"","text":"Линейная регрессия (Linear regression) – модель зависимости переменной x от одной или нескольких других переменных (факторов, регрессоров, независимых переменных) с линейной функцией зависимости. Линейная регрессия относится к задаче определения «линии наилучшего соответствия» через набор точек данных и стала простым предшественником нелинейных методов, которые используют для обучения нейронных сетей.","keywords":["ai","регрессия","линейная","переменных","linear","notebook","regression","аппроксимация","визуализация","данных","других","зависимостей."],"topics":["regression"]},{"id":"chunk-1","heading":"","text":"Цель линейной регрессии — поиск линии, которая наилучшим образом соответствует этим точкам. Напомним, что общее уравнение для прямой есть 𝑓 (𝑥) = 𝑏 + 𝑚 ⋅ 𝑥 +, где 𝑚 – наклон линии, а 𝑏 – его сдвиг.","keywords":["ai","𝑏","𝑚","𝑥","линии","𝑓","notebook","аппроксимация","визуализация","его","зависимостей.","которая"],"topics":[]},{"id":"chunk-2","heading":"","text":"Функция потерь – это мера количества ошибок, которые наша линейная регрессия делает на наборе данных. Хотя есть разные функции потерь, все они вычисляют расстояние между предсказанным значением 𝑦(х) и его фактическим значением. Одна очень распространенная функция потерь называется средней квадратичной ошибкой MSE. Чтобы вычислить MSE, мы просто берем все значения ошибок, считаем их квадраты длин и усредняем.","keywords":["ai","потерь","ошибок","регрессия","функция","mse","mse.","notebook","𝑦","аппроксимация","берем","визуализация"],"topics":["regression"]},{"id":"chunk-3","heading":"","text":"Допустим у нас есть много экспериментальных точек. Необходимо через них провести кривую, которая как можно ближе проходила к этим точкам. При этом необходимо минимизировать среднюю квадратичную ошибку (MSE). Для решения данной задачи в Python есть множество библиотек. Самыми распостраненными выступают:","keywords":["ai","необходимо","mse","notebook","python","аппроксимация","библиотек.","ближе","визуализация","выступают","данной","допустим"],"topics":["python"]},{"id":"chunk-4","heading":"","text":"numpy numpy.linalg.lstsq scipy scipy.linalg (содержит все функции из numpy.linalg плюс часть новых функций, которых нет в numpy.linalg).","keywords":["ai","numpy.linalg","notebook","numpy","numpy.linalg.lstsq","scipy","scipy.linalg","аппроксимация","визуализация","зависимостей.","которых","линейные"],"topics":["numpy"]},{"id":"chunk-5","heading":"","text":"перепишем линейное уравнение y = mx + c как y =Ap, где A = [[ x 1 ]] и p = [[m],[c]] Построим A по x:","keywords":["ai","ap","mx","notebook","аппроксимация","визуализация","зависимостей.","линейное","линейные","модели","перепишем","построим"],"topics":[]},{"id":"chunk-6","heading":"","text":"Используем метод lstsq для решения кго относительно вектора p. m, c = np.linalg.lstsq(A, y, rcond = None)[0] print(m,c)","keywords":["ai","lstsq","none","notebook","np.linalg.lstsq","p.","print","rcond","аппроксимация","вектора","визуализация","зависимостей."],"topics":["numpy"]},{"id":"chunk-7","heading":"","text":"Построим график полученной прямой и укажем на нем точки. plt.plot(x,y,'o',label = 'Исходные данные', markersize=10) plt.plot(x,m x+c,\"r\",label = 'Линейгая экстраполяция') plt.legend() plt.show() ````","keywords":["ai","label","plt.plot","10","markersize","notebook","plt.legend","plt.show","x+c","аппроксимация","визуализация","график"],"topics":[]},{"id":"chunk-8","heading":"","text":"Задача: Пусть 𝑥, 𝑦 – вектора длиной 𝑛 3 (точек 3). Задача заключается в построении эстраполяционного полинома второго порядка (параболы). Таким образом, необходимо найти такие коэффициенты поринома 𝑎, 𝑏, 𝑐 по методу наименьших квадратов. Данные мтогут быть получены в результате измерений. Покажем пример генерации данных случайным образом и загрузки их из файла.","keywords":["ai","задача","образом","𝑎","𝑏","𝑐","𝑛","notebook","𝑥","𝑦","аппроксимация","быть"],"topics":[]},{"id":"chunk-9","heading":"","text":"````python from numpy import from numpy.random import генерируем случайные x и y delta = 1.0 x = linspace( 5,5,11) y = x 2+delta (rand(11) 0.5) x += delta (rand(11) 0.5)","keywords":["11","ai","0.5","delta","import","rand","1.0","2+delta","linspace","notebook","numpy","numpy.random"],"topics":["python","numpy"]},{"id":"chunk-10","heading":"","text":"записываем данные в файл x.tofile('x data.txt', '\\n') y.tofile('y data.txt', '\\n')","keywords":["ai","data.txt","notebook","x.tofile","y.tofile","аппроксимация","визуализация","данные","зависимостей.","записываем","линейные","модели"],"topics":[]},{"id":"chunk-11","heading":"читаем данные из файлов","text":"x = fromfile('x data.txt', float, sep='\\n') y = fromfile('y data.txt', float, sep='\\n')","keywords":["ai","data.txt","float","fromfile","sep","notebook","аппроксимация","визуализация","данные","зависимостей.","линейные","модели"],"topics":[]},{"id":"chunk-12","heading":"рисуем кривую вида y = ax^2 + bx + c, подставляя из решения коэффициенты s[0], s[1], s[2]","text":"plt.plot(x prec, s[0] x prec 2 + s[1] x prec+s[2], ' ', lw=2) plt.grid() plt.savefig('парабола.png') ````","keywords":["ai","prec","ax","bx","lw","notebook","plt.grid","plt.plot","plt.savefig","prec+s","аппроксимация","вида"],"topics":[]},{"id":"chunk-13","heading":"рисуем кривую вида y = ax^2 + bx + c, подставляя из решения коэффициенты s[0], s[1], s[2]","text":"По данным предыдущего примера постройте эстраполяционного полинома третьего порядка","keywords":["ai","ax","bx","notebook","аппроксимация","вида","визуализация","данным","зависимостей.","коэффициенты","кривую","линейные"],"topics":[]},{"id":"chunk-14","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"plt.plot(x prec, s[0] x prec 3 + s[1] x prec 2 + s[2] x prec + s[3], ' ', lw=3) plt.grid() plt.savefig('полином 3 й степени.png') ````","keywords":["prec","ai","ax","bx","cx","lw","notebook","plt.grid","plt.plot","plt.savefig","аппроксимация","вида"],"topics":[]},{"id":"chunk-15","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"Представьте собственные данные и постройте эктраполяцию полиномами первой, второй и третьей степени.","keywords":["ai","ax","bx","cx","notebook","аппроксимация","вида","визуализация","второй","данные","зависимостей.","коэффициенты"],"topics":[]},{"id":"chunk-16","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"python x=array([0,7,12,15]) y=array([3,6,9,12]) A=vstack((x,ones(len(x)))).T m,c = np.linalg.lstsq(A,y,rcond=None)[0] plt.plot(x,y,'o',markersize=10) plt.plot(x,m x+c,'r') plt.legend() plt.show() delta = 1.0 x= linspace( 10,10,10) y= x 2+delta (rand(10)) x+= delta (rand(10)) m= vstack((x 2, x, ones(10))).T s= linalg.lstsq(m, y, rcond=None)[0] x prec=linspace( 10,10,100) plt.plot(x,y,'D') plt.plot(x prec,s[0] x prec","keywords":["10","plt.plot","ai","prec",".t","12","array","delta","linspace","none","ones","rand"],"topics":["python","numpy"]},{"id":"chunk-17","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"2+s[1] x prec+s[2],' ',lw=2) plt.grid() plt.savefig('парабола.png') plt.show() m=vstack((x 3, x 2, x, ones(10))).T s=linalg.lstsq(m, y, rcond=None)[0] x prec=linspace( 10,10,100) plt.plot(x,y,'D') plt.plot(x prec,s[0] x prec 3+s[1] x prec 2+s[2] x prec+s[3],' ',lw=3) plt.grid() plt.savefig('полином 3 й степени.png') plt.show()","keywords":["prec","10","ai","2+s","lw","plt.grid","plt.plot","plt.savefig","plt.show","prec+s",".t","100"],"topics":[]},{"id":"chunk-18","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"Задача: Необходимо проверить гипотезу, что наши точечно заданная функция ложится на кривую вида 𝑓(𝑥, 𝑏) = 𝑏0 + 𝑏1 𝑒𝑥𝑝(−𝑏^2 𝑥^2)","keywords":["ai","𝑏","𝑥","вида","кривую","ax","𝑏0","𝑏1","bx","cx","𝑒𝑥𝑝","𝑓"],"topics":[]},{"id":"chunk-19","heading":"рисуем кривую вида y = ax^3 + bx^2 + cx + d, подставляя коэффициенты s[0], s[1], s[2], s[3]","text":"````python Добавим шума в данные, сделанные по функции f(x, b) с коэффициентами b = (0.25, 0.75, 0.5) beta=(0.25,0.75,0.5) def f(x,b0,b1,b2): return b0+b1 exp( b2 x 2)","keywords":["ai","0.25","0.5","0.75","b2","ax","b0","b0+b1","b1","beta","bx","cx"],"topics":["python"]},{"id":"chunk-20","heading":"зашумляем эти данные","text":"Используем функцию для получения решения в виде коэффициентов функции f(x) для указанных xdata и ydata from scipy.optimize import curve fit beta opt,beta cov = curve fit(f, xdata, ydata) beta opt","keywords":["ai","beta","curve","fit","opt","xdata","ydata","cov","import","notebook","scipy.optimize","аппроксимация"],"topics":[]},{"id":"chunk-21","heading":"зашумляем эти данные","text":"Вычислим квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-22","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata,ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata,f(xdata, beta opt),'b',lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\",fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\",fontsize=18) plt.show()","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-23","heading":"зашумляем эти данные","text":"Задача: Необходимо проверить гипотезу, что наши точечно заданная функция ложится на кривые вида:","keywords":["ai","notebook","аппроксимация","вида","визуализация","гипотезу","данные","зависимостей.","заданная","задача","зашумляем","кривые"],"topics":[]},{"id":"chunk-24","heading":"зашумляем эти данные","text":"````python решение 1 Добавим шума в данные, сделанные по функции f(x,b) c коэффицентами beta=(0.25,0.75) beta=(0.25,0.75) def f(x,b0,b1): return b0+b1 x зададим массив точек xi xdata = linspace(0, 5, 50) cоздаем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 0.05 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt) Вычисляемлинейное","keywords":["beta","xdata","ai","данные","0.25","0.75","opt","ydata","зашумляем","точек","шума","0.05"],"topics":["python"]},{"id":"chunk-25","heading":"зашумляем эти данные","text":"отклонение lin dev = sum(beta cov[0]) print(lin dev) Вычисляем квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","beta","dev","fres","lin","print","residuals","sum","отклонение","cov","notebook","opt"],"topics":[]},{"id":"chunk-26","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show()","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-27","heading":"зашумляем эти данные","text":"решение 2 Добавим шума в данные, сделанные по функции f(x,b) с коэффицентами b = (0.25б 0.75,0.5) beta = (0.25, 0.75, 0.5) def f(x, b0, b1, b2): return b0 + b1 x + b2 x 2 зададим массив точек xi xdata = linspace(0, 5, 50) создаем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 0.05 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt)","keywords":["beta","xdata","ai","данные","0.5","0.75","b0","b1","b2","opt","ydata","зашумляем"],"topics":[]},{"id":"chunk-28","heading":"зашумляем эти данные","text":"Вычислим линейное отклонение lin dev = sum(beta cov[0]) print(lin dev)","keywords":["ai","dev","lin","beta","cov","notebook","print","sum","аппроксимация","визуализация","вычислим","данные"],"topics":[]},{"id":"chunk-29","heading":"зашумляем эти данные","text":"вычислить квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-30","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show()","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-31","heading":"зашумляем эти данные","text":"решение 3 Добавим шума в данные, сделанные по функции f(x,b) с коэффицентами b = (1,2) beta = (1, 2) def f(x, b0, b1): return b0 + b1 log(x) заданим массив точек xi xdata = linspace(1, 5, 50) созадем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 0.05 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt) Вычислим линейное отклонение","keywords":["beta","xdata","ai","данные","b0","b1","opt","ydata","зашумляем","точек","шума","0.05"],"topics":[]},{"id":"chunk-32","heading":"зашумляем эти данные","text":"lin dev = sum(beta cov[0]) print(lin dev)","keywords":["ai","dev","lin","beta","cov","notebook","print","sum","аппроксимация","визуализация","данные","зависимостей."],"topics":[]},{"id":"chunk-33","heading":"зашумляем эти данные","text":"Вычислим квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-34","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show()","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-35","heading":"зашумляем эти данные","text":"решение 4 Добавим шума в данные, сделанные по функции f(x,b) с коэффицентами b = (1,2) beta = (1, 2) def f(x, b0, b1): return b0 x b1 заданим массив точек xi xdata = linspace(1, 5, 50) созадем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 0.05 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt) Вычислим линейное отклонение lin dev","keywords":["beta","xdata","ai","данные","b0","b1","opt","ydata","зашумляем","точек","шума","0.05"],"topics":[]},{"id":"chunk-36","heading":"зашумляем эти данные","text":"= sum(beta cov[0]) print(lin dev)","keywords":["ai","beta","cov","dev","lin","notebook","print","sum","аппроксимация","визуализация","данные","зависимостей."],"topics":[]},{"id":"chunk-37","heading":"зашумляем эти данные","text":"Вычислим квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-38","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 5) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show() ````","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","ax","ax.scatter","fig","notebook"],"topics":[]},{"id":"chunk-39","heading":"зашумляем эти данные","text":"Подставьте собственные данные и поэкспериментируйте с представленными функциями. Проанализируйте динамику изменения данных.","keywords":["ai","данные","notebook","аппроксимация","визуализация","данных.","динамику","зависимостей.","зашумляем","изменения","линейные","модели"],"topics":[]},{"id":"chunk-40","heading":"зашумляем эти данные","text":"````python beta = (8,88,888) def f(x, b0, b1, b2): return b0 + b1 x + b2 x 2 заданим массив точек xi xdata = linspace( 7, 7, 100) созадем теоретически правильные значения точек yi(без шума) y = f(xdata, beta) зашумляем эти данные ydata = y + 50 randn(len(xdata)) beta opt, beta cov = curve fit(f, xdata, ydata) print(beta opt) Вычислим линейное отклонение lin dev = sum(beta cov[0]) print(lin dev)","keywords":["beta","xdata","ai","b0","b1","b2","cov","dev","lin","opt","print","ydata"],"topics":["python"]},{"id":"chunk-41","heading":"зашумляем эти данные","text":"Вычислим квадратичное отклонение residuals = ydata f(xdata, beta opt) fres = sum(residuals 2) print(fres)","keywords":["ai","fres","residuals","beta","notebook","opt","print","sum","xdata","ydata","аппроксимация","визуализация"],"topics":[]},{"id":"chunk-42","heading":"зашумляем эти данные","text":"fig, ax = plt.subplots() ax.scatter(xdata, ydata) ax.plot(xdata, y, 'r', lw=2) ax.plot(xdata, f(xdata, beta opt), 'b', lw=2) ax.set xlim(0, 50) ax.set xlabel(r\"$x$\", fontsize=18) ax.set ylabel(r\"$f(x, \\beta)$\", fontsize=18) plt.show() ````","keywords":["xdata","ai","ax.set","18","ax.plot","beta","fontsize","lw","50","ax","ax.scatter","fig"],"topics":[]},{"id":"chunk-43","heading":"зашумляем эти данные","text":"Линейная регрессия это широко используемый метод статистического анализа, который использует регрессионный анализ в математической статистике для определения количественной взаимосвязи между двумя или более переменными. Если регрессионный анализ включает две или более независимых переменных, а связь между зависимой и независимой переменными является линейной, тогда имееи дело с множественной линейной регрессией.","keywords":["ai","анализ","более","линейной","между","регрессионный","регрессия","notebook","анализа","аппроксимация","взаимосвязи","визуализация"],"topics":["regression"]},{"id":"chunk-44","heading":"зашумляем эти данные","text":"В этом разделе мы увидим, как библиотеку Scikit Learn в Python для машинного обучения можно использовать для реализации функций регрессии. Мы начнем с простой линейной регрессии с участием двух переменных, а затем перейдем к линейной регрессии с участием нескольких переменных.","keywords":["ai","линейной","мы","регрессии","участием","learn","notebook","python","scikit","аппроксимация","библиотеку","визуализация"],"topics":["python"]},{"id":"chunk-45","heading":"зашумляем эти данные","text":"Задача: Построим простую линейную регрессию в Python с использованием библиотеки scikit learn","keywords":["ai","learn","notebook","python","scikit","аппроксимация","библиотеки","визуализация","данные","зависимостей.","задача","зашумляем"],"topics":["python"]},{"id":"chunk-46","heading":"зашумляем эти данные","text":"````python Импортируем необходимые библиотеки используем pandas и numpy для обработки данных matpoltlib для визуализации и sklearn для обучения набора данных и импорта моделей from pandas import DataFrame, Series from sklearn.model selection import train test split from sklearn.linear model import LinearRegression","keywords":["ai","import","pandas","данных","dataframe","linearregression","matpoltlib","model","notebook","numpy","python","selection"],"topics":["python","ai","pandas","numpy","testing","regression"]},{"id":"chunk-47","heading":"зашумляем эти данные","text":"создадим набор данных для описания взаимосвязи между временем обучения студентов и успеваймостью my dict = { 'Учебное время': [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, 5.0, 5.5], 'Оценка': [10, 22, 13, 43, 20, 22, 33, 50, 62, 48, 55, 75, 62, 73, 81, 76, 64, 82, 90, 93] }","keywords":["ai","22","62","0.5","0.75","1.0","1.25","1.5","1.75","10","13","2.0"],"topics":[]},{"id":"chunk-48","heading":"зашумляем эти данные","text":"Рисуем диаграмму plt.scatter(dataset['Учебное время'], dataset['Оценка'], color='b', label='Данные экзамена') plt.xlabel('Часы') plt.ylabel('Оценка') plt.show() ````","keywords":["ai","dataset","данные","оценка","color","label","notebook","plt.scatter","plt.show","plt.xlabel","plt.ylabel","аппроксимация"],"topics":[]},{"id":"chunk-49","heading":"зашумляем эти данные","text":"После того как мы получили представление о данных, разделим информацию на «атрибуты» и «метки». Атрибуты – это независимые переменные, а метки – это зависимые переменные, значения которых должны быть предсказаны. В нашем наборе всего два столбца и необходимо предсказать оценку в зависимости от количества часов. Чтобы извлечь атрибуты и метки, выполните следующий скрипт:","keywords":["ai","атрибуты","метки","переменные","notebook","аппроксимация","быть","визуализация","всего","выполните","данные","данных"],"topics":[]},{"id":"chunk-50","heading":"зашумляем эти данные","text":"````python x = dataset.iloc[:, : 1].values y = dataset.iloc[:, 1].values print(x) print(y)","keywords":["ai",".values","dataset.iloc","print","notebook","python","аппроксимация","визуализация","данные","зависимостей.","зашумляем","линейные"],"topics":["python"]},{"id":"chunk-51","heading":"зашумляем эти данные","text":"Теперь когда у нас есть атрибуты и метки, необходимость разделить их на а обучающий и тестовый наборы. приведенный фрагмент 80 % данных на обучающий набор, а 20% данных на набор тестов x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0) далее можно обучить алгоритм линейной регрессии необходимо импортировать класс LinelRegression, создать его экземпляр и вызвать метод fit()","keywords":["test","ai","train","данных","набор","обучающий","0.2","20","80","fit","linelregression","notebook"],"topics":["ai","oop","testing","algorithms","regression"]},{"id":"chunk-52","heading":"зашумляем эти данные","text":"regressor = LinearRegression() regressor.fit(x train, y train) приведем полученные коэффиценты для линейной регрессии print(regressor.intercept ) print(regressor.coef ) ````","keywords":["ai","print","train","linearregression","notebook","regressor","regressor.coef","regressor.fit","regressor.intercept","аппроксимация","визуализация","данные"],"topics":["ai","regression"]},{"id":"chunk-53","heading":"зашумляем эти данные","text":"code [[1.0000e+00 3.5710e+03 1.9760e+03 5.2500e 01] [2.0000e+00 4.0920e+03 1.2500e+03 5.7200e 01] [3.0000e+00 3.8650e+03 1.5860e+03 5.8000e 01] [4.0000e+00 4.8700e+03 2.3510e+03 5.2900e 01] [3.0000e+00 4.3990e+03 4.3100e+02 5.4400e 01] [4.0000e+00 5.3420e+03 1.3330e+03 5.7100e 01] [5.0000e+00 5.3190e+03 1.1868e+04 4.5100e 01] [3.0000e+00 5.1260e+03 2.1380e+03 5.5300e 01] [5.0000e+00 4.4470e+03 8.5770e+03 5.2900e 01]","keywords":["01","3.0000e+00","ai","4.0000e+00","5.0000e+00","5.2900e","1.0000e+00","1.1868e+04","1.2500e+03","1.3330e+03","1.5860e+03","1.9760e+03"],"topics":[]},{"id":"chunk-54","heading":"зашумляем эти данные","text":"[5.0000e+00 4.5120e+03 8.5070e+03 5.5200e 01] [4.0000e+00 4.3910e+03 5.9390e+03 5.3000e 01] [5.0000e+00 5.1260e+03 1.4186e+04 5.2500e 01] [4.0000e+00 4.8170e+03 6.9300e+03 5.7400e 01] [5.0000e+00 4.2070e+03 6.5800e+03 5.4500e 01] [4.0000e+00 4.3320e+03 8.1590e+03 6.0800e 01] [5.0000e+00 4.3180e+03 1.0340e+04 5.8600e 01] [6.0000e+00 4.2060e+03 8.5080e+03 5.7200e 01] [0.0000e+00 3.7180e+03 4.7250e+03 5.4000e 01]","keywords":["01","5.0000e+00","4.0000e+00","ai","0.0000e+00","1.0340e+04","1.4186e+04","3.7180e+03","4.2060e+03","4.2070e+03","4.3180e+03","4.3320e+03"],"topics":[]},{"id":"chunk-55","heading":"зашумляем эти данные","text":"[6.0000e+00 4.7160e+03 5.9150e+03 7.2400e 01] [3.0000e+00 4.3410e+03 6.0100e+03 6.7700e 01] [1.0000e+00 4.5930e+03 7.8340e+03 6.6300e 01] [3.0000e+00 4.9830e+03 6.0200e+02 6.0200e 01] [1.0000e+00 4.8970e+03 2.4490e+03 5.1100e 01]] [3571. 4092. 3865. 4870. 4399. 5342. 5319. 5126. 4447. 4512. 4391. 5126. 4817. 4207. 4332. 4318. 4206. 3718. 4716. 4341. 4593. 4983","keywords":["01","ai","1.0000e+00","3.0000e+00","5126.","2.4490e+03","3571.","3718.","3865.","4.3410e+03","4.5930e+03","4.7160e+03"],"topics":[]},{"id":"chunk-56","heading":"зашумляем эти данные","text":". 4897.] 9.094947017729282e 13 [5.75398478e 13 1.00000000e+00 1.11022302e 16 3.44246345e 14]","keywords":["ai","13","1.00000000e+00","1.11022302e","14","16","3.44246345e","4897.","5.75398478e","9.094947017729282e","notebook","аппроксимация"],"topics":[]},{"id":"chunk-57","heading":"зашумляем эти данные","text":"Получившийся результат можно интерпретировать следующим образом: с каждым затраченным часом на обучение результат экзамена повышается приблизительно на 17 баллов. Далее можно построить прогнозы. Для этого мы будем использовать наши тестовые данные и посмотрим, насколько точно наш алгоритм предсказывает процентную оценку. Чтобы сделать прогноз на тестовых данных необходимо выполнить следующий код:","keywords":["ai","данные","можно","результат","17","notebook","алгоритм","аппроксимация","баллов.","будем","визуализация","выполнить"],"topics":["algorithms"]},{"id":"chunk-58","heading":"зашумляем эти данные","text":"````python y pred = regressor.predict(x test) сравним фактические значения с прогнозиремыми df = DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","df","pred","test","actual","dataframe","notebook","predicted","python","regressor.predict","аппроксимация","визуализация"],"topics":["python","pandas","testing"]},{"id":"chunk-59","heading":"зашумляем эти данные","text":"визуализируем результат сравнения df.plot(kind='bar') plt.grid(which='major', linestyle=' ', linewidth='0.5', color='green') plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') plt.show() построим линию регресии с тестовыми данными plt.scatter(x test, y test, color='gray') plt.plot(x test, y pred, color='red', linewidth=2) plt.show() ````","keywords":["color","ai","linewidth","test","0.5","linestyle","plt.grid","plt.show","which","bar","black","df.plot"],"topics":["testing"]},{"id":"chunk-60","heading":"зашумляем эти данные","text":"Постройте модель линейной регрессии для произвольных данных из двух столбцов. Для примера можно взять точечную зависимость заработной платы от опыта работы:","keywords":["ai","notebook","аппроксимация","взять","визуализация","данные","данных","двух","зависимостей.","зависимость","заработной","зашумляем"],"topics":[]},{"id":"chunk-61","heading":"зашумляем эти данные","text":"(https://raw.githubusercontent.com/AnnaShestova/salary years simple linear\u0002regression/master/Salary Data.csv).","keywords":["ai","salary","annashestova","data.csv","https","linear","master","notebook","raw.githubusercontent.com","regression","simple","years"],"topics":["python","regression"]},{"id":"chunk-62","heading":"зашумляем эти данные","text":"````python url = r'https://raw.githubusercontent.com/AnnaShestova/salary years simple linear regression/master/Salary Data.csv' dataset = pd.read csv(url) print(dataset.head(5))","keywords":["ai","salary","url","annashestova","csv","data.csv","dataset","dataset.head","https","linear","master","notebook"],"topics":["python","regression"]},{"id":"chunk-63","heading":"зашумляем эти данные","text":"plt.scatter(dataset['YearsExperience'], dataset['Salary'], color='b', label='Salary data') plt.xlabel('Experience') plt.ylabel('Salary') plt.show()","keywords":["ai","salary","dataset","color","data","experience","label","notebook","plt.scatter","plt.show","plt.xlabel","plt.ylabel"],"topics":[]},{"id":"chunk-64","heading":"зашумляем эти данные","text":"x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0)","keywords":["test","ai","train","0.2","notebook","random","size","split","state","аппроксимация","визуализация","данные"],"topics":["ai","testing"]},{"id":"chunk-65","heading":"зашумляем эти данные","text":"y pred = regressor.predict(x test) df = DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","df","pred","test","actual","dataframe","notebook","predicted","regressor.predict","аппроксимация","визуализация","данные"],"topics":["pandas","testing"]},{"id":"chunk-66","heading":"зашумляем эти данные","text":"df.plot(kind='bar') plt.grid(which='major', linestyle=' ', linewidth='0.5', color='green') plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black') plt.show()","keywords":["ai","0.5","color","linestyle","linewidth","plt.grid","which","bar","black","df.plot","green","kind"],"topics":[]},{"id":"chunk-67","heading":"зашумляем эти данные","text":"plt.scatter(x test, y test, color='blue') plt.plot(x test, y pred, color='green', linewidth=2) plt.show() ````","keywords":["ai","test","color","blue","green","linewidth","notebook","plt.plot","plt.scatter","plt.show","pred","аппроксимация"],"topics":["testing"]},{"id":"chunk-68","heading":"зашумляем эти данные","text":"В предыдущем примере мы проиллюстрировали линейную регрессию с двумя переменными. Однако, почти все реальные задачи имеют больше параметров. Линейная регрессия с участием нескольких переменных называется «множественной линейной регрессией» или многомерной линейной регрессией. Шаги для выполнения множественной линейной регрессии аналогичны шагам для простой . Разница заключается в оценке","keywords":["ai","линейной","множественной","регрессия","notebook","аналогичны","аппроксимация","больше","визуализация","выполнения","данные","двумя"],"topics":["regression"]},{"id":"chunk-69","heading":"зашумляем эти данные","text":". Вы можете использовать множественную регрессию, чтобы узнать, какой фактор оказывает наибольшее влияние на прогнозируемый результат или как различные переменные связаны друг с другом.","keywords":["ai","notebook","аппроксимация","визуализация","влияние","вы","данные","друг","другом.","зависимостей.","зашумляем","использовать"],"topics":[]},{"id":"chunk-70","heading":"зашумляем эти данные","text":"Задача: Для решения задачи множественной регрессии можно задействовать уже известный метод numpy.linalg.lstsq.","keywords":["ai","notebook","numpy.linalg.lstsq.","аппроксимация","визуализация","данные","зависимостей.","задача","задачи","задействовать","зашумляем","известный"],"topics":["numpy"]},{"id":"chunk-71","heading":"зашумляем эти данные","text":"y = [1,2,3,4,3,4,5,3,5,5,4,5,4,5,4,5,6,0,6,3,1,3,1] x = [[0,2,4,1,5,4,5,9,9,9,3,7,8,8,6,6,5,5,5,6,6,5,5], [4,1,2,3,4,5,6,7,5,8,7,8,7,8,7,8,6,8,9,2,1,5,6], [4,1,2,5,6,7,8,9,7,8,7,8,7,4,3,1,2,3,4,1,3,9,7]]","keywords":["ai","notebook","аппроксимация","визуализация","данные","зависимостей.","зашумляем","линейные","модели","регрессионных","регрессия"],"topics":[]},{"id":"chunk-72","heading":"зашумляем эти данные","text":"x = np.transpose(x) transpose to input vectors x = np.c [x, np.ones(x.shape[0])] add bias term linreg = np.linalg.lstsq(x, y, rcond=None)[0] print(linreg) ````","keywords":["ai","linreg","add","bias","input","none","notebook","np.c","np.linalg.lstsq","np.ones","np.transpose","print"],"topics":["numpy"]},{"id":"chunk-73","heading":"зашумляем эти данные","text":"Задача: Для данных из предыдущей задачи построить модель множественной линейной регрессии с использованием средств библиотеки sсikit learn.","keywords":["ai","learn.","notebook","sсikit","аппроксимация","библиотеки","визуализация","данные","данных","зависимостей.","задача","задачи"],"topics":[]},{"id":"chunk-74","heading":"зашумляем эти данные","text":"````python from sklearn.model selection import train test split from sklearn.linear model import LinearRegression from sklearn import metrics","keywords":["ai","import","linearregression","metrics","model","notebook","python","selection","sklearn","sklearn.linear","sklearn.model","split"],"topics":["python","ai","testing","regression"]},{"id":"chunk-75","heading":"зашумляем эти данные","text":"y = [1,2,3,4,3,4,5,3,5,5,4,5,4,5,4,5,6,0,6,3,1,3,1] x = [[0,2,4,1,5,4,5,9,9,9,3,7,8,8,6,6,5,5,5,6,6,5,5], [4,1,2,3,4,5,6,7,5,8,7,8,7,8,7,8,6,8,9,2,1,5,6], [4,1,2,5,6,7,8,9,7,8,7,8,7,4,3,1,2,3,4,1,3,9,7]]","keywords":["ai","notebook","аппроксимация","визуализация","данные","зависимостей.","зашумляем","линейные","модели","регрессионных","регрессия"],"topics":[]},{"id":"chunk-76","heading":"формируем DataFrame из двух списков","text":"new y = np.array(y) new y = new y.transpose() df1 = pd.DataFrame(new y) new x = np.array(x) new x = new x.transpose() df2 = pd.DataFrame(new x) df1 = df1.rename(columns={0: 'y'}, inplace=False) df2 = df2.rename(columns={0: 'x1', 1: 'x2', 2: 'x3'}, inplace=False)","keywords":["new","ai","columns","df1","df2","false","inplace","np.array","pd.dataframe","dataframe","df1.rename","df2.rename"],"topics":["pandas","numpy"]},{"id":"chunk-77","heading":"формируем DataFrame из двух списков","text":"frames = [df1, df2] dataset = pd.concat([df1, df2], axis=1, join='inner') print(dataset.head())","keywords":["ai","df1","df2","axis","dataframe","dataset","dataset.head","frames","inner","join","notebook","pd.concat"],"topics":["pandas"]},{"id":"chunk-78","heading":"формируем DataFrame из двух списков","text":"разделим данные на метки и атрибуты x = dataset[['x1', 'x2', 'x3']] y = dataset['y']","keywords":["ai","dataset","dataframe","notebook","x1","x2","x3","аппроксимация","атрибуты","визуализация","данные","двух"],"topics":["pandas"]},{"id":"chunk-79","heading":"формируем DataFrame из двух списков","text":"раздлеим данные на обуччающую и тестовую выборки x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0)","keywords":["test","ai","train","0.2","dataframe","notebook","random","size","split","state","аппроксимация","визуализация"],"topics":["ai","pandas","testing"]},{"id":"chunk-80","heading":"формируем DataFrame из двух списков","text":"для обучения алгоритма мы выполняем тот же код, что и раньше, используя метод fit() класса LinearRegression regressor = LinearRegression() regressor.fit(x train, y train)","keywords":["ai","linearregression","train","dataframe","fit","notebook","regressor","regressor.fit","алгоритма","аппроксимация","визуализация","выполняем"],"topics":["ai","pandas","oop","algorithms","regression"]},{"id":"chunk-81","heading":"формируем DataFrame из двух списков","text":"выведем кожффициенты модели coeff df = pd.DataFrame(regressor.coef , x.columns, columns=['Coefficient']) coeff df","keywords":["ai","coeff","df","модели","coefficient","columns","dataframe","notebook","pd.dataframe","regressor.coef","x.columns","аппроксимация"],"topics":["pandas"]},{"id":"chunk-82","heading":"формируем DataFrame из двух списков","text":"Чтобы сделать прогнозы на тестовых данных, выполните следующий код y pred = regressor.predict(x test) df = pd.DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","df","pred","test","actual","dataframe","notebook","pd.dataframe","predicted","regressor.predict","аппроксимация","визуализация"],"topics":["pandas","testing"]},{"id":"chunk-83","heading":"формируем DataFrame из двух списков","text":"Последний шаг оценить производительность алгоритма. Мы сделаем это, найдя значения для MSE print('Mean Squared Error:', metrics.mean squared error(y test, y pred)) ````","keywords":["ai","error","squared","dataframe","mean","metrics.mean","mse","notebook","pred","print","test","алгоритма."],"topics":["pandas","testing","algorithms"]},{"id":"chunk-84","heading":"формируем DataFrame из двух списков","text":"code y x1 x2 x3 0 1 0 4 4 1 2 2 1 1 2 3 4 2 2 3 4 1 3 5 4 3 5 4 6 (23, 4) y x1 x2 x3 count 23.000000 23.000000 23.000000 23.000000 mean 3.565217 5.347826 5.521739 5.043478 std 1.674029 2.404706 2.428422 2.704849 min 0.000000 0.000000 1.000000 1.000000 25% 3.000000 4.500000 4.000000 3.000000 50% 4.000000 5.000000 6.000000 5.000000 75% 5.000000 6.500000 7.500000 7.000000 max 6.000000 9.000000 9.000000 9.000000 Mean","keywords":["23.000000","5.000000","9.000000","ai","0.000000","1.000000","3.000000","4.000000","6.000000","mean","x1","x2"],"topics":["pandas"]},{"id":"chunk-85","heading":"формируем DataFrame из двух списков","text":"Squared Error: 1.3272699242343076","keywords":["ai","1.3272699242343076","dataframe","error","notebook","squared","аппроксимация","визуализация","двух","зависимостей.","линейные","модели"],"topics":["pandas"]},{"id":"chunk-86","heading":"формируем DataFrame из двух списков","text":"Постройте модель множественной линейной регрессии для произвольных данных из нескольких столбцов. Для примера можно взять потребления газа (в миллионах галлонов) в 48 штатах США или набор данных о качестве красного вина (1) и (2) соответственно. Найдите коэффициенты множественной регрессии. Постройте прогноз. 1. https://raw.githubusercontent.com/likarajo/petrol consumption/master/data/pe trol consumption.csv 2","keywords":["ai","данных","множественной","постройте","1.","48","consumption","consumption.csv","data","dataframe","https","likarajo"],"topics":["python","pandas"]},{"id":"chunk-87","heading":"формируем DataFrame из двух списков","text":". https://raw.githubusercontent.com/aniruddhachoudhury/Red Wine\u0002Quality/master/winequality red.csv","keywords":["ai","aniruddhachoudhury","dataframe","https","master","notebook","quality","raw.githubusercontent.com","red","red.csv","wine","winequality"],"topics":["python","pandas"]},{"id":"chunk-88","heading":"формируем DataFrame из двух списков","text":"````python url = r'https://raw.githubusercontent.com/likarajo/petrol consumption/master/data/petrol consumption.csv' dataset = pd.read csv(url) print(dataset.head(5))","keywords":["ai","petrol","url","consumption","consumption.csv","csv","data","dataframe","dataset","dataset.head","https","likarajo"],"topics":["python","pandas"]},{"id":"chunk-89","heading":"формируем DataFrame из двух списков","text":"y = list(dataset['Petrol tax']) x = [list(dataset['Average income']), list(dataset['Paved Highways']), list(dataset['Population Driver licence(%)']), list(dataset['Petrol Consumption'])]","keywords":["dataset","list","ai","petrol","average","consumption","dataframe","driver","highways","income","licence","notebook"],"topics":["pandas"]},{"id":"chunk-90","heading":"формируем DataFrame из двух списков","text":"new y = new y.transpose() df1 = pd.DataFrame(new y) new x = np.array(x) new x = new x.transpose() df2 = pd.DataFrame(new x) df1 = df1.rename(columns={0: 'y'}, inplace=False) df2 = df2.rename(columns={0: 'x1', 1: 'x2', 2: 'x3'}, inplace=False)","keywords":["new","ai","columns","df1","df2","false","inplace","pd.dataframe","dataframe","df1.rename","df2.rename","notebook"],"topics":["pandas","numpy"]},{"id":"chunk-91","heading":"формируем DataFrame из двух списков","text":"frames = [df1, df2] dataset = pd.concat([df1, df2], axis=1, join='inner') print(dataset.head())","keywords":["ai","df1","df2","axis","dataframe","dataset","dataset.head","frames","inner","join","notebook","pd.concat"],"topics":["pandas"]},{"id":"chunk-92","heading":"формируем DataFrame из двух списков","text":"x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0)","keywords":["test","ai","train","0.2","dataframe","notebook","random","size","split","state","аппроксимация","визуализация"],"topics":["ai","pandas","testing"]},{"id":"chunk-93","heading":"формируем DataFrame из двух списков","text":"coeff df = pd.DataFrame(regressor.coef , x.columns, columns=['Coefficient']) coeff df","keywords":["ai","coeff","df","coefficient","columns","dataframe","notebook","pd.dataframe","regressor.coef","x.columns","аппроксимация","визуализация"],"topics":["pandas"]},{"id":"chunk-94","heading":"формируем DataFrame из двух списков","text":"y pred = regressor.predict(x test) df = pd.DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","df","pred","test","actual","dataframe","notebook","pd.dataframe","predicted","regressor.predict","аппроксимация","визуализация"],"topics":["pandas","testing"]},{"id":"chunk-95","heading":"формируем DataFrame из двух списков","text":"````code Petrol tax Average income Paved Highways Population Driver licence(%) \\ 0 9.0 3571 1976 0.525 1 9.0 4092 1250 0.572 2 9.0 3865 1586 0.580 3 7.5 4870 2351 0.529 4 8.0 4399 431 0.544","keywords":["9.0","ai","0.525","0.529","0.544","0.572","0.580","1250","1586","1976","2351","3571"],"topics":["pandas"]},{"id":"chunk-96","heading":"формируем DataFrame из двух списков","text":"Petrol Consumption 0 541 1 524 2 561 3 414 4 410 y x1 x2 x3 3 0 1 3571.0 1976.0 0.525 541.0 1 2 4092.0 1250.0 0.572 524.0 2 3 3865.0 1586.0 0.580 561.0 3 4 4870.0 2351.0 0.529 414.0 4 3 4399.0 431.0 0.544 410.0 (23, 5) y x1 x2 x3 3 count 23.000000 23.000000 23.000000 23.000000 23.000000 mean 3.565217 4529.913043 5573.652174 0.567957 540.869565 std 1.674029 484.049764 3856.453123 0.058789 112.101539 min 0.000000","keywords":["23.000000","ai","x1","x2","x3","0.000000","0.058789","0.525","0.529","0.544","0.567957","0.572"],"topics":["pandas"]},{"id":"chunk-97","heading":"формируем DataFrame из двух списков","text":"3571.000000 431.000000 0.451000 344.000000 25% 3.000000 4262.500000 2057.000000 0.529500 465.500000 50% 4.000000 4447.000000 5939.000000 0.553000 525.000000 75% 5.000000 4883.500000 8333.000000 0.583000 591.500000 max 6.000000 5342.000000 14186.000000 0.724000 865.000000 Mean Squared Error: 4.749488311367661 ````","keywords":["ai","0.451000","0.529500","0.553000","0.583000","0.724000","14186.000000","2057.000000","25","3.000000","344.000000","3571.000000"],"topics":["pandas"]}]},{"id":"ai/notebook-05-decision-trees","href":"/docs/ai/notebook-05-decision-trees","slug":["ai","notebook-05-decision-trees"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 5 — Деревья решений","description":"Деревья решений и базовые техники интерпретации классификаторов.","preview":"Теоретический материал – Деревья принятия решений Деревья решений являются одним из наиболее эффективных инструментов интеллектуального анализа данных и предсказательной аналитики, которые позволяют решать задачи классификации и регрессии. Перед тем как непоср","keywords":["print","self","def","test","d2","d1","setosa","данных","enter","if","мы","train","virginica","class","init","left","python","warrior"],"topics":["python","ai","knn","pandas","numpy","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"","text":"Деревья решений являются одним из наиболее эффективных инструментов интеллектуального анализа данных и предсказательной аналитики, которые позволяют решать задачи классификации и регрессии.","keywords":["ai","деревья","решений","notebook","анализа","аналитики","базовые","данных","задачи","инструментов","интеллектуального","интерпретации"],"topics":["oop"]},{"id":"chunk-1","heading":"","text":"Перед тем как непосредственно перейти к решению задач с использование данного инструмента рассмотрим общее понятие \"дерево\" в информатике и способы задания деревьев в языке Python.","keywords":["ai","деревья","решений","notebook","python.","базовые","данного","дерево","деревьев","задания","задач","инструмента"],"topics":["python"]},{"id":"chunk-2","heading":"","text":"Деревья принадлежат к числу основных структур данных, используемых в программировании. Древовидная структура является одним из способов представления иерархической структуры в графическом виде. Такое название она получила потому, что граф выглядит как перевернутое дерево. Корень дерева (корневой узел) находится на самом верху, а листья (потомки) — внизу.","keywords":["ai","деревья","решений","notebook","базовые","верху","виде.","внизу.","выглядит","граф","графическом","данных"],"topics":[]},{"id":"chunk-3","heading":"","text":"Деревья широко применяются в компьютерных технологиях. Примером является файловая система, представляющая собой иерархическую структуру из файлов и каталогов.","keywords":["ai","деревья","решений","notebook","базовые","иерархическую","интерпретации","каталогов.","классификаторов.","компьютерных","представляющая","применяются"],"topics":[]},{"id":"chunk-4","heading":"","text":"На рисунке изображены родительские отношения (ребра, ветви дерева) между узлами (вершинами) дерева. На верхнем уровне каждый «родитель» указывает на своих «потомков». То есть в этой иерархической структуре вершина всегда «знает» своих потомков.","keywords":["ai","деревья","решений","своих","notebook","базовые","верхнем","вершина","вершинами","ветви","всегда","дерева"],"topics":[]},{"id":"chunk-5","heading":"","text":"Для того чтобы более точно оперировать структурой Дерево, нужно дать определение некоторым ключевым понятиям:","keywords":["ai","деревья","решений","notebook","базовые","более","дать","дерево","интерпретации","классификаторов.","ключевым","некоторым"],"topics":[]},{"id":"chunk-6","heading":"","text":"− лист, листовой или терминальный узел — конечный узел, то есть не имеющий потомков;","keywords":["ai","деревья","решений","узел","notebook","базовые","имеющий","интерпретации","классификаторов.","конечный","лист","листовой"],"topics":[]},{"id":"chunk-7","heading":"","text":"С корневого узла начинается выполнение большинства операций над деревом. Чтобы получить доступ к любому элементу структуры, необходимо, переходя по ветвям, перебирать элементы, начиная с головы — корневого узла. Корневой узел — это своеобразный вход в дерево. Большинство алгоритмов работы с деревом строятся на том, что каждый узел дерева рассматриваются как корневой узел поддерева, «растущего» из этого узла","keywords":["ai","узел","деревья","корневого","корневой","решений","узла","notebook","алгоритмов","базовые","большинства","большинство"],"topics":["algorithms"]},{"id":"chunk-8","heading":"","text":". Такой подход дает возможность зацикливать выполнение операций при прохождении по элементам дерева. Но в связи с тем, что при прохождении по дереву (в отличие от массива) неизвестно сколько шагов будет в этом цикле, используется другой инструмент — рекурсивный вызов.","keywords":["ai","деревья","при","прохождении","решений","notebook","базовые","будет","возможность","вызов.","выполнение","дает"],"topics":[]},{"id":"chunk-9","heading":"","text":"Двоичное (бинарное) дерево — это древовидная структура данных, где каждый узел имеет не более двух детей. Этих детей называют левым (Л) и правым (П) потомком или «сыном». На рисунке выше дерево является двоичным.","keywords":["ai","дерево","деревья","решений","notebook","базовые","бинарное","более","выше","данных","двоичное","двоичным."],"topics":[]},{"id":"chunk-10","heading":"","text":"В предыдущих разделах мы рассматривали в основном традиционное программирование на Python, когда вся программа разбивается (или не разбивается) на отдельные модули, содержащие функции. Такое программирование соответствует парадигме структурного программирования. Само структурное программирование оказалось колоссальным шагом в построении программ","keywords":["ai","программирование","деревья","разбивается","решений","notebook","python","базовые","вся","интерпретации","классификаторов.","когда"],"topics":["python"]},{"id":"chunk-11","heading":"","text":". Однако еще большим шагом является парадигма объектно ориентированного программирования. В этом подходе программа состоит из отдельных классов, которые объединяют в себе как переменные, называемые полями класса, так и функции, называемые методами класса.","keywords":["ai","деревья","называемые","решений","notebook","базовые","большим","еще","интерпретации","класса","класса.","классификаторов."],"topics":["oop"]},{"id":"chunk-12","heading":"","text":"На самом деле мы уже сталкивались с классами, когда создавали объекты для решения задач классификации и регрессии в Scikit learn. В данном разделе подробнее познакомимся с основами объектно\u0002ориентированного программирования (ООП).","keywords":["ai","деревья","решений","learn.","notebook","scikit","базовые","данном","деле","задач","интерпретации","классами"],"topics":["oop"]},{"id":"chunk-13","heading":"","text":"Рассмотрим на примерах эти понятия. Первое инкапсуляция это объединение в одном объекте данных и программного кода таким образом, что для внешней работы внутренняя часть объекта может быть скрыта от пользователя. Инкапсуляция может быть реализована не только с помощью классов, но и с помощью модулей, но классы позволяют сделать инкапсуляцию естественным путем. Создадим класс в Python","keywords":["ai","быть","деревья","инкапсуляция","может","помощью","решений","notebook","python","базовые","внешней","внутренняя"],"topics":["python","oop"]},{"id":"chunk-14","heading":"","text":". Для этого необходимо определить класс (новый тип данных) и создать объект, называемый экземпляром класса. Мы рекомендуем имена классов начинать с заглавной буквы \"T\", подчеркивая тем самым, что речь идет о типе данных.","keywords":["ai","деревья","решений","notebook","базовые","буквы","данных","данных.","заглавной","идет","имена","интерпретации"],"topics":["oop"]},{"id":"chunk-15","heading":"","text":"class TAnimal: name = \"\" def init (self, name): self.name = name def say(self): print(self.name)","keywords":["ai","name","def","self","self.name","деревья","решений","class","init","notebook","print","say"],"topics":[]},{"id":"chunk-16","heading":"","text":"Теперь создадим экземпляр этого класса. Экземпляр класса представляет собой переменную, с которой можно работать обычным образом.","keywords":["ai","деревья","решений","экземпляр","notebook","базовые","интерпретации","класса","класса.","классификаторов.","которой","можно"],"topics":["oop"]},{"id":"chunk-17","heading":"","text":"Рассмотрим синтаксис Python при создании классов. Все начинается с ключевого слова class. Далее в блоке из отступов мы определяем переменные, которые будем называть полями и функции, которые называются методами. Методы определяются, как обычные функции и могут возвращать значения","keywords":["ai","деревья","которые","решений","функции","class.","notebook","python","базовые","блоке","будем","возвращать"],"topics":["python","oop"]},{"id":"chunk-18","heading":"","text":". Единственное отличие состоит в том, что у всех методов есть обязательный первый параметр, который по традиции всегда называем self в котором передается ссылка на экземпляр класса. Поэтому когда внутри класса метод хочет обратиться к своему полю, то необходимо использовать конструкцию self.name. Заметим, что при вызове методов мы первый параметр не задаем.","keywords":["ai","деревья","методов","параметр","первый","решений","notebook","self","self.name.","базовые","внутри","всегда"],"topics":["oop"]},{"id":"chunk-19","heading":"","text":"Далее, у каждого класса есть метод, с именем init , который называется конструктором класса. Этот метод вызывается в момент создания экземпляра Animal = TAnimal(\"Обезьяна\"). Конструктор может иметь любое количество параметров. Предположим, что теперь нам нужно сделать класс для описания конкретного животного кошки","keywords":["ai","деревья","метод","решений","animal","init","notebook","tanimal","базовые","вызывается","далее","животного"],"topics":["oop"]},{"id":"chunk-20","heading":"","text":". Для это мы используем наследование классов, когда можно определять новые классы, как наследники существующих. При этом новый класс будет иметь все поля и методы наследуемого класса. Вот как это делается:","keywords":["ai","деревья","решений","notebook","базовые","будет","вот","делается","иметь","интерпретации","используем","класс"],"topics":["oop"]},{"id":"chunk-21","heading":"","text":"class TAnimal: name = \"\" def init (self, name): self.name = name def say(self): print(self.name) class TCat(TAnimal): def may(self): print(\"Мяу!\") Cat = TCat(\"Кошка\") Cat.say() Cat.may()","keywords":["ai","def","name","self","class","print","self.name","tanimal","tcat","деревья","решений","cat"],"topics":[]},{"id":"chunk-22","heading":"","text":"Мы видим, что у наследованного класса сохранился конструктор и метод say. В последнем примере мы выдели, что наследный класс, также как и исходный имеет конструктор, который принимает в качестве параметра название животного тогда, что в данном случае излишне. Для решения этой проблемы мы воспользуемся объектно ориентированным механизмом полиморфизмом. Полиморфизм это возможность замены методов при наследовании","keywords":["ai","мы","деревья","конструктор","решений","notebook","say.","базовые","видим","возможность","воспользуемся","выдели"],"topics":["oop"]},{"id":"chunk-23","heading":"","text":". Сделаем так, чтобы не нужно было передавать в конструкторе название \"Кошка\".","keywords":["ai","деревья","решений","notebook","базовые","было","интерпретации","классификаторов.","конструкторе","кошка","название","нужно"],"topics":[]},{"id":"chunk-24","heading":"","text":"class TCat(TAnimal): def init (self): super(). init (\"Кошка\") def may(self): print(\"Мяу!\") Cat = TCat() Cat.say() Cat.may()","keywords":["ai","def","init","self","tcat","деревья","решений","cat","cat.may","cat.say","class","may"],"topics":[]},{"id":"chunk-25","heading":"","text":"Результат выполнения этой программы будет аналогичный, но теперь при использовании этого класса нам не нужно передавать в конструкторе никаких параметров. Полиморфное перекрытие методов делается простым объявлением метода (в данном случае конструктора). При этом нельзя можно менять входные параметры","keywords":["ai","деревья","при","решений","notebook","аналогичный","базовые","будет","входные","выполнения","данном","делается"],"topics":["oop"]},{"id":"chunk-26","heading":"","text":". Если в результате написания кода метода возникает необходимость вызвать перекрытый метод, то для этого необходимо использовать функцию super(), которая по сути просто возвращает ссылку на родительский класс. Самое удивительное в полиморфизме, что изменяя метод, он меняется даже когда на него есть ссылки родительского класса. Рассмотрим еще один пример. Пусть у нас есть класс:","keywords":["ai","деревья","метод","решений","notebook","super","базовые","возвращает","возникает","вызвать","даже","если"],"topics":["oop"]},{"id":"chunk-27","heading":"","text":"class TDo: def Operation(self, x, y): return x + y def Run(self): x = int(input(\"Enter x \")) y = int(input(\"Enter y \")) z = self.Operation(x, y) print(\"Result = \" + z. str ()) Do = TDo() Do.Run()","keywords":["ai","def","enter","input","int","self","tdo","деревья","решений","class","do","do.run"],"topics":["numpy"]},{"id":"chunk-28","heading":"","text":"Необходимо разработать виртуальную модель процесса обучения. В программе должны быть объекты ученики, учитель, кладезь знаний. Потребуется три класса – \"учитель\", \"ученик\", \"данные\". Учитель и ученик во многом похожи, оба – люди. Значит, их классы могут принадлежать одному надклассу \"человек\". Однако в контексте данной задачи у учителя и ученика вряд ли найдутся общие атрибуты","keywords":["ai","учитель","деревья","решений","ученик","notebook","атрибуты","базовые","быть","виртуальную","вряд","данной"],"topics":["oop"]},{"id":"chunk-29","heading":"","text":". Определим, что должны уметь объекты для решения задачи \"увеличить знания\":","keywords":["ai","деревья","решений","notebook","базовые","должны","задачи","знания","интерпретации","классификаторов.","объекты","определим"],"topics":[]},{"id":"chunk-30","heading":"","text":"• Данные могут представлять собой список знаний. Элементы будут извлекаться по индексу.","keywords":["ai","деревья","решений","notebook","базовые","будут","данные","знаний.","извлекаться","индексу.","интерпретации","классификаторов."],"topics":[]},{"id":"chunk-31","heading":"","text":"````python class Data: def init (self, info): self.info = list(info) def getitem (self, i): return self.info[i]","keywords":["ai","def","info","self","self.info","деревья","решений","class","data","getitem","init","list"],"topics":["python"]},{"id":"chunk-32","heading":"","text":"class Pupil: def init (self): self.knowledge = [] def take(self, info): self.knowledge.append(info)","keywords":["ai","def","info","self","деревья","решений","class","init","notebook","pupil","self.knowledge","self.knowledge.append"],"topics":[]},{"id":"chunk-33","heading":"","text":"lesson = Data('class', 'object', 'inheritance', 'polymorphism', 'encapsulation') marIvanna = Teacher() vasy = Pupil() pety = Pupil() marIvanna.teach(lesson[2], vasy, pety) marIvanna.teach(lesson[0], pety) print(vasy.knowledge) print(pety.knowledge) ````","keywords":["ai","lesson","pety","marivanna.teach","print","pupil","vasy","деревья","решений","class","data","encapsulation"],"topics":[]},{"id":"chunk-34","heading":"","text":"Напишите программу по следующему описанию. Есть класс \"Воин\". От него создаются два экземпляра юнита. Каждому устанавливается здоровье в 100 очков. В случайном порядке они бьют друг друга. Тот, кто бьет, здоровья не теряет. У того, кого бьют, оно уменьшается на 20 очков от одного удара. После каждого удара надо выводить сообщение, какой юнит атаковал, и сколько у противника осталось здоровья","keywords":["ai","бьют","деревья","здоровья","от","решений","100","20","notebook","атаковал","базовые","бьет"],"topics":["oop"]},{"id":"chunk-35","heading":"","text":". Как только у кого то заканчивается ресурс здоровья, программа завершается сообщением о том, кто одержал победу.","keywords":["ai","деревья","решений","notebook","базовые","завершается","заканчивается","здоровья","интерпретации","классификаторов.","кого","кто"],"topics":[]},{"id":"chunk-36","heading":"","text":"def hit(self, target, target1): if target.health 0: target.health = 20 if target1 == warrior1: target1 = 'Warrior1' if target1 == warrior2: target1 = 'Warrior2' print(target1, ' has attacked') print(target.health, ' left') if target.health == 0: print(target1, ' has won')","keywords":["target1","if","target.health","ai","print","has","warrior1","warrior2","деревья","решений","20","attacked"],"topics":[]},{"id":"chunk-37","heading":"","text":"warrior1 = Warrior(100) warrior2 = Warrior(100) q = int(input('Enter 1 to attack. Enter 2 to stop program:'))","keywords":["ai","100","enter","warrior","деревья","решений","attack.","input","int","notebook","program","stop"],"topics":["numpy"]},{"id":"chunk-38","heading":"","text":"while q != 2: if q == 1: j = random.randint(1, 2) if j % 2 == 0: warrior1.hit(warrior2, warrior1) q = int(input('Enter 1 to let some warrior attack:')) else: warrior2.hit(warrior1, warrior2) q = int(input('Enter 1 to let some warrior attack:')) else: print('Wrong input.') break ````","keywords":["ai","attack","else","enter","if","input","int","let","some","warrior","warrior1","warrior2"],"topics":["numpy"]},{"id":"chunk-39","heading":"","text":"code Enter 1 to attack. Enter 2 to stop program:1 Warrior2 has attacked 80 left Enter 1 to let some warrior attack:1 Warrior2 has attacked 60 left Enter 1 to let some warrior attack:1 Warrior2 has attacked 40 left Enter 1 to let some warrior attack:1 Warrior2 has attacked 20 left Enter 1 to let some warrior attack:1 Warrior1 has attacked 80 left Enter 1 to let some warrior attack:1 Warrior1 has attacked 60 left Enter","keywords":["enter","attacked","has","left","attack","let","some","warrior","warrior2","ai","60","80"],"topics":[]},{"id":"chunk-40","heading":"","text":"1 to let some warrior attack:1 Warrior1 has attacked 40 left Enter 1 to let some warrior attack:1 Warrior1 has attacked 20 left Enter 1 to let some warrior attack:1 Warrior2 has attacked 0 left Warrior2 has won Enter 1 to let some warrior attack:2","keywords":["attack","has","let","some","warrior","ai","attacked","enter","left","warrior1","warrior2","деревья"],"topics":[]},{"id":"chunk-41","heading":"","text":"Создайте класс по работе с дробями. В классе должна быть реализована следующая функциональность:","keywords":["ai","деревья","решений","notebook","базовые","быть","должна","дробями.","интерпретации","класс","классе","классификаторов."],"topics":["oop"]},{"id":"chunk-42","heading":"","text":"def init (self, n, d): if n == 0: self.num = 0 self.den = 1 else: z = self.sgn(n) self.sgn(d) n = abs(n) d = abs(d) k = self.gcd(n, d) self.num = z n // k self.den = d // k","keywords":["ai","abs","self.den","self.num","self.sgn","деревья","решений","def","else","if","init","notebook"],"topics":[]},{"id":"chunk-43","heading":"","text":"def str (self): if self.num == 0: return '0' else: return str(self.num) + '/' + str(self.den)","keywords":["ai","str","return","self.num","деревья","решений","def","else","if","notebook","self","self.den"],"topics":[]},{"id":"chunk-44","heading":"","text":"def add (self, o): n1 = self.num d1 = self.den if type(o) == int: n2 = o d2 = 1 else: n2 = o.num d2 = o.den n = n1 d2 + n2 d1 d = d1 d2 return Rational(n, d)","keywords":["d2","ai","d1","n2","n1","деревья","решений","add","def","else","if","int"],"topics":[]},{"id":"chunk-45","heading":"","text":"def radd (self, o): n1 = self.num d1 = self.num if type(o) == int: n2 = o d2 = 1 else: n2 = o.num d2 = o.den n = n1 d2 + n2 d1 d = d1 d2 return Rational(n, d)","keywords":["d2","ai","d1","n2","n1","self.num","деревья","решений","def","else","if","int"],"topics":[]},{"id":"chunk-46","heading":"","text":"def sub (self, o): n1 = self.num d1 = self.den n2 = o.num d2 = o.den n = n1 d2 n2 d1 d = d1 d2 return Rational(n, d)","keywords":["ai","d1","d2","n1","n2","деревья","решений","def","notebook","o.den","o.num","rational"],"topics":[]},{"id":"chunk-47","heading":"","text":"def mul (self, o): n1 = self.num d1 = self.den n2 = o.num d2 = o.den n = n1 n2 d = d1 d2 return Rational(n, d)","keywords":["ai","d1","d2","n1","n2","деревья","решений","def","mul","notebook","o.den","o.num"],"topics":[]},{"id":"chunk-48","heading":"","text":"def floordiv (self, o): n1 = self.num d1 = self.den n2 = o.num d2 = o.den n = n1 d2 d = d1 n2 return Rational(n, d)","keywords":["ai","d1","d2","n1","n2","деревья","решений","def","floordiv","notebook","o.den","o.num"],"topics":[]},{"id":"chunk-49","heading":"","text":"d1 = Rational(1, 2) d2 = Rational(1, 3) d3 = d1 + d2 print(d3) d4 = d1 d2 print(d4) d5 = d1 d2 print(d5) d6 = d1 d2 print(d6) d7 = d1 // d2 print(d7) d8 = 6 + d1 print(d8) ````","keywords":["d1","d2","print","ai","d3","d4","d5","d6","d7","d8","rational","деревья"],"topics":[]},{"id":"chunk-50","heading":"","text":"Создайте класс по работе с тригонометрическими функциями. В классе должны быть реализованы функции вычисления:","keywords":["ai","деревья","решений","notebook","базовые","быть","вычисления","должны","интерпретации","класс","классе","классификаторов."],"topics":["oop"]},{"id":"chunk-51","heading":"","text":"Любое представление графов, естественно, можно использовать для представления деревьев, потому что деревья — это особый вид графов. Однако, деревья играют свою большую роль в алгоритмах, и для них разработано много соответствующих структур и методов","keywords":["деревья","ai","решений","notebook","алгоритмах","базовые","большую","вид","графов","графов.","деревьев","естественно"],"topics":["algorithms"]},{"id":"chunk-52","heading":"","text":". Большинство алгоритмов на деревьях (например, поиск по деревьям) можно рассматривать в терминах теории графов, но специальные структуры данных делают их проще в реализации.","keywords":["ai","деревья","решений","notebook","алгоритмов","базовые","большинство","графов","данных","делают","деревьям","деревьях"],"topics":["algorithms"]},{"id":"chunk-53","heading":"","text":"Проще всего описать представление дерева с корнем, в котором ребра спускаются вниз от корня. Такие деревья часто отображают иерархическое ветвление данных, где корень отображает все объекты (которые, возможно, хранятся в листьях), а каждый внутренний узел показывает объекты, содержащиеся в дереве, корень которого — этот узел","keywords":["ai","деревья","корень","объекты","решений","узел","notebook","базовые","ветвление","вниз","внутренний","возможно"],"topics":[]},{"id":"chunk-54","heading":"","text":". Это описание можно использовать, представив каждое поддерево списком, содержащим все его поддеревья потомки. Рассмотрим простое дерево, показанное на рисунке ниже.","keywords":["ai","деревья","решений","notebook","базовые","дерево","его","интерпретации","использовать","каждое","классификаторов.","можно"],"topics":[]},{"id":"chunk-55","heading":"","text":"Каждый список в сущности является списком потомков каждого из внутренних узлов. Во втором примере мы обращаемся к третьему потомку корня, затем ко второму его потомку и в конце концов — к первому потомку предыдущего узла (этот путь отмечен на рисунке). В ряде случаев возможно заранее определить максимальное число потомков каждого узла. (Например, каждый узел бинарного дерева может иметь до двух потомков)","keywords":["ai","потомков","потомку","деревья","каждого","каждый","решений","notebook","базовые","бинарного","внутренних","возможно"],"topics":[]},{"id":"chunk-56","heading":"","text":". Поэтому можно использовать другие представления, скажем, объекты с отдельным атрибутом для каждого из потомков как в листинге ниже.","keywords":["ai","деревья","решений","notebook","атрибутом","базовые","другие","интерпретации","использовать","каждого","классификаторов.","листинге"],"topics":[]},{"id":"chunk-57","heading":"","text":"Определите класс бинарного дерева и задайте его объекты с отдельным атрибутом для каждого из потомков.","keywords":["ai","деревья","решений","notebook","атрибутом","базовые","бинарного","дерева","его","задайте","интерпретации","каждого"],"topics":["oop"]},{"id":"chunk-58","heading":"","text":"````python class Tree: def init (self, left, right): self.left = left self.right = right","keywords":["ai","left","right","деревья","решений","class","def","init","notebook","python","self","self.left"],"topics":["python"]},{"id":"chunk-59","heading":"","text":"Для обозначения отсутствующих потомков можно использовать None (в случае если у узла только один потомок). Само собой, можно комбинировать разные методы (например, использовать списки или множества потомков для каждого узла).","keywords":["ai","деревья","использовать","можно","потомков","решений","узла","none","notebook","базовые","если","интерпретации"],"topics":[]},{"id":"chunk-60","heading":"","text":"Распространенный способ реализации деревьев, особенно на языках, не имеющих встроенной поддержки списков, это так называемое представление «первый потомок, следующий брат». В нем каждый узел имеет два «указателя» или атрибута, указывающих на другие узлы, как в бинарном дереве. Однако, первый из этих атрибутов ссылается на первого потомка узла, а второй — на его следующего брата (т.е","keywords":["ai","деревья","первый","решений","notebook","атрибута","атрибутов","базовые","бинарном","брат","брата","встроенной"],"topics":[]},{"id":"chunk-61","heading":"","text":". узел, имеющий того же родителя, но находящийся правее, — прим. перев). Иными словами, каждый узел дерева имеет указатель на связанный список его потомков, а каждый из этих потомков ссылается на свой собственный аналогичный список. Таким образом, небольшая модификация бинарного дерева даст нам многопутевое дерево, показанное в листинге ниже.","keywords":["ai","дерева","деревья","каждый","потомков","решений","узел","notebook","аналогичный","базовые","бинарного","даст"],"topics":[]},{"id":"chunk-62","heading":"","text":"````python class Tree: def init (self, kids, next=None): self.kids = self.val = kids self.next = next","keywords":["ai","kids","next","деревья","решений","class","def","init","none","notebook","python","self"],"topics":["python"]},{"id":"chunk-63","heading":"","text":"Представьте дерево показанное на рисунке с использованием списка из списков. Выведите на печать корень дерева, а также его левое и правое поддеревья.","keywords":["ai","деревья","решений","notebook","базовые","выведите","дерева","дерево","его","интерпретации","использованием","классификаторов."],"topics":[]},{"id":"chunk-64","heading":"","text":"````python class Tree: def init (self, kids, next=None): self.kids = self.val = kids self.next = next","keywords":["ai","kids","next","деревья","решений","class","def","init","none","notebook","python","self"],"topics":["python"]},{"id":"chunk-65","heading":"","text":"print('left subtree = ', end='') print(t.kids.next.val, end=', ') print(t.kids.next.next.val, end=', ') print(t.kids.next.next.next)","keywords":["print","ai","end","деревья","решений","left","notebook","subtree","t.kids.next.next.next","t.kids.next.next.val","t.kids.next.val","базовые"],"topics":[]},{"id":"chunk-66","heading":"","text":"print('right subtree = ', end='') print(t.next.kids, end=', ') print(t.next.next.kids) ````","keywords":["ai","print","end","деревья","решений","notebook","right","subtree","t.next.kids","t.next.next.kids","базовые","интерпретации"],"topics":[]},{"id":"chunk-67","heading":"","text":"class Tree: def init (self, data): self.left = None self.right = None self.data = data def PrintTree(self): print(self.data)","keywords":["ai","data","def","none","self","self.data","деревья","решений","class","init","notebook","print"],"topics":[]},{"id":"chunk-68","heading":"","text":"Реализуйте в классе функцию для вставки нового элемента в дерево по следующим правилам:","keywords":["ai","деревья","решений","notebook","базовые","вставки","дерево","интерпретации","классе","классификаторов.","нового","правилам"],"topics":["oop"]},{"id":"chunk-69","heading":"","text":"• Левое поддерево узла содержит только узлы со значениями меньше, чем значение в узле.","keywords":["ai","деревья","решений","notebook","базовые","значение","значениями","интерпретации","классификаторов.","левое","меньше","поддерево"],"topics":[]},{"id":"chunk-70","heading":"","text":"• Правое поддерево узла содержит только узлы со значениями меньше, чем значение в узле.","keywords":["ai","деревья","решений","notebook","базовые","значение","значениями","интерпретации","классификаторов.","меньше","поддерево","правое"],"topics":[]},{"id":"chunk-71","heading":"","text":"• Каждое из левого и правого поддеревьев также должно быть бинарным деревом поиска.","keywords":["ai","деревья","решений","notebook","базовые","бинарным","быть","деревом","должно","интерпретации","каждое","классификаторов."],"topics":[]},{"id":"chunk-72","heading":"","text":"Метод вставки сравнивает значение узла с родительским узлом и решает куда доваить элемент (в левое или правое поддерево). Перепишите, метод PrintTree для печати полной версии дерева.","keywords":["ai","деревья","метод","решений","notebook","printtree","базовые","версии","вставки","дерева.","доваить","значение"],"topics":[]},{"id":"chunk-73","heading":"","text":"````python class Tree: def init (self, data): self.left = None self.right = None self.data = data","keywords":["ai","data","none","деревья","решений","class","def","init","notebook","python","self","self.data"],"topics":["python"]},{"id":"chunk-74","heading":"","text":"def PrintTree(self): if self.left: self.left.PrintTree() print(self.data) if self.right: self.right.PrintTree()","keywords":["ai","if","деревья","решений","def","notebook","print","printtree","self","self.data","self.left","self.left.printtree"],"topics":[]},{"id":"chunk-75","heading":"","text":"def AddItem(self, item): if self.data: if item self.data: if self.right is None: self.right = Tree(item) else: self.right.AddItem(item) else: self.data = item","keywords":["item","ai","if","self.data","else","self.right","деревья","решений","additem","def","none","notebook"],"topics":[]},{"id":"chunk-76","heading":"","text":"tree = Tree(5) tree.AddItem(1) tree.AddItem(2) tree.AddItem(6) tree.AddItem(7) tree.PrintTree() ````","keywords":["tree.additem","ai","tree","деревья","решений","notebook","tree.printtree","базовые","интерпретации","классификаторов.","техники"],"topics":[]},{"id":"chunk-77","heading":"","text":"Дерево решений – это один из наиболее часто и широко используемых алгоритмов контролируемого машинного обучения, который может выполнять как регрессионные, так и классификационные задачи. Использование деревьев решений для прогнозного анализа имеет ряд преимуществ:","keywords":["решений","ai","деревья","notebook","алгоритмов","анализа","базовые","выполнять","дерево","деревьев","задачи.","имеет"],"topics":["oop","algorithms"]},{"id":"chunk-78","heading":"","text":"1. Деревья решений могут быть использованы для прогнозирования как непрерывных, так и дискретных значений, т. е. они хорошо работают как для задач регрессии, так и для задач классификации.","keywords":["ai","деревья","решений","задач","так","1.","notebook","базовые","быть","дискретных","е.","значений"],"topics":["oop"]},{"id":"chunk-79","heading":"","text":"4. Они очень быстры и эффективны по сравнению с KNN и другими алгоритмами классификации.","keywords":["ai","деревья","решений","4.","knn","notebook","алгоритмами","базовые","быстры","другими","интерпретации","классификаторов."],"topics":["knn","oop","algorithms"]},{"id":"chunk-80","heading":"","text":"Решим модельные примеры классификации и регрессии, разобранные в предыдущих раочих тетрадях, но с использованием деревьев принятия решений.","keywords":["ai","деревья","решений","notebook","базовые","деревьев","интерпретации","использованием","классификаторов.","классификации","модельные","предыдущих"],"topics":["oop"]},{"id":"chunk-81","heading":"","text":"Построим дерево решений для задачи классификации, для этого, построим границу решения для каждого класса. В качестве данных будем использовать уже знакомый нам и встроенный в библиотеку sklearn набор данных ирисов Фишера. Импортируем библиотеки, набор данных и посмотрим его характеристики.","keywords":["ai","данных","решений","деревья","набор","построим","notebook","sklearn","базовые","библиотеки","библиотеку","будем"],"topics":["oop"]},{"id":"chunk-82","heading":"","text":"code sepal length sepal width petal length petal width species 0 5.1 3.5 1.4 0.2 setosa 1 4.9 3.0 1.4 0.2 setosa 2 4.7 3.2 1.3 0.2 setosa 3 4.6 3.1 1.5 0.2 setosa 4 5.0 3.6 1.4 0.2 setosa","keywords":["0.2","setosa","1.4","ai","length","petal","sepal","width","деревья","решений","1.3","1.5"],"topics":[]},{"id":"chunk-83","heading":"","text":"Далее, разделим наши данные на атрибуты и метки, а затем выделим в общей совокупности полученных данных обучающие и тестовые наборы. Таким образом, мы можем обучить наш алгоритм на одном наборе данных, а затем протестировать его на совершенно на другом наборе, который алгоритм еще не видел. Это дает вам более точное представление о том, как на самом деле будет работать ваш обученный алгоритм.","keywords":["ai","алгоритм","данных","деревья","затем","наборе","решений","notebook","алгоритм.","атрибуты","базовые","более"],"topics":["algorithms"]},{"id":"chunk-84","heading":"","text":"x train, x test, y train, y test = train test split( dataset.iloc[:, : 1], dataset.iloc[:, 1], test size = 0.20 )","keywords":["test","ai","train","dataset.iloc","деревья","решений","0.20","notebook","size","split","базовые","интерпретации"],"topics":["ai","testing"]},{"id":"chunk-85","heading":"","text":"x train.shape, x test.shape, y train.shape, y test.shape x train.head() y train.head() ````","keywords":["ai","test.shape","train.head","train.shape","деревья","решений","notebook","базовые","интерпретации","классификаторов.","техники"],"topics":["ai","testing"]},{"id":"chunk-86","heading":"","text":"code 49 setosa 58 versicolor 68 versicolor 35 setosa 122 virginica Name: species, dtype: object","keywords":["ai","setosa","versicolor","деревья","решений","122","35","49","58","68","code","dtype"],"topics":[]},{"id":"chunk-87","heading":"","text":"После того, как данные были разделены на обучающие и тестовые наборы, последний шаг состоит в том, чтобы обучить алгоритм дерева решений на этих данных и сделать прогнозы. Scikit Learn содержит библиотеку tree , которая содержит встроенные классы/методы для различных алгоритмов дерева решений","keywords":["решений","ai","дерева","деревья","содержит","learn","notebook","scikit","tree","алгоритм","алгоритмов","базовые"],"topics":["oop","algorithms"]},{"id":"chunk-88","heading":"","text":". Поскольку мы собираемся выполнить здесь задачу классификации, мы будем использовать класс DecisionTreeClassifier для этого примера. Метод fit этого класса вызывается для обучения алгоритма на обучающих данных, которые передаются в качестве параметра методу fit . Выполним следующий сценарий для обучения алгоритма.","keywords":["ai","fit","деревья","мы","обучения","решений","этого","decisiontreeclassifier","notebook","алгоритма","алгоритма.","базовые"],"topics":["oop","algorithms"]},{"id":"chunk-89","heading":"","text":"````python from sklearn.tree import DecisionTreeClassifier classifier = DecisionTreeClassifier() classifier.fit(x train, y train)","keywords":["ai","decisiontreeclassifier","train","деревья","решений","classifier","classifier.fit","import","notebook","python","sklearn.tree","базовые"],"topics":["python","ai"]},{"id":"chunk-90","heading":"","text":"Теперь, когда наш классификатор обучен, давайте сделаем прогнозы по тестовым данным. Для составления прогнозов используется метод predict класса Decision Tree Classifier. Взгляните на следующий код для использования.","keywords":["ai","деревья","решений","classifier.","decision","notebook","predict","tree","базовые","взгляните","давайте","данным."],"topics":["oop"]},{"id":"chunk-91","heading":"","text":"code array(['virginica', 'setosa', 'virginica', 'virginica', 'setosa', 'versicolor', 'virginica', 'versicolor', 'setosa', 'versicolor', 'virginica', 'virginica', 'versicolor', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'setosa', 'setosa', 'virginica', 'versicolor', 'setosa', 'versicolor'], dtype=object)","keywords":["virginica","setosa","versicolor","ai","деревья","решений","array","code","dtype","notebook","object","базовые"],"topics":[]},{"id":"chunk-92","heading":"","text":"На данный момент мы обучили наш алгоритм и сделали некоторые прогнозы. Теперь посмотрим, насколько точен наш алгоритм. Для задач классификации обычно используются такие метрики, как матрица путаницы, точность. Библиотека Scikit Learn metrics содержит методы classification report и confusion matrix, которые могут быть использованы для расчета этих метрик.","keywords":["ai","деревья","наш","решений","classification","confusion","learn","matrix","metrics","notebook","report","scikit"],"topics":["oop","algorithms"]},{"id":"chunk-93","heading":"","text":"python from sklearn.metrics import classification report, confusion matrix print(confusion matrix(y test, y pred)) print(classification report(y test, y pred))","keywords":["ai","classification","confusion","matrix","pred","print","report","test","деревья","решений","import","notebook"],"topics":["python","testing"]},{"id":"chunk-94","heading":"","text":"setosa 1.00 1.00 1.00 11 versicolor 1.00 0.78 0.88 9 virginica 0.83 1.00 0.91 10","keywords":["1.00","ai","деревья","решений","0.78","0.83","0.88","0.91","10","11","notebook","setosa"],"topics":[]},{"id":"chunk-95","heading":"","text":"accuracy 0.93 30 macro avg 0.94 0.93 0.93 30 weighted avg 0.94 0.93 0.93 30 ````","keywords":["0.93","30","ai","0.94","avg","деревья","решений","accuracy","macro","notebook","weighted","базовые"],"topics":[]},{"id":"chunk-96","heading":"","text":"Из матрицы оценок алгоритма вы можете видеть, что из 30 тестовых экземпляров наш алгоритм неправильно классифицировал только 3. Это приблизительно 91 % точности.","keywords":["ai","деревья","решений","3.","30","91","notebook","алгоритм","алгоритма","базовые","видеть","вы"],"topics":["oop","algorithms"]},{"id":"chunk-97","heading":"","text":"данные x = np.array([[ 1, 1], [ 2, 1], [ 3, 2], [1, 1], [2, 1], [3, 2]]) target = [0, 0, 0, 1, 1, 1]","keywords":["ai","деревья","решений","notebook","np.array","target","базовые","данные","интерпретации","классификаторов.","техники"],"topics":["numpy"]},{"id":"chunk-98","heading":"","text":"````python from sklearn.metrics import classification report, confusion matrix x = np.array([[ 1, 1], [ 2, 1], [ 3, 2], [1, 1], [2, 1], [3, 2]]) target = [0, 0, 0, 1, 1, 1]","keywords":["ai","деревья","решений","classification","confusion","import","matrix","notebook","np.array","python","report","sklearn.metrics"],"topics":["python","numpy"]},{"id":"chunk-99","heading":"","text":"x train, x test, y train, y test = train test split( dataset.iloc[:, : 1], dataset.iloc[:, 1], test size=0.20 )","keywords":["test","ai","train","dataset.iloc","деревья","решений","0.20","notebook","size","split","базовые","интерпретации"],"topics":["ai","testing"]},{"id":"chunk-100","heading":"","text":"x train.shape, x test.shape, y train.shape, y test.shape x train.head() y train.head()","keywords":["ai","test.shape","train.head","train.shape","деревья","решений","notebook","базовые","интерпретации","классификаторов.","техники"],"topics":["ai","testing"]},{"id":"chunk-101","heading":"","text":"print(confusion matrix(y test, y pred)) print(classification report(y test, y pred, zero division=0)) ````","keywords":["ai","pred","print","test","деревья","решений","classification","confusion","division","matrix","notebook","report"],"topics":["testing"]},{"id":"chunk-102","heading":"","text":"Процесс решения регрессионной задачи с деревом решений с помощью Scikit Learn очень похож на процесс классификации. Однако для регрессии мы используем класс DecisionTreeRegressor древовидной библиотеки. Кроме того, оценочные показатели регрессии отличаются от показателей классификации. В остальном процесс почти такой же. Построим регрессию с использованием дерева решений в Python и библиотеки scikit learn","keywords":["решений","ai","процесс","learn","scikit","деревья","классификации.","регрессии","decisiontreeregressor","notebook","python","базовые"],"topics":["python","oop"]},{"id":"chunk-103","heading":"","text":". В качестве исходного набора данных будем использовать зависимость заработной платы от опыта работы из предыдущей тетради:","keywords":["ai","деревья","решений","notebook","базовые","будем","данных","зависимость","заработной","интерпретации","использовать","исходного"],"topics":[]},{"id":"chunk-104","heading":"","text":"https://raw.githubusercontent.com/AnnaShestova/salary years simple\u0002linear regression/master/Salary Data.csv","keywords":["ai","salary","деревья","решений","annashestova","data.csv","https","linear","master","notebook","raw.githubusercontent.com","regression"],"topics":["python","regression"]},{"id":"chunk-105","heading":"","text":"url = r'https://raw.githubusercontent.com/AnnaShestova/salary years simple linear regression/master/Salary Data.csv' dataset = pd.read csv(url) print(dataset.head())","keywords":["ai","salary","url","деревья","решений","annashestova","csv","data.csv","dataset","dataset.head","https","linear"],"topics":["python","regression"]},{"id":"chunk-106","heading":"","text":"plt.scatter(dataset['YearsExperience'], dataset['Salary'], color='b', label='Заработная плата') plt.xlabel('Опыт(лет)') plt.ylabel('Заработная плата') plt.show()","keywords":["ai","dataset","деревья","заработная","плата","решений","color","label","notebook","plt.scatter","plt.show","plt.xlabel"],"topics":[]},{"id":"chunk-107","heading":"","text":"from sklearn.tree import DecisionTreeRegressor x = dataset.iloc[:, : 1].values y = dataset.iloc[:, 1].values print(x) print(y)","keywords":["ai",".values","dataset.iloc","print","деревья","решений","decisiontreeregressor","import","notebook","sklearn.tree","базовые","интерпретации"],"topics":[]},{"id":"chunk-108","heading":"","text":"x train, x test, y train, y test = train test split(x, y, test size=0.2, random state=0)","keywords":["test","ai","train","деревья","решений","0.2","notebook","random","size","split","state","базовые"],"topics":["ai","testing"]},{"id":"chunk-109","heading":"","text":"from sklearn import metrics print('Mean Squared Error:', metrics.mean squared error(y test, y pred)) print('Mean Absolute Error:', metrics.mean absolute error(y test, y pred))","keywords":["error","ai","absolute","mean","metrics.mean","pred","print","squared","test","деревья","решений","import"],"topics":["testing"]},{"id":"chunk-110","heading":"","text":"Средняя абсолютная ошибка для нашего алгоритма составляет 4120.66, что составляет менее 6 процентов от среднего значения всех значений в столбце.","keywords":["ai","деревья","решений","составляет","4120.66","notebook","абсолютная","алгоритма","базовые","всех","значений","значения"],"topics":["algorithms"]},{"id":"chunk-111","heading":"","text":"Постройте модель регрессии для данных из предыдущей рабочей тетради.Для примера можно взять потребления газа (в миллионах галлонов) в 48 штатах США или набор данных о качестве красного вина: https://raw.githubusercontent.com/likarajo/petrol consumption/master/data/petrol consumption.csv https://raw.githubusercontent.com/aniruddhachoudhury/Red WineQuality/master/winequality red.csv","keywords":["ai","https","master","petrol","raw.githubusercontent.com","winequality","данных","деревья","решений","48","aniruddhachoudhury","consumption"],"topics":["python"]},{"id":"chunk-112","heading":"","text":"````python from sklearn.tree import DecisionTreeRegressor from sklearn import tree from sklearn import metrics","keywords":["ai","import","sklearn","деревья","решений","decisiontreeregressor","metrics","notebook","python","sklearn.tree","tree","базовые"],"topics":["python"]},{"id":"chunk-113","heading":"","text":"url = r'https://raw.githubusercontent.com/likarajo/petrol consumption/master/data/petrol consumption.csv' dataset = pd.read csv(url) print(dataset.head())","keywords":["ai","petrol","url","деревья","решений","consumption","consumption.csv","csv","data","dataset","dataset.head","https"],"topics":["python"]},{"id":"chunk-114","heading":"","text":"plt.scatter(dataset['Petrol tax'],dataset['Average income'],color='b',label='Average income') plt.xlabel('Average income') plt.ylabel('Petrol tax') plt.show()","keywords":["ai","average","income","dataset","petrol","tax","деревья","решений","color","label","notebook","plt.scatter"],"topics":[]},{"id":"chunk-115","heading":"","text":"x train,x test,y train,y test = train test split(x,y,test size=0.2,random state=0)","keywords":["test","ai","train","деревья","решений","0.2","notebook","random","size","split","state","базовые"],"topics":["ai","testing"]},{"id":"chunk-116","heading":"","text":"tree.plot tree(regressor) y pred = regressor.predict(x test) y pred df = pd.DataFrame({'Actual': y test, 'Predicted': y pred}) df","keywords":["ai","pred","df","test","деревья","решений","actual","notebook","pd.dataframe","predicted","regressor","regressor.predict"],"topics":["pandas","testing"]},{"id":"chunk-117","heading":"","text":"print('Squared ', metrics.mean squared error(y test, y pred)) print('Absolute', metrics.mean absolute error(y test, y pred)) metrics.mean absolute error(y test, y pred) / np.average(y) 100 ````","keywords":["absolute","ai","error","metrics.mean","pred","test","print","squared","деревья","решений","100","notebook"],"topics":["numpy","testing"]}]},{"id":"ai/notebook-06-genetic-and-annealing","href":"/docs/ai/notebook-06-genetic-and-annealing","slug":["ai","notebook-06-genetic-and-annealing"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 6 — Генетические и эволюционные методы","description":"Оптимизация: эволюционные стратегии и имитация отжига.","preview":"Теоретический материал – Эволюционные методы Эволюционные методы Эволюционные методы относятся к числу эффективных средств решения задач оптимизации и структурного синтеза проектных решений. Они основаны на использовании принципов оптимального приспособления о","keywords":["def","distances","item","bestroute","if","return","distance","range","arrv","decrement","else","news","sumlength","отжига","max","длины","len","newv"],"topics":["python","ai","oop","algorithms"],"chunks":[{"id":"chunk-0","heading":"","text":"Эволюционные методы относятся к числу эффективных средств решения задач оптимизации и структурного синтеза проектных решений. Они основаны на использовании принципов оптимального приспособления организмов в живой природе к условиям окружающей среды. К числу эволюционных относятся методы генетические, колонии муравьев, поведения толпы. Наиболее развиты и востребованы в настоящее время генетические алгоритмы","keywords":["ai","генетические","методы","эволюционные","относятся","числу","notebook","алгоритмы","востребованы","время","живой","задач"],"topics":["algorithms"]},{"id":"chunk-1","heading":"","text":". По мере развития техники и технологий растет доля сложных задач проектирования и управления, для решения которых применение традиционных методов проблематично. Поэтому все большее внимание уделяется применению методов искусственного интеллекта. Генетические алгоритмы Для применения ГА необходимо:","keywords":["ai","генетические","методов","эволюционные","notebook","алгоритмы","большее","внимание","га","доля","задач","имитация"],"topics":["algorithms"]},{"id":"chunk-2","heading":"","text":"1. выделить совокупность свойств объекта, характеризуемых внутренними параметрами и влияющих на его полезность, т.е. выделить множество управляемых параметровX=(x 1,x 2,…,x n) среди x i могут быть величины различных типов (real, integer, Boolean, enumeration). Наличие нечисловых величин (enumeration) обусловливает возможность решения задач не только параметрической, но и структурной оптимизации;","keywords":["ai","enumeration","выделить","эволюционные","1.","boolean","integer","notebook","real","быть","величин","величины"],"topics":[]},{"id":"chunk-3","heading":"","text":"2. сформулировать количественную оценку полезности вариантов объекта — функцию полезности F. Если в исходном виде задача многокритериальна, то такая формулировка означает выбор скалярного (обобщенного) критерия;","keywords":["ai","полезности","эволюционные","2.","f.","notebook","вариантов","виде","выбор","генетические","если","задача"],"topics":[]},{"id":"chunk-4","heading":"","text":"code for (k=0; k<G; k++) { for (j=0; j<N; j++) { Выбор родительской пары хромосом; Кроссовер; Мутации; Оценка функции полезности F потомков; Селекция; } Замена текущего поколения новым; }","keywords":["ai","эволюционные","code","j++","k++","notebook","выбор","генетические","замена","имитация","кроссовер","методы"],"topics":[]},{"id":"chunk-5","heading":"","text":"Пусть дана начальная популяция из четырех хромосом с двумя генами x и y. Показатель качества хромосомы оценивается функцией Z. При равном качестве хромосом предпочтение отдается хромосоме с большим номером. На каждом этапе хромосома a с высшим качеством порождает четыре новых хромосомы 𝑏1, 𝑐1, 𝑏2, 𝑐2, обмениваясь генами с двумя хромосомами b и c более низкого качества по указанной схеме:","keywords":["ai","генами","двумя","качества","хромосом","хромосомы","эволюционные","𝑏1","𝑏2","𝑐1","𝑐2","notebook"],"topics":[]},{"id":"chunk-6","heading":"","text":"Последняя хромоcома (с низшим качеством) выбывает из популяции. Найти максимальный показатель качества хромосомы в популяции и общее качество популяции после четырех этапов эволюции.","keywords":["ai","популяции","эволюционные","notebook","выбывает","генетические","имитация","качества","качество","качеством","максимальный","методы"],"topics":[]},{"id":"chunk-7","heading":"","text":"````python функция качества хромосомы def qZ(x, y): return (x 3 y + 1) / (3 x 2 + 3 y 2 + 1) сумма качества хромосом def qSumZ(Z): return sum(Z)","keywords":["ai","def","return","качества","эволюционные","notebook","python","qsumz","qz","sum","генетические","имитация"],"topics":["python"]},{"id":"chunk-8","heading":"","text":"def exchangeScheme(oldX, oldY, sortedId): x = [0 for i in range(4)] y = [0 for i in range(4)]","keywords":["ai","range","эволюционные","def","exchangescheme","notebook","oldx","oldy","sortedid","генетические","имитация","методы"],"topics":[]},{"id":"chunk-9","heading":"","text":"def sorting(Z): sortedId = sorted(range(len(Z)), key=lambda k: Z[k]) return sortedId","keywords":["ai","sortedid","эволюционные","def","key","lambda","len","notebook","range","return","sorted","sorting"],"topics":[]},{"id":"chunk-10","heading":"","text":"шаг эволюции def evoStep(x, y, z): , minId = min((value, id) for (id, value) in enumerate(z)) x = x[:] y = y[:] z = z[:]","keywords":["ai","id","value","эволюционные","def","enumerate","evostep","min","minid","notebook","генетические","имитация"],"topics":[]},{"id":"chunk-11","heading":"","text":"шаги эволюции (конечная функция), по умолчанию 4 шага def evoSteps(x, y, steps num=4): results = []","keywords":["ai","эволюционные","def","evosteps","notebook","num","results","steps","генетические","имитация","конечная","методы"],"topics":[]},{"id":"chunk-12","heading":"","text":"qualityArrZ = [] for i in range(len(results[2])): qualityArrZ += results[2][i][3]","keywords":["ai","qualityarrz","results","эволюционные","len","notebook","range","генетические","имитация","методы","оптимизация","отжига."],"topics":[]},{"id":"chunk-13","heading":"","text":"Выполните по вариантам соответственно реализацию генетического алгоритма в соответствии с приложенными начальными данными.","keywords":["ai","эволюционные","notebook","алгоритма","вариантам","выполните","генетические","генетического","данными.","имитация","методы","начальными"],"topics":["algorithms"]},{"id":"chunk-14","heading":"","text":"````python функция качества хромосомы def qZ(x, y): return (x 3 y + 1) / (3 x 2 + y 2 + 1) сумма качества хромосом def qSumZ(Z): return sum(Z)","keywords":["ai","def","return","качества","эволюционные","notebook","python","qsumz","qz","sum","генетические","имитация"],"topics":["python"]},{"id":"chunk-15","heading":"","text":"def exchangeScheme(oldX, oldY, sortedId): x = [0 for i in range(4)] y = [0 for i in range(4)]","keywords":["ai","range","эволюционные","def","exchangescheme","notebook","oldx","oldy","sortedid","генетические","имитация","методы"],"topics":[]},{"id":"chunk-16","heading":"","text":"def sorting(Z): sortedId = sorted(range(len(Z)), key=lambda k: Z[k]) return sortedId","keywords":["ai","sortedid","эволюционные","def","key","lambda","len","notebook","range","return","sorted","sorting"],"topics":[]},{"id":"chunk-17","heading":"","text":"шаг эволюции def evoStep(x, y, z): , minId = min((value, id) for (id, value) in enumerate(z)) x = x[:] y = y[:] z = z[:]","keywords":["ai","id","value","эволюционные","def","enumerate","evostep","min","minid","notebook","генетические","имитация"],"topics":[]},{"id":"chunk-18","heading":"","text":"шаги эволюции (конечная функция), по умолчанию 4 шага def evoSteps(x, y, steps num=4): results = []","keywords":["ai","эволюционные","def","evosteps","notebook","num","results","steps","генетические","имитация","конечная","методы"],"topics":[]},{"id":"chunk-19","heading":"","text":"qualityArrZ = [] for i in range(len(results[2])): qualityArrZ += results[2][i][3]","keywords":["ai","qualityarrz","results","эволюционные","len","notebook","range","генетические","имитация","методы","оптимизация","отжига."],"topics":[]},{"id":"chunk-20","heading":"","text":"code max 1 step: 2.2941176470588234 max 2 step: 0.9714285714285713 max 3 step: 4.823529411764706 max 4 step: 5.828571428571428 max Z: 2.0","keywords":["max","step","ai","эволюционные","0.9714285714285713","2.0","2.2941176470588234","4.823529411764706","5.828571428571428","code","notebook","генетические"],"topics":[]},{"id":"chunk-21","heading":"","text":"Алгоритм отжига – это метод оптимизации, который называется отжигом, или симуляцией восстановления (Simulated annealing). Как ясно из названия, метод поиска моделирует процесс восстановления. Восстановление – это физический процесс, который заключается в нагреве и последующем контролируемом охлаждении субстанции","keywords":["ai","который","метод","процесс","эволюционные","annealing","notebook","simulated","алгоритм","восстановление","восстановления","восстановления."],"topics":["algorithms"]},{"id":"chunk-22","heading":"","text":". В результате получается прочная кристаллическая структура, которая отличается от структуры с дефектами, образующейся при быстром беспорядочном охлаждении. Структура здесь представляет собой кодированное решение, а температура используется для того, чтобы указать, как и когда будут приниматься новые решения.","keywords":["ai","структура","эволюционные","notebook","беспорядочном","будут","быстром","генетические","дефектами","здесь","имитация","используется"],"topics":[]},{"id":"chunk-23","heading":"","text":"Метод отжига может быть эффективным при решении задач различных классов, требующих оптимизации. Ниже приводится их краткий список:","keywords":["ai","эволюционные","notebook","быть","генетические","задач","имитация","их","классов","краткий","метод","методы"],"topics":["oop"]},{"id":"chunk-24","heading":"","text":"Поскольку метод отжига представляет собой процесс генерации случайных чисел, поиск решения с использованием данного алгоритма может занять значительное время. В некоторых случаях алгоритм вообще не находит решение или выбирает не самое оптимальное. Алгоритм отжига как способ выполнения процедур поиска и оптимизации","keywords":["ai","алгоритм","отжига","эволюционные","notebook","алгоритма","вообще","время.","выбирает","выполнения","генерации","генетические"],"topics":["algorithms"]},{"id":"chunk-25","heading":"","text":". Данный метод является аналогом процесса нагревания тела до состояния плавления с последующим постепенным охлаждением. При высоких температурах поиск ведется по всему диапазону. При снижении температуры диапазон поиска уменьшается до небольшой области вокруг текущего решения.","keywords":["ai","до","при","эволюционные","notebook","аналогом","ведется","вокруг","всему","высоких","генетические","данный"],"topics":[]},{"id":"chunk-26","heading":"","text":"Рассмотрим решение задачи поиска оптимального маршрута на графе методом имитации отжига Для этого, представим формальную постановку задачи и рассмотрим пример, который иллюстрирует алгоритм решения.","keywords":["ai","задачи","рассмотрим","эволюционные","notebook","алгоритм","генетические","графе","иллюстрирует","имитации","имитация","который"],"topics":["algorithms"]},{"id":"chunk-27","heading":"","text":"Итак, необходимо Найти длину гамильтонова цикла 𝑆4 в полном графе 𝐾6 после четырех циклов решения задачи методом отжига. Даны расстояния 𝐿𝑖,𝑗 между вершинами. Даны также: начальная последовательность вершин 𝐿0, последовательность замен вершин 𝑍 и выпавшие при этом вероятности перехода 𝑃𝑘, 𝑘 = 1, . . . , 4.","keywords":["ai","вершин","даны","отжига.","последовательность","эволюционные","4.","𝑗","𝑘","𝐾6","𝐿0","𝐿𝑖"],"topics":[]},{"id":"chunk-28","heading":"","text":"Переход на худшее (∆𝑆𝑘 = 𝑆𝑘 − 𝑆𝑘−1 0) решение допустим, если 𝑃∗ = 100где снижение температуры происходит по закону 𝑇𝑘+1 = 0.5𝑇𝑘 от 𝑇1 = 100.","keywords":["ai","𝑆𝑘","эволюционные","0.5𝑇𝑘","100.","100где","notebook","𝑃","𝑇1","𝑇𝑘+1","генетические","допустим"],"topics":[]},{"id":"chunk-29","heading":"","text":"Итак, начальные условия задачи представляют собой следующий граф с расстояниями между ребрами: image.png","keywords":["ai","эволюционные","image.png","notebook","генетические","граф","задачи","имитация","итак","между","методы","начальные"],"topics":[]},{"id":"chunk-30","heading":"","text":"distances = [(1, 2, 20), (1, 3, 40), (1, 4, 42), (1, 5, 33), (1, 6, 21), (2, 3, 26), (2, 4, 38), (2, 5, 42), (2, 6, 17), (3, 4, 22), (3, 5, 43), (3, 6, 21), (4, 5, 27), (4, 6, 22), (5, 6, 26)] длины рёбер","keywords":["ai","21","22","26","42","эволюционные","17","20","27","33","38","40"],"topics":[]},{"id":"chunk-31","heading":"","text":"V = [1, 4, 5, 2, 6, 3, 1] последовательность прохождения маршрута Z = [(3, 4), (4, 6), (5, 2), (6, 2)] последовательность замен вершин P = [49, 54, 43, 54] случайные числа, выпавшие в процессе счёта","keywords":["ai","54","последовательность","эволюционные","43","49","notebook","вершин","выпавшие","генетические","замен","имитация"],"topics":[]},{"id":"chunk-32","heading":"","text":"функция изменения температуры def reductTemp(prevT): nextT = .5 prevT return nextT","keywords":["ai","nextt","prevt","эволюционные",".5","def","notebook","reducttemp","return","генетические","изменения","имитация"],"topics":[]},{"id":"chunk-33","heading":"","text":"graph = nx.Graph() создание пустого графа graph.add weighted edges from(distances) добавление весов рёбер","keywords":["ai","эволюционные","distances","edges","graph","graph.add","notebook","nx.graph","weighted","весов","генетические","графа"],"topics":[]},{"id":"chunk-34","heading":"","text":"отрисовка графа с заданными вершинами nx.draw kamada kawai(graph, node color=' fb7258', node size=2000, with labels=True) ````","keywords":["ai","node","эволюционные","2000","color","fb7258","graph","kamada","kawai","labels","notebook","nx.draw"],"topics":["ai"]},{"id":"chunk-35","heading":"","text":"````python вычисление длины ребра def edgeLength(i, j, distances, roundTrip=True): if roundTrip: return max([(item[2] if (item[0] == i and item[1] == j) or (item[1] == i and item[0] == j) else 1) for item in distances]) else: return max([(item[2] if (item[0] == i and item[1] == j) else 1) for item in distances])","keywords":["item","ai","distances","else","if","max","return","roundtrip","эволюционные","def","edgelength","notebook"],"topics":["python"]},{"id":"chunk-36","heading":"","text":"одна перестановка в пути def routeOneReplacement(arrV, Z, replacementByName=True): decrement = 1 if replacementByName else 0","keywords":["ai","replacementbyname","эволюционные","arrv","decrement","def","else","if","notebook","routeonereplacement","true","генетические"],"topics":[]},{"id":"chunk-37","heading":"","text":"arrV[Z[0] decrement], arrV[Z[1] decrement] = arrV[Z[1] decrement], arrV[Z[0] decrement]","keywords":["arrv","decrement","ai","эволюционные","notebook","генетические","имитация","методы","оптимизация","отжига.","стратегии"],"topics":[]},{"id":"chunk-38","heading":"","text":"перестановка в пути def routeReplacement(V, Z): for z in Z: V = routeOnereplacement(V, z) return V","keywords":["ai","эволюционные","def","notebook","return","routeonereplacement","routereplacement","генетические","имитация","методы","оптимизация","отжига."],"topics":[]},{"id":"chunk-39","heading":"","text":"выбор нужного пути методом отжига def chooseRoute(distances, V, Z, T, P): sumLength = routeLength(V, distances) нахождение длины пути arrSum = [sumLength] массив сумм длин","keywords":["ai","distances","sumlength","пути","эволюционные","arrsum","chooseroute","def","notebook","routelength","выбор","генетические"],"topics":["ai"]},{"id":"chunk-40","heading":"","text":"циклы методом отжига for i in range(len(Z)): newV = routeOneReplacement(V[:], Z[i]) новый маршрут после перестановки newS = routeLength(newV, distances) длина нового маршрута arrSum.append(newS) deltaS = newS sumLength разница между длиной нового и старого маршрутов","keywords":["ai","news","newv","нового","эволюционные","arrsum.append","deltas","distances","len","notebook","range","routelength"],"topics":["ai"]},{"id":"chunk-41","heading":"","text":"в случае, если разница между длинами больше 0, то вычисляется вероятность if deltaS 0: p = probability(deltaS, T) подсчёт вероятности","keywords":["ai","deltas","эволюционные","if","notebook","probability","больше","вероятности","вероятность","вычисляется","генетические","длинами"],"topics":[]},{"id":"chunk-42","heading":"","text":"если заданная вероятность попадает в интервал от 0 до p, то новый маршрут выбирается if p P[i]: V = newV sumLength = newS else: V = newV sumLength = newS","keywords":["ai","news","newv","sumlength","эволюционные","else","if","notebook","вероятность","выбирается","генетические","до"],"topics":["ai"]},{"id":"chunk-43","heading":"","text":"отрисовка графа по заданному маршруту def drawRouteGraph(distances, bestRoute): newDistances = [] прохождение по вектору for i in range(len(bestRoute) 1): for distance in distances: if distance[0] == bestRoute[i] and distance[1] == bestRoute[i + 1] or distance[1] == bestRoute[i] and distance[0] == bestRoute[i + 1]: newDistances.append(distance)","keywords":["bestroute","distance","ai","distances","эволюционные","def","drawroutegraph","if","len","newdistances","newdistances.append","notebook"],"topics":[]},{"id":"chunk-44","heading":"","text":"graph.add weighted edges from(newDistances) добавление весов рёбер отрисовка графа с заданными вершинами nx.draw kamada kawai(graph, node color=' fb7258', node size=2000, with labels=True)","keywords":["ai","node","эволюционные","2000","color","edges","fb7258","graph","graph.add","kamada","kawai","labels"],"topics":["ai"]},{"id":"chunk-45","heading":"","text":"print(f'Лучший выбранный маршрут: {bestRoute}') print(f'Длина лучшего выбранного маршрута: {routeLength(bestRoute, distances)}') print(f'Длины всех рассмотренных маршрутов: {arrLength}')","keywords":["ai","print","bestroute","эволюционные","arrlength","distances","notebook","routelength","всех","выбранного","выбранный","генетические"],"topics":[]},{"id":"chunk-46","heading":"","text":"Найти длину гамильтонова цикла S4 в полном графе K6 после четырех циклов решения задачи методом отжига по вариантам ниже.","keywords":["ai","эволюционные","k6","notebook","s4","вариантам","гамильтонова","генетические","графе","длину","задачи","имитация"],"topics":[]},{"id":"chunk-47","heading":"","text":"````python distances = [(1, 2, 24), (1, 3, 41), (1, 4, 36), (1, 5, 22), (1, 6, 19), (2, 3, 21), (2, 4, 33), (2, 5, 33), (2, 6, 14), (3, 4, 27), (3, 5, 39), (3, 6, 23), (4, 5, 20), (4, 6, 20), (5, 6, 19)] длины рёбер","keywords":["ai","19","20","33","эволюционные","14","21","22","23","24","27","36"],"topics":["python"]},{"id":"chunk-48","heading":"","text":"V = [1, 3, 4, 5, 6, 2, 1] последовательность прохождения маршрута Z = [(3, 4), (4, 6), (5, 2), (6, 2)] последовательность замен вершин P = [33, 82, 51, 76] случайные числа, выпавшие в процессе счёта","keywords":["ai","последовательность","эволюционные","33","51","76","82","notebook","вершин","выпавшие","генетические","замен"],"topics":[]},{"id":"chunk-49","heading":"","text":"функция изменения температуры def reductTemp(prevT): nextT = .5 prevT return nextT","keywords":["ai","nextt","prevt","эволюционные",".5","def","notebook","reducttemp","return","генетические","изменения","имитация"],"topics":[]},{"id":"chunk-50","heading":"","text":"вычисление длины ребра def edgeLength(i, j, distances, roundTrip=True): if roundTrip: return max([(item[2] if (item[0] == i and item[1] == j) or (item[1] == i and item[0] == j) else 1) for item in distances]) else: return max([(item[2] if (item[0] == i and item[1] == j) else 1) for item in distances])","keywords":["item","ai","distances","else","if","max","return","roundtrip","эволюционные","def","edgelength","notebook"],"topics":[]},{"id":"chunk-51","heading":"","text":"одна перестановка в пути def routeOneReplacement(arrV, Z, replacementByName=True): decrement = 1 if replacementByName else 0","keywords":["ai","replacementbyname","эволюционные","arrv","decrement","def","else","if","notebook","routeonereplacement","true","генетические"],"topics":[]},{"id":"chunk-52","heading":"","text":"arrV[Z[0] decrement], arrV[Z[1] decrement] = arrV[Z[1] decrement], arrV[Z[0] decrement]","keywords":["arrv","decrement","ai","эволюционные","notebook","генетические","имитация","методы","оптимизация","отжига.","стратегии"],"topics":[]},{"id":"chunk-53","heading":"","text":"перестановка в пути def routeReplacement(V, Z): for z in Z: V = routeOnereplacement(V, z) return V","keywords":["ai","эволюционные","def","notebook","return","routeonereplacement","routereplacement","генетические","имитация","методы","оптимизация","отжига."],"topics":[]},{"id":"chunk-54","heading":"","text":"выбор нужного пути методом отжига def chooseRoute(distances, V, Z, T, P): sumLength = routeLength(V, distances) нахождение длины пути arrSum = [sumLength] массив сумм длин","keywords":["ai","distances","sumlength","пути","эволюционные","arrsum","chooseroute","def","notebook","routelength","выбор","генетические"],"topics":["ai"]},{"id":"chunk-55","heading":"","text":"циклы методом отжига for i in range(len(Z)): newV = routeOneReplacement(V[:], Z[i]) новый маршрут после перестановки newS = routeLength(newV, distances) длина нового маршрута arrSum.append(newS) deltaS = newS sumLength разница между длиной нового и старого маршрутов","keywords":["ai","news","newv","нового","эволюционные","arrsum.append","deltas","distances","len","notebook","range","routelength"],"topics":["ai"]},{"id":"chunk-56","heading":"","text":"в случае, если разница между длинами больше 0, то вычисляется вероятность if deltaS 0: p = probability(deltaS, T) подсчёт вероятности","keywords":["ai","deltas","эволюционные","if","notebook","probability","больше","вероятности","вероятность","вычисляется","генетические","длинами"],"topics":[]},{"id":"chunk-57","heading":"","text":"если заданная вероятность попадает в интервал от 0 до p, то новый маршрут выбирается if p P[i]: V = newV sumLength = newS else: V = newV sumLength = newS","keywords":["ai","news","newv","sumlength","эволюционные","else","if","notebook","вероятность","выбирается","генетические","до"],"topics":["ai"]},{"id":"chunk-58","heading":"","text":"отрисовка графа по заданному маршруту def drawRouteGraph(distances, bestRoute): newDistances = [] прохождение по вектору for i in range(len(bestRoute) 1): for distance in distances: if distance[0] == bestRoute[i] and distance[1] == bestRoute[i + 1] or distance[1] == bestRoute[i] and distance[0] == bestRoute[i + 1]: newDistances.append(distance)","keywords":["bestroute","distance","ai","distances","эволюционные","def","drawroutegraph","if","len","newdistances","newdistances.append","notebook"],"topics":[]},{"id":"chunk-59","heading":"","text":"graph.add weighted edges from(newDistances) добавление весов рёбер отрисовка графа с заданными вершинами nx.draw kamada kawai(graph, node color=' fb7258', node size=2000, with labels=True)","keywords":["ai","node","эволюционные","2000","color","edges","fb7258","graph","graph.add","kamada","kawai","labels"],"topics":["ai"]},{"id":"chunk-60","heading":"","text":"print(f'Лучший выбранный маршрут: {bestRoute}') print(f'Длина лучшего выбранного маршрута: {routeLength(bestRoute, distances)}') print(f'Длины всех рассмотренных маршрутов: {arrLength}')","keywords":["ai","print","bestroute","эволюционные","arrlength","distances","notebook","routelength","всех","выбранного","выбранный","генетические"],"topics":[]},{"id":"chunk-61","heading":"","text":"code Лучший выбранный маршрут: [1, 6, 5, 4, 3, 2, 1] Длина лучшего выбранного маршрута: 130 Длины всех рассмотренных маршрутов: [145, 158, 183, 130, 146]","keywords":["ai","130","эволюционные","145","146","158","183","code","notebook","всех","выбранного","выбранный"],"topics":[]}]},{"id":"ai/notebook-07-neural-networks","href":"/docs/ai/notebook-07-neural-networks","slug":["ai","notebook-07-neural-networks"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 7 — Нейронные сети","description":"Персептрон, MLP и базовый цикл обучения нейросети.","preview":"1.1. Теоретический материал – Нейронные сети Обучение персептрона Персептрон представляет собой элементарную часть нейронной сети. Одиночный персептрон является линейным бинарным классификатором. В этой лекции мы рассмотрим процедуру обучения персептрона для к","keywords":["print","test","out","train","mlp","weights","bias","def","python","self","neuron","np.array","мы","10","h1","h2","init","𝑦"],"topics":["python","ai","pandas","numpy","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"","text":"Персептрон представляет собой элементарную часть нейронной сети. Одиночный персептрон является линейным бинарным классификатором. В этой лекции мы рассмотрим процедуру обучения персептрона для классификации данных. Поскольку персептрон представляет собой бинарный классификатор, то мы будем рассматривать лишь два класса","keywords":["персептрон","ai","мы","обучения","представляет","собой","mlp","notebook","базовый","бинарный","бинарным","будем"],"topics":["oop"]},{"id":"chunk-1","heading":"","text":". Пусть мы рассматриваем некоторое множество (конечное или бесконечное) n мерных векторов, которые будем обозначать 𝑥 = (𝑥1, 𝑥2, . . . , 𝑥𝑛)","keywords":["ai","mlp","notebook","𝑥","𝑥1","𝑥2","𝑥𝑛","базовый","бесконечное","будем","векторов","конечное"],"topics":[]},{"id":"chunk-2","heading":"","text":"Будем считать, что это множество разбивается на два класса, которые мы будем обозначать +1 и 1. Поэтому возникает задача построения функции, которая задана на нашем множестве векторов, и принимает значения в множестве \\{+1, −1\\}. В качестве такой функции может выступать персептрон. С алгебраической точки зрения персептрон состоит из вектора весов 𝑤 = (𝑤0, 𝑤1, 𝑤2, . . . , 𝑤𝑛).","keywords":["ai","+1","будем","множестве","персептрон","функции","1.","mlp","notebook","𝑤","𝑤0","𝑤1"],"topics":["oop"]},{"id":"chunk-3","heading":"","text":"Приведем алгоритм обучения персептрона. Пусть у нас есть набор обучающих данных \\{(𝑥, 𝑑)\\}, где 𝑥 это различные вектора, а 𝑑 из множества \\{+1, −1\\} указывает к какому классу относится наш вектор. 1. Положим вектор весов 𝑤 равным нулю. 2. Повторять 𝑁 раз следующие шаги: 3. Для каждого тестового набора (𝑥, 𝑑): 4. Вычислить 𝑦 = 𝑠𝑖𝑔𝑛[(𝑥, 𝑤)]. 5","keywords":["𝑥","ai","𝑑","𝑤","обучения","+1","1.","2.","3.","4.","mlp","𝑁"],"topics":["oop","algorithms"]},{"id":"chunk-4","heading":"","text":". Если 𝑦𝑑 < 0, то скорректировать веса 𝑤0 = 𝑤0 + 𝑎𝑑, 𝑤𝑖 = 𝑤𝑖 + 𝑎𝑑𝑥𝑖 , 𝑖 = 1,2, . . . , 𝑛.","keywords":["ai","𝑤0","𝑤𝑖","𝑎𝑑","𝑎𝑑𝑥𝑖","𝑖","mlp","𝑛.","notebook","𝑦𝑑","базовый","веса"],"topics":[]},{"id":"chunk-5","heading":"","text":"Рассмотрим программу обучения персептрона на языке Python. Сначала рассмотрим основной класс персептрона, который умеет учиться по тестовым данным.","keywords":["ai","обучения","персептрона","рассмотрим","mlp","notebook","python.","базовый","данным.","класс","который","нейронные"],"topics":["python","oop"]},{"id":"chunk-6","heading":"","text":"python класс который реализует персептрон и его обучение class Perceptron: def init (self, N): создать нулевые веса self.w = list() for i in range(N): self.w.append(0) метод для вычисления значения персептрона def calc(self, x): res = 0 for i in range(len(self.w)): res = res + self.w[i] x[i] return res пороговая функция активации персептрона def sign(self, x): if self.calc(x) 0: return 1 else: return 1 обучение на","keywords":["res","ai","def","return","self","self.w","range","обучение","персептрон","персептрона","calc","class"],"topics":["python","oop"]},{"id":"chunk-7","heading":"","text":"одном примере def learn(self, la, x, y): обучаем только, когда результат неверный if y self.calc(x) <= 0: for i in range(len(self.w)): self.w[i] = self.w[i] + la y x[i] обучение по всем данным Т кортеж примеров def learning(self, la, T): цикл обучения for n in range(100): обучение по всему набору примеров for t in T: self.learn(la, t[0], t[1])","keywords":["la","ai","self.w","def","range","self","обучение","обучения","примеров","цикл","100","if"],"topics":[]},{"id":"chunk-8","heading":"","text":"В строке 25 мы осуществляем корректировку весов. Посмотрим, как учится и работает наш персептрон.","keywords":["ai","25","mlp","notebook","базовый","весов.","корректировку","мы","наш","нейронные","нейросети.","обучения"],"topics":[]},{"id":"chunk-9","heading":"","text":"python создаем класс двумерного персептрона perceptron = Perceptron(2) la = 0.1 константа обучения создаем примеры T = list() T.append([[2,1], 1]) T.append([[3,2], 1]) T.append([[4,1], 1]) T.append([[1,2], 1]) T.append([[2,3], 1]) T.append([[5,7], 1]) perceptron.learning(la, T) обучение персептрона print(perceptron.w) печатаем веса проверим работу на тестовых примерах print(perceptron.sign([1.5, 2]))","keywords":["t.append","ai","la","perceptron","print","обучения","персептрона","создаем","0.1","1.5","list","mlp"],"topics":["python","oop"]},{"id":"chunk-10","heading":"","text":"print(perceptron.sign([3, 1.5])) print(perceptron.sign([5, 1])) print(perceptron.sign([5, 10]))","keywords":["ai","perceptron.sign","print","1.5","10","mlp","notebook","базовый","нейронные","нейросети.","обучения","персептрон"],"topics":[]},{"id":"chunk-11","heading":"","text":"Видим, что что наш персептрон отлично научился распознавать образы, относя к классу 1 те вектора, у которых первая компонента больше второй, и к классу 1 в противном случае. Хотя устройство персептронов довольно простое эти конструкции могут решать и практические задачи. Кроме того, из таких персептронов состоят нейронные сети.","keywords":["ai","классу","нейронные","персептрон","персептронов","mlp","notebook","базовый","больше","вектора","видим","второй"],"topics":["oop"]},{"id":"chunk-12","heading":"","text":"Нейронная сеть — это функциональная единица машинного или глубокого обучения. Она имитирует поведение человеческого мозга, поскольку основана на концепции биологических нейронных сетей.","keywords":["ai","mlp","notebook","базовый","биологических","глубокого","единица","имитирует","концепции","машинного","мозга","нейронная"],"topics":["ai"]},{"id":"chunk-13","heading":"","text":"Наиболее распространенный тип нейронной сети, называемый многослойным персептроном (MLP), представляет собой функцию, которая отображает входные данные в выходные данные. MLP имеет один входной слой и один выходной слой. Между ними может быть один или несколько скрытых слоев. Входной слой имеет тот же набор нейронов, что и признаки. Скрытые слои также могут иметь более одного нейрона","keywords":["ai","mlp","один","входной","имеет","сети","слой","notebook","базовый","более","быть","входные"],"topics":["ai"]},{"id":"chunk-14","heading":"","text":". Каждый нейрон представляет собой линейную функцию, к которой применяется функция активации для решения сложных задач. Выход каждого слоя подается в качестве входных данных для всех нейронов следующих слоев.","keywords":["ai","mlp","notebook","активации","базовый","всех","входных","выход","данных","задач.","каждого","каждый"],"topics":[]},{"id":"chunk-15","heading":"","text":"Нейронные сети способны решать множество задач. В основном они состоят из таких компонентов:","keywords":["ai","нейронные","сети","mlp","notebook","базовый","задач.","компонентов","множество","нейросети.","обучения","они"],"topics":[]},{"id":"chunk-16","heading":"","text":"Чтобы реализовать нейросеть, необходимо понимать, как ведут себя нейроны. Нейрон одновременно принимает несколько входов, обрабатывает эти данные и выдает один выход. Нейронная сеть представляет собой блоки ввода и вывода, где каждое соединение имеет соответствующие веса (это сила связи нейронов; чем вес больше, тем один нейрон сильнее влияет на другой). Данные всех входов умножаются на веса:","keywords":["ai","веса","входов","данные","нейрон","один","mlp","notebook","базовый","блоки","больше","ввода"],"topics":["ai"]},{"id":"chunk-17","heading":"","text":"Полученное значение пропускается через функцию активации (сигмоиду), которая преобразует входы в один выход:","keywords":["ai","mlp","notebook","активации","базовый","входы","выход","значение","которая","нейронные","нейросети.","обучения"],"topics":[]},{"id":"chunk-18","heading":"","text":"Интервал результатов сигмоиды — от 0 до 1. Отрицательные числа стремятся к нулю, а положительные — к единице.","keywords":["ai","1.","mlp","notebook","базовый","до","единице.","интервал","нейронные","нейросети.","нулю","обучения"],"topics":[]},{"id":"chunk-19","heading":"","text":"````python def sigmoid(x): функция активации: f(x) = 1/(1+e^( x)) return 1 / (1 + np.exp( x)) class Neuron: def init (self, weights, bias): self.weights = weights self.bias = bias def feedforward(self, inputs): total = np.dot(self.weights, inputs) + self.bias return sigmoid(total)","keywords":["ai","def","bias","inputs","return","self","self.bias","self.weights","sigmoid","total","weights","1+e"],"topics":["python","numpy"]},{"id":"chunk-20","heading":"","text":"weights = np.array([0, 1]) w1=0, w2=1 bias = 4 c=4 n = Neuron(weights, bias) x = np.array([2, 3]) x=2, y =3 print(n.feedforward(x)) 0.9990889488055994 ````","keywords":["ai","bias","np.array","weights","0.9990889488055994","mlp","n.feedforward","neuron","notebook","print","w1","w2"],"topics":["numpy"]},{"id":"chunk-21","heading":"","text":"Нейросеть состоит из множества соединенных между собой нейронов. Пример несложной нейронной сети где:","keywords":["ai","сети","mlp","notebook","базовый","между","множества","нейронной","нейронные","нейронов.","нейросети.","нейросеть"],"topics":["ai"]},{"id":"chunk-22","heading":"","text":"Представим, что нейроны из графика выше имеют веса [0, 1]. Пороговое значение (𝑏) у обоих нейронов равно 0 и они имеют идентичную сигмоиду.","keywords":["ai","имеют","𝑏","mlp","notebook","базовый","веса","выше","графика","значение","идентичную","нейронные"],"topics":[]},{"id":"chunk-23","heading":"","text":"Входные данные по нейронам передаются до тех пор, пока не получатся выходные значения.","keywords":["ai","mlp","notebook","базовый","входные","выходные","данные","до","значения.","нейронам","нейронные","нейросети."],"topics":[]},{"id":"chunk-24","heading":"","text":"````python class OurNeuralNetwork: Данные нейросети: два входа два нейрона в скрыттых слоях(h1,h2) выход(о1) Нейроны имеют идентичные веса и пороги: w=[0,1] b=0 def init (self): weights = np.array ([0, 1]) bias = 0 Класс Neuron из предыдущего раздела self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias) def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 =","keywords":["bias","neuron","weights","ai","def","h1","h2","out","self","два","class","feedforward"],"topics":["python","numpy","oop"]},{"id":"chunk-25","heading":"","text":"self.h2.feedforward(x) входы для о1 это выходы для h1 и h2 out o1 = self.o1.feedforward(np.array([out h1, out h2])) return out o1","keywords":["out","ai","h1","h2","o1","mlp","notebook","np.array","return","self.h2.feedforward","self.o1.feedforward","базовый"],"topics":["numpy"]},{"id":"chunk-26","heading":"","text":"network = OurNeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) 0.7216325609518421 ````","keywords":["ai","0.7216325609518421","mlp","network","network.feedforward","notebook","np.array","ourneuralnetwork","print","базовый","нейронные","нейросети."],"topics":["numpy"]},{"id":"chunk-27","heading":"","text":"Обучение нейросети — это подбор весов, которые соответствуют всем входам для решения поставленных задач. Класс нейронной сети:","keywords":["ai","сети","mlp","notebook","базовый","весов","всем","входам","задач.","класс","которые","нейронной"],"topics":["oop"]},{"id":"chunk-28","heading":"","text":"class NeuralNetwork: def init (self, x, y): self.input = x self.weights1 = np.random.rand(self.input.shape[1], 4) self.weights2 = np.random.rand(4,1) self.y = y self.output = np.zeros(y.shape) Каждый этап процесса обучения состоит из: прямого распространения (прогнозируемый выход); обратного распространения (обновление весов и смещений).","keywords":["ai","np.random.rand","обучения","распространения","class","def","init","mlp","neuralnetwork","notebook","np.zeros","self"],"topics":["numpy"]},{"id":"chunk-29","heading":"","text":"В данном случае на выход ŷ влияют только две переменные — 𝑤 (веса) и 𝑏 (смещение). Настройку весов и смещений из данных входа или процесс обучения нейросети можно изобразить так:","keywords":["ai","обучения","𝑏","mlp","notebook","𝑤","базовый","веса","весов","влияют","входа","выход"],"topics":[]},{"id":"chunk-30","heading":"","text":"Как видно, формула прямого распространения представляет собой несложное вычисление:","keywords":["ai","mlp","notebook","базовый","видно","вычисление","нейронные","нейросети.","несложное","обучения","персептрон","представляет"],"topics":[]},{"id":"chunk-31","heading":"","text":"``` class NeuralNetwork: def init (self, x, y): self.input = x self.weights1 = np.random.rand(self.input.shape[1], 4) self.weights2 = np.random.rand(4,1) self.y = y self.output = np.zeros(self.y.shape)","keywords":["ai","np.random.rand","class","def","init","mlp","neuralnetwork","notebook","np.zeros","self","self.input","self.input.shape"],"topics":["numpy"]},{"id":"chunk-32","heading":"","text":"def feedforward(self): self.layer1 = sigmoid(np.dot(self.input, self.weights1)) self.output = sigmoid(np.dot(self.layer1, self.weights2)) ``` Чтобы вычислить ошибку прогноза, необходимо использовать функцию потери. В примере уместно воспользоваться формулой суммы квадратов ошибок — средним значением между прогнозируемым и фактическим результатами:","keywords":["ai","np.dot","self.layer1","sigmoid","def","feedforward","mlp","notebook","self","self.input","self.output","self.weights1"],"topics":["numpy"]},{"id":"chunk-33","heading":"","text":"Обратное распространение позволяет измерить производные в обратном порядке — от конца к началу, и скорректировать веса и смещения. Для этого необходимо узнать производную функции потери — тангенс угла наклона.","keywords":["ai","mlp","notebook","базовый","веса","измерить","конца","наклона.","началу","нейронные","нейросети.","необходимо"],"topics":[]},{"id":"chunk-34","heading":"","text":"Производная функции по отношению к весам и смещениям позволяет узнать градиентный спуск. Производная функции потери не содержит весов и смещений, для ее вычисления необходимо добавить правило цепи:","keywords":["ai","производная","функции","mlp","notebook","базовый","весам","весов","вычисления","градиентный","добавить","ее"],"topics":[]},{"id":"chunk-35","heading":"","text":"𝐿𝑜𝑠𝑠 (𝑦, 𝑦̂) = ∑(𝑦 − 𝑦̂)2𝑛𝑖=1𝜕𝐿𝑜𝑠𝑠 (𝑦, 𝑦̂)𝜕𝑊=𝜕𝐿𝑜𝑠𝑠 (𝑦, 𝑦̂)𝜕𝑦̂∙𝜕𝑦̂𝜕𝑧∙𝜕𝑧𝜕𝑊 == 2(𝑦 − 𝑦̂)∙ производную сигмоиды ∙ 𝑥 == 2(𝑦 − 𝑦̂) ∙ 𝑧(1 − 𝑧) ∙ 𝑥,","keywords":["𝑦","𝑧","ai","𝐿𝑜𝑠𝑠","𝑊","𝑥","2𝑛𝑖","mlp","notebook","базовый","нейронные","нейросети."],"topics":[]},{"id":"chunk-36","heading":"","text":"Благодаря этому правилу можно регулировать веса. Добавляем в код Python функцию обратного распространения:","keywords":["ai","mlp","notebook","python","базовый","благодаря","веса.","добавляем","код","можно","нейронные","нейросети."],"topics":["python"]},{"id":"chunk-37","heading":"","text":"``` class NeuralNetwork: def init (self, x, y): self.input = x self.weights1 = np.random.rand(self.input.shape[1], 4) self.weights2 = np.random.rand(4,1) self.y = y self.output = np.zeros(self.y.shape)","keywords":["ai","np.random.rand","class","def","init","mlp","neuralnetwork","notebook","np.zeros","self","self.input","self.input.shape"],"topics":["numpy"]},{"id":"chunk-38","heading":"","text":"def feedforward(self): self.layer1 = sigmoid(np.dot(self.input, self.weights1)) self.output = sigmoid(np.dot(self.layer1, self.weights2))","keywords":["ai","np.dot","self.layer1","sigmoid","def","feedforward","mlp","notebook","self","self.input","self.output","self.weights1"],"topics":["numpy"]},{"id":"chunk-39","heading":"","text":"def backprop(self): d weights2 = np.dot(self.layer1.T, (2 (self.y self.output) sigmoid derevative(self.output))) d weights1 = np.dot(self.input.T, (np.dot(2 (self.y self.output) sigmoid derivative(self.output), self.weights2.T) sigmoid derivative(self.layer1)))","keywords":["self.output","ai","np.dot","sigmoid","derivative","self.y","backprop","def","derevative","mlp","notebook","self"],"topics":["numpy"]},{"id":"chunk-40","heading":"","text":"``` Нейронные сети базируются на определенных алгоритмах и математических функциях. Сначала может казаться, что разобраться в них довольно сложно. Но существуют готовые библиотеки машинного обучения для построения и тренировки нейросетей, позволяющие не углубляться в их устройство.","keywords":["ai","нейронные","обучения","сети","mlp","notebook","алгоритмах","базируются","базовый","библиотеки","готовые","довольно"],"topics":["algorithms"]},{"id":"chunk-41","heading":"","text":"Реализовать классы нейросетей по аналогии с классом OurNeuralNetwork. Данные нейросети:","keywords":["ai","mlp","notebook","ourneuralnetwork.","аналогии","базовый","данные","классом","классы","нейронные","нейросетей","нейросети"],"topics":["oop"]},{"id":"chunk-42","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([0.5, 0.5, 0.5]) bias = 0 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.h3 = Neuron(weights, bias) self.o1 = Neuron(weights, bias) def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out h3 = self.h3.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2, out h3])) return out","keywords":["out","bias","weights","neuron","0.5","ai","def","h1","h2","h3","np.array","self"],"topics":["python","numpy"]},{"id":"chunk-43","heading":"","text":"o1","keywords":["ai","mlp","notebook","o1","базовый","нейронные","нейросети.","обучения","персептрон","сети","цикл"],"topics":[]},{"id":"chunk-44","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3, 4]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-45","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([1, 0]) bias = 1 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias) self.o2 = Neuron(weights, bias) def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2])) out o2 = self.o2.feedforward(np.array([out h2, out h1]))","keywords":["out","bias","weights","neuron","ai","h1","h2","np.array","def","self","class","feedforward"],"topics":["python","numpy"]},{"id":"chunk-46","heading":"","text":"return np.array([out o1, out o2])","keywords":["ai","out","mlp","notebook","np.array","o1","o2","return","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-47","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-48","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([1, 0]) bias = 1 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias)","keywords":["bias","weights","ai","neuron","class","def","init","mlp","neuralnetwork","notebook","np.array","python"],"topics":["python","numpy"]},{"id":"chunk-49","heading":"","text":"def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2])) return sigmoid(out o1)","keywords":["out","ai","h1","h2","o1","def","feedforward","mlp","notebook","np.array","return","self"],"topics":["numpy"]},{"id":"chunk-50","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-51","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([1, 0]) bias = 1 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias)","keywords":["bias","weights","ai","neuron","class","def","init","mlp","neuralnetwork","notebook","np.array","python"],"topics":["python","numpy"]},{"id":"chunk-52","heading":"","text":"def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2])) return np.tanh(out o1)","keywords":["out","ai","h1","h2","o1","def","feedforward","mlp","notebook","np.array","np.tanh","return"],"topics":["numpy"]},{"id":"chunk-53","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-54","heading":"","text":"````python class NeuralNetwork: def init (self): weights = np.array ([1, 0]) bias = 1 self.h1 = Neuron(weights, bias) self.h2 = Neuron(weights, bias) self.o1 = Neuron(weights, bias)","keywords":["bias","weights","ai","neuron","class","def","init","mlp","neuralnetwork","notebook","np.array","python"],"topics":["python","numpy"]},{"id":"chunk-55","heading":"","text":"def feedforward(self, x): out h1 = self.h1.feedforward(x) out h2 = self.h2.feedforward(x) out o1 = self.o1.feedforward(np.array([out h1, out h2])) return max(0, out o1)","keywords":["out","ai","h1","h2","o1","def","feedforward","max","mlp","notebook","np.array","return"],"topics":["numpy"]},{"id":"chunk-56","heading":"","text":"network = NeuralNetwork() x = np.array([2, 3]) print(network.feedforward(x)) ````","keywords":["ai","mlp","network","network.feedforward","neuralnetwork","notebook","np.array","print","базовый","нейронные","нейросети.","обучения"],"topics":["numpy"]},{"id":"chunk-57","heading":"","text":"Теперь мы знаем, что такое нейронные сети и какие шаги необходимо выполнить, чтобы построить простую нейронную сеть с плотными связями. В этом разделе мы попытаемся построить простую нейронную сеть, которая предсказывает класс, к которому принадлежит данное растение ириса. Мы будем использовать библиотеку Python Scikit Learn для создания нашей нейронной сети.","keywords":["ai","мы","нейронную","нейронные","построить","простую","сети","сеть","learn","mlp","notebook","python"],"topics":["python","oop"]},{"id":"chunk-58","heading":"","text":"Sklearn предоставляет 2 оценщика для задач классификации и регрессии соответственно:","keywords":["ai","mlp","notebook","sklearn","базовый","задач","классификации","нейронные","нейросети.","обучения","оценщика","персептрон"],"topics":["oop"]},{"id":"chunk-59","heading":"","text":"Набор данных цифр: мы будем использовать набор данных цифр, который имеет изображения размером 8x8 для цифр 0 9. Ниже мы будем использовать цифровые данные для задач классификации.","keywords":["ai","цифр","будем","данных","использовать","мы","набор","8x8","9.","mlp","notebook","базовый"],"topics":["oop"]},{"id":"chunk-60","heading":"","text":"Набор данных о жилье в Бостоне: мы будем использовать набор данных о жилье в Бостоне, который содержит информацию о различных свойствах дома, таких как среднее количество комнат, уровень преступности на душу населения в городе и т. д. Мы будем использовать его для задач регрессии.","keywords":["ai","бостоне","будем","данных","жилье","использовать","мы","набор","mlp","notebook","базовый","городе"],"topics":[]},{"id":"chunk-61","heading":"","text":"Sklearn предоставляет оба этих набора данных. Мы можем загрузить их, вызвав методы load digits() и load boston().","keywords":["ai","load","boston","digits","mlp","notebook","sklearn","базовый","вызвав","данных.","загрузить","их"],"topics":[]},{"id":"chunk-62","heading":"","text":"digits = load digits() X digits, Y digits = digits.data, digits.target print('Dataset Sizes : ' , X digits.shape, Y digits.shape) ````","keywords":["digits","ai","digits.shape","dataset","digits.data","digits.target","load","mlp","notebook","print","sizes","базовый"],"topics":[]},{"id":"chunk-63","heading":"","text":"python boston = load boston() X boston, Y boston = digits.data, digits.target print('Dataset Sizes : ', X boston.shape, Y boston.shape)","keywords":["boston","ai","boston.shape","dataset","digits.data","digits.target","load","mlp","notebook","print","python","sizes"],"topics":["python"]},{"id":"chunk-64","heading":"","text":"MLPClassifier — это класс, доступный как часть модуля neuro network sklearn для выполнения задач классификации с использованием многослойного персептрона.","keywords":["ai","mlp","mlpclassifier","network","neuro","notebook","sklearn","базовый","выполнения","доступный","задач","использованием"],"topics":["ai","oop"]},{"id":"chunk-65","heading":"","text":"Функция train test split модуля model selection sklearn поможет нам разделить данные на два набора: 80% для обучения и 20% для тестирования.","keywords":["ai","обучения","20","80","mlp","model","notebook","selection","sklearn","split","test","train"],"topics":["ai","testing"]},{"id":"chunk-66","heading":"","text":"Мы также используем seed(random state=123) с train test split, чтобы мы всегда получали одно и то же разделение и могли сравнивать и воспроизволить результаты в будущем.","keywords":["ai","мы","123","mlp","notebook","random","seed","split","state","test","train","базовый"],"topics":["ai","testing"]},{"id":"chunk-67","heading":"","text":"X train, X test, Y train, Y test = train test split(X digits, Y digits, train size= 0.80, test size= 0.20, stratify=Y digits, random state=123) print('Train/Test Sizes : ', X train.shape, X test.shape, Y train.shape, Y test.shape) ````","keywords":["test","train","ai","digits","size","test.shape","train.shape","0.20","0.80","123","mlp","notebook"],"topics":["ai","testing"]},{"id":"chunk-68","heading":"","text":"Для начала натренируем модель MLPClassifier с параметрами по умолчанию для тренировочных данных.","keywords":["ai","mlp","mlpclassifier","notebook","базовый","данных.","модель","натренируем","начала","нейронные","нейросети.","обучения"],"topics":["ai"]},{"id":"chunk-69","heading":"","text":"mlp classifier = MLPClassifier(random state=123) mlp classifier.fit(X train, Y train) MLPClassifier(activation='relu', alpha=0.0001, batch size='auto', beta 1=0.9, beta 2=0.999, early stopping=False, epsilon=1e 08, hidden layer sizes= (100,), learning rate='constant', learning rate init=0.001, max iter=200, momentum=0.9, n iter no change=10, nesterovs momentum=True, power t=0.5, random state=123, shuffle=True,","keywords":["ai","mlp","0.9","123","beta","iter","learning","mlpclassifier","momentum","random","rate","state"],"topics":["ai"]},{"id":"chunk-70","heading":"","text":"solver='adam' , tol=0.0001, validation fraction=0.1, verbose=False, warm start=False) ````","keywords":["ai","false","0.0001","0.1","adam","fraction","mlp","notebook","solver","start","tol","validation"],"topics":[]},{"id":"chunk-71","heading":"","text":"print(Y preds[:15]) print(Y test[:15]) Метод Score для оценки точностей моделей классификации print('Test Accuracy : %.3f'%mlp classifier.score(X test, Y test))","keywords":["test","ai","print","15","mlp",".3f","accuracy","classifier.score","notebook","preds","score","базовый"],"topics":["ai","oop","testing"]},{"id":"chunk-72","heading":"","text":"Cоздадим метод plot confusion matrix(), который принимает исходные и предсказанные метки данных по модели. Затем он строит матрицу путаницы, используя matplotlib.","keywords":["ai","confusion","cоздадим","matplotlib.","matrix","mlp","notebook","plot","базовый","данных","затем","используя"],"topics":[]},{"id":"chunk-73","heading":"","text":"def plot confusion matrix(Y test, Y preds): conf mat = confusion matrix(Y test, Y preds) fig = plt.figure(figsize=(6,6)) plt.matshow(conf mat, cmap=plt.cm.Blues, fignum=1) plt.yticks(range(10), range(10)) plt.xticks(range(10), range(10)) plt.colorbar(); for i in range(10): for j in range(10): plt.text(i 0.2, j+0.1, str(conf mat[j, i]), color ='tab:red')","keywords":["10","range","ai","conf","mat","confusion","matrix","preds","test","0.2","cmap","color"],"topics":["testing"]},{"id":"chunk-74","heading":"","text":"Ниже приведен список важных атрибутов, доступных с MLPClassifier, которые могут предоставить значимую информацию после обучения модели.","keywords":["ai","обучения","mlp","mlpclassifier","notebook","атрибутов","базовый","важных","доступных","значимую","информацию","которые"],"topics":["ai"]},{"id":"chunk-75","heading":"","text":"− coefs — возвращает массив длины n layers 1, где каждый элемент представляет веса, связанные с уровнем i.","keywords":["ai","coefs","i.","layers","mlp","notebook","базовый","веса","возвращает","длины","каждый","массив"],"topics":[]},{"id":"chunk-76","heading":"","text":"− intercepts — возвращает массив длины n layers 1, где каждый элемент представляет собой перехват, связанный с персептронами слоя i.","keywords":["ai","i.","intercepts","layers","mlp","notebook","базовый","возвращает","длины","каждый","массив","нейронные"],"topics":[]},{"id":"chunk-77","heading":"","text":"python print('Loss : ', mlp classifier.loss ) print('Number of Coefs : ',len(mlp classifier.coefs )) print('Number of Intercepts : ',len(mlp classifier.intercepts )) print('Number of Intercepts for which Estimator Ran : ', mlp classifier.n iter ) print('Name of Output layer Activation Function : ', mlp classifier.out activation )","keywords":["mlp","print","ai","number","activation","intercepts","len","classifier.coefs","classifier.intercepts","classifier.loss","classifier.n","classifier.out"],"topics":["python","ai"]},{"id":"chunk-78","heading":"","text":"MLPRegressor — это класс, доступный как часть библиотеки neuro network sklearn для выполнения задач регрессии с использованием многослойного персептрона. Также разделим набор данных на две части:","keywords":["ai","mlp","mlpregressor","network","neuro","notebook","sklearn","базовый","библиотеки","выполнения","данных","две"],"topics":["ai","oop"]},{"id":"chunk-79","heading":"","text":"− тестовые данные (20%), по которым будет проверяться точность обученной модели.","keywords":["ai","20","mlp","notebook","базовый","будет","данные","которым","модели.","нейронные","нейросети.","обучения"],"topics":[]},{"id":"chunk-80","heading":"","text":"python X train, X test, Y train, Y test = train test split(X boston, Y boston, train size= 0.80, test size= 0.20, stratify=Y digits, random state=123) print('Train/Test Sizes : ', X train.shape, X test.shape, Y train.shape, Y test.shape)","keywords":["test","train","ai","boston","size","test.shape","train.shape","0.20","0.80","123","digits","mlp"],"topics":["python","ai","testing"]},{"id":"chunk-81","heading":"","text":"mlp regressor = MLPRegressor(random state=123) mlp regressor.fit(X train, Y train) MLPClassifier(activation='relu', alpha=0.0001, batch size='auto', beta 1=0.9, beta 2=0.999, early stopping=False, epsilon=1e 08, hidden layer sizes= (100,), learning rate='constant', learning rate init=0.001, max iter=200, momentum=0.9, n iter no change=10, nesterovs momentum=True, power t=0.5, random state=123, shuffle=True,","keywords":["ai","mlp","0.9","123","beta","iter","learning","momentum","random","rate","state","train"],"topics":["ai"]},{"id":"chunk-82","heading":"","text":"solver='adam' , tol=0.0001, validation fraction=0.1, verbose=False, warm start=False) ````","keywords":["ai","false","0.0001","0.1","adam","fraction","mlp","notebook","solver","start","tol","validation"],"topics":[]},{"id":"chunk-83","heading":"","text":"print(Y preds[:10]) print(Y test[:10]) Метод Score оценивает точность модели классификации print('Test R^2 Score : %.3f'%mlp regressor.score(X test, Y test)) print('Training R^2 Score : %.3f'%mlp regressor.score(X train, Y train)) ````","keywords":["print","test","ai","mlp","score",".3f","10","regressor.score","train","notebook","preds","training"],"topics":["ai","oop","testing"]},{"id":"chunk-84","heading":"","text":"python print('Number of Coefs : ', len(mlp regressor.coefs )) [weights.shape for weights in mlp regressor.coefs ]","keywords":["ai","mlp","regressor.coefs","coefs","len","notebook","number","print","python","weights","weights.shape","базовый"],"topics":["python","ai"]},{"id":"chunk-85","heading":"","text":"python print('Number of Iterations for which estimator Ran : ', mlp regressor.n iter )","keywords":["ai","mlp","estimator","iter","iterations","notebook","number","print","python","ran","regressor.n","which"],"topics":["python","ai"]},{"id":"chunk-86","heading":"","text":"python print('Name of Output Layer Activation Function : ', mlp regressor.out activation )","keywords":["ai","activation","mlp","function","layer","name","notebook","output","print","python","regressor.out","базовый"],"topics":["python","ai"]},{"id":"chunk-87","heading":"","text":"Используйте классы MLPClassified и MLPRegressor для классификации и регрессии произвольных данных из интернета. Проведите анализ атрибуты, полученных моделей.","keywords":["ai","mlp","mlpclassified","mlpregressor","notebook","анализ","атрибуты","базовый","данных","интернета.","используйте","классификации"],"topics":["ai","oop"]},{"id":"chunk-88","heading":"","text":"https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv","keywords":["ai","6f9306ad21398ea43cba4f7d537619d0e07d5ae3","8836201","gist.githubusercontent.com","https","iris.csv","mlp","netj","notebook","raw","базовый","нейронные"],"topics":[]},{"id":"chunk-89","heading":"","text":"https://raw.githubusercontent.com/AnnaShestova/salary years simple linearregression/master/Salary Data.csv","keywords":["ai","salary","annashestova","data.csv","https","linearregression","master","mlp","notebook","raw.githubusercontent.com","simple","years"],"topics":["python","regression"]},{"id":"chunk-90","heading":"","text":"````python from sklearn.datasets import load iris from sklearn.neural network import MLPClassifier from sklearn.model selection import train test split","keywords":["ai","import","iris","load","mlp","mlpclassifier","network","notebook","python","selection","sklearn.datasets","sklearn.model"],"topics":["python","ai","testing"]},{"id":"chunk-91","heading":"","text":"iris = load iris() X iris, Y iris = iris.data, iris.target print('Dataset Sizes: ', X iris, Y iris) ````","keywords":["iris","ai","dataset","iris.data","iris.target","load","mlp","notebook","print","sizes","базовый","нейронные"],"topics":[]},{"id":"chunk-92","heading":"","text":"python X train, X test, Y train, Y test = train test split(X iris, Y iris, train size= 0.80, test size= 0.20, stratify=Y iris, random state=123) print('Train/Test Sizes : ', X train.shape, X test.shape, Y train.shape, Y test.shape)","keywords":["test","train","ai","iris","size","test.shape","train.shape","0.20","0.80","123","mlp","notebook"],"topics":["python","ai","testing"]},{"id":"chunk-93","heading":"","text":"python mlp classifier = MLPClassifier(random state=123) mlp classifier.fit(X train, Y train) MLPClassifier(activation='relu', alpha=0.0001, batch size='auto', beta 1=0.9, beta 2=0.999, early stopping=False, epsilon=1e 08, hidden layer sizes= (100,), learning rate='constant', learning rate init=0.001, max iter=200, momentum=0.9, n iter no change=10, nesterovs momentum=True, power t=0.5, random state=123, shuffle=True,","keywords":["ai","mlp","0.9","123","beta","iter","learning","mlpclassifier","momentum","random","rate","state"],"topics":["python","ai"]},{"id":"chunk-94","heading":"","text":"solver='adam' , tol=0.0001, validation fraction=0.1, verbose=False, warm start=False)","keywords":["ai","false","0.0001","0.1","adam","fraction","mlp","notebook","solver","start","tol","validation"],"topics":[]},{"id":"chunk-95","heading":"","text":"print(Y preds[:15]) print(Y test[:15]) print('Test Accuracy : %.3f'%mlp classifier.score(X test, Y test)) print('Training Accuracy : %.3f'%mlp classifier.score(X train, Y train)) ````","keywords":["print","test","ai","mlp",".3f","15","accuracy","classifier.score","train","notebook","preds","training"],"topics":["ai","testing"]},{"id":"chunk-96","heading":"","text":"````python from sklearn.neural network import MLPRegressor from sklearn.model selection import train test split","keywords":["ai","import","mlp","mlpregressor","network","notebook","python","selection","sklearn.model","sklearn.neural","split","test"],"topics":["python","ai","testing"]},{"id":"chunk-97","heading":"","text":"dataframe = pd.read csv('https://raw.githubusercontent.com/AnnaShestova/salary years simple linear regression/master/Salary Data.csv')","keywords":["ai","salary","annashestova","csv","data.csv","dataframe","https","linear","master","mlp","notebook","pd.read"],"topics":["python","pandas","regression"]},{"id":"chunk-98","heading":"","text":"python X train, X test, Y train, Y test = train test split(X salary, Y salary, train size = 0.8, test size=0.2, random state = 123) print('Train/Test Sizes : ', X train.shape, X test.shape, Y train.shape, Y test.shape)","keywords":["test","train","ai","salary","size","test.shape","train.shape","0.2","0.8","123","mlp","notebook"],"topics":["python","ai","testing"]},{"id":"chunk-99","heading":"","text":"python mlp regressor = MLPRegressor(random state=123) mlp regressor.fit(X train, Y train) MLPClassifier(activation='relu', alpha=0.0001, batch size='auto', beta 1=0.9, beta 2=0.999, early stopping=False, epsilon=1e 08, hidden layer sizes= (100,), learning rate='constant', learning rate init=0.001, max iter=200, momentum=0.9, n iter no change=10, nesterovs momentum=True, power t=0.5, random state=123, shuffle=True,","keywords":["ai","mlp","0.9","123","beta","iter","learning","momentum","random","rate","state","train"],"topics":["python","ai"]},{"id":"chunk-100","heading":"","text":"solver='adam' , tol=0.0001, validation fraction=0.1, verbose=False, warm start=False)","keywords":["ai","false","0.0001","0.1","adam","fraction","mlp","notebook","solver","start","tol","validation"],"topics":[]},{"id":"chunk-101","heading":"","text":"python Y preds = mlp regressor.predict(X test) print(Y preds[:10]) print(Y test[:10]) print('Test R^2 Score : %.3f'%mlp regressor.score(X test, Y test)) print('Training R^2 Score : %.3f'%mlp regressor.score(X train, Y train))","keywords":["test","mlp","print","ai",".3f","10","preds","regressor.score","score","train","notebook","python"],"topics":["python","ai","testing"]},{"id":"chunk-102","heading":"","text":"python print('Number of Coefs : ', len(mlp regressor.coefs )) [weights.shape for weights in mlp regressor.coefs ]","keywords":["ai","mlp","regressor.coefs","coefs","len","notebook","number","print","python","weights","weights.shape","базовый"],"topics":["python","ai"]},{"id":"chunk-103","heading":"","text":"python print('Number of Iterations for which estimator Ran : ', mlp regressor.n iter )","keywords":["ai","mlp","estimator","iter","iterations","notebook","number","print","python","ran","regressor.n","which"],"topics":["python","ai"]},{"id":"chunk-104","heading":"","text":"python print('Name of Output Layer Activation Function : ', mlp regressor.out activation )","keywords":["ai","activation","mlp","function","layer","name","notebook","output","print","python","regressor.out","базовый"],"topics":["python","ai"]}]},{"id":"ai/notebook-08-clustering","href":"/docs/ai/notebook-08-clustering","slug":["ai","notebook-08-clustering"],"section":"ai","sectionTitle":"AI","title":"AI Notebook 8 — Кластеризация","description":"Неподконтрольное обучение и методы группировки данных.","preview":"Теоретический материал Кластеризация Кластеризация — разбиение множества объектов на подмножества, называемые кластерами. Кластеризация, будучи математическим алгоритм имеет широкое применение во многих сферах: начиная с таких естественно научных областей как","keywords":["данных","набор","кластеризацию","выполним","загрузим","centers","10","cluster","clusters","data","python","100","plt.scatter","кластеризация","кластеров","figsize","алгоритм","кластеризации"],"topics":["python","ai","pandas","numpy","oop","algorithms","clustering"],"chunks":[{"id":"chunk-0","heading":"","text":"Кластеризация — разбиение множества объектов на подмножества, называемые кластерами. Кластеризация, будучи математическим алгоритм имеет широкое применение во многих сферах: начиная с таких естественно научных областей как биология и физиология, и заканчивая маркетингом в социальных сетях и поисковой оптимизацией","keywords":["ai","кластеризация","notebook","алгоритм","биология","будучи","группировки","данных.","естественно","заканчивая","имеет","кластерами."],"topics":["algorithms","clustering"]},{"id":"chunk-1","heading":"","text":". Цель – разделить данные таким образом, чтобы точки, находящие в одном и том же кластере, были очень схожи друг с другом, а точки, находящиеся в разных кластерах, отличались друг от друга. Как и алгоритмы классификации, алгоритмы кластеризации присваивают (или прогнозируют) каждой точке данных номер кластера, которому она принадлежит.","keywords":["ai","алгоритмы","друг","точки","notebook","были","группировки","данные","данных","данных.","друга.","другом"],"topics":["oop","algorithms"]},{"id":"chunk-2","heading":"","text":"Задача кластеризации относится к широкому классу задач обучения без учителя. Кластеризацию применяют для анализа и поиска признаков по которым можно объединить объекты, сжатия данных и поиска новизны (что не входит ни в один кластер) В чем отличие классификации и кластеризации: при классификации у вас есть набор предопределенных классов, вы обучаете ИИ на наборе примеров и потом хотите знать, к какому классу","keywords":["ai","классификации","классу","кластеризации","поиска","notebook","анализа","без","вас","входит","вы","группировки"],"topics":["oop"]},{"id":"chunk-3","heading":"","text":"принадлежит новый объект. При кластеризации вы используете алгоритм, который пытается сгруппировать набор объектов и определить, существует ли какая либо взаимосвязь между объектами.","keywords":["ai","notebook","алгоритм","взаимосвязь","вы","группировки","данных.","используете","какая","кластеризации","кластеризация","который"],"topics":["algorithms"]},{"id":"chunk-4","heading":"","text":"Кластеризация k средних – один из самых простых и наиболее часто используемых алгоритмов кластеризации. Сначала выбирается число кластеров k. После выбора значения k алгоритм k средних отбирает точки, которые будут представлять центры кластеров (cluster centers). Затем для каждой точки данных вычисляется его евклидово расстояние до каждого центра кластера. Каждая точка назначается ближайшему центру кластера","keywords":["ai","кластеризация","кластеров","средних","точки","centers","cluster","k.","notebook","алгоритм","алгоритмов","ближайшему"],"topics":["algorithms","clustering"]},{"id":"chunk-5","heading":"","text":". Алгоритм вычисляет центроиды (centroids) – центры тяжести кластеров. Каждый центроид – это вектор, элементы которого представляют собой средние значения характеристик, вычисленные по всем точкам кластера. Центр кластера смещается в его центроид. Точки заново назначаются ближайшему центру кластера","keywords":["ai","кластера","centroids","notebook","алгоритм","ближайшему","вектор","всем","вычисленные","вычисляет","группировки","данных."],"topics":["algorithms"]},{"id":"chunk-6","heading":"","text":". Этапы изменения центров кластеров и переназначения точек итеративно повторяются до тех пор, пока границы кластеров и расположение центроидов не перестанут изменяться, т.е. на каждой итерации в каждый кластер будут попадать одни и те же точки данных.","keywords":["ai","данных.","кластеров","notebook","будут","границы","группировки","до","же","изменения","изменяться","итеративно"],"topics":[]},{"id":"chunk-7","heading":"","text":"Алгоритм k средних, наверное, самый популярный и простой алгоритм кластеризации и очень легко представляется в виде простого псевдокода:","keywords":["ai","алгоритм","notebook","виде","группировки","данных.","кластеризации","кластеризация","легко","методы","наверное","неподконтрольное"],"topics":["algorithms"]},{"id":"chunk-8","heading":"","text":"Выбрать количество кластеров k, которое нам кажется оптимальным для наших данных. Высыпать случайным образом в пространство наших данных k точек (центроидов). Для каждой точки нашего набора данных посчитать, к какому центроиду она ближе. Переместить каждый центроид в центр выборки, которую мы отнесли к этому центроиду","keywords":["ai","данных","данных.","наших","центроиду","notebook","ближе.","выборки","выбрать","высыпать","группировки","каждой"],"topics":[]},{"id":"chunk-9","heading":"","text":". Повторять последние два шага фиксированное число раз, либо до тех пор пока центроиды не \"сойдутся\" (обычно это значит, что их смещение относительно предыдущего положения не превышает какого то заранее заданного небольшого значения).","keywords":["ai","notebook","группировки","данных.","два","до","заданного","заранее","значения","значит","их","какого"],"topics":[]},{"id":"chunk-10","heading":"","text":"Стоит заметить, что можно рассчитывать расстояние между центройдами по любой метрике (Евклидовой, Хемминговой и т.д.).","keywords":["ai","notebook","группировки","данных.","евклидовой","заметить","кластеризация","любой","между","методы","метрике","можно"],"topics":[]},{"id":"chunk-11","heading":"","text":"Сгенерируем 2D набор данных, содержащий 4 разных больших объекта, а затем применим алгоритм k средних, чтобы увидеть результат.","keywords":["ai","2d","notebook","алгоритм","больших","группировки","данных","данных.","затем","кластеризация","методы","набор"],"topics":["algorithms"]},{"id":"chunk-12","heading":"сгенерируем данные","text":"from sklearn.datasets import make blobs x, y true = make blobs(n samples=400, centers=4, cluster std=0.60, random state=0) plt.scatter(x[:, 0], x[:, 1], s=20) plt.show() ````","keywords":["ai","blobs","make","0.60","20","400","centers","cluster","import","notebook","plt.scatter","plt.show"],"topics":["clustering"]},{"id":"chunk-13","heading":"Построим и визуализируем центры кластера, выбранные с помощью k средних оценки Python","text":"from sklearn.datasets import make blobs x, y true = make blobs(n samples=400, centers=4, cluster std=0.60, random state=0) plt.scatter(x[:, 0], x[:, 1], c=y kmeans, s=20, cmap='summer') centers = kmeans.cluster centers plt.scatter(centers[:, 0], centers[:, 1], c='blue', s=100, alpha=.9) plt.show() ````","keywords":["centers","ai","blobs","make","plt.scatter",".9","0.60","100","20","400","alpha","blue"],"topics":["python","clustering"]},{"id":"chunk-14","heading":"Построим и визуализируем центры кластера, выбранные с помощью k средних оценки Python","text":"Проведем кластеризацию K средних к набору простых цифр. K means попытается идентифицировать похожие цифры","keywords":["ai","средних","means","notebook","python","визуализируем","выбранные","группировки","данных.","идентифицировать","кластера","кластеризацию"],"topics":["python"]},{"id":"chunk-15","heading":"загрузим набор цифр из sklearn","text":"from sklearn.datasets import load digits digits = load digits() digits.data.shape ````","keywords":["ai","digits","load","digits.data.shape","import","notebook","sklearn","sklearn.datasets","группировки","данных.","загрузим","кластеризация"],"topics":[]},{"id":"chunk-16","heading":"выполним кластеризацию","text":"kmeans = KMeans(n clusters=10, random state=0) clusters = kmeans.fit predict(digits.data) kmeans.cluster centers .shape ````","keywords":["ai","clusters","kmeans",".shape","10","centers","digits.data","kmeans.cluster","kmeans.fit","notebook","predict","random"],"topics":["clustering"]},{"id":"chunk-17","heading":"выполним кластеризацию","text":"code /usr/local/lib/python3.10/dist packages/sklearn/cluster/ kmeans.py:870: FutureWarning: The default value of n init will change from 10 to 'auto' in 1.4. Set the value of n init explicitly to suppress the warning warnings.warn(","keywords":["ai","init","value","1.4.","10","870","auto","change","cluster","code","default","dist"],"topics":["python","clustering"]},{"id":"chunk-18","heading":"выполним кластеризацию","text":"python fig, ax = plt.subplots(2, 5, figsize=(8, 3)) centers = kmeans.cluster centers .reshape(10, 8, 8) for axi, center in zip(ax.flat, centers): axi.set(xticks=[], yticks=[]) axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)","keywords":["ai","centers","center",".reshape","10","ax","ax.flat","axi","axi.imshow","axi.set","cmap","fig"],"topics":["python","clustering"]},{"id":"chunk-19","heading":"выполним кластеризацию","text":"Дан массив данных. Требуется провести кластерный анализ данных методом k средних. Поэкспериментируйте с количеством кластеров.","keywords":["ai","данных.","notebook","анализ","выполним","группировки","дан","данных","кластеризацию","кластеризация","кластерный","кластеров."],"topics":[]},{"id":"chunk-20","heading":"выполним кластеризацию","text":"````python def cluster analysis(x, clusters): x, y true = make blobs(n samples=clusters 100, centers=clusters, cluster std=.6, random state=0) plt.scatter(x[:, 0], x[:, 1], s=20) plt.show()","keywords":["ai","clusters","cluster",".6","100","20","analysis","blobs","centers","def","make","notebook"],"topics":["python","clustering"]},{"id":"chunk-21","heading":"выполним кластеризацию","text":"x, y true = make blobs(n samples=clusters 100, centers=clusters, cluster std=.6, random state=0) plt.scatter(x[:, 0], x[:, 1], c=y kmeans, s=20, cmap='summer') centers = kmeans.cluster centers plt.scatter(centers[:, 0], centers[:, 1], c='blue', s=100, alpha=.9) plt.show()","keywords":["centers","ai","100","clusters","plt.scatter",".6",".9","20","alpha","blobs","blue","cluster"],"topics":["clustering"]},{"id":"chunk-22","heading":"выполним кластеризацию","text":"X = np.array([[5, 3], [10, 15], [15, 15], [24, 10], [30, 45], [85, 70], [71, 80], [60, 78], [55, 52], [80, 91]]) cluster analysis(X, 4) количество кластеров = 4 cluster analysis(X, 10) количество кластеров = 10 ````","keywords":["10","15","ai","80","analysis","cluster","кластеров","количество","24","30","45","52"],"topics":["numpy","clustering"]},{"id":"chunk-23","heading":"выполним кластеризацию","text":"Выполните кластеризацию для набора данных ирисов Фишера. Выполните предсказания для модели. Поэкспериментируйте с количеством кластеров.","keywords":["ai","выполните","кластеризацию","notebook","выполним","группировки","данных","данных.","ирисов","кластеризация","кластеров.","количеством"],"topics":[]},{"id":"chunk-24","heading":"выполним кластеризацию","text":"def clusterization(x, amount): print(x.data.shape) kmeans = KMeans(n clusters=amount, random state=0) clusters = kmeans.fit predict(x.data) print(kmeans.cluster centers .shape)","keywords":["ai","amount","clusters","kmeans","print",".shape","centers","clusterization","def","kmeans.cluster","kmeans.fit","notebook"],"topics":["clustering"]},{"id":"chunk-25","heading":"выполним кластеризацию","text":"fig, ax = plt.subplots(amount // 2, 2, figsize=(10, 3)) centers = kmeans.cluster centers .reshape(amount, 2, 2) for axi, center in zip(ax.flat, centers): axi.set(xticks=[], yticks=[]) axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)","keywords":["ai","centers","amount","center",".reshape","10","ax","ax.flat","axi","axi.imshow","axi.set","cmap"],"topics":["clustering"]},{"id":"chunk-26","heading":"выполним кластеризацию","text":"Алгомеративная кластеризация относится к семейству алгоритмов кластеризации, в основе которых лежат одинаковые принципы: алгоритм начинает свою работу с того, что каждую точку данных заносит в свой собственный кластер и по мере выполнения объединяет два наиболее схожих между собой кластера до тех пор, пока не будет удовлетворен определенный критерий остановки","keywords":["ai","кластеризация","notebook","алгомеративная","алгоритм","алгоритмов","будет","выполнения","выполним","группировки","данных","данных."],"topics":["algorithms","clustering"]},{"id":"chunk-27","heading":"выполним кластеризацию","text":". Зачастую данным критерием выступает это количество кластеров, поэтому схожие между собой кластеры объединяются до тех пор, пока не останется заданное число кластеров.","keywords":["ai","notebook","выполним","выступает","группировки","данным","данных.","до","заданное","зачастую","кластеризацию","кластеризация"],"topics":[]},{"id":"chunk-28","heading":"выполним кластеризацию","text":"Результатом агломеративной кластеризации является иерархическая кластеризация. Кластеризация выполняется итеративно, и каждая точка совершает путь от отдельной точки кластера до участника итогового кластера. На каждом промежуточном шаге происходит кластеризация данных (с разным количеством кластеров). Иногда полезно сразу взглянуть на все возможные кластеризации","keywords":["ai","кластеризация","кластеризации","notebook","агломеративной","взглянуть","возможные","выполним","выполняется","группировки","данных","данных."],"topics":["clustering"]},{"id":"chunk-29","heading":"выполним кластеризацию","text":". Следующий пример показывает наложение всех возможных кластеризаций, показанных на рис. и дает некоторое представление о том, как каждый кластер распадается на более мелкие кластеры.","keywords":["ai","notebook","более","возможных","всех","выполним","группировки","дает","данных.","каждый","кластер","кластеризаций"],"topics":[]},{"id":"chunk-30","heading":"выполним кластеризацию","text":"python X = np.array( [[7, 8], [12, 20], [17, 19], [26, 15], [32, 37], [87, 75], [73, 85], [62, 80], [73, 60], [87, 96]]) labels = range(1, 11) plt.figure(figsize=(10, 7)) plt.subplots adjust(bottom=.1) plt.scatter(X[:, 0], X[:, 1], label='True Position') for label, x, y in zip(labels, X[:, 0], X[:, 1]): plt.annotate( label, xy=(x, y), xytext=( 3, 3), textcoords='offset points', ha='right', va='bottom') plt.show()","keywords":["ai","label","73","87","bottom","labels",".1","10","11","12","15","17"],"topics":["python","numpy"]},{"id":"chunk-31","heading":"выполним кластеризацию","text":"python from scipy.cluster.hierarchy import dendrogram, linkage from matplotlib import pyplot as plt linked = linkage(X, 'single') label list = range(1, 11) plt.figure(figsize=(10, 7)) dendrogram(linked, orientation='top', labels=label list, distance sort='descending', show leaf counts=True) plt.show()","keywords":["ai","dendrogram","import","label","linkage","linked","list","10","11","counts","descending","distance"],"topics":["python","clustering"]},{"id":"chunk-32","heading":"выполним кластеризацию","text":"Далее нам нужно импортировать класс для кластеризации и вызвать его метод fit predict для прогнозирования кластера.","keywords":["ai","fit","notebook","predict","вызвать","выполним","группировки","далее","данных.","его","импортировать","класс"],"topics":["oop"]},{"id":"chunk-33","heading":"выполним кластеризацию","text":"python from sklearn.cluster import AgglomerativeClustering cluster = AgglomerativeClustering(n clusters=2, affinity='euclidean', linkage='ward') cluster.fit predict(X) plt.scatter(X[:, 0], X[:, 1], c=cluster.labels , cmap='rainbow')","keywords":["ai","agglomerativeclustering","affinity","cluster","cluster.fit","cluster.labels","clusters","cmap","euclidean","import","linkage","notebook"],"topics":["python","ai","clustering"]},{"id":"chunk-34","heading":"выполним кластеризацию","text":"В этом примере мы выполним иерархическую кластеризацию реальных данных и посмотрим, как ее можно использовать для решения реальной проблемы. Выполним кластеризацию данных по набору https://raw.githubusercontent.com/lucko515/clustering python/master/Customer%20in%20Mall%20clusterng/Mall Customers.csv","keywords":["ai","выполним","кластеризацию","данных","20clusterng","20in","20mall","clustering","customer","customers.csv","https","lucko515"],"topics":["python","clustering"]},{"id":"chunk-35","heading":"выполним кластеризацию","text":"Задача, которую мы собираемся решить в этом разделе, состоит в том, чтобы разделить клиентов на разные группы в зависимости от их покупательских тенденций.","keywords":["ai","notebook","выполним","группировки","группы","данных.","зависимости","задача","их","кластеризацию","кластеризация","клиентов"],"topics":[]},{"id":"chunk-36","heading":"загрузим набор данных","text":"url = r'https://raw.githubusercontent.com/lucko515/clustering python/master/Customer%20in%20Mall%20clusterng/Mall Customers.csv' customer data = pd.read csv(url) customer data.head() ````","keywords":["ai","customer","url","20clusterng","20in","20mall","clustering","csv","customers.csv","data","data.head","https"],"topics":["python","clustering"]},{"id":"chunk-37","heading":"загрузим набор данных","text":"code CustomerID Genre Age Annual Income (k$) Spending Score (1 100) 0 1 Male 19 15 39 1 2 Male 21 15 81 2 3 Female 20 16 6 3 4 Female 23 16 77 4 5 Female 31 17 40","keywords":["ai","female","15","16","male","100","17","19","20","21","23","31"],"topics":[]},{"id":"chunk-38","heading":"загрузим набор данных","text":"Наш набор данных состоит из пяти столбцов. Чтобы просмотреть результаты в двумерном пространстве, мы сохраним только два из них:«Годовой доход» (в тысячах долларов) и «Оценка расходов» (1–100). Столбец «Оценка расходов» показывает, как часто человек тратит деньги в торговом центре по шкале от 1 до 100, где 100 — это самый высокий расход","keywords":["100","ai","данных","набор","оценка","расходов","notebook","высокий","годовой","группировки","данных.","два"],"topics":[]},{"id":"chunk-39","heading":"загрузим набор данных","text":". Выполним следующий скрипт, чтобы отфильтровать первые три столбца из нашего набора данных:","keywords":["ai","данных","notebook","выполним","группировки","данных.","загрузим","кластеризация","методы","набор","набора","нашего"],"topics":[]},{"id":"chunk-40","heading":"загрузим набор данных","text":"Наш набор данных состоит из пяти столбцов. Чтобы просмотреть результаты в двумерном пространстве, мы сохраним только два из них:«Годовой доход» (в тысячах долларов) и «Оценка расходов» (1–100). Столбец «Оценка расходов» показывает, как часто человек тратит деньги в торговом центре по шкале от 1 до 100, где 100 — это самый высокий расход","keywords":["100","ai","данных","набор","оценка","расходов","notebook","высокий","годовой","группировки","данных.","два"],"topics":[]},{"id":"chunk-41","heading":"загрузим набор данных","text":". Выполним следующий скрипт, чтобы отфильтровать первые три столбца из нашего набора данных:","keywords":["ai","данных","notebook","выполним","группировки","данных.","загрузим","кластеризация","методы","набор","набора","нашего"],"topics":[]},{"id":"chunk-42","heading":"загрузим набор данных","text":"python figure(figsize=(28, 12), dpi=180) plt.figure(figsize=(10, 7)) plt.title('Customer Dendrograms') dend = shc.dendrogram(shc.linkage(data, method='ward'))","keywords":["ai","figsize","10","12","180","28","customer","data","dend","dendrograms","dpi","figure"],"topics":["python"]},{"id":"chunk-43","heading":"загрузим набор данных","text":"Если мы нарисуем горизонтальную линию, которая проходит через самое длинное расстояние без горизонтальной линии, мы получим 5 кластеров. Теперь мы знаем количество кластеров для нашего набора данных, следующим шагом будет группировка точек данных в эти пять кластеров.","keywords":["ai","данных","мы","кластеров.","notebook","без","будет","горизонтальной","горизонтальную","группировка","группировки","данных."],"topics":[]},{"id":"chunk-44","heading":"загрузим набор данных","text":"Для этого мы снова воспользуемся классом AgglomerativeClustering библиотеки sklearn.cluster.","keywords":["ai","agglomerativeclustering","notebook","sklearn.cluster.","библиотеки","воспользуемся","группировки","данных","данных.","загрузим","классом","кластеризация"],"topics":["oop","clustering"]},{"id":"chunk-45","heading":"загрузим набор данных","text":"cluster = AgglomerativeClustering(n clusters=5, affinity='euclidean', linkage='ward') cluster.fit predict(data) ````","keywords":["ai","affinity","agglomerativeclustering","cluster","cluster.fit","clusters","data","euclidean","linkage","notebook","predict","ward"],"topics":["clustering"]},{"id":"chunk-46","heading":"загрузим набор данных","text":"code /usr/local/lib/python3.10/dist packages/sklearn/cluster/ agglomerative.py:983: FutureWarning: Attribute affinity was deprecated in version 1.2 and will be removed in 1.4. Use metric instead warnings.warn(","keywords":["ai","1.2","1.4.","983","affinity","agglomerative.py","attribute","cluster","code","deprecated","dist","futurewarning"],"topics":["python","clustering"]},{"id":"chunk-47","heading":"загрузим набор данных","text":"code array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 0, 2, 0, 2, 1, 2, 0, 2,","keywords":["ai","array","code","notebook","группировки","данных","данных.","загрузим","кластеризация","методы","набор","неподконтрольное"],"topics":[]},{"id":"chunk-48","heading":"загрузим набор данных","text":"0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2])","keywords":["ai","notebook","группировки","данных","данных.","загрузим","кластеризация","методы","набор","неподконтрольное","обучение"],"topics":[]},{"id":"chunk-49","heading":"загрузим набор данных","text":"В качестве последнего шага давайте построим кластеры, чтобы увидеть, как на самом деле были сгруппированы наши данные:","keywords":["ai","notebook","были","группировки","давайте","данные","данных","данных.","деле","загрузим","качестве","кластеризация"],"topics":[]},{"id":"chunk-50","heading":"загрузим набор данных","text":"python plt.figure(figsize=(10, 7)) plt.scatter(data[:, 0], data[:, 1], c=cluster.labels , cmap='rainbow')","keywords":["ai","data","10","cluster.labels","cmap","figsize","notebook","plt.figure","plt.scatter","python","rainbow","группировки"],"topics":["python","ai","clustering"]},{"id":"chunk-51","heading":"загрузим набор данных","text":"Выполните иерархическую кластеризацию для набора данных об ирисах Фишера. При этом необходимо использовать любые два признака (всего их четыре).","keywords":["ai","данных","notebook","всего","выполните","группировки","данных.","два","загрузим","иерархическую","ирисах","использовать"],"topics":[]},{"id":"chunk-52","heading":"загрузим набор данных","text":"iris data = load iris() data = pd.DataFrame(data=iris data.data, columns=iris data.feature names) data.head() ````","keywords":["iris","ai","data","columns","data.data","data.feature","data.head","load","names","notebook","pd.dataframe","группировки"],"topics":["pandas"]},{"id":"chunk-53","heading":"загрузим набор данных","text":"code sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) 0 5.1 3.5 1.4 0.2 1 4.9 3.0 1.4 0.2 2 4.7 3.2 1.3 0.2 3 4.6 3.1 1.5 0.2 4 5.0 3.6 1.4 0.2","keywords":["0.2","cm","1.4","ai","length","petal","sepal","width","1.3","1.5","3.0","3.1"],"topics":[]},{"id":"chunk-54","heading":"загрузим набор данных","text":"python plt.figure(figsize=(10, 7)) plt.title('Iris Dendrograms') dend = shc.dendrogram(shc.linkage(data, method='ward'))","keywords":["ai","10","data","dend","dendrograms","figsize","iris","method","notebook","plt.figure","plt.title","python"],"topics":["python"]},{"id":"chunk-55","heading":"загрузим набор данных","text":"cluster = AgglomerativeClustering(n clusters=4, affinity='euclidean', linkage='ward') cluster.fit predict(data)","keywords":["ai","affinity","agglomerativeclustering","cluster","cluster.fit","clusters","data","euclidean","linkage","notebook","predict","ward"],"topics":["clustering"]},{"id":"chunk-56","heading":"загрузим набор данных","text":"plt.figure(figsize=(10, 7)) plt.scatter(data[:, 0], data[:, 1], c=cluster.labels , cmap='rainbow') ````","keywords":["ai","data","10","cluster.labels","cmap","figsize","notebook","plt.figure","plt.scatter","rainbow","группировки","данных"],"topics":["ai","clustering"]},{"id":"chunk-57","heading":"загрузим набор данных","text":"code /usr/local/lib/python3.10/dist packages/sklearn/cluster/ agglomerative.py:983: FutureWarning: Attribute affinity was deprecated in version 1.2 and will be removed in 1.4. Use metric instead warnings.warn(","keywords":["ai","1.2","1.4.","983","affinity","agglomerative.py","attribute","cluster","code","deprecated","dist","futurewarning"],"topics":["python","clustering"]}]},{"id":"algorithms","href":"/docs/algorithms","slug":["algorithms"],"section":"algorithms","sectionTitle":"Algorithms","title":"Algorithms","description":"Вводные и вспомогательные материалы StackMIREA Docs.","preview":"Что внутри Введение и навигация по проекту MDX структура и правила оформления Связь с Python и Java треками","keywords":["java","mdx","python","введение","внутри","навигация","оформления","правила","проекту","связь","структура","треками","algorithms","docs.","stackmirea","вводные","вспомогательные","материалы"],"topics":["python","java","algorithms"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"Введение и навигация по проекту MDX структура и правила оформления Связь с Python и Java треками","keywords":["algorithms","docs.","java","mdx","python","stackmirea","введение","вводные","внутри","вспомогательные","материалы","навигация"],"topics":["python","java"]}]},{"id":"bigdata","href":"/docs/bigdata","slug":["bigdata"],"section":"bigdata","sectionTitle":"BigData","title":"BigData","description":"Практики по анализу больших данных, ML, классификации и кластеризации.","preview":"BigData обзор В этом разделе собраны практические работы по дисциплине BigData в формате MDX. Материалы охватывают вводную часть, анализ данных, регрессию, прикладные кейсы со страховыми данными, классификацию, кластеризацию, ансамблевые методы и итоговую рабо","keywords":["практика","bigdata","данных","анализ","практики","данными","материалы","методы","работа","часть","mdx.","ансамблевое","ансамблевые","вводную","данных.","дисциплине","итоговая","итоговую"],"topics":["ai","oop","clustering","regression"],"chunks":[{"id":"chunk-0","heading":"BigData обзор","text":"В этом разделе собраны практические работы по дисциплине BigData в формате MDX. 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Документация собрана по исходному коду этой практики.","keywords":["java","алгоритмы","базовые","остановки.","рекурсивные","условия","10","task","документация","задание","исходному","коду"],"topics":["algorithms"]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task10/Recursion11.java Task10/Recursion12.java Task10/Recursion13.java Task10/Recursion14.java","keywords":["task10","java","10","recursion11.java","recursion12.java","recursion13.java","recursion14.java","task","алгоритмы","базовые","исходные","остановки."],"topics":["java"]},{"id":"chunk-2","heading":"Task10/Recursion11.java","text":"public class Recursion11 { public static int Recurs(){ Scanner s = new Scanner(System.in); int n = s.nextInt(); if (n==1){ int m = s.nextInt(); if (m == 1){ return Recurs()+n+m; } else{ int k = s.nextInt(); if(k == 1){ return Recurs()+m+n+k; } else{ return n+m+k; } } } else{ int m = s.nextInt(); if (m==1){ return Recurs()+m+n; } else{ return n+m","keywords":["int","return","else","if","recurs","s.nextint","java","public","scanner","+m+n","+m+n+k","+n+m"],"topics":["java"]},{"id":"chunk-3","heading":"Task10/Recursion11.java","text":"; } } } public static void main(String[] args){ System.out.println(Recurs()); } }","keywords":["java","10","args","main","public","recurs","recursion11.java","static","string","system.out.println","task","task10"],"topics":["ai","java"]},{"id":"chunk-4","heading":"Task10/Recursion12.java","text":"public class Recursion12 { public static void Recurs(){ Scanner s = new Scanner(System.in); int n = s.nextInt(); if (n 0){ if (n % 2 == 1){ System.out.println(n); Recurs(); } else{ Recurs(); } } } public static void main(String[] args){ Recurs(); } }","keywords":["recurs","public","if","java","scanner","static","void","10","args","class","else","int"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task10/Recursion13.java","text":"public class Recursion13 { public static void Recurs(){ Scanner s = new Scanner(System.in); int n = s.nextInt(); if (n 0){ System.out.println(n); int m = s.nextInt(); if (m 0){ Recurs(); } } } public static void main(String[] args){ Recurs(); } }","keywords":["public","recurs","if","int","java","s.nextint","scanner","static","void","10","args","class"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task10/Recursion14.java","text":"public class Recursion14 { public static int Recurs(int n){ if (n < 10){ return n; } else{ System.out.println(n % 10 + \" \"); return Recurs(n / 10); } } public static void main(String[] args){ System.out.println(Recurs(123)); } }","keywords":["10","public","recurs","int","java","return","static","system.out.println","123","args","class","else"],"topics":["ai","java"]},{"id":"chunk-7","heading":"Описание","text":"В этом модуле используется 4 Java файлов. Ключевые сущности: Recursion11, Recursion12, Recursion13, Recursion14.","keywords":["java","10","recursion11","recursion12","recursion13","recursion14.","task","алгоритмы","базовые","используется","ключевые","модуле"],"topics":["java"]},{"id":"chunk-8","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","10","task","алгоритмы","базовые","демо","задачи","запускайте","затем","кейсами.","класс","модель"],"topics":["oop"]}]},{"id":"java/task-11-sorting-students","href":"/docs/java/task-11-sorting-students","slug":["java","task-11-sorting-students"],"section":"java","sectionTitle":"Java","title":"Task 11 — Сортировка студентов","description":"Сортировка и сравнение объектов по метрикам.","preview":"Задание Сортировка и сравнение объектов по метрикам. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task11/Connection.java Task11/SortingStudentsByGPA.java Task11/Student.java Task11/Co","keywords":["new","task11","list","string","public","student","int","name","sortingstudentsbygpa","r.add","sortingstudentsbygpa.java","connection.java","idnumber","return","сортировка","c.add","mergesort","result.add"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Сортировка и сравнение объектов по метрикам. Документация собрана по исходному коду этой практики.","keywords":["сортировка","java","метрикам.","объектов","сравнение","11","task","документация","задание","исходному","коду","практики."],"topics":[]},{"id":"chunk-1","heading":"Task11/Connection.java","text":"c.add(new String[]{\"Max\", \"10\"}); c.add(new String[]{\"Nicolai\", \"20\"}); c.add(new String[]{\"Tolic\", \"19\"}); c.add(new String[]{\"Dane\", \"17\"});","keywords":["c.add","new","string","java","сортировка","10","11","17","19","20","connection.java","dane"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task11/Connection.java","text":"List r = new ArrayList< (); r.add(new String[]{\"Denes\",\"23\"}); r.add(new String[]{\"Dasha\", \"30\"}); r.add(new String[]{\"Oleg\", \"27\"}); //c.addAll(r); //System.out.print(c); List t = mergeSort(c, r); for (String[] d: t){ System.out.println(\"[\" +d[0]+\", \"+d[1] +\"]\"); } } private static List mergeSort(List c, List r) { if (c.size() left1 = c.subList(0, m1); List right1 = c.subList(m1, c.size())","keywords":["list","new","string","r.add","+d","c.size","c.sublist","java","m1","mergesort","сортировка","11"],"topics":["java"]},{"id":"chunk-3","heading":"Task11/Connection.java","text":"; List left2 = r.subList(0,m2); List right2 = r.subList(m2, r.size()); List sortleft = mergeSort(left1, left2); List sortright = mergeSort(right1, right2); return merge(sortleft, sortright); } public static List merge(List left, List right) { List result = new ArrayList< (); int i = 0; int j = 0","keywords":["list","int","java","left2","m2","merge","mergesort","r.sublist","right2","sortleft","sortright","сортировка"],"topics":["java"]},{"id":"chunk-4","heading":"Task11/Connection.java","text":"; while (i < left.size() && j < right.size()) { if (Integer.parseInt(left.get(i)[1]) < Integer.parseInt(right.get(j)[1])) { result.add(left.get(i)); i++; } else { result.add(right.get(j)); j++; } } while (i < left.size()) { result.add(left.get(i)); i++; } while (j < right.size()) { result.add(right.get(j)); j++; } return result; } }","keywords":["result.add","left.get","right.get","while","i++","integer.parseint","j++","java","left.size","right.size","сортировка","11"],"topics":["java"]},{"id":"chunk-5","heading":"Task11/SortingStudentsByGPA.java","text":"```java title=\"Task11/SortingStudentsByGPA.java\" package Task11; public class SortingStudentsByGPA implements Comparable { public String name; public int bal;","keywords":["java","public","task11","sortingstudentsbygpa.java","сортировка","11","bal","class","comparable","implements","int","name"],"topics":["java"]},{"id":"chunk-6","heading":"Task11/SortingStudentsByGPA.java","text":"public SortingStudentsByGPA(String name, int bal) { this.name = name; this.bal = bal; }","keywords":["bal","java","name","сортировка","11","int","public","sortingstudentsbygpa","sortingstudentsbygpa.java","string","task","task11"],"topics":["java"]},{"id":"chunk-7","heading":"Task11/SortingStudentsByGPA.java","text":"@Override public int compareTo(SortingStudentsByGPA o) { return o.bal this.bal; } public static void main(String[] args){ List r = new ArrayList< (); r.add(new SortingStudentsByGPA(\"Nicolai\", 123)); r.add(new SortingStudentsByGPA(\"Oleg\", 200)); r.add(new SortingStudentsByGPA(\"Sasha\", 260)); r.add(new SortingStudentsByGPA(\"Nikita\", 250)); Collections.sort(r)","keywords":["new","sortingstudentsbygpa","r.add","java","public","сортировка","11","123","200","250","260","args"],"topics":["ai","java"]},{"id":"chunk-8","heading":"Task11/SortingStudentsByGPA.java","text":"; for (SortingStudentsByGPA s : r){ System.out.println(s.name+ \" Баллы \"+s.bal); } } }","keywords":["java","сортировка","+s.bal","11","s.name+","sortingstudentsbygpa","sortingstudentsbygpa.java","system.out.println","task","task11","баллы","метрикам."],"topics":["java"]},{"id":"chunk-9","heading":"Task11/Student.java","text":"public Student(String name, int iDNumber) { this.name = name; this.iDNumber = iDNumber; } public void setName(String name){ this.name=name; } public String getName(){ return name; } public int getiDNumber() { return iDNumber; } public void setiDNumber(int iDNumber) { this.iDNumber = iDNumber; } public static void main(String[] args){ Student[] s = new Student[6]; s[0]=new Student(\"Sol\",1); s[1]=new Student(\"Pol\",2)","keywords":["public","idnumber","name","student","string","int","new","void","java","return","this.idnumber","this.name"],"topics":["ai","java"]},{"id":"chunk-10","heading":"Task11/Student.java","text":"; s[2]=new Student(\"Kol\",3); s[3]=new Student(\"Tol\",7); s[4]=new Student(\"Fol\",0); s[5]=new Student(\"Rol\",4); for(int i = 1; i =0 && s[j].getiDNumber() current.getiDNumber()){ s[j+1]=s[j]; j ; } s[j+1]=current; } System.out.println(\" \"); for(Student f: s){ System.out.println(\"Name: \"+f.getName()+\" id: \"+f.getiDNumber()); } } }","keywords":["student","new","j+1","java","system.out.println","сортировка",".getidnumber","+f.getidnumber","+f.getname","11","current","current.getidnumber"],"topics":["java"]},{"id":"chunk-11","heading":"Описание","text":"В этом модуле используется 3 Java файлов. Ключевые сущности: Connection, SortingStudentsByGPA, Student.","keywords":["java","сортировка","11","connection","sortingstudentsbygpa","student.","task","используется","ключевые","метрикам.","модуле","объектов"],"topics":["java"]},{"id":"chunk-12","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","сортировка","11","task","демо","задачи","запускайте","затем","кейсами.","класс","метрикам.","модель"],"topics":["oop"]},{"id":"chunk-13","heading":"Вывод","text":"Task 11 — Сортировка студентов документирует реальное решение из исходного кода.","keywords":["сортировка","11","java","task","студентов","вывод","документирует","исходного","кода.","метрикам.","объектов","реальное"],"topics":[]}]},{"id":"java/task-12-drunkard-game","href":"/docs/java/task-12-drunkard-game","slug":["java","task-12-drunkard-game"],"section":"java","sectionTitle":"Java","title":"Task 12 — Игра Пьяница","description":"Моделирование игры через очереди и коллекции.","preview":"Задание Моделирование игры через очереди и коллекции. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task12/Drunkard.java Task12/Drunkard.java java title=\"Task12/Drunkard.java\" package","keywords":["int","b1","b1.length","new","b2.length","drunkard.java","newb1","newb2","system.out.println","task12","b2","count","system.arraycopy","card2","t1","t2","10","card1"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Моделирование игры через очереди и коллекции. Документация собрана по исходному коду этой практики.","keywords":["java","игры","коллекции.","моделирование","очереди","через","12","task","документация","задание","игра","исходному"],"topics":[]},{"id":"chunk-1","heading":"Task12/Drunkard.java","text":"public class Drunkard { public static void main(String[] args) throws InterruptedException { System.out.println(\"Ведите 5 карт для первого игрока: \"); Scanner s = new Scanner(System.in); Scanner r = new Scanner(System.in); int t1 = s.nextInt(); System.out.println(\"Ведите 5 карт для второго игрока: \"); int t2 = r.nextInt(); int[] b1 = new int[5]; int[] b2 = new int[5]; while (t1 0) { for (int i = b1.length 1; i = 0","keywords":["int","new","scanner","java","public","system.in","system.out.println","t1","ведите","игрока","карт","12"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task12/Drunkard.java","text":"; i ) { b1[i] = t1 % 10; t1 = t1 / 10; } } while (t2 0) { for (int i = b2.length 1; i = 0; i ) { b2[i] = t2 % 10; t2 = t2 / 10; } } int count = 0; while (b1.length != 0 && b2.length != 0 && count card2 (card1 == 0 && card2 == 9)) && (card1!=9&&card2!=0)) { int[] newb1 = new int[b1.length + 1]; System.arraycopy(b1, 1, newb1, 0, b1.length 1); newb1[newb1.length 2] = card1; newb1[newb1.length 1] = card2; b1 = newb1","keywords":["newb1","10","card2","int","t2","b1","b1.length","card1","t1","b2.length","count","java"],"topics":["java"]},{"id":"chunk-3","heading":"Task12/Drunkard.java","text":"; int[] newb2 = new int[b2.length 1]; System.arraycopy(b2, 1, newb2, 0, b2.length 1); b2 = newb2; count += 1; for(int i =0; i<b2.length;i++) { System.out.println(b2[i]); } System.out.println(\"==========================\"); } else { int[] newb2 = new int[b2.length + 1]; System.arraycopy(b2, 1, newb2, 0, b2.length 1); newb2[newb2.length 2] = card2; newb2[newb2.length 1] = card1; b2 = newb2","keywords":["newb2","b2","b2.length","int","java","new","newb2.length","system.arraycopy","system.out.println","12","card1","card2"],"topics":["java"]},{"id":"chunk-4","heading":"Task12/Drunkard.java","text":"; int[] newb1 = new int[b1.length 1]; System.arraycopy(b1, 1, newb1, 0, b1.length 1); b1 = newb1; count += 1; } } if (count == 106) { System.out.println(\"botva\"); } if (b2.length == 0) { System.out.println(\"first \" + count); } else { System.out.println(\"second \" + count); } //Удаление переменной из массива / int index = 0; int[] newArray = new int[b1.length 1]; System.arraycopy(b1,0,newArray,0,index)","keywords":["int","count","b1","b1.length","newb1","system.out.println","if","index","java","new","newarray","system.arraycopy"],"topics":["java"]},{"id":"chunk-5","heading":"Task12/Drunkard.java","text":"; System.arraycopy(b1, index+1,newArray,index,b1.length index 1); b1=newArray; for(int i= 0; i<b1.length;i++) { System.out.println(b1[i]); } / } }","keywords":["b1","b1.length","index","java","newarray","12","drunkard.java","i++","index+1","int","system.arraycopy","system.out.println"],"topics":["java"]},{"id":"chunk-6","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","12","task","демо","задачи","запускайте","затем","игра","игры","кейсами.","класс","коллекции."],"topics":["oop"]}]},{"id":"java/task-13-file-io","href":"/docs/java/task-13-file-io","slug":["java","task-13-file-io"],"section":"java","sectionTitle":"Java","title":"Task 13 — Файловый ввод-вывод","description":"Чтение и запись файлов, обработка ошибок I/O.","preview":"Задание Чтение и запись файлов, обработка ошибок I/O. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task13/file.java Task13/test.txt Task13/file.java java title=\"Task13/file.java\" pack","keywords":["task13","file.java","задание","new","filewriter","scanner","system.out.println","test.txt","catch","ex","ex.getmessage","ideaprojects","ioexception","o.","src","string","try","untitled"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Чтение и запись файлов, обработка ошибок I/O. Документация собрана по исходному коду этой практики.","keywords":["java","o.","запись","обработка","ошибок","файлов","чтение","13","task","ввод-вывод","документация","задание"],"topics":[]},{"id":"chunk-1","heading":"Task13/file.java","text":"public class file { public static void main(String[] args) { //Задание 1 и 3 try (FileWriter writer = new FileWriter(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task13\\\\test.txt\", false)){ Scanner s = new Scanner(System.in); String w = s.nextLine(); writer.write(w); writer.flush(); } catch (IOException ex) { System.out.println(ex.getMessage())","keywords":["filewriter","java","new","public","scanner","string","task13","13","args","catch","class","ex"],"topics":["ai","java","testing"]},{"id":"chunk-2","heading":"Task13/file.java","text":"; } //Задание 2 try (FileReader reader = new FileReader(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task13\\\\test.txt\")){ int c; while ((c=reader.read())!= 1){ System.out.println((char)c); } }catch (IOException ex){ System.out.println(ex.getMessage()); } //Задание 4 try (FileWriter writer = new FileWriter(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task13\\\\test.txt\", true)){ Scanner s = new Scanner(System.in); String w = s.nextLine()","keywords":["new","task13","filereader","filewriter","ideaprojects","java","scanner","src","system.out.println","test.txt","try","untitled"],"topics":["java","testing"]},{"id":"chunk-3","heading":"Task13/file.java","text":"; writer.append(\"\\n\"); writer.write(w); writer.flush(); }catch (IOException ex){ System.out.println(ex.getMessage()); } //Задание 4 } }","keywords":["java","13","catch","ex","ex.getmessage","file.java","ioexception","o.","system.out.println","task","task13","writer.append"],"topics":["java"]},{"id":"chunk-4","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","13","o.","task","ввод-вывод","демо","задачи","запись","запускайте","затем","кейсами.","класс"],"topics":["oop"]}]},{"id":"java/task-14-generics-and-queues","href":"/docs/java/task-14-generics-and-queues","slug":["java","task-14-generics-and-queues"],"section":"java","sectionTitle":"Java","title":"Task 14 — Generics и очереди","description":"Обобщения и безопасная работа с очередями.","preview":"Задание Обобщения и безопасная работа с очередями. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task14/BoundedWaitList.java Task14/IWaitList.java Task14/Main.java Task14/UnfairWaitLis","keywords":["task14","public","system.out.println","element","res","new","waitlist","boolean","if","int","bout.add","capacity","return","boundedwaitlist","content","content.add","main.java","unfairwaitlist"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Обобщения и безопасная работа с очередями. Документация собрана по исходному коду этой практики.","keywords":["java","безопасная","обобщения","очередями.","работа","14","generics","task","документация","задание","исходному","коду"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task14/BoundedWaitList.java Task14/IWaitList.java Task14/Main.java Task14/UnfairWaitList.java Task14/WaitList.java","keywords":["task14","java","14","boundedwaitlist.java","generics","iwaitlist.java","main.java","task","unfairwaitlist.java","waitlist.java","безопасная","исходные"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task14/BoundedWaitList.java","text":"public class BoundedWaitList extends WaitList { private int capacity; public BoundedWaitList(int capacity){ if (capacity 0) this.capacity = capacity; else System.out.println(\"Очередь должна состоять больше чем из 0 элементов\"); } public int getCapacity() { return capacity; } public void add(E element){ if(capacity content.size()) content.add(element); else System.out.println(\"Очередь переполнена\"); }","keywords":["capacity","public","int","boundedwaitlist","element","else","if","java","system.out.println","очередь","14","add"],"topics":["ai","java"]},{"id":"chunk-3","heading":"Task14/IWaitList.java","text":"public interface IWaitList { void add(E element); E remove(); boolean contains(E element); boolean containsAll(Collection c); boolean isEmpty(); }","keywords":["boolean","element","java","14","add","collection","contains","containsall","generics","interface","isempty","iwaitlist"],"topics":["ai","java"]},{"id":"chunk-4","heading":"Task14/Main.java","text":"public class Main { static public void main(String[] args){ WaitList wait = new WaitList< (); ArrayList al = new ArrayList< (); al.add(\"O\"); wait.add(\"L\"); wait.add(\"O\"); wait.add(\"L\"); System.out.println(wait); wait.remove(); System.out.println(wait); System.out.println(\"Есть ли 'L' ? \" + wait.contains(\"L\")); al.add(\"D\"); System.out.println(\"Есть ли 'D'? \" + wait.containsAll(al)); System.out.println(\"Список пуст","keywords":["system.out.println","wait","wait.add","al","al.add","arraylist","java","main","new","public","waitlist","ли"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task14/Main.java","text":"? \" + wait.isEmpty()); System.out.println(\" \"); BoundedWaitList bout = new BoundedWaitList< (4); bout.add(\"f\"); bout.add(\"a\"); bout.add(\"B\"); bout.add(\"a\"); bout.add(\"!\"); System.out.println(bout); bout.remove(); System.out.println(bout); bout.add(\"!\"); System.out.println(bout); System.out.println(\"Объем списка: \"+bout.getCapacity()); System.out.println(\" \"); UnfairWaitList unf = new UnfairWaitList< (); unf.add(\"L\")","keywords":["bout.add","system.out.println","bout","boundedwaitlist","java","new","unfairwaitlist","+bout.getcapacity","14","bout.remove","generics","main.java"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task14/Main.java","text":"; unf.add(\"O\"); unf.add(\"T\"); unf.add(\"p\"); System.out.println(unf); unf.remove(\"L\"); System.out.println(\"Попытка удаления первого элемента: \"+unf); unf.remove(\"O\"); System.out.println(\"Удаление элемента: \"+unf); unf.moveToBack(\"T\"); System.out.println(\"Перенос элемента в конец: \"+unf);","keywords":["system.out.println","+unf","unf.add","элемента","java","unf.remove","14","generics","main.java","task","task14","unf"],"topics":["ai","java"]},{"id":"chunk-7","heading":"Task14/UnfairWaitList.java","text":"public class UnfairWaitList extends WaitList { public UnfairWaitList(){} public void remove(E element){ for(int i =0; i<content.size();i = i) { E el = content.remove(); if(!el.equals(element) (el.equals(element) && i == 0)){ content.add(el); i++; } } } public void moveToBack(E element){ remove(element); content.add(element); } }","keywords":["element","public","content.add","el","el.equals","java","remove","unfairwaitlist","void","14","class","content.remove"],"topics":["ai","java"]},{"id":"chunk-8","heading":"Task14/WaitList.java","text":"public class WaitList implements IWaitList { protected ConcurrentLinkedQueue content; public WaitList(){ content = new ConcurrentLinkedQueue< (); } public WaitList(Collection c){ content = new ConcurrentLinkedQueue< (c); } @Override public String toString() { return \"WaitList{\" + \"content=\" + content + '}'; }","keywords":["content","public","waitlist","concurrentlinkedqueue","java","new","14","class","collection","generics","implements","iwaitlist"],"topics":["ai","java"]},{"id":"chunk-9","heading":"Task14/WaitList.java","text":"@Override public E remove() { if(isEmpty()){ throw new IllegalStateException(\"Очередь пуста\"); } return content.remove(); }","keywords":["java","14","content.remove","generics","if","illegalstateexception","isempty","new","override","public","remove","return"],"topics":["ai","java"]},{"id":"chunk-10","heading":"Task14/WaitList.java","text":"@Override public boolean contains(E element) { boolean res = false; for (int i = 0; i < content.size(); i++) { E cur = content.remove(); if (cur.equals(element)) res = true; content.add(cur); } return res; }","keywords":["res","boolean","cur","element","java","14","contains","content.add","content.remove","content.size","cur.equals","false"],"topics":["ai","java"]},{"id":"chunk-11","heading":"Task14/WaitList.java","text":"@Override public boolean containsAll(Collection c) { ArrayList al = new ArrayList< (c); boolean res = false; for(int i = 0; i < c.size(); i++){ res = false; for(int j = 0; j < content.size(); j++){ E el = content.remove(); if(el.equals(al.get(i))) res = true; content.add(el); } if(!res) return res; } return res; }","keywords":["res","arraylist","boolean","el","false","if","int","java","return","14","al","al.get"],"topics":["ai","java"]},{"id":"chunk-12","heading":"Описание","text":"В этом модуле используется 5 Java файлов. Ключевые сущности: BoundedWaitList, IWaitList, Main, UnfairWaitList, WaitList.","keywords":["java","14","boundedwaitlist","generics","iwaitlist","main","task","unfairwaitlist","waitlist.","безопасная","используется","ключевые"],"topics":["ai","java"]},{"id":"chunk-13","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","14","generics","task","безопасная","демо","задачи","запускайте","затем","кейсами.","класс","модель"],"topics":["oop"]}]},{"id":"java/task-15-mvc-pattern","href":"/docs/java/task-15-mvc-pattern","slug":["java","task-15-mvc-pattern"],"section":"java","sectionTitle":"Java","title":"Task 15 — MVC паттерн","description":"Разделение модели, представления и контроллера.","preview":"Задание Разделение модели, представления и контроллера. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task15/Employee.java Task15/EmployeeController.java Task15/EmployeeView.java Task1","keywords":["task15","public","string","new","void","model","name","view","private","mvcpatterndemomovie.java","class","return","employee","movieview.java","student","system.out.println","movie","255"],"topics":["ai","mvc","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Разделение модели, представления и контроллера. Документация собрана по исходному коду этой практики.","keywords":["java","контроллера.","модели","представления","разделение","15","mvc","task","документация","задание","исходному","коду"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task15/Employee.java Task15/EmployeeController.java Task15/EmployeeView.java Task15/Movie.java Task15/MovieController.java Task15/MovieView.java Task15/MVCPatternDemoEmployee.java Task15/MVCPatternDemoMovie.java Task15/MVCPatternDemoStudent.java Task15/Student.java Task15/StudentController.java Task15/StudentView.java","keywords":["task15","java","15","employee.java","employeecontroller.java","employeeview.java","movie.java","moviecontroller.java","movieview.java","mvc","mvcpatterndemoemployee.java","mvcpatterndemomovie.java"],"topics":["mvc","java"]},{"id":"chunk-2","heading":"Task15/Employee.java","text":"```java title=\"Task15/Employee.java\" package Task15; public class Employee { private String Name; private double salary; public String getName() { return Name; } public void setName(String name) { Name = name; } public double getSalary() { return salary; }","keywords":["name","public","java","string","task15","double","employee.java","private","return","salary","15","class"],"topics":["java"]},{"id":"chunk-3","heading":"Task15/EmployeeController.java","text":"```java title=\"Task15/EmployeeController.java\" package Task15; public class EmployeeController { private Employee model; private EmployeeView view; public EmployeeController(Employee model, EmployeeView view) { this.model = model; this.view = view; } public void setEmployeeName(String name){ model.setName(name); } public String getEmployeeName(){ return model.getName()","keywords":["public","java","model","task15","view","employee","employeecontroller","employeecontroller.java","employeeview","name","private","string"],"topics":["java"]},{"id":"chunk-4","heading":"Task15/EmployeeController.java","text":"; } public void setEmployeeSalary(double salary){ model.setSalary(salary); } public double getEmployeeSalary(){ return model.getSalary(); } public void updateView(){ view.printEmployeeDetails(model.getName(), model.getSalary()); } public void nds(){ model.setSalary(model.getSalary() (model.getSalary() 0.13)); } public void theend(String e){ view.printEnd(e); } }","keywords":["public","model.getsalary","void","double","java","model.setsalary","salary","0.13","15","employeecontroller.java","getemployeesalary","model.getname"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task15/EmployeeView.java","text":"public class EmployeeView { public void printEmployeeDetails(String name, double salary){ System.out.println(\"Имя: \" + name); System.out.println(\"Зарплата: \"+salary); } public void printEnd(String e){ System.out.println(\"End: \"+e); } }","keywords":["public","system.out.println","java","name","string","void","+e","+salary","15","class","double","employeeview"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task15/Movie.java","text":"public void setOpis(String opis) { this.opis = opis; } public String getDate() { return date; }","keywords":["java","opis","public","string","15","date","getdate","movie.java","mvc","return","setopis","task"],"topics":["java"]},{"id":"chunk-7","heading":"Task15/MovieController.java","text":"public class MovieController { private Movie model; private MovieView view; public MovieController(Movie model, MovieView view){ this.model = model; this.view = view; } public void updet(String imagePath, String date, String opis){ model.setDate(date); model.setOpis(opis); view.updateView(imagePath, date, opis); }","keywords":["date","model","opis","public","string","view","imagepath","java","movie","moviecontroller","movieview","private"],"topics":["java"]},{"id":"chunk-8","heading":"Task15/MovieView.java","text":"public class MovieView extends JFrame { private JLabel name movie; private JLabel prince; private JLabel time;","keywords":["jlabel","private","java","15","class","extends","jframe","movie","movieview","movieview.java","mvc","name"],"topics":["java"]},{"id":"chunk-9","heading":"Task15/MovieView.java","text":"MovieView() throws IOException, InterruptedException { super(\"Расписание кино\"); setDefaultCloseOperation(JFrame.EXIT ON CLOSE);","keywords":["java","15","close","interruptedexception","ioexception","jframe.exit","movieview","movieview.java","mvc","setdefaultcloseoperation","super","task"],"topics":["java"]},{"id":"chunk-10","heading":"Task15/MovieView.java","text":"JPanel panel = new JPanel(); panel.setLayout(new BoxLayout(panel, BoxLayout.Y AXIS)); panel.setBackground(new Color(255,255,255)); panel.add(Box.createVerticalGlue());","keywords":["255","new","java","jpanel","panel","15","axis","box.createverticalglue","boxlayout","boxlayout.y","color","movieview.java"],"topics":["java"]},{"id":"chunk-11","heading":"Task15/MovieView.java","text":"JPanel imagePanel = new JPanel(); imagePanel.setPreferredSize(new Dimension(800, 600)); imagePanel.setMaximumSize(new Dimension(800, 600)); imagePanel.setBackground(new Color(255,255,255)); imagePanel.add(name movie);","keywords":["new","255","600","800","dimension","java","jpanel","15","color","imagepanel","imagepanel.add","imagepanel.setbackground"],"topics":["java"]},{"id":"chunk-12","heading":"Task15/MovieView.java","text":"prince.setAlignmentX(Component.CENTER ALIGNMENT); time.setAlignmentX(Component.CENTER ALIGNMENT);","keywords":["alignment","component.center","java","15","movieview.java","mvc","prince.setalignmentx","task","task15","time.setalignmentx","контроллера.","модели"],"topics":["java"]},{"id":"chunk-13","heading":"Task15/MovieView.java","text":"getContentPane().add(panel); setSize(800, 800); setDefaultCloseOperation(JFrame.EXIT ON CLOSE); setVisible(true); }","keywords":["800","java",".add","15","close","getcontentpane","jframe.exit","movieview.java","mvc","panel","setdefaultcloseoperation","setsize"],"topics":["java"]},{"id":"chunk-14","heading":"Task15/MovieView.java","text":"try { BufferedImage img = ImageIO.read(new File(filename)); JLabel picture = new JLabel(); picture.setIcon(new ImageIcon(img)); } catch (IOException e) { System.out.println(\"Ошибка при получении картинки\"); } } public void updateView(String imagePath, String date, String description){ name movie.setIcon(new ImageIcon(imagePath)); prince.setText(date); time.setText(\" \"+ description +\" \"); } }","keywords":["new","string","date","description","imageicon","imagepath","img","java","jlabel","15","bufferedimage","catch"],"topics":["java"]},{"id":"chunk-15","heading":"Task15/MVCPatternDemoEmployee.java","text":"public class MVCPatternDemoEmployee { public static void main(String[] args){ Employee model = retriveEmployeeFromDa(); EmployeeView view = new EmployeeView(); EmployeeController controller = new EmployeeController(model, view); controller.updateView(); controller.nds(); //controller.setEmployeeSalary(30000); System.out.println(\"С вычитом подоходового налога\"); controller.updateView()","keywords":["controller.updateview","employeecontroller","employeeview","java","model","new","public","view","15","30000","args","class"],"topics":["ai","mvc","java"]},{"id":"chunk-16","heading":"Task15/MVCPatternDemoEmployee.java","text":"; controller.theend(\"Завершение работы\"); }","keywords":["java","15","controller.theend","mvc","mvcpatterndemoemployee.java","task","task15","завершение","контроллера.","модели","паттерн","представления"],"topics":["mvc","java"]},{"id":"chunk-17","heading":"Task15/MVCPatternDemoEmployee.java","text":"private static Employee retriveEmployeeFromDa() { Employee model = new Employee(); model.setName(\"Артем\"); model.setSalary(30000); return model; } }","keywords":["employee","java","model","15","30000","model.setname","model.setsalary","mvc","mvcpatterndemoemployee.java","new","private","retriveemployeefromda"],"topics":["mvc","java"]},{"id":"chunk-18","heading":"Task15/MVCPatternDemoMovie.java","text":"public class MVCPatternDemoMovie { public static void main(String[] args) throws IOException, InterruptedException { Movie model = new Movie(\"Дата показа 12.12.2023\", \"Когда речь идет о деньгах, совесть молчит. А уж если речь об огромных деньгах!.","keywords":["java","movie","public","деньгах","речь","12.12.2023","15","args","class","interruptedexception","ioexception","main"],"topics":["ai","mvc","java"]},{"id":"chunk-19","heading":"Task15/MVCPatternDemoMovie.java","text":". Это основанная на реальных событиях история нескольких провидцев, которые независимо друг от друга предсказали мировой экономический кризис 2008 года задолго до того, как о нем зашептались в кулуарах на Уолл стрит. И предсказав, стали на нем зарабатывать. Сами того не желая.\"); MovieView view = new MovieView(); MovieController controller = new MovieController(model, view)","keywords":["java","moviecontroller","movieview","new","view","нем","того","15","2008","controller","model","mvc"],"topics":["mvc","java"]},{"id":"chunk-20","heading":"Task15/MVCPatternDemoMovie.java","text":"; controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\2.jpg\", model.getOpis(), model.getDate()); view.addMouseListener(new MouseAdapter() { int click = 1;","keywords":["java","task15","15","2.jpg","click","controller.updet","ideaprojects","int","model.getdate","model.getopis","mouseadapter","mvc"],"topics":["mvc","java"]},{"id":"chunk-21","heading":"Task15/MVCPatternDemoMovie.java","text":"@Override public void mouseClicked(MouseEvent e) { switch (click) { case 0: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\2.jpg\", \"Дата показа 13.12.2023\", \"Когда речь идет о деньгах, совесть молчит. А уж если речь об огромных деньгах!.","keywords":["java","task15","деньгах","речь","13.12.2023","15","2.jpg","case","click","controller.updet","ideaprojects","mouseclicked"],"topics":["mvc","java"]},{"id":"chunk-22","heading":"Task15/MVCPatternDemoMovie.java","text":". Это основанная на реальных событиях история нескольких провидцев, которые независимо друг от друга предсказали мировой экономический кризис 2008 года задолго до того, как о нем зашептались в кулуарах на Уолл стрит. И предсказав, стали на нем зарабатывать. Сами того не желая.\"); break","keywords":["java","нем","того","15","2008","break","mvc","mvcpatterndemomovie.java","task","task15","года","до"],"topics":["mvc","java"]},{"id":"chunk-23","heading":"Task15/MVCPatternDemoMovie.java","text":"; case 1: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\3.jpg\", \"Дата показа 14.12.2023\", \"Нью йоркский финансист Джордан Белфорт в конце 80 х годов основал одну из крупнейших брокерских компаний, но через десять лет его осудили за мошенничество с ценными бумагами","keywords":["java","task15","14.12.2023","15","3.jpg","80","case","controller.updet","ideaprojects","mvc","mvcpatterndemomovie.java","src"],"topics":["mvc","java"]},{"id":"chunk-24","heading":"Task15/MVCPatternDemoMovie.java","text":". С самых высот финансового мира Белфорду приходиться опуститься на самое дно, он борется с алкоголизмом и наркотической зависимостью, но после тюремного заключения бывший брокер переосмысливает свою жизнь, начинает читать лекции и выпускает книгу.\"); break","keywords":["java","15","break","mvc","mvcpatterndemomovie.java","task","task15","алкоголизмом","белфорду","борется","брокер","бывший"],"topics":["mvc","java"]},{"id":"chunk-25","heading":"Task15/MVCPatternDemoMovie.java","text":"; case 2: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\4.jpg\", \"Дата показа 15.12.2023\",\"Американское семейство отправляется из Чикаго в Европу, но в спешке сборов бестолковые родители забывают дома… одного из своих детей. Юное создание, однако, не теряется и демонстрирует чудеса изобретательности. И когда в дом залезают грабители, им приходится не раз пожалеть о встрече с милым крошкой.\"); break","keywords":["java","task15","15","15.12.2023","4.jpg","break","case","controller.updet","ideaprojects","mvc","mvcpatterndemomovie.java","src"],"topics":["mvc","java"]},{"id":"chunk-26","heading":"Task15/MVCPatternDemoMovie.java","text":"; case 3: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\5.jpg\", \"Дата показа 16.12.2023\", \"Кобб — талантливый вор, лучший из лучших в опасном искусстве извлечения: он крадет ценные секреты из глубин подсознания во время сна, когда человеческий разум наиболее уязвим","keywords":["java","task15","15","16.12.2023","5.jpg","case","controller.updet","ideaprojects","mvc","mvcpatterndemomovie.java","src","task"],"topics":["mvc","java"]},{"id":"chunk-27","heading":"Task15/MVCPatternDemoMovie.java","text":". Редкие способности Кобба сделали его ценным игроком в привычном к предательству мире промышленного шпионажа, но они же превратили его в извечного беглеца и лишили всего, что он когда либо любил.\"); break; case 4: controller.updet(\"D:\\\\IdeaProjects\\\\untitled\\\\src\\\\Task15\\\\1.png\", \"Дата показа 17.12.2023\", \"Вскоре после отмщения за смерть жены и сына, Макс Рокатански покинул ряды «Основного силового патруля» и уехал","keywords":["java","task15","его","1.png","15","17.12.2023","break","case","controller.updet","ideaprojects","mvc","mvcpatterndemomovie.java"],"topics":["mvc","java"]},{"id":"chunk-28","heading":"Task15/MVCPatternDemoMovie.java","text":"в глушь, где скитается в одиночестве, пока мир медленно падает впоследствии нефтяного кризиса и глобальной войны. Не имея ничего, кроме своей машины «Перехватчик», Максу предстоит научиться, как выжить в пост апокалиптической пустоши и сражаться с жестокими, безжалостными воинами, которые населяют её.\"); break; } click++; if (click == 5) { click = 0; } } }); } }","keywords":["click","java","15","break","click++","if","mvc","mvcpatterndemomovie.java","task","task15","апокалиптической","безжалостными"],"topics":["mvc","java"]},{"id":"chunk-29","heading":"Task15/MVCPatternDemoStudent.java","text":"public class MVCPatternDemoStudent { public static void main(String[] args){ Student model = retriveStudentFromDa(); StudentView view = new StudentView(); StudentController controller = new StudentController(model, view); controller.updateView(); controller.setStudentRollNo(\"01\"); System.out.println(\" \"); controller.updateView(); } private static Student retriveStudentFromDa() { Student student = new Student()","keywords":["student","new","controller.updateview","java","model","public","retrivestudentfromda","static","studentcontroller","studentview","view","01"],"topics":["ai","mvc","java"]},{"id":"chunk-30","heading":"Task15/MVCPatternDemoStudent.java","text":"; student.setName(\"Николай\"); student.setRollNo(\"09\"); return student; } }","keywords":["java","09","15","mvc","mvcpatterndemostudent.java","return","student","student.setname","student.setrollno","task","task15","контроллера."],"topics":["mvc","java"]},{"id":"chunk-31","heading":"Task15/StudentController.java","text":"public class StudentController { private Student model; private StudentView view;","keywords":["java","private","15","class","model","mvc","public","student","studentcontroller","studentcontroller.java","studentview","task"],"topics":["java"]},{"id":"chunk-32","heading":"Task15/StudentController.java","text":"public StudentController(Student model, StudentView view) { this.model = model; this.view = view; } public void setStudentName(String name){ model.setName(name); } public String getStudentName(){ return model.getName(); } public void setStudentRollNo(String rollNo){ model.setRollNo(rollNo); } public String getStudentRollNo(){ return model.getRollNo()","keywords":["public","string","java","model","name","return","rollno","view","void","15","getstudentname","getstudentrollno"],"topics":["java"]},{"id":"chunk-33","heading":"Task15/StudentController.java","text":"; } public void updateView(){ view.printStudentDetails(model.getName(), model.getRollNo()); } }","keywords":["java","15","model.getname","model.getrollno","mvc","public","studentcontroller.java","task","task15","updateview","view.printstudentdetails","void"],"topics":["ai","java"]},{"id":"chunk-34","heading":"Task15/StudentView.java","text":"public class StudentView { public void printStudentDetails(String name, String rollNo){ System.out.println(\"Name: \" + name); System.out.println(\"RollNo: \" + rollNo); } }","keywords":["name","rollno","java","public","string","system.out.println","15","class","mvc","printstudentdetails","studentview","studentview.java"],"topics":["ai","java"]},{"id":"chunk-35","heading":"Описание","text":"В этом модуле используется 12 Java файлов. Ключевые сущности: Employee, EmployeeController, EmployeeView, Movie, MovieController, MovieView.","keywords":["java","12","15","employee","employeecontroller","employeeview","movie","moviecontroller","movieview.","mvc","task","используется"],"topics":["java"]},{"id":"chunk-36","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","15","mvc","task","демо","задачи","запускайте","затем","кейсами.","класс","контроллера.","модели"],"topics":["oop"]}]},{"id":"java/task-16-exceptions","href":"/docs/java/task-16-exceptions","slug":["java","task-16-exceptions"],"section":"java","sectionTitle":"Java","title":"Task 16 — Исключения","description":"Обработка исключений и безопасное выполнение кода.","preview":"Задание Обработка исключений и безопасное выполнение кода. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task16/Exception1.java Task16/Exception2.java Task16/Exception3.java Task16/Exc","keywords":["string","key","public","task16","system.out.println","void","static","new","scanner","exception","getdetails","null","args","class","exceptiondemo","intstring","main","catch"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Обработка исключений и безопасное выполнение кода. Документация собрана по исходному коду этой практики.","keywords":["java","безопасное","выполнение","исключений","кода.","обработка","16","task","документация","задание","исключения","исходному"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task16/Exception1.java Task16/Exception2.java Task16/Exception3.java Task16/Exception4.java Task16/ThrowsDemo5.java Task16/ThrowsDemo6.java Task16/ThrowsDemo7.java Task16/ThrowsDemo8.java","keywords":["task16","java","16","exception1.java","exception2.java","exception3.java","exception4.java","task","throwsdemo5.java","throwsdemo6.java","throwsdemo7.java","throwsdemo8.java"],"topics":["java"]},{"id":"chunk-2","heading":"Task16/Exception1.java","text":"public class Exception1 { public static void exceptionDemo(){ System.out.println(2/0); } public static void exceptionDemo1(){ System.out.println(2.0/0.0); } public static void exceptionDemo2(){ try { System.out.println(2/0); }catch (ArithmeticException e){ System.out.println(\"Attempted division by zero\"); } } public static void main(String[] args){ exceptionDemo1(); exceptionDemo2(); exceptionDemo(); } }","keywords":["public","static","system.out.println","void","exceptiondemo","exceptiondemo1","exceptiondemo2","java","0.0","16","2.0","args"],"topics":["ai","java"]},{"id":"chunk-3","heading":"Task16/Exception2.java","text":"public class Exception2 { public static void exceptionDemo1(){ Scanner myScanner = new Scanner(System.in); System.out.println(\"Enter an integer \"); String intString = myScanner.next(); int i = Integer.parseInt(intString); System.out.println(2/i); } public static void exceptionDemo(){ try { Scanner myScanner = new Scanner(System.in); System.out.println(\"Enter an integer \"); String intString = myScanner.next()","keywords":["scanner","intstring","public","system.out.println","enter","integer","java","myscanner","myscanner.next","new","static","string"],"topics":["java"]},{"id":"chunk-4","heading":"Task16/Exception2.java","text":"; int i = Integer.parseInt(intString); System.out.println(2/i); }catch (NumberFormatException e){ System.out.println(\"error\"); } } public static void main(String[] args){ exceptionDemo(); exceptionDemo1(); } }","keywords":["java","system.out.println","16","args","catch","error","exception2.java","exceptiondemo","exceptiondemo1","int","integer.parseint","intstring"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task16/Exception3.java","text":"public class Exception3 { public static void exceptionDemo() { try { Scanner myScanner = new Scanner(System.in); System.out.println(\"Enter an integer \"); String intString = myScanner.next(); int i = Integer.parseInt(intString); System.out.println(2 / i); } catch (Exception e) { System.err.println(\"error\"); } } public static void main(String[] args){ exceptionDemo(); } }","keywords":["public","exceptiondemo","intstring","java","scanner","static","string","system.out.println","void","16","args","catch"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task16/Exception4.java","text":"public class Exception4 { public static void exceptionDemo(){ try { Scanner myScanner = new Scanner(System.in); System.out.println(\"Enter an integer \"); String intString = myScanner.next(); int i = Integer.parseInt(intString); System.out.println(2/i); }catch (NumberFormatException e){ System.out.println(\"error\"); }finally { System.out.println(\"Проверка работы блока finally\")","keywords":["system.out.println","finally","intstring","java","public","scanner","16","catch","class","enter","error","exception4"],"topics":["java"]},{"id":"chunk-7","heading":"Task16/Exception4.java","text":"; } } public static void main(String[] args){ exceptionDemo(); } }","keywords":["java","16","args","exception4.java","exceptiondemo","main","public","static","string","task","task16","void"],"topics":["ai","java"]},{"id":"chunk-8","heading":"Task16/ThrowsDemo5.java","text":"public class ThrowsDemo5 { public static String getDetails1(String key){ if (key == null) { throw new NullPointerException(\"null key in getDetails\"); } return key; } public static String getDetails(String key){ try { if (key == null){ throw new NullPointerException(\"null key in getDetails\");","keywords":["key","null","string","getdetails","public","if","java","new","nullpointerexception","static","throw","16"],"topics":["ai","java"]},{"id":"chunk-9","heading":"Task16/ThrowsDemo5.java","text":"} }catch (NullPointerException e){} return \"data \" + key; } public static void main(String[] args){ System.out.println(getDetails(null)); System.out.println(getDetails1(null)); } }","keywords":["java","null","system.out.println","16","args","catch","data","getdetails","getdetails1","key","main","nullpointerexception"],"topics":["ai","java"]},{"id":"chunk-10","heading":"Task16/ThrowsDemo6.java","text":"```java title=\"Task16/ThrowsDemo6.java\" package Task16; public class ThrowsDemo6 { public void printMessage(String key){ try { String message = getDetails(key); System.out.println(message); }catch (Exception e){","keywords":["java","task16","key","message","public","string","throwsdemo6.java","16","catch","class","exception","getdetails"],"topics":["ai","java"]},{"id":"chunk-11","heading":"Task16/ThrowsDemo6.java","text":"} System.out.println(key); } public String getDetails(String key){ if (key == null){ throw new NullPointerException(\"null key in getDetails\"); } return \"data for \" + key; } public static void main(String[] args){ ThrowsDemo6 n = new ThrowsDemo6(); n.printMessage(\"tol\"); n.printMessage(null); n.getDetails(\"kol\"); n.getDetails(null); } }","keywords":["key","null","string","getdetails","java","n.getdetails","n.printmessage","new","public","throwsdemo6","16","args"],"topics":["ai","java"]},{"id":"chunk-12","heading":"Task16/ThrowsDemo7.java","text":"public class ThrowsDemo7 { public void getKey() throws Exception { Scanner myScanner = new Scanner(System.in); String key = myScanner.next(); printDetails(key); } public void printDetails(String key) throws Exception { String message = getDetails(key); System.out.println(message);","keywords":["key","public","string","exception","java","message","printdetails","scanner","throws","void","16","class"],"topics":["ai","java"]},{"id":"chunk-13","heading":"Task16/ThrowsDemo7.java","text":"} private String getDetails(String key) throws Exception { if(Objects.equals(key, \"!\")){ throw new Exception(\"Key set to empty string\"); } return \"data for \" + key; } public static void main(String[] args) throws Exception { ThrowsDemo7 n = new ThrowsDemo7(); n.getKey(); } }","keywords":["key","string","exception","java","new","throws","throwsdemo7","16","args","data","empty","getdetails"],"topics":["ai","java"]},{"id":"chunk-14","heading":"Task16/ThrowsDemo8.java","text":"public class ThrowsDemo8 { public void getKey() { boolean r = true; while (r) try { Scanner myScanner = new Scanner(System.in); String key = myScanner.next(); printDetails(key); r = false; }catch (Exception e){ r = true; } } public void printDetails(String key) throws Exception { String message = getDetails(key); System.out.println(message)","keywords":["key","public","string","exception","java","message","printdetails","scanner","true","void","16","boolean"],"topics":["ai","java"]},{"id":"chunk-15","heading":"Task16/ThrowsDemo8.java","text":"; } private String getDetails(String key) throws Exception { if(Objects.equals(key,\"!\")){ throw new Exception(\"Key set to empty string\"); } return \"data for \" + key; } public static void main(String[] args) { ThrowsDemo8 n = new ThrowsDemo8(); n.getKey(); } }","keywords":["key","string","exception","java","new","throwsdemo8","16","args","data","empty","getdetails","if"],"topics":["ai","java"]},{"id":"chunk-16","heading":"Описание","text":"В этом модуле используется 8 Java файлов. Ключевые сущности: Exception1, Exception2, Exception3, Exception4, ThrowsDemo5, ThrowsDemo6.","keywords":["java","16","exception1","exception2","exception3","exception4","task","throwsdemo5","throwsdemo6.","безопасное","выполнение","исключений"],"topics":["java"]},{"id":"chunk-17","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","16","task","безопасное","выполнение","демо","задачи","запускайте","затем","исключений","исключения","кейсами."],"topics":["oop"]}]},{"id":"java/task-17-gui-and-exceptions","href":"/docs/java/task-17-gui-and-exceptions","slug":["java","task-17-gui-and-exceptions"],"section":"java","sectionTitle":"Java","title":"Task 17 — GUI + исключения","description":"Связка UI-слоя, доменной логики и пользовательских ошибок.","preview":"Задание Связка UI слоя, доменной логики и пользовательских ошибок. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task17/EmptyStringException.java Task17/FIOINN.java Task17/LabClass.jav","keywords":["task17","public","new","void","fio","string","int","gpa","labclassui.java","students","student","emptystringexception","arr.get","idnumber","override","return","table","this"],"topics":["ai","numpy","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Связка UI слоя, доменной логики и пользовательских ошибок. Документация собрана по исходному коду этой практики.","keywords":["java","доменной","логики","ошибок.","пользовательских","связка","17","gui","task","ui","ui-слоя","документация"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task17/EmptyStringException.java Task17/FIOINN.java Task17/LabClass.java Task17/LabClassDriver.java Task17/LabClassUI.java Task17/Proverka.java Task17/Student.java Task17/StudentNotFoundException.java","keywords":["task17","java","17","emptystringexception.java","fioinn.java","gui","labclass.java","labclassdriver.java","labclassui.java","proverka.java","student.java","studentnotfoundexception.java"],"topics":["java"]},{"id":"chunk-2","heading":"Task17/EmptyStringException.java","text":"public class EmptyStringException extends IllegalArgumentException { public EmptyStringException(LabClassUI t) { JOptionPane.showMessageDialog(t, \"Введите необходимые данные в поле ввода!\"); } }","keywords":["emptystringexception","java","public","17","class","emptystringexception.java","extends","gui","illegalargumentexception","joptionpane.showmessagedialog","labclassui","task"],"topics":["numpy","java"]},{"id":"chunk-3","heading":"Task17/FIOINN.java","text":"public class FIOINN { public void red() { System.out.println(\"Введите ФИО\"); Scanner fi = new Scanner(System.in); String fio = fi.nextLine(); System.out.println(\"Введите ИНН\"); Scanner in = new Scanner(System.in); String[] inn = in.nextLine().split(\"\"); try { if (inn.length == 12) { System.out.println(\"ИНН \" + fio + \" верный!\"); } else { throw new Proverka(fio)","keywords":["scanner","fio","new","system.out.println","java","public","string","system.in","введите","инн",".split","12"],"topics":["java"]},{"id":"chunk-4","heading":"Task17/FIOINN.java","text":"; } }catch (Proverka e){ System.out.println(e.getMessage()); } } public static void main(String[] args){ FIOINN n = new FIOINN(); n.red(); } }","keywords":["fioinn","java","17","args","catch","e.getmessage","fioinn.java","gui","main","n.red","new","proverka"],"topics":["ai","java"]},{"id":"chunk-5","heading":"Task17/LabClass.java","text":"public class LabClass { public static void main(String[] args) { ArrayList new stud = new ArrayList< (); new stud.add(new Student(\"Иванов Иван Иванович\", 123456, 4.0)); new LabClassUI(new stud); } }","keywords":["new","arraylist","java","public","stud","123456","17","4.0","args","class","gui","labclass"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task17/LabClassDriver.java","text":"public class LabClassDriver { public int compare(Student stud1, Student stud2) { return Double.compare(stud1.getGPA(),stud2.getGPA()); } public void quick sort(ArrayList arr, int begin, int end) { if (begin arr, int begin, int end) { Student pt = arr.get(end); int i = (begin 1); for (int j = begin; j = 0) { i++; Student swapTemp = arr.get(i); arr.set(i, arr.get(j)); arr.set(j, swapTemp)","keywords":["int","begin","student","arr.get","end","public","arr","arr.set","java","swaptemp","17","arraylist"],"topics":["java"]},{"id":"chunk-7","heading":"Task17/LabClassDriver.java","text":"; } } Student swapt = arr.get(i + 1); arr.set(i + 1, arr.get(end)); arr.set(end, swapt); return i + 1; } }","keywords":["arr.get","arr.set","end","java","swapt","17","gui","labclassdriver.java","return","student","task","task17"],"topics":["java"]},{"id":"chunk-8","heading":"Task17/LabClassUI.java","text":"public LabClassUI(ArrayList students arr){ super(\"Сведения о студентах\"); setDefaultCloseOperation(EXIT ON CLOSE); setSize(520, 330); setResizable(false); this.students arr = students arr; JPanel panel = new JPanel(new FlowLayout()); JButton add btn = new JButton(\"Добавить\"); JButton sort btn = new JButton(\"Отсортировать по GPA\"); JButton search btn = new JButton(\"Поиск\"); panel.add(add btn); panel.add(search btn)","keywords":["jbutton","btn","new","arr","add","java","jpanel","panel.add","search","students","17","330"],"topics":["java"]},{"id":"chunk-9","heading":"Task17/LabClassUI.java","text":"; panel.add(sort btn);","keywords":["java","17","btn","gui","labclassui.java","panel.add","sort","task","task17","ui-слоя","доменной","исключения"],"topics":["java"]},{"id":"chunk-10","heading":"Task17/LabClassUI.java","text":"Object [][] exist stud = new String[students arr.size()][3]; for(int i = 0; i { try { add(); }catch (IllegalArgumentException ex){ JOptionPane.showMessageDialog(this, ex.getMessage()); } }); search btn.addActionListener(e { try{ find(); } catch (StudentNotFoundException ex){ JOptionPane.showMessageDialog(this, ex.getMessage()); } })","keywords":["catch","ex","ex.getmessage","java","joptionpane.showmessagedialog","this","try","17","add","arr.size","btn.addactionlistener","exist"],"topics":["numpy","java"]},{"id":"chunk-11","heading":"Task17/LabClassUI.java","text":"; sort btn.addMouseListener(new MouseInputListener() { @Override public void mouseClicked(MouseEvent e) { LabClassDriver ms = new LabClassDriver(); ms.quick sort(students arr,0,students arr.size() 1); DefaultTableModel table mod = (DefaultTableModel) table.getModel(); for(int i = 0; i<students arr.size();i++) { table mod.removeRow(i); Student st = students arr.get(i)","keywords":["students","arr.size","defaulttablemodel","java","labclassdriver","new","sort","table","17","arr","arr.get","btn.addmouselistener"],"topics":["numpy","java"]},{"id":"chunk-12","heading":"Task17/LabClassUI.java","text":"; table mod.insertRow(i, new Object[]{st.getFio(), st.getiDNumber(), st.getGPA()}); } } @Override public void mousePressed(MouseEvent e) {} @Override public void mouseReleased(MouseEvent e) {} @Override public void mouseEntered(MouseEvent e) {} @Override public void mouseExited(MouseEvent e) {} @Override public void mouseDragged(MouseEvent e) {} @Override public void mouseMoved(MouseEvent e) {} });","keywords":["mouseevent","override","public","void","java","17","gui","labclassui.java","mod.insertrow","mousedragged","mouseentered","mouseexited"],"topics":["java"]},{"id":"chunk-13","heading":"Task17/LabClassUI.java","text":"getContentPane().add(panel, BorderLayout.SOUTH); getContentPane().add(new JScrollPane(table), BorderLayout.CENTER); setVisible(true); }","keywords":[".add","getcontentpane","java","17","borderlayout.center","borderlayout.south","gui","jscrollpane","labclassui.java","new","panel","setvisible"],"topics":["java"]},{"id":"chunk-14","heading":"Task17/LabClassUI.java","text":"private void add() { String fio = JOptionPane.showInputDialog(\"ФИО студента: \"); if(fio.isEmpty()) try { throw new EmptyStringException(this); } catch (EmptyStringException e) { throw new RuntimeException(e); } String number = JOptionPane.showInputDialog(\"Номер дела:\");","keywords":["emptystringexception","java","joptionpane.showinputdialog","new","string","throw","17","add","catch","fio","fio.isempty","gui"],"topics":["numpy","java"]},{"id":"chunk-15","heading":"Task17/LabClassUI.java","text":"if(number.isEmpty()) try { throw new EmptyStringException(this); } catch (EmptyStringException e) { throw new RuntimeException(e); }","keywords":["emptystringexception","java","new","throw","17","catch","gui","if","labclassui.java","number.isempty","runtimeexception","task"],"topics":["java"]},{"id":"chunk-16","heading":"Task17/LabClassUI.java","text":"String GPA = JOptionPane.showInputDialog(\"GPA:\"); if(GPA.isEmpty()) try { throw new EmptyStringException(this); } catch (EmptyStringException e) { throw new RuntimeException(e); } students arr.add(new Student(fio, Integer.parseInt(number), Double.parseDouble(GPA))); DefaultTableModel table mod = (DefaultTableModel) table.getModel(); table mod.addRow(new Object[]{fio, number,GPA}); }","keywords":["gpa","new","defaulttablemodel","emptystringexception","fio","java","number","table","throw","17","arr.add","catch"],"topics":["numpy","java"]},{"id":"chunk-17","heading":"Task17/LabClassUI.java","text":"private void find() throws StudentNotFoundException { String search val = JOptionPane.showInputDialog(\"Введите ФИО для поиска:\"); for(int i = 0; i < students arr.size(); i++){ if(students arr.get(i).getFio().equals(search val)){ JOptionPane.showMessageDialog(this, \"Результат: \"+ students arr.get(i).toString()); return; } } throw new StudentNotFoundException(search val, this); }","keywords":["search","students","val","arr.get","java","studentnotfoundexception","this",".equals",".getfio",".tostring","17","arr.size"],"topics":["numpy","java"]},{"id":"chunk-18","heading":"Task17/LabClassUI.java","text":"private void remove() { int cnt = table.getSelectedRowCount(); if(cnt != 1) { JOptionPane.showMessageDialog(this, \"Необходимо выбрать строку для удаления\"); return; } cnt = table.getSelectedRow(); students arr.remove(cnt); DefaultTableModel table mod = (DefaultTableModel) table.getModel(); table mod.removeRow(cnt);","keywords":["cnt","defaulttablemodel","java","table","17","arr.remove","gui","if","int","joptionpane.showmessagedialog","labclassui.java","mod"],"topics":["numpy","java"]},{"id":"chunk-19","heading":"Task17/Proverka.java","text":"public class Proverka extends Exception{ public Proverka(String fio){ super(\"ИНН \"+fio+\" не верный\"); } }","keywords":["java","proverka","public","+fio+","17","class","exception","extends","fio","gui","proverka.java","string"],"topics":["java"]},{"id":"chunk-20","heading":"Task17/Student.java","text":"public class Student { private int iDNumber; private String fio; private double GPA; public Student(String fio,int iDNumber, double GPA) { this.iDNumber = iDNumber; this.fio = fio; this.GPA = GPA; } @Override public String toString() { return fio + \" (iDNumber: \" + iDNumber + \", GPA: \" + GPA + \")\" ; } public int getiDNumber() { return iDNumber; } public void setiDNumber(int iDNumber) { this.iDNumber = iDNumber","keywords":["idnumber","gpa","public","fio","int","private","string","double","java","return","student","this.idnumber"],"topics":["java"]},{"id":"chunk-21","heading":"Task17/Student.java","text":"; } public String getFio() { return fio; } public void setFio(String fio) { this.fio = fio; } public double getGPA() { return GPA; } public void setGPA(double GPA) { this.GPA = GPA; } }","keywords":["public","fio","gpa","double","java","return","string","void","17","getfio","getgpa","gui"],"topics":["java"]},{"id":"chunk-22","heading":"Task17/StudentNotFoundException.java","text":"public class StudentNotFoundException extends Throwable { public StudentNotFoundException(String fio, LabClassUI t) { JOptionPane.showMessageDialog(t, \"Студент \" + fio + \" не найден\"); } }","keywords":["fio","java","public","studentnotfoundexception","17","class","extends","gui","joptionpane.showmessagedialog","labclassui","string","studentnotfoundexception.java"],"topics":["numpy","java"]},{"id":"chunk-23","heading":"Описание","text":"В этом модуле используется 8 Java файлов. 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Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task2/Task.java Task2/Task.java java title=\"Task2/Task.java\" package Task2; p","keywords":["int","ty","task2","task.java","структура","j+1","java","system.out.println","входа","минимальная","точка","этой","factorial","input","public","task","tmp","документация"],"topics":["numpy","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Точка входа и минимальная структура Java приложения. 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Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task20/calculator.java Task20/calculator.java java title=\"Task20/calculator.j","keywords":["stack.pop","stack.push","task20","break","calculator.java","case","elem","new","stack","string","исключений.","калькулятора","обработкой","ооп","этой","0.0","double","exception"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"ООП реализация калькулятора с обработкой исключений. 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Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task21/AbstractQueue.java Task21/ArrayQueue.java Task21/ArrayQueueADT.java Ta","keywords":["public","task21","front","int","system.out.println","rear","new","return","if","isempty","queue","size","void","arr","count","element","throw","arrayqueuemodule"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Абстрактные типы данных и разные реализации очереди. Документация собрана по исходному коду этой практики.","keywords":["java","абстрактные","данных","очереди.","разные","реализации","типы","21","adt","queue","task","документация"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task21/AbstractQueue.java Task21/ArrayQueue.java Task21/ArrayQueueADT.java Task21/ArrayQueueModule.java Task21/LinkedQueue.java Task21/Queue.java Task21/TestArray.java Task21/TestLinked.java","keywords":["task21","java","21","abstractqueue.java","adt","arrayqueue.java","arrayqueueadt.java","arrayqueuemodule.java","linkedqueue.java","queue","queue.java","task"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task21/ArrayQueue.java","text":"public ArrayQueue(){ arr = new int[SIZE]; front=rear= 1; count = 0; } public void enqueue(int element){ if(count==SIZE){ throw new IllegalStateException(\"Переполнение массива\"); } if(isEmpty()){ front=rear=0; }else{ rear=(rear+1)%SIZE; } arr[rear] = element; count++; } public int element(){ if (isEmpty()) { throw new IllegalStateException(\"Queue is empty\"); } return arr[front]; }","keywords":["rear","arr","element","front","if","int","new","public","size","count","illegalstateexception","isempty"],"topics":["java"]},{"id":"chunk-3","heading":"Task21/ArrayQueue.java","text":"public int dequeue() { if (isEmpty()) { throw new IllegalStateException(\"Queue is empty\"); } int value = arr[front]; if (front == rear) { front = rear = 1; } else { front = (front + 1) % SIZE; }","keywords":["front","if","int","java","queue","rear","21","adt","arr","arrayqueue.java","dequeue","else"],"topics":["java"]},{"id":"chunk-4","heading":"Task21/ArrayQueue.java","text":"public int size() { return count; } public void clear() { front = rear = 1; count = 0; } public boolean isEmpty(){ if (front== 1){ return true; }else { return false; } } }","keywords":["public","return","count","front","java","21","adt","arrayqueue.java","boolean","clear","else","false"],"topics":["java"]},{"id":"chunk-5","heading":"Task21/ArrayQueueADT.java","text":"public class ArrayQueueADT { public ArrayQueueADT(ArrayQueueModule queue){ this.queue = queue; } private ArrayQueueModule queue; public void enqueue(int elemen) { queue.enqueue(elemen); } public int element(){ return queue.element(); } public int dequeue(){ return queue.dequeue(); } public int size(){ return queue.size(); } public void clear(){ queue.clear(); } public boolean isEmpty(){ return queue.isEmpty(); } }","keywords":["public","int","queue","return","arrayqueueadt","arrayqueuemodule","elemen","java","void","21","adt","arrayqueueadt.java"],"topics":["java"]},{"id":"chunk-6","heading":"Task21/ArrayQueueModule.java","text":"public class ArrayQueueModule { int SIZE = 5; int[] arr; int front, rear; int count;","keywords":["int","java","21","adt","arr","arrayqueuemodule","arrayqueuemodule.java","class","count","front","public","queue"],"topics":["java"]},{"id":"chunk-7","heading":"Task21/ArrayQueueModule.java","text":"public ArrayQueueModule(){ arr = new int[SIZE]; front=rear= 1; count = 0; } public void enqueue(int element){ if(count==SIZE){ throw new IllegalStateException(\"Переполнение массива\"); } if(isEmpty()){ front=rear=0; }else{ rear=(rear+1)%SIZE; } arr[rear] = element; count++; } public int element(){ if (isEmpty()) { throw new IllegalStateException(\"Queue is empty\"); } return arr[front]; }","keywords":["rear","arr","element","front","if","int","new","public","size","count","illegalstateexception","isempty"],"topics":["java"]},{"id":"chunk-8","heading":"Task21/ArrayQueueModule.java","text":"public int dequeue() { if (isEmpty()) { throw new IllegalStateException(\"Queue is empty\"); } int value = arr[front]; if (front == rear) { front = rear = 1; } else { front = (front + 1) % SIZE; }","keywords":["front","if","int","java","queue","rear","21","adt","arr","arrayqueuemodule.java","dequeue","else"],"topics":["java"]},{"id":"chunk-9","heading":"Task21/ArrayQueueModule.java","text":"public int size() { return count; } public void clear() { front = rear = 1; count = 0; } public boolean isEmpty(){ if (front== 1){ return true; }else { return false; } } }","keywords":["public","return","count","front","java","21","adt","arrayqueuemodule.java","boolean","clear","else","false"],"topics":["java"]},{"id":"chunk-10","heading":"Task21/LinkedQueue.java","text":"public class LinkedQueue extends AbstractQueue implements Queue { private LinkedList queue; public LinkedQueue(){ queue=new LinkedList< (); front=0; rear=0; } @Override public void enqueue(Object o) { queue.add(o); rear++; }","keywords":["queue","public","java","linkedlist","linkedqueue","21","abstractqueue","adt","class","enqueue","extends","front"],"topics":["java"]},{"id":"chunk-11","heading":"Task21/LinkedQueue.java","text":"@Override public Object element() { if(isEmpty())throw new IndexOutOfBoundsException(\"Массив пуст!!\"); return queue.get(front); } @Override public Object dequeue() { if (isEmpty()) throw new IndexOutOfBoundsException(\"Массив пуст!!\"); System.out.println(front+\" \"+rear); return queue.remove(front); }","keywords":["front","if","indexoutofboundsexception","isempty","java","new","object","override","public","return","throw","массив"],"topics":["java"]},{"id":"chunk-12","heading":"Task21/LinkedQueue.java","text":"@Override public boolean clear() { queue.clear(); front=0; rear=0; return false; } }","keywords":["java","21","adt","boolean","clear","false","front","linkedqueue.java","override","public","queue","queue.clear"],"topics":["java"]},{"id":"chunk-13","heading":"Task21/Queue.java","text":"public interface Queue { void enqueue(Object o); Object element(); Object dequeue(); boolean isEmpty(); boolean clear(); }","keywords":["object","boolean","java","queue","21","adt","clear","dequeue","element","enqueue","interface","isempty"],"topics":["java"]},{"id":"chunk-14","heading":"Task21/TestArray.java","text":"public class TestArray { public static void main(String[] args) { ArrayQueueModule m = new ArrayQueueModule(); // b.dequeue(); m.enqueue(1); m.enqueue(2); m.enqueue(3); m.enqueue(1); m.enqueue(2); System.out.println(m.size()); System.out.println(m.element()); System.out.println(m.dequeue()); System.out.println(m.isEmpty()); ArrayQueueADT a = new ArrayQueueADT(m); System.out.println(a.size())","keywords":["m.enqueue","system.out.println","arrayqueueadt","arrayqueuemodule","java","new","public","21","a.size","adt","args","b.dequeue"],"topics":["ai","java","testing"]},{"id":"chunk-15","heading":"Task21/TestArray.java","text":"; System.out.println(a.element()); System.out.println(a.dequeue()); System.out.println(a.isEmpty()); ArrayQueue q = new ArrayQueue(); q.enqueue(1); q.enqueue(2); q.enqueue(3); q.enqueue(1); q.enqueue(2); System.out.println(q.size()); System.out.println(q.element()); System.out.println(q.dequeue()); System.out.println(q.isEmpty()); } }","keywords":["system.out.println","q.enqueue","arrayqueue","java","21","a.dequeue","a.element","a.isempty","adt","new","q.dequeue","q.element"],"topics":["java","testing"]},{"id":"chunk-16","heading":"Task21/TestLinked.java","text":"public class TestLinked { public static void main(String[] args){ LinkedQueue l = new LinkedQueue(); l.enqueue(1); l.enqueue(3); l.enqueue(2); System.out.println(l.dequeue()); //System.out.println(l.element()); System.out.println(l.dequeue()); System.out.println(\"Пустой ли массив ? \"+l.isEmpty()); //System.out.println(l.clear()); System.out.println(l.dequeue()); System.out.println(\"Пустой ли массив ? \"+l.isEmpty())","keywords":["system.out.println","l.dequeue","l.enqueue","+l.isempty","java","linkedqueue","public","ли","массив","пустой","21","adt"],"topics":["ai","java","testing"]},{"id":"chunk-17","heading":"Task21/TestLinked.java","text":"; } }","keywords":["java","21","adt","queue","task","task21","testlinked.java","абстрактные","данных","очереди.","разные","реализации"],"topics":["java","testing"]},{"id":"chunk-18","heading":"Описание","text":"В этом модуле используется 8 Java файлов. Ключевые сущности: AbstractQueue, ArrayQueue, ArrayQueueADT, ArrayQueueModule, LinkedQueue, Queue.","keywords":["java","21","abstractqueue","adt","arrayqueue","arrayqueueadt","arrayqueuemodule","linkedqueue","queue","queue.","task","абстрактные"],"topics":["java"]},{"id":"chunk-19","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","21","adt","queue","task","абстрактные","данных","демо","задачи","запускайте","затем","кейсами."],"topics":["oop"]}]},{"id":"java/task-22-design-patterns","href":"/docs/java/task-22-design-patterns","slug":["java","task-22-design-patterns"],"section":"java","sectionTitle":"Java","title":"Task 22 — Паттерны проектирования","description":"Factory, Abstract Factory и MVC в прикладных задачах.","preview":"Задание Factory, Abstract Factory и MVC в прикладных задачах. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task22/one/Complex.java Task22/one/ComplexAbstractFactory.java Task22/one/Co","keywords":["task22","public","three","new","void","string","two","int","class","actionlistener","carcasview.java","override","return","idocument","one","textdocument","complex","image"],"topics":["ai","numpy","mvc","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Factory, Abstract Factory и MVC в прикладных задачах. Документация собрана по исходному коду этой практики.","keywords":["factory","abstract","java","mvc","задачах.","прикладных","22","task","документация","задание","исходному","коду"],"topics":["mvc"]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task22/one/Complex.java Task22/one/ComplexAbstractFactory.java Task22/one/ConcreteFactory.java Task22/one/TestComplex.java Task22/three/CarcasController.java Task22/three/CarcasModel.java Task22/three/CarcasView.java Task22/three/CreateTextDoc.java Task22/three/ICreateDocument.java Task22/three/IDocument.java Task22/three/TestWindow.java Task22/three/TextDocument.java Task22/two/AbstractChairFactory.java","keywords":["task22","three","one","factory","java","22","abstract","abstractchairfactory.java","carcascontroller.java","carcasmodel.java","carcasview.java","complex.java"],"topics":["ai","java","testing"]},{"id":"chunk-2","heading":"Исходные файлы решения","text":"Task22/two/Chair.java Task22/two/ChairFactory.java Task22/two/Client.java Task22/two/FunctionalChair.java Task22/two/MagicChair.java Task22/two/TestChair.java Task22/two/VictorianChair.java","keywords":["task22","two","factory","java","22","abstract","chair.java","chairfactory.java","client.java","functionalchair.java","magicchair.java","mvc"],"topics":["ai","java","testing"]},{"id":"chunk-3","heading":"Task22/one/Complex.java","text":"public class Complex { private int real; private int image; public Complex(){ this.real=0; this.image=0; } public Complex(int real, int image) { this.real=real; this.image=image; } public int getReal() { return real; } public void setReal(int real) { this.real = real; } public int getImage() { return image; } public void setImage(int image) { this.image = image","keywords":["int","public","image","real","complex","this.image","this.real","factory","java","private","return","void"],"topics":["java"]},{"id":"chunk-4","heading":"Task22/one/Complex.java","text":"; } @Override public String toString() { return \"Комплексное число = \"+ real + \" + \" + image + \"i\"; } }","keywords":["factory","java","22","abstract","complex.java","image","mvc","one","override","public","real","return"],"topics":["java"]},{"id":"chunk-5","heading":"Task22/one/ComplexAbstractFactory.java","text":"public interface ComplexAbstractFactory { Complex createComplex(); Complex CreateComplex(int real, int image); }","keywords":["complex","createcomplex","factory","int","java","22","abstract","complexabstractfactory","complexabstractfactory.java","image","interface","mvc"],"topics":["java"]},{"id":"chunk-6","heading":"Task22/one/ConcreteFactory.java","text":"public class ConcreteFactory implements ComplexAbstractFactory { @Override public Complex createComplex() { return new Complex(); } @Override public Complex CreateComplex(int real, int image) { return new Complex(real, image); } }","keywords":["complex","public","createcomplex","factory","image","int","java","new","override","real","return","22"],"topics":["java"]},{"id":"chunk-7","heading":"Task22/one/TestComplex.java","text":"public class TestComplex { public static void main(String[] args){ ComplexAbstractFactory c = new ConcreteFactory(); System.out.println(c.CreateComplex(1,7)); System.out.println(c.createComplex()); } }","keywords":["c.createcomplex","factory","java","public","system.out.println","22","abstract","args","class","complexabstractfactory","concretefactory","main"],"topics":["ai","java","testing"]},{"id":"chunk-8","heading":"Task22/three/CarcasController.java","text":"```java title=\"Task22/three/CarcasController.java\" package Task22.three; public class CarcasController { private final CarcasModel model; private final CarcasView view; public CarcasController() { this.model = new CarcasModel(); this.view = new CarcasView< ();","keywords":["java","carcascontroller","carcascontroller.java","carcasmodel","carcasview","factory","final","new","private","public","task22","three"],"topics":["java"]},{"id":"chunk-9","heading":"Task22/three/CarcasController.java","text":"view.addNewListener(e { model.setDocument(new CreateTextDoc().CreateNew()); view.showDocText((TextDocument) model.getDocument()); });","keywords":["factory","java",".createnew","22","abstract","carcascontroller.java","createtextdoc","model.getdocument","model.setdocument","mvc","new","task"],"topics":["java"]},{"id":"chunk-10","heading":"Task22/three/CarcasController.java","text":"view.addOpenListener(e { try { model.setDocument(new CreateTextDoc().CreateOpen(((CarcasView.openDocA) e).getPath())); view.showDocText((TextDocument) model.getDocument()); } catch (FileNotFoundException exc) { JOptionPane.showMessageDialog(view, \"File does not exist\"); } });","keywords":["factory","java",".createopen",".getpath","22","abstract","carcascontroller.java","carcasview.opendoca","catch","createtextdoc","does","exc"],"topics":["numpy","java"]},{"id":"chunk-11","heading":"Task22/three/CarcasController.java","text":"view.addSaveListener(e { var document = ((CarcasView.saveDocA)e).getDocument(); System.out.println(\"Saved\"); try { document.save(); } catch (IOException ex) { throw new RuntimeException(ex); } }); }","keywords":["ex","factory","java",".getdocument","22","abstract","carcascontroller.java","carcasview.savedoca","catch","document","document.save","ioexception"],"topics":["java"]},{"id":"chunk-12","heading":"Task22/three/CarcasView.java","text":"public class CarcasView extends JFrame { IDocument doc; class newDocA extends ActionEvent { public newDocA(Object obj) { super(obj, 0, \"\"); } } class openDocA extends ActionEvent { private String path;","keywords":["class","extends","actionevent","factory","java","newdoca","obj","public","22","abstract","carcasview","carcasview.java"],"topics":["java"]},{"id":"chunk-13","heading":"Task22/three/CarcasView.java","text":"public String getPath() { return path; } public openDocA(Object source, String path) { super(source, 0, \"\"); this.path = path; } } JMenuBar menu = new JMenuBar(); ArrayList saveListener = new ArrayList< (); ArrayList newListener = new ArrayList< (); ArrayList openListener = new ArrayList< (); class saveDocA extends ActionEvent { private IDocument doc; public saveDocA(Object obj, IDocument doc) { super(obj, 0, \"\")","keywords":["arraylist","new","path","public","doc","factory","idocument","java","jmenubar","obj","object","savedoca"],"topics":["java"]},{"id":"chunk-14","heading":"Task22/three/CarcasView.java","text":"; this.doc = doc; } public IDocument getDocument() { return doc; } }","keywords":["doc","factory","java","22","abstract","carcasview.java","getdocument","idocument","mvc","public","return","task"],"topics":["java"]},{"id":"chunk-15","heading":"Task22/three/CarcasView.java","text":"void addNewListener(ActionListener actionListener) { newListener.add(actionListener); } void addOpenListener(ActionListener actionListener) { openListener.add(actionListener); } void addSaveListener(ActionListener actionListener) { saveListener.add(actionListener); } void setOpenDoc(String path) { for (ActionListener actionListener: openListener) { actionListener.actionPerformed(new openDocA(this, path)); } }","keywords":["actionlistener","void","factory","java","path","22","abstract","actionlistener.actionperformed","addnewlistener","addopenlistener","addsavelistener","carcasview.java"],"topics":["numpy","java"]},{"id":"chunk-16","heading":"Task22/three/CarcasView.java","text":"void setNewDoc() { for (ActionListener actionListener: newListener) { actionListener.actionPerformed(new newDocA(this)); } }","keywords":["actionlistener","factory","java","22","abstract","actionlistener.actionperformed","carcasview.java","mvc","new","newdoca","newlistener","setnewdoc"],"topics":["numpy","java"]},{"id":"chunk-17","heading":"Task22/three/CarcasView.java","text":"void setSaveDoc() { for (ActionListener actionListener: saveListener) { actionListener.actionPerformed(new saveDocA(this, doc)); } }","keywords":["actionlistener","factory","java","22","abstract","actionlistener.actionperformed","carcasview.java","doc","mvc","new","savedoca","savelistener"],"topics":["numpy","java"]},{"id":"chunk-18","heading":"Task22/three/CarcasView.java","text":"CarcasView() { setTitle(\"Каркас\"); setDefaultCloseOperation(EXIT ON CLOSE); setSize(400, 200); JMenu fileItem = new JMenu(\"Файл\");","keywords":["factory","java","jmenu","200","22","400","abstract","carcasview","carcasview.java","close","exit","fileitem"],"topics":["java"]},{"id":"chunk-19","heading":"Task22/three/CarcasView.java","text":"JMenuItem newPosition = new JMenuItem(\"Создать\"); newPosition.addActionListener(e { setNewDoc(); });","keywords":["factory","java","jmenuitem","22","abstract","carcasview.java","mvc","new","newposition","newposition.addactionlistener","setnewdoc","task"],"topics":["java"]},{"id":"chunk-20","heading":"Task22/three/CarcasView.java","text":"JMenuItem openPosition = new JMenuItem(\"Открыть\"); openPosition.addActionListener(e { JFileChooser file choose = new JFileChooser(); if (file choose.showDialog(getContentPane(), \"OK\") == JFileChooser.APPROVE OPTION) { setOpenDoc(file choose.getSelectedFile().getAbsolutePath()); } });","keywords":["file","factory","java","jfilechooser","jmenuitem","new",".getabsolutepath","22","abstract","carcasview.java","choose","choose.getselectedfile"],"topics":["numpy","java"]},{"id":"chunk-21","heading":"Task22/three/CarcasView.java","text":"JMenuItem savePosition = new JMenuItem(\"Сохранить\"); savePosition.addActionListener(e { setSaveDoc(); });","keywords":["factory","java","jmenuitem","22","abstract","carcasview.java","mvc","new","saveposition","saveposition.addactionlistener","setsavedoc","task"],"topics":["java"]},{"id":"chunk-22","heading":"Task22/three/CarcasView.java","text":"JMenuItem exitPosition = new JMenuItem(\"Выход\"); exitPosition.addActionListener(e { setVisible(false); }); fileItem.add(newPosition); fileItem.add(openPosition); fileItem.add(savePosition); fileItem.add(exitPosition); menu.add(fileItem); setJMenuBar(menu); setBounds(0, 0, 390, 200); }","keywords":["fileitem.add","exitposition","factory","java","jmenuitem","200","22","390","abstract","carcasview.java","exitposition.addactionlistener","false"],"topics":["numpy","java"]},{"id":"chunk-23","heading":"Task22/three/CarcasView.java","text":"void showDocText(TextDocument textDocument) { this.doc = (IDocument ) textDocument; setTitle(\"Текущий документ \" + textDocument.getName()); getContentPane().removeAll(); repaint(); JTextArea textFieldBig = new JTextArea(textDocument.getContent())","keywords":["textdocument","factory","java","jtextarea",".removeall","22","abstract","carcasview.java","getcontentpane","idocument","mvc","new"],"topics":["ai","java"]},{"id":"chunk-24","heading":"Task22/three/CarcasView.java","text":"; textFieldBig.getDocument().addDocumentListener(new DocumentListener() { @Override public void insertUpdate(DocumentEvent documentEvent) { ((TextDocument)doc).setContent(textFieldBig.getText()); } @Override public void removeUpdate(DocumentEvent documentEvent) { ((TextDocument)doc).setContent(textFieldBig.getText())","keywords":["documentevent",".setcontent","doc","factory","java","override","public","textdocument","textfieldbig.gettext","void",".adddocumentlistener","22"],"topics":["java"]},{"id":"chunk-25","heading":"Task22/three/CarcasView.java","text":"; } @Override public void changedUpdate(DocumentEvent documentEvent) { ((TextDocument)doc).setContent(textFieldBig.getText()); } }); add(textFieldBig); setBounds(0, 0, 450, 200); }","keywords":["documentevent","factory","java",".setcontent","200","22","450","abstract","add","carcasview.java","changedupdate","doc"],"topics":["java"]},{"id":"chunk-26","heading":"Task22/three/CreateTextDoc.java","text":"public class CreateTextDoc implements ICreateDocument{ public IDocument CreateNew() { return (IDocument ) new TextDocument(\"Безымянный\"); }","keywords":["factory","idocument","java","public","22","abstract","class","createnew","createtextdoc","createtextdoc.java","icreatedocument","implements"],"topics":["java"]},{"id":"chunk-27","heading":"Task22/three/CreateTextDoc.java","text":"@Override public IDocument CreateOpen(String path) throws FileNotFoundException { BufferedReader file = new BufferedReader(new FileReader(path)); return (IDocument ) new TextDocument(path, file.lines().collect(Collectors.joining(\"\\n\"))); }","keywords":["new","path","bufferedreader","factory","idocument","java",".collect","22","abstract","collectors.joining","createopen","createtextdoc.java"],"topics":["java"]},{"id":"chunk-28","heading":"Task22/three/ICreateDocument.java","text":"```java title=\"Task22/three/ICreateDocument.java\" package Task22.three; public interface ICreateDocument { IDocument CreateNew(); IDocument CreateOpen(String path) throws FileNotFoundException;","keywords":["java","factory","icreatedocument.java","idocument","task22","three","22","abstract","createnew","createopen","filenotfoundexception","icreatedocument"],"topics":["java"]},{"id":"chunk-29","heading":"Task22/three/IDocument.java","text":"public interface IDocument { String getName(); void setName(String name); String getContent(); void setContent(String content);","keywords":["string","factory","java","void","22","abstract","content","getcontent","getname","idocument","idocument.java","interface"],"topics":["java"]},{"id":"chunk-30","heading":"Task22/three/TestWindow.java","text":"public class TestWindow { public static void main(String[] args) { try { UIManager.setLookAndFeel(UIManager.getSystemLookAndFeelClassName()); } catch (Exception ignored){}","keywords":["factory","java","public","22","abstract","args","catch","class","exception","ignored","main","mvc"],"topics":["ai","java","testing"]},{"id":"chunk-31","heading":"Task22/three/TestWindow.java","text":"CarcasController controller = new CarcasController(); controller.showWindow(); } }","keywords":["carcascontroller","factory","java","22","abstract","controller","controller.showwindow","mvc","new","task","task22","testwindow.java"],"topics":["java","testing"]},{"id":"chunk-32","heading":"Task22/three/TextDocument.java","text":"public class TextDocument implements IDocument{ private String text; private String name;","keywords":["factory","java","private","string","22","abstract","class","idocument","implements","mvc","name","public"],"topics":["java"]},{"id":"chunk-33","heading":"Task22/three/TextDocument.java","text":"TextDocument(String name, String text) { this.name = name; this.text = text; } @Override public String getName() { return name; } @Override public void setName(String name) { this.name = name; } @Override public String getContent() { return text; } @Override public void setContent(String text) { this.text = text","keywords":["string","name","text","override","public","factory","java","return","this.name","this.text","void","22"],"topics":["java"]},{"id":"chunk-34","heading":"Task22/three/TextDocument.java","text":"; } @Override public void save() throws IOException { BufferedWriter writer = new BufferedWriter(new FileWriter(name)); writer.write(text); writer.close(); }","keywords":["bufferedwriter","factory","java","new","22","abstract","filewriter","ioexception","mvc","name","override","public"],"topics":["java"]},{"id":"chunk-35","heading":"Task22/two/AbstractChairFactory.java","text":"public interface AbstractChairFactory { VictorianChair createVictorianChair(); MagicChair createMagicanChair(); FunctionalChair createFunctionalChair(); }","keywords":["factory","java","22","abstract","abstractchairfactory","abstractchairfactory.java","createfunctionalchair","createmagicanchair","createvictorianchair","functionalchair","interface","magicchair"],"topics":["ai","java"]},{"id":"chunk-36","heading":"Task22/two/ChairFactory.java","text":"@Override public VictorianChair createVictorianChair() { return new VictorianChair(35); }","keywords":["factory","java","victorianchair","22","35","abstract","chairfactory.java","createvictorianchair","mvc","new","override","public"],"topics":["ai","java"]},{"id":"chunk-37","heading":"Task22/two/ChairFactory.java","text":"@Override public MagicChair createMagicanChair() { return new MagicChair(); } @Override public FunctionalChair createFunctionalChair() { return new FunctionalChair(); } }","keywords":["factory","functionalchair","java","magicchair","new","override","public","return","22","abstract","chairfactory.java","createfunctionalchair"],"topics":["ai","java"]},{"id":"chunk-38","heading":"Task22/two/Client.java","text":"public class Client implements Chair { Chair chair; public void sit(){ System.out.println(\"Клиент сел на стул.\"); } public void setChair(Chair chair){ this.chair=chair; } }","keywords":["chair","public","factory","java","void","22","abstract","class","client","client.java","implements","mvc"],"topics":["ai","java"]},{"id":"chunk-39","heading":"Task22/two/FunctionalChair.java","text":"```java title=\"Task22/two/FunctionalChair.java\" package Task22.two; public class FunctionalChair implements Chair { public int sum(int a, int b){ return a+b; } }","keywords":["int","java","factory","functionalchair.java","public","task22","two","22","a+b","abstract","chair","class"],"topics":["ai","java"]},{"id":"chunk-40","heading":"Task22/two/MagicChair.java","text":"```java title=\"Task22/two/MagicChair.java\" package Task22.two; public class MagicChair implements Chair { public void doMagic(){ System.out.println(\"Магический стул\"); } }","keywords":["java","factory","magicchair.java","public","task22","two","22","abstract","chair","class","domagic","implements"],"topics":["ai","java"]},{"id":"chunk-41","heading":"Task22/two/TestChair.java","text":"public class TestChair { public static void main(String[] args){ AbstractChairFactory af = new ChairFactory(); Scanner s = new Scanner(System.in); Client c = new Client(); System.out.println(\"Введите 1если нужен старинный стул, 2если нужен магический стул, 3если нужен функциональный стул.\"); int a = s.nextInt(); if (a ==1){ VictorianChair v = af.createVictorianChair(); System.out.println(v.getAge()+\" лет\")","keywords":["new","нужен","client","factory","java","public","scanner","system.out.println","стул","1если","22","2если"],"topics":["ai","java","testing"]},{"id":"chunk-42","heading":"Task22/two/TestChair.java","text":"; } if (a ==2){ MagicChair m = af.createMagicanChair(); m.doMagic(); } if (a==3){ FunctionalChair f = af.createFunctionalChair(); System.out.println(\"Сумма = \"+f.sum(39,12)); } if (a==1 a==2 a==3){ c.sit(); }else{ System.out.println(\"В каталоге нет такого вида стульев\"); } } }","keywords":["if","factory","java","system.out.println","+f.sum","12","22","39","abstract","af.createfunctionalchair","af.createmagicanchair","c.sit"],"topics":["ai","java","testing"]},{"id":"chunk-43","heading":"Task22/two/VictorianChair.java","text":"public class VictorianChair implements Chair { private int age; public VictorianChair(int age) { this.age = age; }","keywords":["age","factory","int","java","public","victorianchair","22","abstract","chair","class","implements","mvc"],"topics":["ai","java"]},{"id":"chunk-44","heading":"Описание","text":"В этом модуле используется 20 Java файлов. Ключевые сущности: Complex, ComplexAbstractFactory, ConcreteFactory, TestComplex, CarcasController, CarcasModel.","keywords":["java","factory","20","22","abstract","carcascontroller","carcasmodel.","complex","complexabstractfactory","concretefactory","mvc","task"],"topics":["java","testing"]},{"id":"chunk-45","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["factory","java","22","abstract","mvc","task","демо","задачах.","задачи","запускайте","затем","кейсами."],"topics":["oop"]},{"id":"chunk-46","heading":"Вывод","text":"Task 22 — Паттерны проектирования документирует реальное решение из исходного кода.","keywords":["22","factory","java","task","паттерны","проектирования","abstract","mvc","вывод","документирует","задачах.","исходного"],"topics":[]}]},{"id":"java/task-23-orders-and-menu","href":"/docs/java/task-23-orders-and-menu","slug":["java","task-23-orders-and-menu"],"section":"java","sectionTitle":"Java","title":"Task 23 — Заказы и меню","description":"Моделирование меню, заказов и интерфейсов предметной области.","preview":"Задание Моделирование меню, заказов и интерфейсов предметной области. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task23/Dish.java Task23/Drink.java Task23/InternetOrder.java Task23/","keywords":["public","string","task23","return","dish","name","description","int","new","order","private","coast","override","this.description","dish.java","drink.java","getcoast","system.out.println"],"topics":["python","ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Моделирование меню, заказов и интерфейсов предметной области. Документация собрана по исходному коду этой практики.","keywords":["меню","java","заказов","интерфейсов","моделирование","области.","предметной","23","task","документация","задание","заказы"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task23/Dish.java Task23/Drink.java Task23/InternetOrder.java Task23/Item.java Task23/Order.java Task23/Test.java","keywords":["task23","java","меню","23","dish.java","drink.java","internetorder.java","item.java","order.java","task","test.java","заказов"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task23/Dish.java","text":"public final class Dish implements Item{ private int coast; private String name; private String description; Dish(String name,String description){ this.coast = 0; this.name = name; this.description = description; } Dish(int coast,String name,String description){ this.coast = coast; this.name = name; this.description = description; } @Override public int getCoast() { return coast","keywords":["string","description","name","coast","dish","int","private","java","public","this.coast","this.description","this.name"],"topics":["python","java"]},{"id":"chunk-3","heading":"Task23/Dish.java","text":"; } @Override public String getName() { return name; } @Override public String getDescription() { return description; }","keywords":["java","override","public","return","string","меню","23","description","dish.java","getdescription","getname","name"],"topics":["java"]},{"id":"chunk-4","heading":"Task23/Dish.java","text":"} @Override public void setDescription(String description) { this.description = description; } public String toString(){ return \"Coast = \" + getCoast() + \", name = \" + getName() + \", description = \" + getDescription() + \"\\n\"; } } ```","keywords":["description","java","public","string","меню","23","coast","dish.java","getcoast","getdescription","getname","name"],"topics":["python","java"]},{"id":"chunk-5","heading":"Task23/Drink.java","text":"private int coast; private String name; private String description; Drink(String name,String description){ this.coast = 0; this.name = name; this.description = description; } Drink(int coast,String name,String description){ this.coast = coast; this.name = name; this.description = description; } @Override public int getCoast() { return coast; } @Override public String getName() { return name","keywords":["string","name","description","coast","int","private","drink","java","override","public","return","this.coast"],"topics":["python","java"]},{"id":"chunk-6","heading":"Task23/Drink.java","text":"; } @Override public String getDescription() { return description; }","keywords":["java","меню","23","description","drink.java","getdescription","override","public","return","string","task","task23"],"topics":["java"]},{"id":"chunk-7","heading":"Task23/Drink.java","text":"@Override public void setDescription(String description) { this.description = description; } }","keywords":["description","java","меню","23","drink.java","override","public","setdescription","string","task","task23","this.description"],"topics":["java"]},{"id":"chunk-8","heading":"Task23/InternetOrder.java","text":"public class InternetOrder { private Order[] orders; public Order getOrder(String name){ return new Order(1); } public void addDish(Dish dish,String name){ return; } public boolean removeOrder(String name){ return true; } public int freeTableNumber(){ return 0; } public int[] freeTableNumbers(){ return new int[]{}; } public Order[] getOrders(){ return new Order[]{}; } public double orderCoastSummary(){ return 0.0","keywords":["public","return","order","int","name","new","string","dish","java","меню","0.0","23"],"topics":["python","java"]},{"id":"chunk-9","heading":"Task23/InternetOrder.java","text":"; } public int dishQuantity(String dishName){ return 0; } }","keywords":["java","меню","23","dishname","dishquantity","int","internetorder.java","public","return","string","task","task23"],"topics":["java"]},{"id":"chunk-10","heading":"Task23/Order.java","text":"public class Order { private List items; private int size; private Dish[] dishes; Order(int size){ this.items = new ArrayList< (); } public boolean add(Dish dish){ return items.add(dish); } public boolean remove(String Name){ for(int i = 0; i n.getName().equals(Name)).count(); }","keywords":["dish","int","private","public","boolean","java","name","order","size","меню",".count",".equals"],"topics":["java"]},{"id":"chunk-11","heading":"Task23/Order.java","text":"public Dish[] getDishes() { return items.toArray(new Dish[0]); } public double costTotal(){ return items.stream().mapToDouble(Item::getCoast).sum(); } public String[] dishesName(){ return new String[]{items.stream().filter(n n.getName() != \"\").toArray().toString()}; } public Dish[] sortedDishesByCostDesc(){ return items.stream().sorted(Comparator.comparingInt(Item::getCoast)).toList().toArray(new Dish[0]); } }","keywords":["dish","public","return","items.stream","new",".toarray","getcoast","item","java","string","меню",".filter"],"topics":["python","java"]},{"id":"chunk-12","heading":"Task23/Test.java","text":"public class Test { public static void main(String[] args) { Order order = new Order(5); order.add(new Dish(450,\"Оливье\",\"Холодное блюдо\")); order.add(new Dish(300,\"Лимонад\",\"Напиток\")); order.add(new Dish(600,\"Рёбра\",\"Горячее блюдо\")); order.add(new Dish(400,\"Борщ\",\"Горячее блюдо\")); System.out.println(order.remove(\"Лимонад\")); System.out.println(order.dishQuantity()); System.out.println(order.costTotal())","keywords":["new","dish","order.add","order","system.out.println","блюдо","java","public","горячее","лимонад","меню","23"],"topics":["ai","java","testing"]},{"id":"chunk-13","heading":"Task23/Test.java","text":"; System.out.println(order.dishQuantity(\"Борщ\")); System.out.println(Arrays.stream(order.sortedDishesByCostDesc()).toList().toString()); } }","keywords":["java","system.out.println","меню",".tolist",".tostring","23","arrays.stream","order.dishquantity","order.sorteddishesbycostdesc","task","task23","test.java"],"topics":["java","testing"]},{"id":"chunk-14","heading":"Описание","text":"В этом модуле используется 6 Java файлов. Ключевые сущности: Dish, Drink, InternetOrder, Item, Order, Test.","keywords":["java","меню","23","dish","drink","internetorder","item","order","task","test.","заказов","заказы"],"topics":["java","testing"]},{"id":"chunk-15","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","меню","23","task","демо","задачи","заказов","заказы","запускайте","затем","интерфейсов","кейсами."],"topics":["oop"]}]},{"id":"java/task-24-final-order-system","href":"/docs/java/task-24-final-order-system","slug":["java","task-24-final-order-system"],"section":"java","sectionTitle":"Java","title":"Task 24 — Финальный проект","description":"Расширенная система заказов с менеджерами и клиентскими сущностями.","preview":"Задание Расширенная система заказов с менеджерами и клиентскими сущностями. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения task24/Address.java task24/AlcohoTable.java task24/Customer.ja","keywords":["public","task24","int","return","string","override","null","new","private","order","if","menuitem","itemname","head","temp","item","name","system.out.println"],"topics":["python","ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Расширенная система заказов с менеджерами и клиентскими сущностями. Документация собрана по исходному коду этой практики.","keywords":["java","заказов","клиентскими","менеджерами","расширенная","система","сущностями.","24","task","документация","задание","исходному"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"task24/Address.java task24/AlcohoTable.java task24/Customer.java task24/Dish.java task24/Drink.java task24/DrinkTypeEnum.java task24/InternetOrder.java task24/InternetOrdersManager.java task24/MenuItem.java task24/Order.java task24/OrdersManager.java task24/TableOrder.java task24/TableOrdersManager.java task24/Test.java","keywords":["task24","java","24","address.java","alcohotable.java","customer.java","dish.java","drink.java","drinktypeenum.java","internetorder.java","internetordersmanager.java","menuitem.java"],"topics":["java","testing"]},{"id":"chunk-2","heading":"task24/Address.java","text":"public final class Address extends Customer{ private String cityName; private int zipCode; private String streetName; private int buildingNumber; private int buildingLetter; private int apartmentNumber; public Address EMPTY ADDRESSS;","keywords":["private","int","address","java","public","string","24","address.java","addresss","apartmentnumber","buildingletter","buildingnumber"],"topics":["java"]},{"id":"chunk-3","heading":"task24/AlcohoTable.java","text":"interface AlcohoTable { public boolean isAlcoholicDrink(); public double getAlcoholVol();","keywords":["java","public","24","alcohotable","alcohotable.java","boolean","double","getalcoholvol","interface","isalcoholicdrink","task","task24"],"topics":["java"]},{"id":"chunk-4","heading":"task24/Customer.java","text":"public class Customer implements Order { private String firstName; private String secondName; private int age; private Address address; private Customer MATURE UNKNOWN CUSTOMER; private Customer NOT MATURE UNKNOWN CUSTOMER;","keywords":["private","customer","address","java","mature","string","unknown","24","age","class","customer.java","firstname"],"topics":["java"]},{"id":"chunk-5","heading":"task24/Dish.java","text":"public final class Dish extends MenuItem { private int coast; private String name; private String description; Dish(String name,String description){ super(); if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["string","private","description","dish","if","illegalargumentexception","java","name","new","throw","быть","должно"],"topics":["python","java"]},{"id":"chunk-6","heading":"task24/Dish.java","text":"}else { this.coast = 0; this.name = name; this.description = description; } } Dish(int coast,String name,String description){ super(); if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["description","else","if","illegalargumentexception","java","name","new","string","throw","быть","должно","пустым"],"topics":["python","java"]},{"id":"chunk-7","heading":"task24/Dish.java","text":"} else if ( coast < 0){ throw new IllegalArgumentException(\"Стоимость должны быть больше 0\");","keywords":["java","24","coast","dish.java","else","if","illegalargumentexception","new","task","task24","throw","больше"],"topics":["python","java"]},{"id":"chunk-8","heading":"task24/Dish.java","text":"}else { this.coast = coast; this.name = name; this.description = description; } } public Dish(double cost, Scanner name, Scanner opis) { super(); }","keywords":["java","name","scanner","24","coast","cost","description","dish","dish.java","double","else","opis"],"topics":["python","java"]},{"id":"chunk-9","heading":"task24/Drink.java","text":"final class Drink extends MenuItem implements AlcohoTable { private double alcoholVol; private DrinkTypeEnum type; private int coast; private String name; private String description;","keywords":["private","java","string","24","alcoholvol","alcohotable","class","coast","description","double","drink","drink.java"],"topics":["python","java"]},{"id":"chunk-10","heading":"task24/Drink.java","text":"public DrinkTypeEnum getType() { return type; } Drink(String name,String description){ if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["if","illegalargumentexception","java","new","string","throw","быть","должно","пустым","24","description","description.isempty"],"topics":["java"]},{"id":"chunk-11","heading":"task24/Drink.java","text":"}else { this.coast = 0; this.name = name; this.description = description; } } Drink(int coast,String name,String description){ if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["description","else","if","illegalargumentexception","java","name","new","string","throw","быть","должно","пустым"],"topics":["python","java"]},{"id":"chunk-12","heading":"task24/Drink.java","text":"} else if ( coast < 0){ throw new IllegalArgumentException(\"Стоимость должны быть больше 0\");","keywords":["java","24","coast","drink.java","else","if","illegalargumentexception","new","task","task24","throw","больше"],"topics":["python","java"]},{"id":"chunk-13","heading":"task24/Drink.java","text":"}else { this.coast = coast; this.name = name; this.description = description; } } Drink(int coast,String name,String description, int alcoholVol,DrinkTypeEnum type){ if (name.isEmpty()){ throw new IllegalArgumentException(\"Название блюда должно быть не пустым\"); } else if (description.isEmpty()){ throw new IllegalArgumentException(\"Описание должно быть не пустым\");","keywords":["coast","description","else","if","illegalargumentexception","int","java","name","new","string","throw","быть"],"topics":["python","java"]},{"id":"chunk-14","heading":"task24/Drink.java","text":"} else if ( coast < 0){ throw new IllegalArgumentException(\"Стоимость должны быть больше 0\");","keywords":["java","24","coast","drink.java","else","if","illegalargumentexception","new","task","task24","throw","больше"],"topics":["python","java"]},{"id":"chunk-15","heading":"task24/Drink.java","text":"}else { this.coast = coast; this.name = name; this.description = description; this.alcoholVol = alcoholVol; this.type = type; } }","keywords":["java","24","alcoholvol","coast","description","drink.java","else","name","task","task24","this.alcoholvol","this.coast"],"topics":["python","java"]},{"id":"chunk-16","heading":"task24/DrinkTypeEnum.java","text":"public class DrinkTypeEnum { public static String Proverc(String n){ String t = \"\"; if (Objects.equals(n, \"AL\")){ t = \"Предъявите паспор\"; } return t; } }","keywords":["string","java","public","24","al","class","drinktypeenum","drinktypeenum.java","if","objects.equals","proverc","return"],"topics":["java"]},{"id":"chunk-17","heading":"task24/InternetOrder.java","text":"public class InternetOrder implements Order{ private int size; private ListNode head; private ListNode tail; class ListNode{ private ListNode next; private ListNode prev; private MenuItem value; ListNode(MenuItem value){ this.next = null; this.prev = null; this.value = value; }","keywords":["listnode","private","value","class","java","menuitem","null","24","head","implements","int","internetorder"],"topics":["ai","java"]},{"id":"chunk-18","heading":"task24/InternetOrder.java","text":"} InternetOrder(int size){ this.tail = null; this.head = null; this.size = size; } @Override public boolean add(MenuItem item) { ListNode node = new ListNode(item); if(head == null){ head = node; tail = node; return true; }else{ node.next = head; head.prev = node; head = node; return true; } } @Override public String[] itemsNames() { String str[] = new String[size]; ListNode temp = head; int i = 0","keywords":["head","node","listnode","null","size","string","int","item","java","new","override","public"],"topics":["ai","java"]},{"id":"chunk-19","heading":"task24/InternetOrder.java","text":"; while (temp != null & i < size){ str[i] = temp.value.getName(); i++; temp = temp.next; } return str; } @Override public int itemsQuantity() { ListNode temp = head; int count = 0; while (temp != null){ count++; temp = temp.next; } return count; } @Override public int itemsQuantity(String itemName) { ListNode temp = head; int count = 0; while (temp != null){ if(temp.value.getName().equals(itemName)) { count++","keywords":["temp","int","count","null","while","count++","head","itemname","itemsquantity","java","listnode","override"],"topics":["java"]},{"id":"chunk-20","heading":"task24/InternetOrder.java","text":"; } temp = temp.next; } return count; }","keywords":["java","24","count","internetorder.java","return","task","task24","temp","temp.next","заказов","клиентскими","менеджерами"],"topics":["java"]},{"id":"chunk-21","heading":"task24/InternetOrder.java","text":"@Override public int itemsQuantity(MenuItem itemName) { ListNode temp = head; int count = 0; while (temp != null){ if(temp.value.equals(itemName)) { count++; } temp = temp.next; } return count; }","keywords":["temp","count","int","itemname","java","24","count++","head","if","internetorder.java","itemsquantity","listnode"],"topics":["java"]},{"id":"chunk-22","heading":"task24/InternetOrder.java","text":"@Override public Order[] getOrders() { return orderManager.values().toArray(new Order[0]); }","keywords":["java","order",".toarray","24","getorders","internetorder.java","new","ordermanager.values","override","public","return","task"],"topics":["java"]},{"id":"chunk-23","heading":"task24/InternetOrder.java","text":"@Override public int ordersCostSummary() { int sum = 0; ListNode temp = head; while (temp != null){ sum += temp.value.getCost(); temp = temp.next; } return sum; }","keywords":["sum","temp","int","java","24","head","internetorder.java","listnode","null","orderscostsummary","override","public"],"topics":["java"]},{"id":"chunk-24","heading":"task24/InternetOrder.java","text":"@Override public MenuItem[] getItems() { return orderManager.values().toArray(new MenuItem[0]); }","keywords":["java","menuitem",".toarray","24","getitems","internetorder.java","new","ordermanager.values","override","public","return","task"],"topics":["java"]},{"id":"chunk-25","heading":"task24/InternetOrder.java","text":"@Override public boolean remove(MenuItem item) { if (head == null){ return false; } if (head == tail && head.value.equals(item)){ head = null; tail = null; return true; } ListNode temp = head; while (temp != null & temp.value.equals(item)){ temp = temp.next; } temp.prev.next = temp.next; temp.next.prev = temp.prev; temp.prev = null; temp.next = null; return true; }","keywords":["null","head","item","return","temp","temp.next","if","java","tail","temp.prev","true","24"],"topics":["ai","java"]},{"id":"chunk-26","heading":"task24/InternetOrder.java","text":"@Override public int removeAll(String itemName) { if (head == null){ return 0; } if (head == tail & head.value.getName().equals(itemName)){ head = null; tail = null; return 1; } int count = 0; ListNode temp = head; while (temp != null){ if(temp.value.getName().equals(itemName)){ count++; temp.prev.next = temp.next; temp.next.prev = temp.prev; temp.prev = null; temp.next = null; } temp = temp.next; } return count; }","keywords":["null","head","if","itemname","return","temp","temp.next",".equals","count","int","java","tail"],"topics":["ai","java"]},{"id":"chunk-27","heading":"task24/InternetOrder.java","text":"@Override public int removeAll(MenuItem item) { if (head == null){ return 0; } if (head == tail & head.value.equals(item)){ head = null; tail = null; return 1; } int count = 0; ListNode temp = head; while (temp != null){ if(temp.value.equals(item)){ count++; temp.prev.next = temp.next; temp.next.prev = temp.prev; temp.prev = null; temp.next = null; } temp = temp.next; } return count; }","keywords":["null","head","if","item","return","temp","temp.next","count","int","java","tail","temp.prev"],"topics":["ai","java"]},{"id":"chunk-28","heading":"task24/InternetOrder.java","text":"@Override public boolean remove(String itemName) { if (head == null){ return false; } if (head == tail && head.value.getName().equals(itemName)){ head = null; tail = null; return true; } ListNode temp = head; while (temp != null & temp.value.getName().equals(itemName)){ temp = temp.next; } temp.prev.next = temp.next; temp.next.prev = temp.prev; temp.prev = null; temp.next = null; return true; }","keywords":["null","head","itemname","return","temp","temp.next",".equals","if","java","tail","temp.prev","true"],"topics":["ai","java"]},{"id":"chunk-29","heading":"task24/InternetOrder.java","text":"@Override public int costTotal() { orderManager.values().stream().filter(value orderManager.containsValue(value)).toList(); return 0; }","keywords":["java","value",".filter",".stream",".tolist","24","costtotal","int","internetorder.java","ordermanager.containsvalue","ordermanager.values","override"],"topics":["ai","java"]},{"id":"chunk-30","heading":"task24/InternetOrdersManager.java","text":"```java title=\"task24/InternetOrdersManager.java\" package task24; class QueueNode{ private QueueNode next; private QueueNode prev; private Order value; } public class InternetOrdersManager implements OrdersManager{ private QueueNode head; private QueueNode tail; private int size; public boolean add(Order order){ return true; } public Order remove(){ return null; } public Order order(){ return null","keywords":["order","private","queuenode","public","java","return","task24","class","internetordersmanager.java","null","24","add"],"topics":["ai","java"]},{"id":"chunk-31","heading":"task24/InternetOrdersManager.java","text":"; } @Override public int itemsQuantity(String itemName) { return 0; }","keywords":["java","24","int","internetordersmanager.java","itemname","itemsquantity","override","public","return","string","task","task24"],"topics":["java"]},{"id":"chunk-32","heading":"task24/MenuItem.java","text":"public class MenuItem implements Order{ private int cost; private String name; private String description;","keywords":["private","java","string","24","class","cost","description","implements","int","menuitem","menuitem.java","name"],"topics":["java"]},{"id":"chunk-33","heading":"task24/MenuItem.java","text":"@Override public boolean add(MenuItem item) { orderManager.put(item.getName(),item); return true; }","keywords":["item","java","24","add","boolean","item.getname","menuitem","menuitem.java","ordermanager.put","override","public","return"],"topics":["java"]},{"id":"chunk-34","heading":"task24/MenuItem.java","text":"@Override public String[] itemsNames() { return orderManager.keySet().stream().filter(key orderManager.get(key) != null).toArray(String[]::new); }","keywords":["java","key","string",".filter",".stream",".toarray","24","itemsnames","menuitem.java","new","null","ordermanager.get"],"topics":["java"]},{"id":"chunk-35","heading":"task24/MenuItem.java","text":"@Override public int itemsQuantity(String itemName) { return (int) orderManager.keySet().stream().filter(key key.equals(itemName)).count(); }","keywords":["int","itemname","java",".count",".filter",".stream","24","itemsquantity","key","key.equals","menuitem.java","ordermanager.keyset"],"topics":["java"]},{"id":"chunk-36","heading":"task24/MenuItem.java","text":"@Override public int itemsQuantity(MenuItem itemName) { int count = 0; for (Map.Entry entry : orderManager.entrySet()) { Order order = entry.getValue(); if (order.equals(itemName)) { count++; } } return count; }","keywords":["count","int","itemname","java","order","24","count++","entry","entry.getvalue","if","itemsquantity","map.entry"],"topics":["java"]},{"id":"chunk-37","heading":"task24/MenuItem.java","text":"@Override public Order[] getOrders() { return orderManager.values().toArray(new Order[0]); }","keywords":["java","order",".toarray","24","getorders","menuitem.java","new","ordermanager.values","override","public","return","task"],"topics":["java"]},{"id":"chunk-38","heading":"task24/MenuItem.java","text":"@Override public MenuItem[] getItems() { return orderManager.values().toArray(new MenuItem[0]); }","keywords":["java","menuitem",".toarray","24","getitems","menuitem.java","new","ordermanager.values","override","public","return","task"],"topics":["java"]},{"id":"chunk-39","heading":"task24/MenuItem.java","text":"@Override public boolean remove(MenuItem item) { return orderManager.remove(item.getName(),item); }","keywords":["item","java","24","boolean","item.getname","menuitem","menuitem.java","ordermanager.remove","override","public","remove","return"],"topics":["java"]},{"id":"chunk-40","heading":"task24/MenuItem.java","text":"@Override public int removeAll(String itemName) { int initialSize = orderManager.size(); // Запоминаем начальный размер HashMap orderManager.entrySet().removeIf(entry entry.getKey().equals(itemName)); int countDel = initialSize orderManager.size(); // Вычисляем количество удаленных элементов return countDel; }","keywords":["int","countdel","initialsize","itemname","java","ordermanager.size",".equals",".removeif","24","entry","entry.getkey","hashmap"],"topics":["java"]},{"id":"chunk-41","heading":"task24/MenuItem.java","text":"@Override public int removeAll(MenuItem item) { int count = 0; for(int i = 0 ; i < orderManager.size();i++){ if(orderManager.remove(item.name,item)) { count++; } } return count; }","keywords":["int","count","item","java","24","count++","i++","if","item.name","menuitem","menuitem.java","ordermanager.remove"],"topics":["java"]},{"id":"chunk-42","heading":"task24/MenuItem.java","text":"@Override public boolean remove(String itemName) { return orderManager.remove(itemName) != null; } @Override public int costTotal() { return 0; }","keywords":["itemname","java","override","public","return","24","boolean","costtotal","int","menuitem.java","null","ordermanager.remove"],"topics":["java"]},{"id":"chunk-43","heading":"task24/MenuItem.java","text":"@Override public Customer setCustomer(Customer customer) { return null; } @Override public String toString() { return \"cost = \" + cost + \", name = \" + name + \", description = \" + description; } public String prints(){ return orderManager.toString(); } }","keywords":["customer","public","return","cost","description","java","name","override","string","24","menuitem.java","null"],"topics":["java"]},{"id":"chunk-44","heading":"task24/Order.java","text":"public interface Order extends OrdersManager{ public boolean add(MenuItem item); public String[] itemsNames(); public int itemsQuantity(); public int itemsQuantity(String itemName); public int itemsQuantity(MenuItem itemName); public MenuItem[] getItems(); public boolean remove(MenuItem item); public int removeAll(String itemName); public int removeAll(MenuItem item); public boolean remove(String itemName);","keywords":["public","int","menuitem","itemname","string","boolean","item","itemsquantity","java","remove","removeall","24"],"topics":["java"]},{"id":"chunk-45","heading":"task24/Order.java","text":"public int costTotal(); public Customer getCustomer(); public Customer setCustomer(Customer customer);","keywords":["customer","public","java","24","costtotal","getcustomer","int","order.java","setcustomer","task","task24","заказов"],"topics":["java"]},{"id":"chunk-46","heading":"task24/OrdersManager.java","text":"interface OrdersManager{ HashMap orderManager = new HashMap< (); public int itemsQuantity(String itemName); public int itemsQuantity(MenuItem item); public Order[] getOrders(); public int ordersCostSummary(); public int ordersQuantity();","keywords":["public","int","hashmap","itemsquantity","java","24","getorders","interface","item","itemname","menuitem","new"],"topics":["java"]},{"id":"chunk-47","heading":"task24/TableOrder.java","text":"public class TableOrder implements Order{ private int size; private MenuItem[] items;","keywords":["java","private","24","class","implements","int","items","menuitem","order","public","size","tableorder"],"topics":["java"]},{"id":"chunk-48","heading":"task24/TableOrder.java","text":"@Override public boolean add(MenuItem item) { orderManager.put(item.getName(),item); return true; }","keywords":["item","java","24","add","boolean","item.getname","menuitem","ordermanager.put","override","public","return","tableorder.java"],"topics":["java"]},{"id":"chunk-49","heading":"task24/TableOrder.java","text":"@Override public String[] itemsNames() { return orderManager.keySet().stream().filter(key orderManager.get(key) != null).toArray(String[]::new); } @Override public int itemsQuantity() { return orderManager.keySet().size(); }","keywords":["java","key","ordermanager.keyset","override","public","return","string",".filter",".size",".stream",".toarray","24"],"topics":["java"]},{"id":"chunk-50","heading":"task24/TableOrder.java","text":"@Override public int itemsQuantity(String itemName) { return (int) orderManager.keySet().stream().filter(key key.equals(itemName)).count(); } @Override public int itemsQuantity(MenuItem itemName) { int count = 0; for (Map.Entry entry : orderManager.entrySet()) { Order order = entry.getValue(); if (order.equals(itemName)) { count++; } } return count","keywords":["int","itemname","count","itemsquantity","java","order","override","public","return",".count",".filter",".stream"],"topics":["java"]},{"id":"chunk-51","heading":"task24/TableOrder.java","text":"; } @Override public Order[] getOrders() { return orderManager.values().toArray(new Order[0]); } @Override public int ordersCostSummary() { return 0; }","keywords":["java","order","override","public","return",".toarray","24","getorders","int","new","ordermanager.values","orderscostsummary"],"topics":["java"]},{"id":"chunk-52","heading":"task24/TableOrder.java","text":"@Override public MenuItem[] getItems() { return orderManager.values().toArray(new MenuItem[0]); }","keywords":["java","menuitem",".toarray","24","getitems","new","ordermanager.values","override","public","return","tableorder.java","task"],"topics":["java"]},{"id":"chunk-53","heading":"task24/TableOrder.java","text":"@Override public boolean remove(MenuItem item) { return orderManager.remove(item.getName(),item); }","keywords":["item","java","24","boolean","item.getname","menuitem","ordermanager.remove","override","public","remove","return","tableorder.java"],"topics":["java"]},{"id":"chunk-54","heading":"task24/TableOrder.java","text":"@Override public int removeAll(String itemName) { int initialSize = orderManager.size(); // Запоминаем начальный размер HashMap orderManager.entrySet().removeIf(entry entry.getKey().equals(itemName)); int countDel = initialSize orderManager.size(); // Вычисляем количество удаленных элементов return countDel; }","keywords":["int","countdel","initialsize","itemname","java","ordermanager.size",".equals",".removeif","24","entry","entry.getkey","hashmap"],"topics":["java"]},{"id":"chunk-55","heading":"task24/TableOrder.java","text":"@Override public int removeAll(MenuItem item) { int count = 0; for(int i = 0 ; i < orderManager.size();i++){ if(orderManager.remove(item.getName(),item)) { count++; } } return count; }","keywords":["int","count","item","java","24","count++","i++","if","item.getname","menuitem","ordermanager.remove","ordermanager.size"],"topics":["java"]},{"id":"chunk-56","heading":"task24/TableOrder.java","text":"@Override public boolean remove(String itemName) { return orderManager.remove(itemName) != null; } @Override public int costTotal() { return 0; }","keywords":["itemname","java","override","public","return","24","boolean","costtotal","int","null","ordermanager.remove","remove"],"topics":["java"]},{"id":"chunk-57","heading":"task24/TableOrdersManager.java","text":"public class TableOrdersManager implements OrdersManager{ private Order[] orders; public void add(Order order, int tableNumber){} public void addItem(MenuItem item, int tableNumber){} public int freeTableNumber(Order order, int tableNumber){ return 0;} public int[] freeTableNumbers(Order order, int tableNumber){ return null;}","keywords":["order","int","public","tablenumber","java","return","void","24","add","additem","class","freetablenumber"],"topics":["java"]},{"id":"chunk-58","heading":"task24/TableOrdersManager.java","text":"public Order getOrder(int tableNumber){ return null;} public void remove(int tableNumber){} public int remove(Order order){ return 0;} public int removeAll(Order order){return 0;}","keywords":["order","int","public","return","java","remove","tablenumber","24","getorder","null","removeall","tableordersmanager.java"],"topics":["java"]},{"id":"chunk-59","heading":"task24/TableOrdersManager.java","text":"@Override public int itemsQuantity(MenuItem item) { return 0; } @Override public Order[] getOrders() { return new Order[0]; } @Override public int ordersCostSummary() { return 0; } @Override public int ordersQuantity() { return 0; } }","keywords":["override","public","return","int","java","order","24","getorders","item","itemsquantity","menuitem","new"],"topics":["java"]},{"id":"chunk-60","heading":"task24/Test.java","text":"public class Test { public static void main(String[] args) { Scanner s = new Scanner(System.in); Scanner r = new Scanner(System.in); Scanner w = new Scanner(System.in); MenuItem menuItem = new MenuItem(); double cost; boolean bool = true; String name, opis; Scanner nomder = new Scanner(System.in)","keywords":["scanner","new","system.in","menuitem","java","public","string","24","args","bool","boolean","class"],"topics":["ai","java","testing"]},{"id":"chunk-61","heading":"task24/Test.java","text":"; System.out.println(\"Ввелитед 1 если вы делаете заказ в ресторане:\\n\" + \"Введите 2 если делаете заказ на сайте:\\n\" + \"Введите 3 если хотите популярные блюда ресторана:\"); switch (nomder.nextInt()) { case 1: while (bool) { System.out.println(\"Что хотите добавить в заказ \\n1 если еду: \\n2 если напиток:\"); switch (nomder.nextInt()) { case 1: System.out.println(\"Введите название товара: \"); name = r.nextLine()","keywords":["если","system.out.println","введите","заказ","case","java","nomder.nextint","switch","делаете","хотите","24","bool"],"topics":["java","testing"]},{"id":"chunk-62","heading":"task24/Test.java","text":"; System.out.println(\"Введите цену товара: \"); cost = s.nextDouble(); System.out.println(\"Введите описание товара: \"); opis = w.nextLine(); Dish d = new Dish((int) cost, name, opis); menuItem.add(d); System.out.println(\"Ввелите 1 если вы хотите заказать еще или 0 если вы закончили с заказом\"); if (nomder.nextInt() == 1){ bool = true; break; }else { bool=false; break","keywords":["system.out.println","bool","break","cost","dish","java","opis","введите","вы","если","товара","24"],"topics":["java","testing"]},{"id":"chunk-63","heading":"task24/Test.java","text":"; } case 2: System.out.println(\"Введите название напитка: \"); name = r.nextLine(); System.out.println(\"Введите цену напитка: \"); cost = s.nextDouble(); System.out.println(\"Введите описание напитка если он алкагольный введите AL: \"); opis = w.nextLine(); Drink dr = new Drink((int) cost, name, opis); menuItem.add(dr); System.out.println(Proverc(opis))","keywords":["system.out.println","введите","opis","напитка","cost","dr","drink","java","name","24","al","case"],"topics":["java","testing"]},{"id":"chunk-64","heading":"task24/Test.java","text":"; System.out.println(\"Ввелите 1 если вы хотите заказать еще или 0 если вы закончили с заказом\"); if (nomder.nextInt() == 1){ bool = true; break; }else { bool=false; break; } } } break; case 2: while (bool) { System.out.println(\"Что хотите добавить в заказ \\n1 если еду: \\n2 если напиток:\"); switch (nomder.nextInt()) { case 1: System.out.println(\"Введите название товара: \"); name = r.nextLine()","keywords":["если","bool","break","system.out.println","case","java","nomder.nextint","вы","хотите","24","else","false"],"topics":["java","testing"]},{"id":"chunk-65","heading":"task24/Test.java","text":"; System.out.println(\"Введите цену товара: \"); cost = s.nextDouble(); System.out.println(\"Введите описание товара: \"); opis = w.nextLine(); Dish d = new Dish((int) cost, name, opis); menuItem.add(d); System.out.println(\"Ввелите 1 если вы хотите заказать еще или 0 если вы закончили с заказом\"); if (nomder.nextInt() == 1){ bool = true; break; }else { bool=false; break","keywords":["system.out.println","bool","break","cost","dish","java","opis","введите","вы","если","товара","24"],"topics":["java","testing"]},{"id":"chunk-66","heading":"task24/Test.java","text":"; } case 2: System.out.println(\"Введите название напитка: \"); name = r.nextLine(); System.out.println(\"Введите цену напитка: \"); cost = s.nextDouble(); System.out.println(\"Введите описание напитка если он алкагольный введите AL: \"); opis = w.nextLine(); Drink dr = new Drink((int) cost, name, opis); menuItem.add(dr); System.out.println(Proverc(opis))","keywords":["system.out.println","введите","opis","напитка","cost","dr","drink","java","name","24","al","case"],"topics":["java","testing"]},{"id":"chunk-67","heading":"task24/Test.java","text":"; System.out.println(\"Ввелите 1 если вы хотите заказать еще или 0 если вы закончили с заказом\"); if (nomder.nextInt() == 1){ bool = true; break; }else { bool=false; break; } } } break; case 3: Dish dishOne = new Dish(10,\"Название№1\",\"Описание№1\"); Dish dishTwo = new Dish(500,\"Название№2\",\"Описание№2\"); Dish dishFree = new Dish(1000,\"Название№3\",\"Описание№3\"); Drink drink = new Drink(120,\"dsd\",\"dsds\")","keywords":["dish","new","break","drink","название","описание","bool","java","вы","если","10","1000"],"topics":["java","testing"]},{"id":"chunk-68","heading":"task24/Test.java","text":"; menuItem.add(dishOne); menuItem.add(dishTwo); menuItem.add(dishFree); menuItem.add(drink); } System.out.println(Arrays.stream(menuItem.itemsNames()).toList()); } }","keywords":["menuitem.add","java",".tolist","24","arrays.stream","dishfree","dishone","dishtwo","drink","menuitem.itemsnames","system.out.println","task"],"topics":["java","testing"]},{"id":"chunk-69","heading":"Описание","text":"В этом модуле используется 14 Java файлов. Ключевые сущности: Address, AlcohoTable, Customer, Dish, Drink, DrinkTypeEnum.","keywords":["java","14","24","address","alcohotable","customer","dish","drink","drinktypeenum.","task","заказов","используется"],"topics":["java"]},{"id":"chunk-70","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","24","task","демо","задачи","заказов","запускайте","затем","кейсами.","класс","клиентскими","менеджерами"],"topics":["oop"]}]},{"id":"java/task-03-constructors-and-encapsulation","href":"/docs/java/task-03-constructors-and-encapsulation","slug":["java","task-03-constructors-and-encapsulation"],"section":"java","sectionTitle":"Java","title":"Task 3 — Конструкторы и инкапсуляция","description":"Инициализация объектов и контроль состояния через инкапсуляцию.","preview":"Задание Инициализация объектов и контроль состояния через инкапсуляцию. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task3/Author.java Task3/Ball.java Task3/TestAuthor.java Task3/Test","keywords":["public","double","string","task3","void","ball","return","author","email","gender","name","private","char","class","author.java","b1","ball.java","system.out.println"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Инициализация объектов и контроль состояния через инкапсуляцию. Документация собрана по исходному коду этой практики.","keywords":["java","инициализация","инкапсуляцию.","контроль","объектов","состояния","через","task","документация","задание","инкапсуляция","исходному"],"topics":[]},{"id":"chunk-1","heading":"Task3/Author.java","text":"public class Author { private String name; private String email; private char gender; public Author(String n, String e, char g){ name = n; email = e; gender = g; } public void setName(String name){ this.name = name; } public String getName(){ return name; } public void setEmail(String email){ this.email = email; } public String getEmail(){ return email; } public void getGender(char gender){ this.gender = gender","keywords":["string","public","email","name","gender","char","private","void","author","java","return","author.java"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task3/Author.java","text":"; } public char setGender(){ return gender; } public String toString(){ return this.name+\" (\"+gender+\") at \"+email; }","keywords":["java","public","return","+email","+gender+","author.java","char","gender","setgender","string","task","task3"],"topics":["ai","java"]},{"id":"chunk-3","heading":"Task3/Ball.java","text":"public class Ball { private double x = 0.0; private double y = 0.0; public Ball(){} public Ball(double x, double y){ this.x = x; this.y = y; } public double getX(){ return x; } public double getY(){ return y; } public void setX(double x){ this.x = x; } public void setY(double y){ this.y = y; } public void setXY(double x, double y){ this.x = x; this.y = y; } public void move(double xDisp, double yDisp){ x += xDisp","keywords":["double","public","void","ball","this.x","this.y","0.0","java","private","return","xdisp","ball.java"],"topics":["java"]},{"id":"chunk-4","heading":"Task3/Ball.java","text":"; y += yDisp; } public String toString(){ return \"Ball (\"+this.x+\", \"+this.y+\").\"; } }","keywords":["java","+this.x+","+this.y+","ball","ball.java","public","return","string","task","task3","tostring","ydisp"],"topics":["java"]},{"id":"chunk-5","heading":"Task3/TestAuthor.java","text":"public class TestAuthor { public static void main(String[] args){ Author a1 = new Author(\"Petr Grob\", \"peGrob@somewhere.com\", 'M'); System.out.println(a1); }","keywords":["a1","author","java","public","args","class","grob","main","new","pegrob","petr","somewhere.com"],"topics":["ai","java","testing"]},{"id":"chunk-6","heading":"Task3/TestBall.java","text":"public class TestBall { public static void main(String[] args){ Ball b1 = new Ball(100, 100); System.out.println(b1); b1.move(30, 15); System.out.println(b1); } }","keywords":["b1","100","ball","java","public","system.out.println","15","30","args","b1.move","class","main"],"topics":["ai","java","testing"]},{"id":"chunk-7","heading":"Описание","text":"В этом модуле используется 4 Java файлов. Ключевые сущности: Author, Ball, TestAuthor, TestBall.","keywords":["java","author","ball","task","testauthor","testball.","инициализация","инкапсуляцию.","инкапсуляция","используется","ключевые","конструкторы"],"topics":["java","testing"]},{"id":"chunk-8","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","task","демо","задачи","запускайте","затем","инициализация","инкапсуляцию.","инкапсуляция","кейсами.","класс","конструкторы"],"topics":["oop"]},{"id":"chunk-9","heading":"Вывод","text":"Task 3 — Конструкторы и инкапсуляция документирует реальное решение из исходного кода.","keywords":["java","task","инкапсуляция","конструкторы","вывод","документирует","инициализация","инкапсуляцию.","исходного","кода.","контроль","объектов"],"topics":["oop"]}]},{"id":"java/task-04-inheritance-and-composition","href":"/docs/java/task-04-inheritance-and-composition","slug":["java","task-04-inheritance-and-composition"],"section":"java","sectionTitle":"Java","title":"Task 4 — Наследование и композиция","description":"Переиспользование поведения через наследование и композицию.","preview":"Задание Переиспользование поведения через наследование и композицию. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task4/Book.java Task4/BookTest.java Task4/Circle.java Task4/CircleTes","keywords":["public","task4","string","name","return","rost","int","void","author","human.java","new","system.out.println","year","circle","class","static","наследование","book"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Переиспользование поведения через наследование и композицию. Документация собрана по исходному коду этой практики.","keywords":["наследование","java","композицию.","переиспользование","поведения","через","task","документация","задание","исходному","коду","композиция"],"topics":["oop"]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task4/Book.java Task4/BookTest.java Task4/Circle.java Task4/CircleTest.java Task4/Human.java","keywords":["task4","java","наследование","book.java","booktest.java","circle.java","circletest.java","human.java","task","исходные","композицию.","композиция"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task4/Book.java","text":"public Book(String author, String name, int year) { this.author = author; this.name = name; this.year = year; } public String getAuthor() { return author; } public void setAuthor(String author) { this.author = author; } public String getName() { return name; } public void setName(String name) { this.name = name; } public int getYear() { return year; } public void setYear(int year) { this.year = year; } }","keywords":["public","string","author","name","year","int","return","void","java","this.author","this.name","this.year"],"topics":["java"]},{"id":"chunk-3","heading":"Task4/BookTest.java","text":"public class BookTest { public static void main(String[] args){ Book b = new Book(\"Lev\", \"Man\", 2000); b.setAuthor(\"Pol\"); b.setName(\"Dog\"); b.setYear(2000); System.out.println(\"Author: \"+b.getAuthor()+\" name: \"+b.getName()+\" Year: \"+b.getYear()); } }","keywords":["2000","book","java","public","наследование","+b.getauthor","+b.getname","+b.getyear","args","author","b.setauthor","b.setname"],"topics":["ai","java","testing"]},{"id":"chunk-4","heading":"Task4/Circle.java","text":"public class Circle { private double radius; private float diametr; public Circle(double radius, float diametr) { this.radius = radius; this.diametr = diametr; } public double getRadius() { return radius; } public void setRadius(double r) { this.radius = r; }","keywords":["double","public","radius","diametr","circle","float","java","private","this.radius","наследование","circle.java","class"],"topics":["java"]},{"id":"chunk-5","heading":"Task4/CircleTest.java","text":"public class CircleTest { public static void main(String[] args){ Circle c = new Circle(20, 40); c.setRadius(30); System.out.println(\"Новый радиус: \" +c.getRadius()); } }","keywords":["circle","java","public","наследование","+c.getradius","20","30","40","args","c.setradius","circletest","circletest.java"],"topics":["ai","java","testing"]},{"id":"chunk-6","heading":"Task4/Human.java","text":"@Override public String toString() { return \"Head{\" + \"eye='\" + eye + '\\'' + \", volos='\" + volos + '\\'' + '}'; } } static class Leg{ public int Razmer;","keywords":["eye","java","public","volos","наследование","class","head","human.java","int","leg","override","razmer"],"topics":["java"]},{"id":"chunk-7","heading":"Task4/Human.java","text":"@Override public String toString() { return \"Leg{\" + \"Razmer=\" + Razmer + '}'; } } static class Hand{ public int ling;","keywords":["java","public","razmer","наследование","class","hand","human.java","int","leg","ling","override","return"],"topics":["java"]},{"id":"chunk-8","heading":"Task4/Human.java","text":"@Override public String toString() { return \"Hand{\" + \"ling=\" + ling + '}'; } } public int Rost; public String name; public Human(int rost, String name) { Rost = rost; this.name = name; } public int getRost() { return Rost; } public void setRost(int rost) { Rost = rost; } public String getName() { return name; } public void setName(String name) { this.name = name; }","keywords":["public","rost","name","string","int","return","java","ling","this.name","void","наследование","getname"],"topics":["java"]},{"id":"chunk-9","heading":"Task4/Human.java","text":"@Override public String toString() { return \"Human{\" + \"Rost=\" + Rost + \", name='\" + name + '\\'' + '}'; }","keywords":["java","name","rost","наследование","human","human.java","override","public","return","string","task","task4"],"topics":["java"]},{"id":"chunk-10","heading":"Task4/Human.java","text":"static public void main(String[] args){ Human h = new Human(170, \"LOK\"); System.out.println(h); Hand h2 = new Hand(12); System.out.println(h2); Leg l = new Leg(12); System.out.println(l); Head h3 = new Head(\"red\", 12); System.out.println(h3);","keywords":["new","system.out.println","12","h2","h3","hand","head","human","java","leg","наследование","170"],"topics":["ai","java"]},{"id":"chunk-11","heading":"Описание","text":"В этом модуле используется 5 Java файлов. Ключевые сущности: Book, BookTest, Circle, CircleTest, Human.","keywords":["java","наследование","book","booktest","circle","circletest","human.","task","используется","ключевые","композицию.","композиция"],"topics":["java","testing"]},{"id":"chunk-12","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","наследование","task","демо","задачи","запускайте","затем","кейсами.","класс","композицию.","композиция","модель"],"topics":["oop"]},{"id":"chunk-13","heading":"Вывод","text":"Task 4 — Наследование и композиция документирует реальное решение из исходного кода.","keywords":["наследование","java","task","композиция","вывод","документирует","исходного","кода.","композицию.","переиспользование","поведения","реальное"],"topics":["oop"]}]},{"id":"java/task-05-interfaces-and-abstract-classes","href":"/docs/java/task-05-interfaces-and-abstract-classes","slug":["java","task-05-interfaces-and-abstract-classes"],"section":"java","sectionTitle":"Java","title":"Task 5 — Интерфейсы и абстрактные классы","description":"Контракты поведения и разделение абстракций от реализации.","preview":"Задание Контракты поведения и разделение абстракций от реализации. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task5/Circle.java Task5/Movable.java Task5/MovableCircle.java Task5/Mov","keywords":["public","task5","system.out.println","double","movablepoint","void","int","override","return","radius","string","rectangle","width","circle","color","new","false","filled"],"topics":["python","ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Контракты поведения и разделение абстракций от реализации. Документация собрана по исходному коду этой практики.","keywords":["java","абстракций","контракты","от","поведения","разделение","реализации.","task","абстрактные","документация","задание","интерфейсы"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task5/Circle.java Task5/Movable.java Task5/MovableCircle.java Task5/MovablePoint.java Task5/MovableRectangle.java Task5/Rectangle.java Task5/Shape.java Task5/Square.java Task5/TestMovable.java Task5/TestShape.java","keywords":["task5","java","circle.java","movable.java","movablecircle.java","movablepoint.java","movablerectangle.java","rectangle.java","shape.java","square.java","task","testmovable.java"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task5/Circle.java","text":"public class Circle extends Shape { protected double radius; public Circle(){ this.filled = false; this.color = \"blue\"; this.radius = 1; } public Circle(double radius){ this.filled = false; this.color = \"blue\"; this.radius = radius; } public Circle(double radius, String color, boolean filled){ this.radius = radius; this.color = color; this.filled = filled; }","keywords":["radius","circle","public","double","this.color","this.filled","this.radius","blue","color","false","filled","java"],"topics":["java"]},{"id":"chunk-3","heading":"Task5/Circle.java","text":"public double getRadius() { return radius; } public void setRadius(double radius){ this.radius = radius; } @Override public double getArea() { return Math.PI radius radius; } @Override public double getPerimeter() { return 2 Math.PI radius; } @Override public String toString() { return \"Shape: circle radius: \"+this.radius+\", color: \"+this.color+\" Plosh \"+getArea(); } }","keywords":["radius","public","double","return","override","java","math.pi","+getarea","+this.color+","+this.radius+","circle","circle.java"],"topics":["java"]},{"id":"chunk-4","heading":"Task5/Movable.java","text":"public interface Movable { void moveUp(); void moveDown(); void moveLeft(); void moveRight(); }","keywords":["void","java","interface","movable","movable.java","movedown","moveleft","moveright","moveup","public","task","task5"],"topics":["java"]},{"id":"chunk-5","heading":"Task5/MovableCircle.java","text":"public class MovableCircle implements Movable{ private int radius; MovablePoint center; public MovableCircle(int x, int y, int xSpeed, int ySpeed, int radius){ this.center = new MovablePoint(x, y, xSpeed, ySpeed); this.radius = radius","keywords":["int","radius","java","movablecircle","movablepoint","public","xspeed","yspeed","center","class","implements","movable"],"topics":["java"]},{"id":"chunk-6","heading":"Task5/MovableCircle.java","text":"; } @Override public String toString() { return \"MovableCircle{\" + \"radius=\" + this.radius + \", center=\" + this.center.y + \" \"+this.center.ySpeed+\" \"+ this.center.xSpeed+\" \"+ this.center.x+ '}'; } @Override public void moveUp() { MovablePoint ret = this.center; ret.y += this.center.ySpeed; } @Override public void moveDown() { MovablePoint ret = this.center; ret.y = this.center.ySpeed","keywords":["override","public","java","movablepoint","ret","ret.y","this.center","this.center.yspeed","void","+this.center.yspeed+","center","movablecircle"],"topics":["java"]},{"id":"chunk-7","heading":"Task5/MovableCircle.java","text":"; } @Override public void moveLeft() { MovablePoint ret = this.center; ret.x += this.center.xSpeed; } @Override public void moveRight() { MovablePoint ret = this.center; ret.x = this.center.xSpeed; } }","keywords":["java","movablepoint","override","public","ret","ret.x","this.center","this.center.xspeed","void","movablecircle.java","moveleft","moveright"],"topics":["java"]},{"id":"chunk-8","heading":"Task5/MovablePoint.java","text":"public class MovablePoint implements Movable { int x; int y; int xSpeed; int ySpeed; public MovablePoint(int x, int y, int xSpeed, int ySpeed){ this.x=x; this.y=y; this.xSpeed=xSpeed; this.ySpeed=ySpeed; }","keywords":["int","xspeed","yspeed","java","movablepoint","public","class","implements","movable","movablepoint.java","task","task5"],"topics":["java"]},{"id":"chunk-9","heading":"Task5/MovablePoint.java","text":"@Override public String toString() { return \"MovablePoint{\" + \"x=\" + this.x + \", y=\" + this.y + \", xSpeed=\" + this.xSpeed + \", ySpeed=\" + this.ySpeed + '}'; }","keywords":["java","movablepoint","movablepoint.java","override","public","return","string","task","task5","this.x","this.xspeed","this.y"],"topics":["java"]},{"id":"chunk-10","heading":"Task5/MovableRectangle.java","text":"public class MovableRectangle implements Movable{ MovablePoint topLeft; MovablePoint bottomRight; public MovableRectangle(int x1, int y1, int x2, int y2, int xSpeed, int ySpeed){ this.topLeft = new MovablePoint(x1, y1, xSpeed, ySpeed); this.bottomRight = new MovablePoint(x2, y2, xSpeed, ySpeed); } public void proverkX(){ if (this.topLeft.xSpeed != this.bottomRight.xSpeed){ MovablePoint terx","keywords":["int","movablepoint","public","xspeed","yspeed","java","movablerectangle","new","x1","x2","y1","y2"],"topics":["java"]},{"id":"chunk-11","heading":"Task5/MovableRectangle.java","text":"; if(this.topLeft.xSpeed this.bottomRight.xSpeed){ terx = this.topLeft; terx.xSpeed = this.bottomRight.xSpeed; this.topLeft.xSpeed = this.bottomRight.xSpeed; }else{ terx = this.bottomRight; terx.xSpeed = this.topLeft.xSpeed; this.bottomRight.xSpeed = this.topLeft.xSpeed; } } } public void proverkY(){ if (this.topLeft.ySpeed != this.bottomRight.ySpeed){ MovablePoint tery","keywords":["this.bottomright.xspeed","this.topleft.xspeed","if","java","terx","terx.xspeed","else","movablepoint","movablerectangle.java","proverky","public","task"],"topics":["java"]},{"id":"chunk-12","heading":"Task5/MovableRectangle.java","text":"; if(this.topLeft.ySpeed this.bottomRight.ySpeed){ tery = this.topLeft; tery.ySpeed = this.bottomRight.ySpeed; this.topLeft.ySpeed = this.bottomRight.ySpeed; }else{ tery = this.bottomRight; tery.ySpeed = this.topLeft.ySpeed; this.bottomRight.ySpeed = this.topLeft.ySpeed; } } } @Override public String toString() { return \"MovableRectangle{\" + \"topLeft=\" + topLeft + \", bottomRight=\" + bottomRight + '}'","keywords":["this.bottomright.yspeed","this.topleft.yspeed","bottomright","java","tery","tery.yspeed","topleft","else","if","movablerectangle","movablerectangle.java","override"],"topics":["java"]},{"id":"chunk-13","heading":"Task5/MovableRectangle.java","text":"; } @Override public void moveUp() { this.proverkY(); MovablePoint rety = this.topLeft; rety.y += this.topLeft.ySpeed; rety = this.bottomRight; rety.y += this.bottomRight.ySpeed; } @Override public void moveDown() { this.proverkY(); MovablePoint rety = this.topLeft; rety.y = this.topLeft.ySpeed; rety = this.bottomRight; rety.y = this.bottomRight.ySpeed; } @Override public void moveLeft() { this.proverkX()","keywords":["rety","rety.y","override","public","void","java","movablepoint","this.bottomright","this.bottomright.yspeed","this.proverky","this.topleft","this.topleft.yspeed"],"topics":["java"]},{"id":"chunk-14","heading":"Task5/MovableRectangle.java","text":"; MovablePoint retX = this.topLeft; retX.x = this.topLeft.xSpeed; retX = this.bottomRight; retX.x = this.bottomRight.xSpeed; }","keywords":["java","retx","retx.x","movablepoint","movablerectangle.java","task","task5","this.bottomright","this.bottomright.xspeed","this.topleft","this.topleft.xspeed","абстрактные"],"topics":["java"]},{"id":"chunk-15","heading":"Task5/MovableRectangle.java","text":"@Override public void moveRight() { this.proverkX(); MovablePoint retX = this.topLeft; retX.x += this.topLeft.xSpeed; retX = this.bottomRight; retX.x += this.bottomRight.xSpeed; } }","keywords":["java","retx","retx.x","movablepoint","movablerectangle.java","moveright","override","public","task","task5","this.bottomright","this.bottomright.xspeed"],"topics":["java"]},{"id":"chunk-16","heading":"Task5/Rectangle.java","text":"```java title=\"Task5/Rectangle.java\" package Task5; public class Rectangle extends Shape{ protected double width; protected double length; public Rectangle(){ this.width = 6; this.length = 5; this.color = \"red\"; this.filled = false; } public Rectangle(double width, double length){ this.width = width; this.length = length; this.color = \"red\"; this.filled = false","keywords":["double","java","length","public","rectangle","task5","width","false","protected","rectangle.java","red","this.color"],"topics":["java"]},{"id":"chunk-17","heading":"Task5/Rectangle.java","text":"; } public Rectangle(double width, double length, String color, boolean filled){ this.width = width; this.length = length; this.color = color; this.filled = filled; } public double getWidth(){ return width; } public double getLength(){ return length; } public void setWidth(double width){ this.width = width; } public void setLength(double length){ this.length = length","keywords":["double","length","public","width","color","filled","java","return","this.length","this.width","void","boolean"],"topics":["java"]},{"id":"chunk-18","heading":"Task5/Rectangle.java","text":"; } @Override public double getArea() { return width length; } @Override public double getPerimeter() { return 2 (width+length); } @Override public String toString() { return \"Shape: rectangle width: \"+this.width+\", color: \"+this.color+\" Plosh \"+getArea(); } }","keywords":["override","public","return","double","java","width","+getarea","+this.color+","+this.width+","color","getarea","getperimeter"],"topics":["java"]},{"id":"chunk-19","heading":"Task5/Shape.java","text":"```java title=\"Task5/Shape.java\" package Task5; public abstract class Shape { protected String color; protected boolean filled; public Shape(){","keywords":["java","task5","protected","public","shape","shape.java","abstract","boolean","class","color","filled","package"],"topics":["java"]},{"id":"chunk-20","heading":"Task5/Shape.java","text":"} public Shape(String c, boolean f){ this.color = c; this.filled = f; } public String getColor(){ return color; } public void setColor(String color){ this.color = color; } public boolean isFilled(){ return filled; } public void setFilled(boolean filled){ this.filled = filled; } public abstract double getArea(); public abstract double getPerimeter(); public abstract String toString(); }","keywords":["public","string","abstract","boolean","color","filled","double","java","return","this.color","this.filled","void"],"topics":["java"]},{"id":"chunk-21","heading":"Task5/Square.java","text":"```java title=\"Task5/Square.java\" package Task5; public class Square extends Rectangle { public Square(){ this.filled = false; this.color = \"red\"; this.width = 1; } public Square(double side){ this.filled = false; this.color = \"red\"; this.width = side; } public Square(double side, String color, boolean filled){ this.width = side; this.filled = filled; this.color = color; } public double getSide(){ return width","keywords":["public","side","square","double","java","task5","this.color","this.filled","this.width","color","false","filled"],"topics":["java"]},{"id":"chunk-22","heading":"Task5/Square.java","text":"; } public void setSide(double side){ this.width = side; } @Override public double getArea() { return width width; } @Override public double getPerimeter() { return 4 width; } @Override public String toString() { return \"Shape: rectangle width: \"+this.width+\", color: \"+this.color+\" Plosh \"+getArea(); } }","keywords":["public","width","double","override","return","java","side","+getarea","+this.color+","+this.width+","color","getarea"],"topics":["java"]},{"id":"chunk-23","heading":"Task5/TestMovable.java","text":"public class TestMovable { public static void main(String[] args){ MovablePoint m1 = new MovablePoint(3, 4, 6, 7); MovableRectangle r1 = new MovableRectangle(5,0,5, 0,9, 8);","keywords":["java","movablepoint","movablerectangle","new","public","args","class","m1","main","r1","static","string"],"topics":["ai","java","testing"]},{"id":"chunk-24","heading":"Task5/TestMovable.java","text":"MovableCircle c1 = new MovableCircle(4, 6, 8, 9, 10); //System.out.println(((MovableCircle)c1).toString()); System.out.println(r1); c1.moveDown(); r1.moveUp(); m1.moveLeft(); //System.out.println(((MovableCircle)c1).toString()); System.out.println(r1); //System.out.println(m1); } }","keywords":["system.out.println","movablecircle","c1",".tostring","java","r1","10","c1.movedown","m1","m1.moveleft","new","r1.moveup"],"topics":["java","testing"]},{"id":"chunk-25","heading":"Task5/TestShape.java","text":"```java title=\"Task5/TestShape.java\" package Task5; public class TestShape { public static void main(String[] args) { / Scanner scanner = new Scanner(System.in); String color = scanner.nextLine(); boolean filled = scanner.nextBoolean(); double radius = scanner.nextDouble();","keywords":["java","scanner","task5","public","string","testshape.java","args","boolean","class","color","double","filled"],"topics":["ai","java","testing"]},{"id":"chunk-26","heading":"Task5/TestShape.java","text":"Circle c1 = new Circle(34, \"red\", false); Square s1 = new Square(32, \"red\", false); Rectangle r1 = new Rectangle(12, 11, \"red\", false); System.out.println(c1.toString()); System.out.println(s1.toString()); System.out.println(r1.toString()); } }","keywords":["false","new","red","system.out.println","circle","java","rectangle","square","11","12","32","34"],"topics":["java","testing"]},{"id":"chunk-27","heading":"Task5/TestShape.java","text":"Shape s1 = new Circle(5.5, \"RED\", false); // Upcast Circle toShape System.out.println(s1); // which version? System.out.println(s1.getArea()); // which version? System.out.println(s1.getPerimeter()); // which version? System.out.println(s1.getColor()); System.out.println(s1.isFilled()); System.out.println(((Circle)s1).getRadius()); Circle c1 = (Circle)s1; // downcast back to Circle System.out.println(c1)","keywords":["system.out.println","circle","s1","version","which","c1","java",".getradius","5.5","back","downcast","false"],"topics":["python","java","testing"]},{"id":"chunk-28","heading":"Task5/TestShape.java","text":"; System.out.println(c1.getArea()); System.out.println(c1.getPerimeter()); System.out.println(c1.getColor()); System.out.println(c1.isFilled()); System.out.println(c1.getRadius()); Shape s3 = new Rectangle(1.0, 2.0, \"RED\", false); // upcast System.out.println(s3); System.out.println(s3.getArea()); System.out.println(s3.getPerimeter()); System.out.println(s3.getColor()); System.out.println(((Rectangle)s3).getLength())","keywords":["system.out.println","s3","java","rectangle",".getlength","1.0","2.0","c1.getarea","c1.getcolor","c1.getperimeter","c1.getradius","c1.isfilled"],"topics":["python","java","testing"]},{"id":"chunk-29","heading":"Task5/TestShape.java","text":"; Rectangle r1 = (Rectangle)s3; // downcast System.out.println(r1); System.out.println(r1.getArea()); System.out.println(r1.getColor()); System.out.println(r1.getLength()); Shape s4 = new Square(6.6); // Upcast System.out.println(s4); System.out.println(s4.getArea()); System.out.println(s4.getColor()); System.out.println(((Square) s4).getSide()); Rectangle r2 = (Rectangle)s4; System.out.println(r2)","keywords":["system.out.println","rectangle","s4","java","r1","r2","square",".getside","6.6","downcast","new","r1.getarea"],"topics":["python","java","testing"]},{"id":"chunk-30","heading":"Task5/TestShape.java","text":"; System.out.println(r2.getArea()); System.out.println(r2.getColor()); System.out.println(((Square)r2).getSide()); System.out.println(r2.getLength()); Square sq1 = (Square)r2; System.out.println(sq1); System.out.println(sq1.getArea()); System.out.println(sq1.getColor()); System.out.println(sq1.getSide()); System.out.println(sq1.getLength()); } } ```","keywords":["system.out.println","square","java","r2","sq1",".getside","r2.getarea","r2.getcolor","r2.getlength","sq1.getarea","sq1.getcolor","sq1.getlength"],"topics":["java","testing"]},{"id":"chunk-31","heading":"Описание","text":"В этом модуле используется 10 Java файлов. Ключевые сущности: Circle, Movable, MovableCircle, MovablePoint, MovableRectangle, Rectangle.","keywords":["java","10","circle","movable","movablecircle","movablepoint","movablerectangle","rectangle.","task","абстрактные","абстракций","интерфейсы"],"topics":["java"]},{"id":"chunk-32","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","task","абстрактные","абстракций","демо","задачи","запускайте","затем","интерфейсы","кейсами.","класс","классы"],"topics":["oop"]},{"id":"chunk-33","heading":"Вывод","text":"Task 5 — Интерфейсы и абстрактные классы документирует реальное решение из исходного кода.","keywords":["java","task","абстрактные","интерфейсы","классы","абстракций","вывод","документирует","исходного","кода.","контракты","от"],"topics":["oop"]}]},{"id":"java/task-06-class-hierarchies","href":"/docs/java/task-06-class-hierarchies","slug":["java","task-06-class-hierarchies"],"section":"java","sectionTitle":"Java","title":"Task 6 — Иерархии классов","description":"Построение связанных сущностей в единой иерархии.","preview":"Задание Построение связанных сущностей в единой иерархии. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task6/Bed.java Task6/Bylidog.java Task6/Chair.java Task6/Cup.java Task6/Dish.jav","keywords":["public","string","task6","double","name","return","color","material","override","filled","class","furniture","void","year","dish","new","int","tostring"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Построение связанных сущностей в единой иерархии. Документация собрана по исходному коду этой практики.","keywords":["java","единой","иерархии.","построение","связанных","сущностей","task","документация","задание","иерархии","исходному","классов"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task6/Bed.java Task6/Bylidog.java Task6/Chair.java Task6/Cup.java Task6/Dish.java Task6/Dog.java Task6/Furniture.java Task6/FurnitureShop.java Task6/Haski.java Task6/Pan.java Task6/Plate.java Task6/TestDish.java Task6/TestDog.java Task6/TestFurnitureShop.java","keywords":["task6","java","bed.java","bylidog.java","chair.java","cup.java","dish.java","dog.java","furniture.java","furnitureshop.java","haski.java","pan.java"],"topics":["ai","java","testing"]},{"id":"chunk-2","heading":"Task6/Bed.java","text":"public class Bed extends Furniture{ private int count, maxw; private String NameMatras; public Bed(double h, double w, double l, String Name, String material, int count, int maxw, String NameMatras) { super(h, w, l, Name, material); this.count = count; this.maxw = maxw; this.NameMatras = NameMatras; }","keywords":["string","count","double","int","maxw","namematras","bed","java","material","name","private","public"],"topics":["java"]},{"id":"chunk-3","heading":"Task6/Bed.java","text":"@Override public String toString() { return \"Bed{\" + \"count=\" + count + \", maxw=\" + maxw + \", NameMatras='\" + NameMatras + '\\'' + \", h=\" + h + \", w=\" + w + \", l=\" + l + \", Name='\" + Name + '\\'' + \", material='\" + material + '\\'' + '}'; } }","keywords":["count","java","material","maxw","name","namematras","bed","bed.java","override","public","return","string"],"topics":["java"]},{"id":"chunk-4","heading":"Task6/Bylidog.java","text":"public class Bylidog extends Dog { public Bylidog(String name, int year) { super(name, year); }","keywords":["bylidog","java","name","public","year","bylidog.java","class","dog","extends","int","string","super"],"topics":["java"]},{"id":"chunk-5","heading":"Task6/Bylidog.java","text":"@Override public String toString() { return \"Bylidog{\" + \"name='\" + name + '\\'' + \", year=\" + year + '}'; } }","keywords":["java","name","year","bylidog","bylidog.java","override","public","return","string","task","task6","tostring"],"topics":["java"]},{"id":"chunk-6","heading":"Task6/Chair.java","text":"public class Chair extends Furniture{ public String nameMaterial; public Chair(double h, double w, double l, String Name, String material, String nameMaterial) { super(h, w, l, Name, material); this.nameMaterial = nameMaterial; }","keywords":["string","double","namematerial","public","chair","java","material","name","chair.java","class","extends","furniture"],"topics":["ai","java"]},{"id":"chunk-7","heading":"Task6/Chair.java","text":"public void setNameMaterial(String nameMaterial) { this.nameMaterial = nameMaterial; }","keywords":["java","namematerial","chair.java","public","setnamematerial","string","task","task6","this.namematerial","void","единой","иерархии"],"topics":["ai","java"]},{"id":"chunk-8","heading":"Task6/Chair.java","text":"@Override public String toString() { return \"Chair{\" + \"nameMaterial='\" + nameMaterial + '\\'' + \", h=\" + h + \", w=\" + w + \", l=\" + l + \", Name='\" + Name + '\\'' + \", material='\" + material + '\\'' + '}'; } }","keywords":["java","material","name","namematerial","chair","chair.java","override","public","return","string","task","task6"],"topics":["ai","java"]},{"id":"chunk-9","heading":"Task6/Cup.java","text":"public class Cup extends Dish{ protected double h; protected double r; public Cup(String color, boolean filled, double h, double r) { super(color, filled); this.r = r; this.h = h; } public double getH() { return h; } public void setH(double h) { this.h = h; } public double getR() { return r; } public void setR(double r) { this.r = r; }","keywords":["double","public","color","cup","filled","java","protected","return","this.h","this.r","void","boolean"],"topics":["java"]},{"id":"chunk-10","heading":"Task6/Cup.java","text":"@Override public String toString() { return \"Chahca{\" + \"h=\" + h + \", r=\" + r + \", color='\" + color + '\\'' + \", filled=\" + filled + '}'; } / @Override public double Radus() { return 2 r; } @Override public double Diametr() { return 0; }","keywords":["override","public","return","color","double","filled","java","chahca","cup.java","diametr","radus","string"],"topics":["java"]},{"id":"chunk-11","heading":"Task6/Dish.java","text":"public class Dish { String color; boolean filled; public Dish(String c, boolean f) { this.color = c; this.filled = f; } public String getC() { return color; } public void setC(String c) { this.color = c; } public boolean getFilled() { return filled; } public void setFilled(boolean f) { this.filled = f; } @Override public String toString() { return \"Dish{\" + \"color = \" + color + \", r=\" + filled + '}'; } }","keywords":["public","string","boolean","color","dish","filled","return","java","this.color","this.filled","void","class"],"topics":["java"]},{"id":"chunk-12","heading":"Task6/Dog.java","text":"public class Dog { public String name; public int year; public Dog(String name, int year) { this.name = name; this.year = year; } public String getName() { return name; }","keywords":["public","name","string","year","dog","int","java","class","dog.java","getname","return","task"],"topics":["java"]},{"id":"chunk-13","heading":"Task6/Dog.java","text":"@Override public String toString() { return \"Dog{\" + \"name='\" + name + '\\'' + \", year=\" + year + '}'; } }","keywords":["java","name","year","dog","dog.java","override","public","return","string","task","task6","tostring"],"topics":["java"]},{"id":"chunk-14","heading":"Task6/Furniture.java","text":"public abstract class Furniture { public double h, w, l; public String Name, material;","keywords":["public","java","abstract","class","double","furniture","furniture.java","material","name","string","task","task6"],"topics":["java"]},{"id":"chunk-15","heading":"Task6/Furniture.java","text":"public Furniture(double h, double w, double l, String Name, String material) { this.h = h; this.w = w; this.l = l; this.Name = Name; this.material = material; } public double getH() { return h; } public void setH(double h) { this.h = h; } public double getW() { return w; } public void setW(double w) { this.w = w; } public double getL() { return l; } public void setL(double l) { this.l = l","keywords":["double","public","return","void","java","material","name","string","this.h","this.l","this.w","furniture"],"topics":["java"]},{"id":"chunk-16","heading":"Task6/Furniture.java","text":"; } public String getName() { return Name; } public void setName(String Name) { this.Name = Name; } public String getMaterial() { return material; } public void setMaterial(String material) { this.material = material; } @Override public String toString() { return \"Furniture{\" + \"h=\" + h + \", w=\" + w + \", l=\" + l + \", name='\" + Name + '\\'' + \", material='\" + material + '\\'' + '}'; } }","keywords":["material","name","public","string","return","java","void","furniture","furniture.java","getmaterial","getname","override"],"topics":["java"]},{"id":"chunk-17","heading":"Task6/FurnitureShop.java","text":"public class FurnitureShop { private Furniture[] goods; public FurnitureShop(int count){ goods = new Furniture[count]; for(int i=0; i =count) break; goods[i++] = new Bed(43, 23, 45, \"lul\", \"derevo\", 2, 45, \"len\"); if (i =count) break; goods[i++] = new Chair(43, 23, 45, \"lyl\", \"derevo\", \"derevo12\"); } } public Furniture[] getGoods() { return goods; } public Furniture buy(int number){ Furniture f = goods[number]","keywords":["goods","furniture","count","public","45","int","new","23","43","break","derevo","furnitureshop"],"topics":["ai","java"]},{"id":"chunk-18","heading":"Task6/FurnitureShop.java","text":"; Furniture[] prod = new Furniture[goods.length 1]; for (int i=0; i number) prod[i 1] = goods[i]; } goods=prod; return f; } public String Asortiment(){ String vr = \"Асортимент магазина: \\n\"; for (Furniture f : goods){ vr += f.toString()+\"\\n\"; } return vr; } }","keywords":["furniture","goods","prod","vr","java","return","string","asortiment","f.tostring","furnitureshop.java","goods.length","int"],"topics":["java"]},{"id":"chunk-19","heading":"Task6/Haski.java","text":"public class Haski extends Dog{ public Haski(String name, int year) { super(name, year); }","keywords":["haski","java","name","public","year","class","dog","extends","haski.java","int","string","super"],"topics":["java"]},{"id":"chunk-20","heading":"Task6/Haski.java","text":"@Override public String toString() { return \"Haski{\" + \"name='\" + name + '\\'' + \", year=\" + year + '}'; } }","keywords":["java","name","year","haski","haski.java","override","public","return","string","task","task6","tostring"],"topics":["java"]},{"id":"chunk-21","heading":"Task6/Pan.java","text":"public class Pan extends Dish{ protected double r; protected double h; public Pan (String color, boolean filled, double r, double h) { super(color, filled); this.r = r; this.h = h; }","keywords":["double","color","filled","java","pan","protected","public","boolean","class","dish","extends","pan.java"],"topics":["java"]},{"id":"chunk-22","heading":"Task6/Pan.java","text":"@Override public String toString() { return \"Pan{\" + \"r=\" + r + \", h=\" + h + \", color='\" + color + '\\'' + \", filled=\" + filled + '}'; } / @Override public double Radus() { return 2; }","keywords":["color","filled","java","override","public","return","double","pan","pan.java","radus","string","task"],"topics":["java"]},{"id":"chunk-23","heading":"Task6/Plate.java","text":"public class Plate extends Dish{ protected String name; protected double r; public Plate(String color, boolean filled, String name, double r) { super(color, filled); this.name = name; this.r = r; }","keywords":["name","string","color","double","filled","java","plate","protected","public","boolean","class","dish"],"topics":["java"]},{"id":"chunk-24","heading":"Task6/Plate.java","text":"@Override public String toString() { return \"Plate{\" + \"name='\" + name + '\\'' + \", r=\" + r + \", color='\" + color + '\\'' + \", filled=\" + filled + '}'; } / @Override public double Radus() { return 0; } @Override public double Diametr() { return 0; } @Override public double Scovoroda() { return 0; }","keywords":["override","public","return","double","color","filled","java","name","diametr","plate","plate.java","radus"],"topics":["java"]},{"id":"chunk-25","heading":"Task6/TestDish.java","text":"public class TestDish { public static void main(String[] args) { Dish d = new Cup(\"red\", true, 4, 5); Dish d1 = new Dish(\"black\", false); Dish d2 = new Pan(\"white\", false, 78, 56); Dish d3 = new Plate(\"blue\", true, \"Boorg\", 32); System.out.println(d); System.out.println(d1); System.out.println(d2); System.out.println(d3); } }","keywords":["dish","new","system.out.println","d1","d2","d3","false","java","public","true","32","56"],"topics":["ai","java","testing"]},{"id":"chunk-26","heading":"Task6/TestDog.java","text":"public class TestDog { public static void main(String[] args){ Dog d1 = new Dog(\"kol\", 4); Dog d2 = new Bylidog(\"ser\", 5); Dog d3 = new Haski(\"pol\", 6); System.out.println(d1); System.out.println(d2); System.out.println(d3); } }","keywords":["dog","new","system.out.println","d1","d2","d3","java","public","args","bylidog","class","haski"],"topics":["ai","java","testing"]},{"id":"chunk-27","heading":"Task6/TestFurnitureShop.java","text":"public class TestFurnitureShop { public static void main(String[] args){ FurnitureShop f = new FurnitureShop(4); System.out.println(f.Asortiment()); //f.buy(3); System.out.println(\"Куплено: \"+\"\\n\"+f.buy(2));","keywords":["furnitureshop","java","public","system.out.println","+f.buy","args","class","f.asortiment","f.buy","main","new","static"],"topics":["ai","java","testing"]},{"id":"chunk-28","heading":"Описание","text":"В этом модуле используется 14 Java файлов. Ключевые сущности: Bed, Bylidog, Chair, Cup, Dish, Dog.","keywords":["java","14","bed","bylidog","chair","cup","dish","dog.","task","единой","иерархии","иерархии."],"topics":["ai","java"]},{"id":"chunk-29","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","task","демо","единой","задачи","запускайте","затем","иерархии","иерархии.","кейсами.","класс","классов"],"topics":["oop"]}]},{"id":"java/task-07-basic-gui","href":"/docs/java/task-07-basic-gui","slug":["java","task-07-basic-gui"],"section":"java","sectionTitle":"Java","title":"Task 7 — Базовый GUI","description":"Создание простого графического интерфейса на Java.","preview":"Задание Создание простого графического интерфейса на Java. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task7/Windows.java Task7/Windows.java java title=\"Task7/Windows.java\" package T","keywords":["100","task7","new","windows.java","jlabel","add","public","winner","12","if","jbutton","madrid","milan","result","windows","windows.this.lbl3.settext","windows.this.madrid","windows.this.milan"],"topics":["python","ai","numpy","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Создание простого графического интерфейса на Java. Документация собрана по исходному коду этой практики.","keywords":["java","java.","графического","интерфейса","простого","создание","gui","task","базовый","документация","задание","исходному"],"topics":["java"]},{"id":"chunk-1","heading":"Task7/Windows.java","text":"package Task7; public class Windows extends JFrame{ int milan; int madrid; JButton but1 = new JButton(\"AC Milan\"); JButton but2 = new JButton(\"Real Madrid\"); JLabel lbl1 = new JLabel(\"Result: 0 X 0\"); JLabel lbl2 = new JLabel(\"Last Scorer: N/A\"); JLabel lbl3 = new JLabel(\"Winner: DRAW\"); public Windows(){ super(\"Result\"); setLayout((LayoutManager) null); but1.setBounds(12, 12, 100, 100)","keywords":["jlabel","new","jbutton","100","12","int","java","madrid","milan","public","result","task7"],"topics":["python","java"]},{"id":"chunk-2","heading":"Task7/Windows.java","text":"; but2.setBounds(300,12,100,100); lbl1.setBounds(112, 12,100,100); lbl2.setBounds(112,30,100,100); lbl3.setBounds(112,50,100,100); add(but1); add(but2); add(lbl1); add(lbl2); add(lbl3); but1.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent ae) { try { ++Windows.this.milan; Windows.this.lbl1.setText(\"Result: \"+Windows.this.milan+\" X \"+Windows.this.madrid)","keywords":["100","add","112","12","java","++windows.this.milan","+windows.this.madrid","+windows.this.milan+","30","300","50","actionevent"],"topics":["numpy","java"]},{"id":"chunk-3","heading":"Task7/Windows.java","text":"; Windows.this.lbl2.setText(\"Last Scorer: AC Milan\"); if(Windows.this.milan Windows.this.madrid){ Windows.this.lbl3.setText(\"Winner: AC Milan\"); } if (Windows.this.milan == Windows.this.madrid){ Windows.this.lbl3.setText(\"Winner: DRAW\"); } } catch (Exception e){ } } }); but2.addActionListener(new ActionListener() { @Override public void actionPerformed(ActionEvent ae) { try { ++Windows.this.madrid","keywords":["ac","if","java","milan","windows.this.lbl3.settext","windows.this.madrid","windows.this.milan","winner","++windows.this.madrid","actionevent","actionlistener","actionperformed"],"topics":["python","numpy","java"]},{"id":"chunk-4","heading":"Task7/Windows.java","text":"; Windows.this.lbl1.setText(\"Result: \"+Windows.this.milan+\" X \"+Windows.this.madrid); Windows.this.lbl2.setText(\"Last Scorer: Real Madrid\"); if(Windows.this.milan < Windows.this.madrid){ Windows.this.lbl3.setText(\"Winner: Real Madrid\"); } if (Windows.this.milan == Windows.this.madrid){ Windows.this.lbl3.setText(\"Winner: DRAW\"); } } catch (Exception e){} } }); setSize(200,200)","keywords":["200","if","java","madrid","real","windows.this.lbl3.settext","windows.this.madrid","windows.this.milan","winner","+windows.this.madrid","+windows.this.milan+","catch"],"topics":["python","java"]},{"id":"chunk-5","heading":"Task7/Windows.java","text":"; } public static void main(String[] args){ Windows w = new Windows(); w.setVisible(true); w.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); } } ```","keywords":["java","windows","args","close","gui","java.","jframe.exit","main","new","public","static","string"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","gui","java.","task","базовый","графического","демо","задачи","запускайте","затем","интерфейса","кейсами."],"topics":["oop"]}]},{"id":"java/task-08-graphics-and-animation","href":"/docs/java/task-08-graphics-and-animation","slug":["java","task-08-graphics-and-animation"],"section":"java","sectionTitle":"Java","title":"Task 8 — Графика и анимация","description":"Работа с графическими примитивами и обновлением сцены.","preview":"Задание Работа с графическими примитивами и обновлением сцены. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task8/Animation.java Task8/Picture.java Task8/RandomShapes.java Task8/tret.","keywords":["new","int","task8","public","imageicon","color","private","jlabel","count","jpanel","randomshapes.java","void","height","random.nextint","static","width","final","jframe"],"topics":["ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Работа с графическими примитивами и обновлением сцены. Документация собрана по исходному коду этой практики.","keywords":["java","графическими","обновлением","примитивами","работа","сцены.","task","анимация","графика","документация","задание","исходному"],"topics":[]},{"id":"chunk-1","heading":"Task8/Animation.java","text":"public class Animation { public static void main(String[] args) { JFrame frame = new JFrame(\"Анимация\"); frame.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); final ImageIcon[] icon = {new ImageIcon(\"D:\\\\Обои\\\\1234.jpeg\")}; //ImageIcon icon1 = new ImageIcon(\"D:\\\\Обои\\\\1680.jpg\"); JLabel label1; final JLabel[] label2 = new JLabel[1]; label1 = new JLabel(\"lol\", icon[0], SwingConstants.CENTER)","keywords":["new","imageicon","jlabel","final","icon","java","jframe","label1","public","анимация","обои","1234.jpeg"],"topics":["ai","java"]},{"id":"chunk-2","heading":"Task8/Animation.java","text":"; //label2 = new JLabel(\"lop\", icon1, SwingConstants.CENTER); JPanel panel = new JPanel(); panel.setBackground(Color.BLACK); panel.setPreferredSize(new Dimension(1919, 1079)); panel.add(label1); //panel.add(label2); frame.getContentPane().add(panel); frame.pack(); frame.setVisible(true); final int[] count = {0}; Timer t = new Timer(); TimerTask animate = null; t.schedule(animate, 1, 200)","keywords":["new","animate","java","jpanel","label2","panel","panel.add","timer",".add","1079","1919","200"],"topics":["java"]},{"id":"chunk-3","heading":"Task8/Animation.java","text":"; animate = new TimerTask() { @Override public void run() { switch (count[0]) { case 0: ImageIcon icon = new ImageIcon(\"D:\\\\Обои\\\\1680.jpg\"); JLabel label2 = new JLabel(\"lop\", icon, SwingConstants.CENTER); JPanel panel1 = new JPanel(); panel1.add(label2); break; case 1: ImageIcon icon1 = new ImageIcon(\"D:\\\\Обои\\\\1234.jpeg\"); JLabel label3 = new JLabel(\"lop\", icon1, SwingConstants.CENTER); JPanel panel2 = new JPanel()","keywords":["new","imageicon","jlabel","jpanel","case","icon","icon1","java","label2","lop","swingconstants.center","обои"],"topics":["java"]},{"id":"chunk-4","heading":"Task8/Animation.java","text":"; panel2.add(label3); break; } count[0]++; if(count[0] == 2) { count[0] = 0; } } }; } }","keywords":["count","java","++","animation.java","break","if","label3","panel2.add","task","task8","анимация","графика"],"topics":["java"]},{"id":"chunk-5","heading":"Task8/Picture.java","text":"public class Picture { public static void main(String[] args){ String r = \"\"; JFrame frame = new JFrame (\"Картинка\"); frame.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); for (int i=0; i<args.length; i++){ r=args[i]; } ImageIcon icon = new ImageIcon(r); JLabel label1; label1 = new JLabel(\"FreeBSD Left\", icon, SwingConstants.CENTER); //label1.setHorizontalTextPosition(SwingConstants.LEFT)","keywords":["new","args","icon","imageicon","java","jframe","jlabel","label1","public","string","args.length","class"],"topics":["ai","java"]},{"id":"chunk-6","heading":"Task8/Picture.java","text":"; //label1.setVerticalTextPosition(SwingConstants.BOTTOM); JPanel panel = new JPanel(); panel.setBackground(Color.BLACK); panel.setPreferredSize(new Dimension(200, 300)); panel.add(label1); frame.getContentPane().add(panel); frame.pack(); frame.setVisible(true); } }","keywords":["java","jpanel","new","panel",".add","200","300","color.black","dimension","frame.getcontentpane","frame.pack","frame.setvisible"],"topics":["java"]},{"id":"chunk-7","heading":"Task8/RandomShapes.java","text":"public Shape(Color color, int x, int y) { this.color = color; this.x = x; this.y = y; }","keywords":["color","int","java","public","randomshapes.java","shape","task","task8","this.color","this.x","this.y","анимация"],"topics":["java"]},{"id":"chunk-8","heading":"Task8/RandomShapes.java","text":"public RectangleShape(Color color, int x, int y, int width, int height) { super(color, x, y); this.width = width; this.height = height; } public Color getColor(){ return null; } @Override public void draw(Graphics g) { g.setColor(getColor()); g.fillRect(x, y, width, height); } }","keywords":["color","int","height","public","width","getcolor","java","draw","g.fillrect","g.setcolor","graphics","null"],"topics":["java"]},{"id":"chunk-9","heading":"Task8/RandomShapes.java","text":"class CircleShape extends Shape { private int radius; private Random random = new Random(); public CircleShape(Color color, int x, int y, int radius) { super(color, x, y); this.radius = radius; }","keywords":["int","color","radius","random","circleshape","java","private","class","extends","new","public","randomshapes.java"],"topics":["java"]},{"id":"chunk-10","heading":"Task8/RandomShapes.java","text":"@Override public void draw(Graphics g) { g.setColor(getColor()); g.fillOval(x, y, 2 radius, 2 radius); }","keywords":["java","radius","draw","g.filloval","g.setcolor","getcolor","graphics","override","public","randomshapes.java","task","task8"],"topics":["java"]},{"id":"chunk-11","heading":"Task8/RandomShapes.java","text":"private Color getColor() { Color randomColor = new Color(random.nextInt(256), random.nextInt(256), random.nextInt(256)); return randomColor; } }","keywords":["256","color","random.nextint","java","randomcolor","getcolor","new","private","randomshapes.java","return","task","task8"],"topics":["java"]},{"id":"chunk-12","heading":"Task8/RandomShapes.java","text":"public class RandomShapes extends JPanel { private static final int WIDTH = 800; private static final int HEIGHT = 600; private static final int NUM SHAPES = 20;","keywords":["final","int","private","static","java","20","600","800","class","extends","height","jpanel"],"topics":["java"]},{"id":"chunk-13","heading":"Task8/RandomShapes.java","text":"@Override protected void paintComponent(Graphics g) { super.paintComponent(g); for (int i = 0; i < NUM SHAPES; i++) { int x = random.nextInt(WIDTH); int y = random.nextInt(HEIGHT); int shapeType = random.nextInt(2); if (shapeType == 0) { int width = random.nextInt(100) + 20; int height = random.nextInt(100) + 20; RectangleShape rectangle = new RectangleShape(Color.red, x, y, width, height); rectangle.draw(g)","keywords":["int","random.nextint","height","width","100","20","java","rectangleshape","shapetype","color.red","graphics","i++"],"topics":["ai","java"]},{"id":"chunk-14","heading":"Task8/RandomShapes.java","text":"; } else { int radius = random.nextInt(50) + 10; CircleShape circle = new CircleShape(Color.red, x, y, radius); circle.draw(g); } } }","keywords":["circleshape","java","radius","10","50","circle","circle.draw","color.red","else","int","new","random.nextint"],"topics":["java"]},{"id":"chunk-15","heading":"Task8/RandomShapes.java","text":"public static void main(String[] args) { SwingUtilities.invokeLater(() { JFrame frame = new JFrame(\"Random Shapes\"); frame.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); frame.add(new RandomShapes()); frame.pack(); frame.setLocationRelativeTo(null); frame.setVisible(true); }); } }","keywords":["java","jframe","new","args","close","frame","frame.add","frame.pack","frame.setdefaultcloseoperation","frame.setlocationrelativeto","frame.setvisible","jframe.exit"],"topics":["ai","java"]},{"id":"chunk-16","heading":"Task8/tret.java","text":"```java title=\"Task8/tret.java\" package Task8; public class tret extends JPanel{ private static final int HEIGHT = 1080; private static final int WIDTH = 1920; private JFrame frame; private Timer timer; private Image image; public tret() { frame = new JFrame(\"Application name\"); frame.setDefaultCloseOperation(JFrame.EXIT ON CLOSE); frame.setSize(WIDTH, HEIGHT); frame.add(this); frame.setVisible(true)","keywords":["private","java","task8","final","frame","height","image","int","jframe","public","static","timer"],"topics":["java"]},{"id":"chunk-17","heading":"Task8/tret.java","text":"; image = new ImageIcon(\"D:\\\\Обои\\\\1234.jpeg\").getImage(); count = 0; timer = new Timer(); timer.schedule(animate, 1,200); } private int count; TimerTask animate = new TimerTask() {","keywords":["new","animate","count","java","timer","timertask",".getimage","1234.jpeg","200","image","imageicon","int"],"topics":["java"]},{"id":"chunk-18","heading":"Task8/tret.java","text":"@Override public void run() { switch (count) { case 20: image = new ImageIcon(\"D:\\\\Обои\\\\1680.jpg\").getImage(); break; case 40: image = new ImageIcon(\"D:\\\\Обои\\\\03.jpg\").getImage(); break; case 60: image = new ImageIcon(\"D:\\\\Обои\\\\1234.jpeg\").getImage(); break; default: break; } count++; if (count == 80) { count = 0; } repaint(); } }; public void paint(Graphics canvas) { canvas.drawImage(image, 0, 0, null)","keywords":["break","image",".getimage","case","count","imageicon","new","обои","java","public","void","03.jpg"],"topics":["ai","java"]},{"id":"chunk-19","heading":"Task8/tret.java","text":"; } public static void main(String[] args){ new tret(); }","keywords":["java","args","main","new","public","static","string","task","task8","tret","tret.java","void"],"topics":["ai","java"]},{"id":"chunk-20","heading":"Описание","text":"В этом модуле используется 4 Java файлов. Ключевые сущности: Animation, Picture, RandomShapes, tret.","keywords":["java","animation","picture","randomshapes","task","tret.","анимация","графика","графическими","используется","ключевые","модуле"],"topics":["java"]},{"id":"chunk-21","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","task","анимация","графика","графическими","демо","задачи","запускайте","затем","кейсами.","класс","модель"],"topics":["oop"]}]},{"id":"java/task-09-nameable-priceable","href":"/docs/java/task-09-nameable-priceable","slug":["java","task-09-nameable-priceable"],"section":"java","sectionTitle":"Java","title":"Task 9 — Интерфейсы Nameable/Priceable","description":"Единые API для объектов с именем и ценой.","preview":"Задание Единые API для объектов с именем и ценой. Документация собрана по исходному коду этой практики. Решение Полный код решения по этой практике: Исходные файлы решения Task9/Car.java Task9/Dishes.java Task9/Dog.java Task9/Nameable.java Task9/Priceable.java","keywords":["task9","public","nameable","string","car","name","price","priceable","dishes","api","car.java","class","dishes.java","new","return","system.out.println","void","единые"],"topics":["ai","oop","java","testing"],"chunks":[{"id":"chunk-0","heading":"Задание","text":"Единые API для объектов с именем и ценой. Документация собрана по исходному коду этой практики.","keywords":["api","java","единые","именем","объектов","ценой.","nameable","priceable","task","документация","задание","интерфейсы"],"topics":[]},{"id":"chunk-1","heading":"Исходные файлы решения","text":"Task9/Car.java Task9/Dishes.java Task9/Dog.java Task9/Nameable.java Task9/Priceable.java Task9/TestNameable.java","keywords":["task9","java","api","car.java","dishes.java","dog.java","nameable","nameable.java","priceable","priceable.java","task","testnameable.java"],"topics":["java","testing"]},{"id":"chunk-2","heading":"Task9/Car.java","text":"public class Car implements Nameable{ String name; public Car(String name){ this.name = name; } @Override public String getName() { return name; } public String toString(){ return \"Car = \"+ name; } }","keywords":["name","public","string","car","java","nameable","return","api","car.java","class","getname","implements"],"topics":["java"]},{"id":"chunk-3","heading":"Task9/Dishes.java","text":"public class Dishes implements Priceable{ double price; public Dishes(double price){ this.price = price; } @Override public void getPrice() { System.out.printf(\"Цена = %s\", price); //return price; } public static void main(String[] args){ Priceable p = new Dishes(12345); //System.out.println(p); p.getPrice(); } }","keywords":["price","public","dishes","priceable","double","java","void","12345","api","args","class","dishes.java"],"topics":["ai","java"]},{"id":"chunk-4","heading":"Task9/TestNameable.java","text":"public class TestNameable { public static void main(String[] args){ Nameable n = new Dog(\"KOL\"); Nameable n1 = new Car(\"TOYOTA\"); System.out.println(n); System.out.println(n1); } }","keywords":["nameable","java","n1","new","public","system.out.println","api","args","car","class","dog","kol"],"topics":["ai","java","testing"]},{"id":"chunk-5","heading":"Описание","text":"В этом модуле используется 6 Java файлов. Ключевые сущности: Car, Dishes, Dog, Nameable, Priceable, TestNameable.","keywords":["java","nameable","priceable","api","car","dishes","dog","task","testnameable.","единые","именем","интерфейсы"],"topics":["java","testing"]},{"id":"chunk-6","heading":"Описание","text":"Для проверки практики сначала запускайте тестовый/демо класс из папки задачи, затем расширяйте модель новыми кейсами.","keywords":["java","api","nameable","priceable","task","демо","единые","задачи","запускайте","затем","именем","интерфейсы"],"topics":["oop"]},{"id":"chunk-7","heading":"Вывод","text":"Task 9 — Интерфейсы Nameable/Priceable документирует реальное решение из исходного кода.","keywords":["java","nameable","priceable","task","интерфейсы","api","вывод","документирует","единые","именем","исходного","кода."],"topics":[]}]},{"id":"systems-analysis-and-conceptual-modeling-part-1","href":"/docs/systems-analysis-and-conceptual-modeling-part-1","slug":["systems-analysis-and-conceptual-modeling-part-1"],"section":"systems-analysis-and-conceptual-modeling-part-1","sectionTitle":"Анализ и концептуальное моделирование систем (часть 1)","title":"Анализ и концептуальное моделирование систем (часть 1)","description":"Введение в системный анализ и концептуальное моделирование, часть 1.","preview":"О разделе Раздел Анализ и концептуальное моделирование систем (часть 1) добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["анализ","концептуальное","моделирование","часть","систем","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","1.","введение","задач","кода","конспекты"],"topics":[],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Анализ и концептуальное моделирование систем (часть 1) добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["анализ","концептуальное","моделирование","часть","систем","1.","stackmirea","systems-analysis-and-conceptual-modeling-part-1","введение","готов","добавлен","материалами."],"topics":[]}]},{"id":"algorithms/getting-started","href":"/docs/algorithms/getting-started","slug":["algorithms","getting-started"],"section":"algorithms","sectionTitle":"Algorithms","title":"Введение","description":"Навигация по учебной документации проекта StackMIREA.","preview":"StackMIREA Docs Этот сайт собирает практики в формате учебных модулей. Что внутри: раздел Python с практиками по ООП/ФВП и отдельным блоком AI ноутбуков; раздел Java с 24 задачами: от основ до паттернов проектирования; унифицированная структура каждой практики","keywords":["раздел","java","python","практики","24","ai","docs","stackmirea","блоком","до","задание","задачами","каждой","ноутбуков","ооп","основ","от","отдельным"],"topics":["python","ai","oop","java"],"chunks":[{"id":"chunk-0","heading":"StackMIREA Docs","text":"раздел Python с практиками по ООП/ФВП и отдельным блоком AI ноутбуков; раздел Java с 24 задачами: от основ до паттернов проектирования; унифицированная структура каждой практики: задание, решение, описание, важные моменты и вывод.","keywords":["algorithms","раздел","24","ai","docs","java","python","stackmirea","stackmirea.","блоком","важные","введение"],"topics":["python","ai","oop","java"]},{"id":"chunk-1","heading":"Как пользоваться","text":"1. Выберите трек: Python или Java . 2. Откройте нужную практику по номеру. 3. Сначала прочитайте задание, затем разберите пример кода. 4. Сопоставьте материал с исходниками в репозитории.","keywords":["algorithms","1.","2.","3.","4.","java","python","stackmirea.","введение","выберите","документации","задание"],"topics":["python","java"]},{"id":"chunk-2","heading":"Как пользоваться","text":"Используйте правый блок On this page для быстрого перехода по большим документам.","keywords":["algorithms","page","stackmirea.","this","блок","большим","быстрого","введение","документам.","документации","используйте","навигация"],"topics":[]}]},{"id":"procedural-programming/homework-01-basics","href":"/docs/procedural-programming/homework-01-basics","slug":["procedural-programming","homework-01-basics"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 1 — Ввод, арифметика и уравнения","description":"Пять стартовых задач по консольному вводу, базовой арифметике и простым уравнениям.","preview":"Что внутри В первом домашнем задании собраны самые базовые упражнения: ввод строки, арифметические действия, решение линейного и квадратного уравнений, а также логическая задача про освещённость комнаты. 1.1 «Имя» Условие. Нужно запросить имя пользователя, счи","keywords":["cout","else","if","endl","уравнение","return","int","name","ошибка","перезапустите","приложение","double","include","имя","1.4","арифметика","еще","лампа"],"topics":["ai"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"В первом домашнем задании собраны самые базовые упражнения: ввод строки, арифметические действия, решение линейного и квадратного уравнений, а также логическая задача про освещённость комнаты.","keywords":["ввод","procedural-programming","арифметика","арифметике","арифметические","базовой","базовые","вводу","внутри","действия","дз","домашнем"],"topics":[]},{"id":"chunk-1","heading":"1.1 «Имя»","text":"Условие. Нужно запросить имя пользователя, считать одно слово и вывести приветствие.","keywords":["имя","1.1","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","вывести","дз","задач","запросить"],"topics":[]},{"id":"chunk-2","heading":"1.1 «Имя»","text":"Как устроено решение. Исходник читает строку в переменную Name , а затем выводит её обратно в консоль. Это минимальный пример на работу с cin и cout .","keywords":["1.1","cin","cout","name","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","выводит","дз"],"topics":[]},{"id":"chunk-3","heading":"1.1 «Имя»","text":"Что учесть. В сохранённой версии строка приветствия записана в кодировке старого исходника, но сама логика решения остаётся простой и корректной.","keywords":["1.1","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","версии","дз","задач","записана","имя"],"topics":[]},{"id":"chunk-4","heading":"1.1 «Имя»","text":"cpp title=\"resources/procedural programming/home work 1/name.cpp\" include using namespace std; string Name; int main(){ cout Name) { cout << \"Привет, \" << Name; } else { cout << \"Eror\"; } return 0; }","keywords":["cout","name","1.1","cpp","else","eror","home","include","int","main","name.cpp","namespace"],"topics":["ai"]},{"id":"chunk-5","heading":"1.2 «Арифметика»","text":"Условие. По двум введённым числам требуется получить сумму, разность, произведение и, если возможно, частное.","keywords":["арифметика","1.2","procedural-programming","арифметике","базовой","введенным","ввод","вводу","возможно","двум","дз","если"],"topics":[]},{"id":"chunk-6","heading":"1.2 «Арифметика»","text":"Как устроено решение. Сохранённый файл реализует калькулятор на одну выбранную операцию: пользователь вводит два числа и знак + , , или / , после чего программа считает результат.","keywords":["арифметика","1.2","procedural-programming","арифметике","базовой","ввод","вводит","вводу","выбранную","два","дз","задач"],"topics":[]},{"id":"chunk-7","heading":"1.2 «Арифметика»","text":"Что учесть. Это решение не печатает все четыре операции подряд, как просит формулировка из PDF, зато хорошо показывает проверку ввода и отдельную обработку деления на ноль.","keywords":["арифметика","1.2","pdf","procedural-programming","арифметике","базовой","ввод","ввода","вводу","деления","дз","задач"],"topics":[]},{"id":"chunk-8","heading":"1.2 «Арифметика»","text":"cpp title=\"resources/procedural programming/home work 1/Arifmetica.cpp\" include using namespace std; string Name; int a, b, d; int main() { cout a) { cout b) { cout Name) { if (Name == \"+\") { d = a + b; cout << \"Answer: \" << d; } else if (Name == \" \") { d = a b; cout << \"Answer: \" << d; } else if (Name == \" \") { d = a b; cout << \"Answer: \" << d; } else if (Name == \"/\") { if (b == 0) { cout << \"Error\"","keywords":["cout","name","if","answer","else","int","арифметика","1.2","arifmetica.cpp","cpp","error","home"],"topics":["ai"]},{"id":"chunk-9","heading":"1.2 «Арифметика»","text":"; } else { d = a / b; cout << \"Answer: \" << d; } } else { cout << \"Only: +, , , /\"; } } else { cout << \"Error\"; } } else { cout << \"Error\"; } } else { cout << \"Error\"; } }","keywords":["cout","else","error","арифметика","1.2","answer","only","procedural-programming","арифметике","базовой","ввод","вводу"],"topics":[]},{"id":"chunk-10","heading":"1.3 «Уравнение»","text":"Как устроено решение. Программа читает коэффициенты, проверяет случай b != 0 , вычисляет корень x = c / b , а затем отдельно рассматривает вырожденные случаи b = 0 .","keywords":["1.3","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","вырожденные","вычисляет","дз","задач","затем"],"topics":[]},{"id":"chunk-11","heading":"1.3 «Уравнение»","text":"Что учесть. В текущем коде ответ сохраняется в int , поэтому дробная часть может потеряться. Для защиты полезно проговорить, что математически здесь лучше использовать double .","keywords":["1.3","double","int","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","дз","дробная","задач"],"topics":[]},{"id":"chunk-12","heading":"1.3 «Уравнение»","text":"cpp title=\"resources/procedural programming/home work 1/maindesc.cpp\" include include using namespace std; int main() { setlocale(0, \"\"); double b, c; cout b) { cout c) { if (b != 0) { int m = ( c) / b; int r; if (m == 0) { r = 0; } else r = m; cout << \"Решение уравнения вида bx + c = 0 равно: \" << r << endl; } else if (b == 0) { if (c == 0) { cout << \"x любое число\"; } else cout << \"нет решений\"","keywords":["cout","if","else","int","include","уравнения","1.3","bx","cpp","double","endl","home"],"topics":["ai"]},{"id":"chunk-13","heading":"1.3 «Уравнение»","text":"; } } else { cout << \"Ошибка! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } } else { cout << \"Ошибка! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } return 0; }","keywords":["cout","else","endl","введите","ввели","вы","вычисления","данных","ошибка","перезапустите","пожалуйста","приложение"],"topics":[]},{"id":"chunk-14","heading":"1.4 «Ещё уравнение»","text":"Как устроено решение. Код проверяет несколько веток: полное квадратное уравнение, частные случаи b = 0 или c = 0 , а также вырожденный вариант, когда уравнение превращается в линейное или вообще теряет неизвестную.","keywords":["уравнение","1.4","procedural-programming","арифметика","арифметике","базовой","вариант","ввод","вводу","веток","вообще","вырожденный"],"topics":[]},{"id":"chunk-15","heading":"1.4 «Ещё уравнение»","text":"Что учесть. Это самая объёмная задача в первом ДЗ. Хорошо видно, как через if и else if раскладывается задача на отдельные случаи, а корни находятся через дискриминант.","keywords":["if","задача","через","1.4","else","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","видно"],"topics":[]},{"id":"chunk-16","heading":"1.4 «Ещё уравнение»","text":"cpp title=\"resources/procedural programming/home work 1/onemoresolution.cpp\" include include using namespace std; int main() { setlocale(0, \"\"); double a, b, c; cout a) { cout b) { cout c) { if (b == 0 && a != 0 && c 0) { if ((2 a) == 0) { cout << \"Ошибка, деление на ноль! Перезапустите приложение!\" << endl; return 0; } double x1 = ((( b) + (sqrt(d))) / (2 a)); double x2 = ((( b) (sqrt(d))) / (2 a))","keywords":["cout","double","if","include","sqrt","1.4","cpp","endl","home","int","main","namespace"],"topics":["ai"]},{"id":"chunk-17","heading":"1.4 «Ещё уравнение»","text":"; cout << \"Первый корень уравнения равен:\" << x1 << endl; cout << \"Второй корень уравнения равен:\" << x2 << endl; return 0; } } else if (a == 0 && b == 0) { if (c == 0) { cout << \"x любое число\" << endl; } else if (c != 0) { cout << \"нет решений\" << endl; } return 0; } else if (c == 0) { if (a == 0) { cout << \"Ошибка, деление на ноль! Перезапустите приложение!\" << endl; return 0; } double x = 1 (b / a)","keywords":["cout","endl","if","else","return","уравнения","корень","равен","1.4","double","procedural-programming","x1"],"topics":[]},{"id":"chunk-18","heading":"1.4 «Ещё уравнение»","text":"; cout << \"Корень уравнения равен:\" << x << endl; return 0; } else { if (b == 0) { cout << \"Ошибка, деление на ноль! Перезапустите приложение!\" << endl; return 0; } double x = (( c) / b); cout << \"Корень уравнения равен:\" << x << endl; return 0; } } else { cout << \"Ошибка! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } } else { cout << \"Ошибка","keywords":["cout","endl","else","return","ошибка","уравнения","корень","перезапустите","приложение","равен","1.4","double"],"topics":[]},{"id":"chunk-19","heading":"1.4 «Ещё уравнение»","text":"! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } } else { cout << \"Ошибка! Вы ввели не число. Пожалуйста, перезапустите приложение и введите тип данных для вычисления!\" << endl; } return 0; }","keywords":["endl","введите","ввели","вы","вычисления","данных","перезапустите","пожалуйста","приложение","тип","число.","1.4"],"topics":[]},{"id":"chunk-20","heading":"1.5 «Лампа со шторой»","text":"Условие. В комнате светло, если включена лампа или если на улице день и открыты шторы.","keywords":["если","лампа","1.5","procedural-programming","арифметика","арифметике","базовой","ввод","вводу","включена","день","дз"],"topics":[]},{"id":"chunk-21","heading":"1.5 «Лампа со шторой»","text":"Как устроено решение. Программа последовательно спрашивает про день, окно и лампу, валидирует ввод и затем проверяет комбинации состояний через вложенные условия.","keywords":["ввод","1.5","procedural-programming","арифметика","арифметике","базовой","валидирует","вводу","вложенные","день","дз","задач"],"topics":[]},{"id":"chunk-22","heading":"1.5 «Лампа со шторой»","text":"Что учесть. В сохранённой версии при открытых шторах выводится Light и днём, и ночью. Для защиты лучше отдельно проговорить, что по условию стоило бы дополнительно учитывать именно время суток.","keywords":["1.5","light","procedural-programming","арифметика","арифметике","базовой","бы","ввод","вводу","версии","время","выводится"],"topics":[]},{"id":"chunk-23","heading":"1.5 «Лампа со шторой»","text":"```cpp title=\"resources/procedural programming/home work 1/lamp.cpp\" include using namespace std; int Num, a, b, c;","keywords":["1.5","cpp","home","include","int","lamp.cpp","namespace","num","procedural","procedural-programming","programming","resources"],"topics":[]},{"id":"chunk-24","heading":"1.5 «Лампа со шторой»","text":"int main() { cout a; if ((a == 1) (a == 2)) { } else { cout b; if (b == 1 b == 2) { } else { cout c; if (c == 1 c == 2) { } else { cout << \"Error\"; return 0; } if (c == 1) { cout << \"Light\"; } else { if (b == 1) { if (a == 1) { cout << \"Light\"; } else { cout << \"Light\"; } } else { cout << \"Dark\"; } } } ```","keywords":["cout","else","if","light","1.5","dark","error","int","main","procedural-programming","return","арифметика"],"topics":["ai"]}]},{"id":"procedural-programming/homework-02-formulas-and-branching","href":"/docs/procedural-programming/homework-02-formulas-and-branching","slug":["procedural-programming","homework-02-formulas-and-branching"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 2 — Формулы, ветвления и табуляция","description":"Задачи на вычисление выражений, проверку области определения и вывод таблиц значений.","preview":"Что внутри Во втором домашнем задании акцент смещается с простого ввода на вычисление формул, проверку корректности аргументов и вывод серий значений. 2.1 «Конус» Условие. Нужно вычислить объём и полную поверхность усечённого конуса по R , r и h . Как устроено","keywords":["cout","include","return","else","int","2.1","конус","устроено","2.3","2.4","cpp","home","if","namespace","pi","procedural","programming","resources"],"topics":["ai"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"Во втором домашнем задании акцент смещается с простого ввода на вычисление формул, проверку корректности аргументов и вывод серий значений.","keywords":["вывод","вычисление","значений.","проверку","procedural-programming","акцент","аргументов","ввода","ветвления","внутри","втором","выражений"],"topics":[]},{"id":"chunk-1","heading":"2.1 «Конус»","text":"Условие. Нужно вычислить объём и полную поверхность усечённого конуса по R , r и h .","keywords":["2.1","procedural-programming","ветвления","вывод","выражений","вычисление","вычислить","дз","задачи","значений.","конус","конуса"],"topics":[]},{"id":"chunk-2","heading":"2.1 «Конус»","text":"Как устроено решение. Программа читает размеры, вычисляет образующую l , затем по формулам получает объём V и площадь S .","keywords":["2.1","procedural-programming","ветвления","вывод","выражений","вычисление","вычисляет","дз","задачи","затем","значений.","конус"],"topics":[]},{"id":"chunk-3","heading":"2.1 «Конус»","text":"Что учесть. В сохранённом исходнике pi объявлена как int , поэтому значение 3.14 обрезается до 3 . В разборе важно понимать, что сама идея верна, но точность страдает.","keywords":["2.1","3.14","int","pi","procedural-programming","важно","верна","ветвления","вывод","выражений","вычисление","дз"],"topics":[]},{"id":"chunk-4","heading":"2.1 «Конус»","text":"```cpp title=\"resources/procedural programming/home work 2/conus.cpp\" include include include using namespace std;","keywords":["include","2.1","conus.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-5","heading":"2.1 «Конус»","text":"int main() { int l; int pi = 3.14; float R, r, h, V, S; cout h && h = 0) { cout r && r = 0) { cout R && R = 0) { l = sqrt(h h + (R r) (R r)); pi = 3.14; V = (((pi h) / 3) (R R + r R + r r)); S = (pi (R R + (R + r) l + (r r))); cout << \"V: \" << V << \"\\n\"; cout << \"S: \" << S; } else { cout << \"Immposible\"; return 0; } } else { cout << \"Immposible\"; return 0; } } else { cout << \"Immposible\"; return 0; } } ```","keywords":["cout","pi","else","immposible","int","return","3.14","2.1","float","main","procedural-programming","sqrt"],"topics":["ai"]},{"id":"chunk-6","heading":"2.2 «Разветвление»","text":"Как устроено решение. Сохранённый файл работает по двум веткам: если x < 1 , считается w = a log( x ) , иначе программа пытается найти w = sqrt(a x^2) .","keywords":["2.2","log","procedural-programming","sqrt","ветвления","веткам","вывод","выражений","вычисление","двум","дз","если"],"topics":[]},{"id":"chunk-7","heading":"2.2 «Разветвление»","text":"Что учесть. Здесь хорошо видно, зачем проверять область определения: логарифм требует положительный аргумент, а корень требует неотрицательное подкоренное выражение.","keywords":["определения","требует","2.2","procedural-programming","аргумент","ветвления","видно","вывод","выражение.","выражений","вычисление","дз"],"topics":[]},{"id":"chunk-8","heading":"2.2 «Разветвление»","text":"cpp title=\"resources/procedural programming/home work 2/branchingout.cpp\" include include using namespace std; int main() { setlocale(0, \"\"); double x, a, w, xm; cout x; cout a; if (cin.fail()) { cin.clear(); cin.ignore(); cout = 1) { if ((a x x) < 0) { cout << \"Ответ: отрицательное подкоренное выражение! Нет ответа.\"; return 0; } else { w = sqrt(a x x); } } } cout << \"Ответ: \" << w; return 0; }","keywords":["cout","if","include","return","ответ","2.2","branchingout.cpp","cin.clear","cin.fail","cin.ignore","cpp","double"],"topics":["ai"]},{"id":"chunk-9","heading":"2.3 «Функция»","text":"Условие. Нужно вычислить выражение от b , x и y с логарифмом и квадратным корнем.","keywords":["2.3","procedural-programming","ветвления","вывод","выражение","выражений","вычисление","вычислить","дз","задачи","значений.","квадратным"],"topics":[]},{"id":"chunk-10","heading":"2.3 «Функция»","text":"Как устроено решение. В текущем варианте сначала считаются j = b y и u = b x , затем при корректной области определения программа выводит z = log(j) sqrt(u) .","keywords":["области","определения","2.3","log","procedural-programming","sqrt","варианте","ветвления","вывод","выводит","выражений","вычисление"],"topics":[]},{"id":"chunk-11","heading":"2.3 «Функция»","text":"Что учесть. Для такой задачи критично уметь назвать ограничения: b y 0 , а b x = 0 . Именно эти проверки защищают программу от некорректных вычислений.","keywords":["задачи","2.3","procedural-programming","ветвления","вывод","выражений","вычисление","вычислений.","дз","защищают","значений.","именно"],"topics":[]},{"id":"chunk-12","heading":"2.3 «Функция»","text":"```cpp title=\"resources/procedural programming/home work 2/main.cpp\" include include include using namespace std;","keywords":["include","2.3","cpp","home","main.cpp","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":["ai"]},{"id":"chunk-13","heading":"2.3 «Функция»","text":"int main() { float x, y, b, z; int j, u; cout b) { cout x) { cout y) { j = b y; u = b x; if (j 0) { if (u != 0) { z = log(j) sqrt(u); cout << \"Answer: \" << z; return 0; } else if (u = 0) { cout << \"Net resh\"; } } else { cout << \"Net resh\"; return 0; } } else { cout << \"Eror\"; return 0; } } else { cout << \"Eror\"; return 0; } } else { cout << \"Eror\"; return 0; } } ```","keywords":["cout","else","return","eror","if","int","net","resh","2.3","answer","float","log"],"topics":["ai"]},{"id":"chunk-14","heading":"2.4 «Порядок»","text":"Условие. Нужно вывести 10 натуральных чисел подряд, начиная с корректного значения N .","keywords":["10","2.4","procedural-programming","ветвления","вывести","вывод","выражений","вычисление","дз","задачи","значений.","значения"],"topics":[]},{"id":"chunk-15","heading":"2.4 «Порядок»","text":"Как устроено решение. Исходник читает N , задаёт границу N + 10 и печатает числа в цикле while .","keywords":["10","2.4","procedural-programming","while","ветвления","вывод","выражений","вычисление","границу","дз","задает","задачи"],"topics":[]},{"id":"chunk-16","heading":"2.4 «Порядок»","text":"Что учесть. В PDF отдельно оговариваются дробные, нулевые и отрицательные случаи. В текущем файле они не обработаны: решение работает как простая печать десяти последовательных целых чисел от введённого N .","keywords":["2.4","pdf","procedural-programming","введенного","ветвления","вывод","выражений","вычисление","десяти","дз","дробные","задачи"],"topics":[]},{"id":"chunk-17","heading":"2.4 «Порядок»","text":"```cpp title=\"resources/procedural programming/home work 2/linerow.cpp\" include include include using namespace std;","keywords":["include","2.4","cpp","home","linerow.cpp","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-18","heading":"2.4 «Порядок»","text":"while (N != N10) { cout << N << \", \"; N = N + 1; } } else { cout << \"Eror\"; return 0; } } ```","keywords":["cout","2.4","else","eror","n10","procedural-programming","return","while","ветвления","вывод","выражений","вычисление"],"topics":[]},{"id":"chunk-19","heading":"2.5 «Табуляция»","text":"Как устроено решение. В сохранённой программе табулируется выражение y = (x^2 2x + 2) / (x 1) . Значения от 4 до 0 и от 1.5 до 4 печатаются отдельно, чтобы не попасть на деление на ноль при x = 1 .","keywords":["до","от","табуляция","1.5","2.5","2x","procedural-programming","ветвления","вывод","выражение","выражений","вычисление"],"topics":[]},{"id":"chunk-20","heading":"2.5 «Табуляция»","text":"Что учесть. Это хороший пример, где область определения влияет не только на вычисление, но и на организацию цикла.","keywords":["вычисление","определения","табуляция","2.5","procedural-programming","ветвления","влияет","вывод","выражений","дз","задачи","значений."],"topics":[]},{"id":"chunk-21","heading":"2.5 «Табуляция»","text":"```cpp title=\"resources/procedural programming/home work 2/tabulation.cpp\" include include include using namespace std;","keywords":["include","табуляция","2.5","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","tabulation.cpp"],"topics":[]},{"id":"chunk-22","heading":"2.5 «Табуляция»","text":"int main() { setlocale(0, \"\"); double y; for (double x = 4; x <= 0; x = x + 0.5) { y = (((x x) 2 x + 2) / (x 1)); cout << \"Ответ для x = \" << x << \" y = \" << y << endl; } cout << \"Ответ для x = 1 не существует\" << endl; for (double x = 1.5; x <= 4; x = x + 0.5) { y = (((x x) 2 x + 2) / (x 1)); cout << \"Ответ для x = \" << x << \" y = \" << y << endl; } return 0; } ```","keywords":["cout","double","endl","ответ","0.5","табуляция","1.5","2.5","int","main","procedural-programming","return"],"topics":["ai"]}]},{"id":"procedural-programming/homework-03-files-and-strings","href":"/docs/procedural-programming/homework-03-files-and-strings","slug":["procedural-programming","homework-03-files-and-strings"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 3 — Заём, файлы и работа со строками","description":"Формулы по кредитам, чтение файлов, фильтрация чисел и сортировка символов.","preview":"Что внутри Третья домашняя работа совмещает два блока тем: расчётные задачи по формулам займа и базовую работу с файлами и строками. 3.1 «Заём» Условие. Нужно вычислить ежемесячный платёж m по сумме S , проценту p и сроку n . Как устроено решение. Программа пр","keywords":["cout","include","else","заем","3.1","if","return","решение.","устроено","3.2","cpp","home","int","main","namespace","procedural","programming","resources"],"topics":["ai","algorithms"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"Третья домашняя работа совмещает два блока тем: расчётные задачи по формулам займа и базовую работу с файлами и строками.","keywords":["работа","procedural-programming","базовую","блока","внутри","два","дз","домашняя","задачи","заем","займа","кредитам"],"topics":[]},{"id":"chunk-1","heading":"3.1 «Заём»","text":"Условие. Нужно вычислить ежемесячный платёж m по сумме S , проценту p и сроку n .","keywords":["заем","3.1","procedural-programming","вычислить","дз","ежемесячный","кредитам","нужно","платеж","программирование","процедурное","проценту"],"topics":[]},{"id":"chunk-2","heading":"3.1 «Заём»","text":"Как устроено решение. Программа проверяет диапазоны входных данных, отдельно рассматривает случай p = 0 , а в общем случае подставляет значения в формулу аннуитетного платежа.","keywords":["заем","3.1","procedural-programming","аннуитетного","входных","данных","дз","диапазоны","значения","кредитам","общем","отдельно"],"topics":[]},{"id":"chunk-3","heading":"3.1 «Заём»","text":"Что учесть. Это хороший пример, где сначала надо проверить корректность параметров, и только потом переходить к вычислению формулы.","keywords":["заем","3.1","procedural-programming","вычислению","дз","корректность","кредитам","надо","параметров","переходить","потом","пример"],"topics":[]},{"id":"chunk-4","heading":"3.1 «Заём»","text":"```cpp title=\"resources/procedural programming/home work 3/3.1.cpp\" include include using namespace std;","keywords":["include","заем","3.1","3.1.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std"],"topics":[]},{"id":"chunk-5","heading":"3.1 «Заём»","text":"int main() { double S, p, n, r; double m, q, y; cout S; if (S p; if (p = 1 pow(10, 4) && p n; if (n = 1 && n < pow(10, 2) && n != 0) { if (p == 0) { m = S / n / 12; cout << \"Mi nashli : \" << m << endl; return 0; } else { r = p / 100; } q = S r pow((1 + r), n); y = 12 (pow(1 + r, n) 1); m = q / y; cout << \"Mi nashli: \" << m << \" Mecyachnaya viplata.\"; return 0;","keywords":["if","pow","cout","10","12","double","mi","nashli","return","заем","100","3.1"],"topics":["ai"]},{"id":"chunk-6","heading":"3.1 «Заём»","text":"} else { cout << \"Nevenie dannie, restart\"; return 0; } } else { cout << \"Dannie ne verni, perezapustite\"; return 0; } } else { cout << \"Dannie ne verni, perezapustite\"; return 0; } } ```","keywords":["cout","dannie","else","return","ne","perezapustite","verni","заем","3.1","nevenie","procedural-programming","restart"],"topics":[]},{"id":"chunk-7","heading":"3.2 «Ссуда»","text":"Условие. По сумме S , месячному платежу m и сроку n нужно определить процент p .","keywords":["3.2","procedural-programming","дз","заем","кредитам","месячному","нужно","определить","платежу","программирование","процедурное","процент"],"topics":[]},{"id":"chunk-8","heading":"3.2 «Ссуда»","text":"Как устроено решение. Сохранённый код не выводит процент аналитически, а перебирает его от 100 до 100 с маленьким шагом и сравнивает расчётный платёж с заданным.","keywords":["100","3.2","procedural-programming","аналитически","выводит","дз","до","его","заданным.","заем","код","кредитам"],"topics":[]},{"id":"chunk-9","heading":"3.2 «Ссуда»","text":"Что учесть. Такой способ медленнее формульного, но хорошо показывает идею численного подбора.","keywords":["3.2","procedural-programming","дз","заем","идею","кредитам","медленнее","подбора.","показывает","программирование","процедурное","работа"],"topics":[]},{"id":"chunk-10","heading":"3.2 «Ссуда»","text":"```cpp title=\"resources/procedural programming/home work 3/3.2.cpp\" include include using namespace std;","keywords":["include","3.2","3.2.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-11","heading":"3.2 «Ссуда»","text":"int main() { setlocale(0, \"\"); float s, n, r, m, p = 0, i = 100000, m1; cout s) { cout m) { cout n) { while (( 100 = m) { cout << \"Процент займа = \" << p << \"%\" << endl; break; } } } else { cout << \"Перезапустите приложение и введите числа\"; } } else { cout << \"Перезапустите приложение и введите числа\"; } } else { cout << \"Перезапустите приложение и введите числа\"; } return 0; } ```","keywords":["cout","else","введите","перезапустите","приложение","числа","100","100000","3.2","break","endl","float"],"topics":["ai"]},{"id":"chunk-12","heading":"3.3 «Копирование файла»","text":"Условие. Нужно открыть текстовый файл, проверить его существование и вывести содержимое на экран без потери форматирования.","keywords":["3.3","procedural-programming","без","вывести","дз","его","заем","копирование","кредитам","нужно","открыть","потери"],"topics":[]},{"id":"chunk-13","heading":"3.3 «Копирование файла»","text":"Как устроено решение. В ресурсе сохранён закомментированный черновик. Ниже приведён тот же вариант в читаемом виде: файл открывается через fstream , затем посимвольно печатается в консоль.","keywords":["3.3","fstream","procedural-programming","вариант","виде","дз","же","заем","закомментированный","затем","консоль.","копирование"],"topics":[]},{"id":"chunk-14","heading":"3.3 «Копирование файла»","text":"```cpp title=\"resources/procedural programming/home work 3/3.3.cpp\" include include using namespace std;","keywords":["include","3.3","3.3.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-15","heading":"3.3 «Копирование файла»","text":"int main() { fstream fl; fl.open(\"f.txt\"); if (fl.is open()) { char c; while (fl.get(c)) { cout << c; } fl.close(); } else { cout << \"ne\"; } } ```","keywords":["cout","3.3","char","else","f.txt","fl","fl.close","fl.get","fl.is","fl.open","fstream","if"],"topics":["ai"]},{"id":"chunk-16","heading":"3.4 «Фильтр»","text":"Как устроено решение. В исходнике файл читается посимвольно, а на экран попадают только символы, для которых isdigit(c) возвращает истину.","keywords":["3.4","isdigit","procedural-programming","возвращает","дз","заем","истину.","исходнике","которых","кредитам","попадают","посимвольно"],"topics":[]},{"id":"chunk-17","heading":"3.4 «Фильтр»","text":"Что учесть. Такой вариант извлекает именно цифры, а не полноценные целые числа как отдельные токены. Для базовой задачи на фильтрацию этого достаточно, но на защите стоит понимать разницу.","keywords":["3.4","procedural-programming","базовой","вариант","дз","достаточно","задачи","заем","защите","извлекает","именно","кредитам"],"topics":[]},{"id":"chunk-18","heading":"3.4 «Фильтр»","text":"```cpp title=\"resources/procedural programming/home work 3/3.4.cpp\" include include using namespace std;","keywords":["include","3.4","3.4.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-19","heading":"3.4 «Фильтр»","text":"int main() { fstream fl; fl.open(\"2.txt\"); if (fl.is open()) { char c; while (fl.get(c)) { if (isdigit(c)) { cout << c; } } fl.close(); } else { cout << \"ne rabotaet\"; } } ```","keywords":["cout","if","2.txt","3.4","char","else","fl","fl.close","fl.get","fl.is","fl.open","fstream"],"topics":["ai"]},{"id":"chunk-20","heading":"3.5 «Сортировка букв»","text":"Как устроено решение. Пользователь вводит строку, после чего вызывается стандартная sort , которая упорядочивает символы в диапазоне begin() — end() .","keywords":["сортировка","3.5","begin","end","procedural-programming","sort","букв","вводит","вызывается","дз","диапазоне","заем"],"topics":[]},{"id":"chunk-21","heading":"3.5 «Сортировка букв»","text":"Что учесть. Это короткая, но очень полезная задача на работу со строками и стандартными алгоритмами STL.","keywords":["со","сортировка","строками","3.5","procedural-programming","stl.","алгоритмами","букв","дз","задача","заем","короткая"],"topics":["algorithms"]},{"id":"chunk-22","heading":"3.5 «Сортировка букв»","text":"```cpp title=\"resources/procedural programming/home work 3/3.5.cpp\" include include include using namespace std;","keywords":["include","сортировка","3.5","3.5.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std"],"topics":[]},{"id":"chunk-23","heading":"3.5 «Сортировка букв»","text":"int main() { system(\"CHCP 1251\"); cout line; sort(line.begin(), line.end()); cout << line << endl; return 0; } ```","keywords":["cout","line","сортировка","1251","3.5","chcp","endl","int","line.begin","line.end","main","procedural-programming"],"topics":["ai"]}]},{"id":"procedural-programming/homework-04-functions-and-automation","href":"/docs/procedural-programming/homework-04-functions-and-automation","slug":["procedural-programming","homework-04-functions-and-automation"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 4 — Функции, файлы, графика и системы счисления","description":"Девять задач на подпрограммы, файлы, псевдографику, матрицы и преобразование чисел.","preview":"Что внутри Четвёртая домашняя работа самая объёмная: здесь есть файловый ввод вывод, собственные функции, псевдографика, генератор чисел, матрицы и перевод между системами счисления. 4.1 «Файл» Условие. Нужно записать 10 вещественных чисел в файл, затем открыт","keywords":["if","return","else","int","cout","include","ch","i++","cf","endl","main","cpp","home","procedural","programming","resources","title","work"],"topics":["ai"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"Четвёртая домашняя работа самая объёмная: здесь есть файловый ввод вывод, собственные функции, псевдографика, генератор чисел, матрицы и перевод между системами счисления.","keywords":["матрицы","файлы","функции","procedural-programming","ввод","внутри","вывод","генератор","графика","девять","дз","домашняя"],"topics":[]},{"id":"chunk-1","heading":"4.1 «Файл»","text":"Условие. Нужно записать 10 вещественных чисел в файл, затем открыть его снова и посчитать сумму.","keywords":["файл","файлы","10","4.1","procedural-programming","вещественных","графика","девять","дз","его","задач","записать"],"topics":[]},{"id":"chunk-2","heading":"4.1 «Файл»","text":"Как устроено решение. Программа сначала пишет десять введённых чисел в 1.txt , затем вторым проходом считывает их из файла и накапливает сумму в переменной sum .","keywords":["файлы","1.txt","4.1","procedural-programming","sum","введенных","вторым","графика","девять","десять","дз","задач"],"topics":[]},{"id":"chunk-3","heading":"4.1 «Файл»","text":"```cpp title=\"resources/procedural programming/home work 4/4.1 main.cpp\" include include using namespace std;","keywords":["4.1","include","файлы","cpp","home","main.cpp","namespace","procedural","procedural-programming","programming","resources","std"],"topics":["ai"]},{"id":"chunk-4","heading":"4.1 «Файл»","text":"int main() { double q, a, sum = 0; int i = 0; ofstream file(\"1.txt\"); setlocale(0, \"\"); cout q) { i++; file << q << endl; } else { cout << \"Введите только числа\"; return 0; } } file.close();","keywords":["cout","file","int","файлы","1.txt","4.1","double","else","endl","file.close","i++","main"],"topics":["ai"]},{"id":"chunk-5","heading":"4.1 «Файл»","text":"ifstream fin(\"1.txt\"); while (fin a) { sum += a; } cout << \"Сумма=\" << sum; fin.close(); } ```","keywords":["fin","sum","файлы","1.txt","4.1","cout","fin.close","ifstream","procedural-programming","while","графика","девять"],"topics":[]},{"id":"chunk-6","heading":"4.2 «Знак числа»","text":"Условие. Нужно определить знак числа через отдельную подпрограмму функцию sign .","keywords":["знак","файлы","числа","4.2","procedural-programming","sign","графика","девять","дз","задач","матрицы","нужно"],"topics":[]},{"id":"chunk-7","heading":"4.2 «Знак числа»","text":"Как устроено решение. Функция ptt принимает число и печатает 1 , 0 или 1 в зависимости от его значения.","keywords":["файлы","4.2","procedural-programming","ptt","графика","девять","дз","его","зависимости","задач","знак","значения."],"topics":[]},{"id":"chunk-8","heading":"4.2 «Знак числа»","text":"```cpp title=\"resources/procedural programming/home work 4/4.2 main.cpp\" include include","keywords":["4.2","include","файлы","cpp","home","main.cpp","procedural","procedural-programming","programming","resources","title","work"],"topics":["ai"]},{"id":"chunk-9","heading":"4.2 «Знак числа»","text":"int main() { setlocale(0, \"\"); double S; cout S) { ptt(S); } else { cout << \"Введите число\"; } } ```","keywords":["cout","файлы","4.2","double","else","int","main","procedural-programming","ptt","setlocale","введите","графика"],"topics":["ai"]},{"id":"chunk-10","heading":"4.3 «Геометрические фигуры»","text":"Условие. Нужно вычислять площадь прямоугольника, треугольника или круга через отдельные функции.","keywords":["файлы","4.3","procedural-programming","вычислять","геометрические","графика","девять","дз","задач","круга","матрицы","нужно"],"topics":[]},{"id":"chunk-11","heading":"4.3 «Геометрические фигуры»","text":"Как устроено решение. Для каждой фигуры выделена собственная подпрограмма: prog1 , prog2 и prog3 . В main пользователь выбирает, какую именно фигуру считать.","keywords":["файлы","фигуры","4.3","main","procedural-programming","prog1","prog2","prog3","выбирает","выделена","геометрические","графика"],"topics":["ai"]},{"id":"chunk-12","heading":"4.3 «Геометрические фигуры»","text":"```cpp title=\"resources/procedural programming/home work 4/4.3 main.cpp\" include include using namespace std; void prog1() { double a, b; cout a; cin b; cout a ; cin h; cout r; cout << \"S=\" << 3.14 r r; }","keywords":["cout","4.3","cin","include","файлы","3.14","cpp","double","home","main.cpp","namespace","procedural"],"topics":["ai"]},{"id":"chunk-13","heading":"4.3 «Геометрические фигуры»","text":"int main() { setlocale(0, \"\"); string S; cout S; if (S == \"prog1\") { prog1(); } else if (S == \"prog2\") { prog2(); } else if (S == \"prog3\") { prog3(); } } ```","keywords":["if","else","prog1","prog2","prog3","файлы","4.3","cout","int","main","procedural-programming","setlocale"],"topics":["ai"]},{"id":"chunk-14","heading":"4.4 «Былая слава»","text":"Условие. Нужно вывести в консоль флаг в виде псевдографики, обязательно используя цикл или рекурсию.","keywords":["файлы","4.4","procedural-programming","былая","виде","вывести","графика","девять","дз","задач","используя","консоль"],"topics":[]},{"id":"chunk-15","heading":"4.4 «Былая слава»","text":"Как устроено решение. Программа печатает первые шесть строк с блоком звёзд и полосами, затем ещё шесть строк только из полос.","keywords":["строк","файлы","шесть","4.4","procedural-programming","блоком","былая","графика","девять","дз","еще","задач"],"topics":[]},{"id":"chunk-16","heading":"4.4 «Былая слава»","text":"cpp title=\"resources/procedural programming/home work 4/4.4 main.cpp\" include include using namespace std; int main() { cout << endl; for (int i = 0; i < 6; i++) { for (int i = 0; i < 8; i++) { cout << \" \"; } for (int i = 0; i < 41; i++) { cout << \" \"; } cout << endl; } for (int i = 0; i < 6; i++) { for (int i = 0; i < 49; i++) { cout << \" \"; } cout << endl; } }","keywords":["cout","int","i++","endl","4.4","include","файлы","41","49","cpp","home","main"],"topics":["ai"]},{"id":"chunk-17","heading":"4.5 «Синусоида»","text":"Как устроено решение. Сначала в массив sinx сохраняются значения синуса по точкам, затем двумерный массив graf заполняется символами или пробелами, после чего печатается построчно.","keywords":["массив","файлы","4.5","graf","procedural-programming","sinx","графика","двумерный","девять","дз","задач","заполняется"],"topics":[]},{"id":"chunk-18","heading":"4.5 «Синусоида»","text":"cpp title=\"resources/procedural programming/home work 4/4.5 main.cpp\" include include using namespace std; int main() { const int size = 89, height = 23; char graf[height][size]; double sinx[size]; for (int i = 0; i < size; i++) sinx[i] = 10 sin(i / 10.0); for (int i = 0; i < height; i++) for (int j = 0; j < size; j++) if ( 1 < 10.0 i round(sinx[j]) and 10.0 i round(sinx[j]) < 1) graf[i][j] = ' '","keywords":["int","size","sinx","10.0","height","4.5","graf","i++","include","round","файлы","10"],"topics":["ai"]},{"id":"chunk-19","heading":"4.5 «Синусоида»","text":"; else graf[i][j] = ' '; for (int i = 0; i < height; i++) { for (int j = 0; j < size; j++) cout << graf[i][j]; cout << endl; } }","keywords":["cout","graf","int","файлы","4.5","else","endl","height","i++","j++","procedural-programming","size"],"topics":[]},{"id":"chunk-20","heading":"4.6 «Автоматный распознаватель»","text":"Условие. Нужно перевести число из римской записи в арабскую и отследить некорректный ввод.","keywords":["файлы","4.6","procedural-programming","автоматный","арабскую","ввод.","графика","девять","дз","задач","записи","матрицы"],"topics":[]},{"id":"chunk-21","heading":"4.6 «Автоматный распознаватель»","text":"Как устроено решение. Функция cf сопоставляет римские символы их значениям, а основной цикл проходит по строке и складывает или вычитает значения в зависимости от соседних символов.","keywords":["файлы","4.6","cf","procedural-programming","автоматный","вычитает","графика","девять","дз","зависимости","задач","значения"],"topics":[]},{"id":"chunk-22","heading":"4.6 «Автоматный распознаватель»","text":"Что учесть. Проверка корректности здесь частичная: реализация демонстрирует общий принцип разбора, но не покрывает все правила записи римских чисел.","keywords":["файлы","чисел.","4.6","procedural-programming","автоматный","графика","девять","демонстрирует","дз","задач","записи","здесь"],"topics":[]},{"id":"chunk-23","heading":"4.6 «Автоматный распознаватель»","text":"```cpp title=\"resources/procedural programming/home work 4/4.6.cpp\" include include using namespace std; int cf(char x) { if (x == 'I') return(1); else if (x == 'X') return(10); else if (x == 'V') return(5); else if (x == 'IV') return(4); else if (x == 'L') return(50); else if (x == 'C') return(100); else if (x == 'D') return(500); else if (x == 'M') return(1000); else if (x == '0') return(0); else return(0)","keywords":["return","else","if","include","файлы","10","100","1000","4.6","4.6.cpp","50","500"],"topics":[]},{"id":"chunk-24","heading":"4.6 «Автоматный распознаватель»","text":"; } int main() { setlocale(0, \"\"); string ch; int s = 0; int i = 0; char v = 'I'; cout ch; if (ch.length() 2) { if (ch[0] == ch[1] and ch[0] == v and cf(ch[2]) 1) { cout << \"eror\"; return 0; } for (int i = 0; i < (ch.length() 3); i++) { if (ch[i] == ch[i + 1] and ch[i] == v and cf(ch[i + 2]) <= 1) { cout << \"Eror\"; return 0; } } }","keywords":["ch","int","cout","if","cf","ch.length","eror","return","файлы","4.6","char","i++"],"topics":["ai"]},{"id":"chunk-25","heading":"4.6 «Автоматный распознаватель»","text":"for (int i = 0; i < (ch.length()); i++) { if (cf(ch[i]) < cf(ch[i + 1]) and cf(ch[i]) != 0) { s = s + cf(ch[i + 1]) cf(ch[i]); ch[i + 1] = '0'; } else s = s + cf(ch[i]); } cout << s; } ```","keywords":["ch","cf","файлы","4.6","ch.length","cout","else","i++","if","int","procedural-programming","автоматный"],"topics":[]},{"id":"chunk-26","heading":"4.7 «Генератор случайных чисел»","text":"Условие. Нужно построить линейный конгруэнтный генератор по рекуррентной формуле.","keywords":["генератор","файлы","4.7","procedural-programming","графика","девять","дз","задач","конгруэнтный","линейный","матрицы","нужно"],"topics":[]},{"id":"chunk-27","heading":"4.7 «Генератор случайных чисел»","text":"Как устроено решение. Глобальная переменная s хранит текущее состояние, функция f() пересчитывает его по формуле (37 s + 3) % 64 , а затем main несколько раз вызывает генератор.","keywords":["файлы","37","4.7","64","main","procedural-programming","вызывает","генератор","генератор.","глобальная","графика","девять"],"topics":["ai"]},{"id":"chunk-28","heading":"4.7 «Генератор случайных чисел»","text":"cpp title=\"resources/procedural programming/home work 4/4.7.cpp\" include using namespace std; int s = 0; int f() { s = (37 s + 3) % 64; return s; } int main() { int n; cin n; for (int i = 1; i <= n + 1; ++i) { cout << f() << endl; } return 0; }","keywords":["int","return","файлы","++i","37","4.7","4.7.cpp","64","cin","cout","cpp","endl"],"topics":["ai"]},{"id":"chunk-29","heading":"4.8 «Умножение матриц»","text":"Условие. Нужно задать матрицы продаж и цен, получить произведение C = A x B и ответить на вопросы о выручке и комиссионных.","keywords":["матрицы","файлы","4.8","procedural-programming","вопросы","выручке","графика","девять","дз","задать","задач","комиссионных."],"topics":[]},{"id":"chunk-30","heading":"4.8 «Умножение матриц»","text":"Как устроено решение. Код вручную задаёт две константные матрицы, перемножает их, затем проходит по строкам результата и находит максимумы, минимумы и суммарные значения.","keywords":["матрицы","файлы","4.8","procedural-programming","вручную","графика","две","девять","дз","задает","задач","затем"],"topics":[]},{"id":"chunk-31","heading":"4.8 «Умножение матриц»","text":"```cpp title=\"resources/procedural programming/home work 4/4.8.cpp\" include include include include using namespace std; int main() { double maks = 90, mensh = 9999, maksk = 90, menshk = 990, obsh, obshk, obshd, x1, x2, x3, x4, x5 = 0, x6 = 0; double a[3][4] = { 5,2,0,10,3,5,2,5,20,0,0,0 }, b[4][2] = { 1.20,0.50,2.80,0.40,5,1,2,1.5 }, c[3][2] = {0}; for (int i = 0; i < 3; i++) { for (int j = 0; j < 4","keywords":["include","int","90","double","файлы","0.40","0.50","1.20","1.5","10","2.80","20"],"topics":["ai"]},{"id":"chunk-32","heading":"4.8 «Умножение матриц»","text":"; j++) { c[i][0] += a[i][j] b[j][0]; c[i][1] += a[i][j] b[j][1]; } }","keywords":["файлы","4.8","j++","procedural-programming","графика","девять","дз","задач","матриц","матрицы","подпрограммы","преобразование"],"topics":[]},{"id":"chunk-33","heading":"4.8 «Умножение матриц»","text":"for (int i = 0; i c[i][0]) { mensh = c[i][0]; x2 = i; } if (maksk c[i][1]) { menshk = c[i][1]; x4 = i; } x5 += c[i][0]; x6 += c[i][1];","keywords":["файлы","4.8","if","int","maksk","mensh","menshk","procedural-programming","x2","x4","x5","x6"],"topics":[]},{"id":"chunk-34","heading":"4.8 «Умножение матриц»","text":"for (int j = 0; j < 2; j++) { cout << c[i][j] << \" \"; } cout << endl; } cout << \"1) \" << x1 + 1 << \" \" << x2 + 1 << endl; cout << \"2) \" << x3 + 1 << \" \" << x4 + 1 << endl; cout << \"3) \" << x5 << endl; cout << \"4) \" << x6 << endl; cout << \"5) \" << x5 + x6 << endl; } ```","keywords":["cout","endl","x5","x6","файлы","4.8","int","j++","procedural-programming","x1","x2","x3"],"topics":[]},{"id":"chunk-35","heading":"4.9 «Системы счисления»","text":"Как устроено решение. Сначала символы исходной записи переводятся в десятичное значение через функцию cf , затем число делится на новое основание, а остатки собираются в ответ.","keywords":["системы","счисления","файлы","4.9","cf","procedural-programming","графика","девять","делится","десятичное","дз","задач"],"topics":[]},{"id":"chunk-36","heading":"4.9 «Системы счисления»","text":"Что учесть. Сама схема перевода правильная: сначала перевод в десятичную систему, потом в целевую. Это главная идея, которую стоит помнить.","keywords":["системы","счисления","файлы","4.9","procedural-programming","главная","графика","девять","десятичную","дз","задач","идея"],"topics":[]},{"id":"chunk-37","heading":"4.9 «Системы счисления»","text":"cpp title=\"resources/procedural programming/home work 4/4.9.cpp\" include include include using namespace std; int cf(char x) { if (x == '1') return(1); else if (x == '2') return(2); else if (x == '3') return(3); else if (x == '4') return(4); else if (x == '5') return(5); else if (x == '6') return(6); else if (x == '7') return(7); else if (x == '8') return(8); else if (x == '9') return(9); else if (x == '0') return(0)","keywords":["if","return","else","include","системы","счисления","файлы","4.9","4.9.cpp","cf","char","cpp"],"topics":[]},{"id":"chunk-38","heading":"4.9 «Системы счисления»","text":"; else if (x == 'A') return(10); else if (x == 'B') return(11); else if (x == 'C') return(12); else if (x == 'D') return(13); else if (x == 'E') return(14); else if (x == 'F') return(15); else if (x == 'G') return(16); else if (x == 'H') return(17); else if (x == 'I') return(18); else if (x == 'J') return(19); else if (x == 'K') return(20); else if (x == 'L') return(21); else if (x == 'M') return(22)","keywords":["else","if","return","системы","счисления","файлы","10","11","12","13","14","15"],"topics":[]},{"id":"chunk-39","heading":"4.9 «Системы счисления»","text":"; else if (x == 'N') return(23); else if (x == 'O') return(24); else if (x == 'P') return(25); else if (x == 'Q') return(26); else if (x == 'R') return(27); else if (x == 'S') return(28); else if (x == 'T') return(29); else if (x == 'U') return(30); else if (x == 'V') return(31); else if (x == 'W') return(32); else if (x == 'X') return(33); else if (x == 'Y') return(34); else if (x == 'Z') return(35); return 0","keywords":["return","else","if","системы","счисления","файлы","23","24","25","26","27","28"],"topics":[]},{"id":"chunk-40","heading":"4.9 «Системы счисления»","text":"; } int bk(char x) { if (x == 10) return('A'); else if (x == 11) return('B'); else if (x == 12) return('C'); else if (x == 13) return('D'); else if (x == 14) return('E'); else if (x == 15) return('F'); else if (x == 16) return('G'); else if (x == 17) return('H'); else if (x == 18) return('I'); else if (x == 19) return('J'); else if (x == 20) return('K'); else if (x == 'L') return(21); else if (x == 'M') return(22)","keywords":["if","return","else","системы","счисления","файлы","10","11","12","13","14","15"],"topics":[]},{"id":"chunk-41","heading":"4.9 «Системы счисления»","text":"; else if (x == 'N') return(23); else if (x == 'O') return(24); else if (x == 'P') return(25); else if (x == 'Q') return(26); else if (x == 'R') return(27); else if (x == 'S') return(28); else if (x == 'T') return(29); else if (x == 'U') return(30); else if (x == 'V') return(31); else if (x == 'W') return(32); else if (x == 'X') return(33); else if (x == 'Y') return(34); else if (x == 'Z') return(35); else return(0)","keywords":["else","return","if","системы","счисления","файлы","23","24","25","26","27","28"],"topics":[]},{"id":"chunk-42","heading":"4.9 «Системы счисления»","text":"; } int main() { setlocale(0, \"\"); string ch, otv = \"\"; string vr; long int s = 0, p = 4000, o, e, n, d = 0, pr; cout e; cout n; cout ch; if (e == n) { cout = e) { cout = n) { pr = d % n; d /= n; if (pr 9) { otv += bk(pr); } else { vr = to string(pr); otv += vr; } i++; } i++; if (d 9) { otv += bk(d); } else { vr = to string(d); otv += vr; } } reverse(otv.begin(), otv.end()); cout << otv; }","keywords":["cout","otv","pr","vr","string","if","bk","ch","else","i++","int","системы"],"topics":["ai"]}]},{"id":"procedural-programming/homework-05-number-theory-and-combinatorics","href":"/docs/procedural-programming/homework-05-number-theory-and-combinatorics","slug":["procedural-programming","homework-05-number-theory-and-combinatorics"],"section":"procedural-programming","sectionTitle":"Процедурное программирование","title":"ДЗ 5 — НОД, простые числа, индивидуальные варианты и шарики","description":"Пятое домашнее задание по теории чисел, файлам и перебору перестановок.","preview":"Что внутри В пятом домашнем задании есть как стандартные алгоритмы, так и индивидуальные варианты. По PDF видно, что пункты 5.3 , 5.4 и 5.5 должны были быть уникальными для каждого студента, поэтому в ресурсах есть готовый код только для части задач. 5.1 «Алго","keywords":["if","else","kol","num","5.3","int","balls","обработка","текстовых","файлов","include","шарики","5.6","cout","position","5.4","count","cpp"],"topics":["python","ai","oop","algorithms"],"chunks":[{"id":"chunk-0","heading":"Что внутри","text":"В пятом домашнем задании есть как стандартные алгоритмы, так и индивидуальные варианты. По PDF видно, что пункты 5.3 , 5.4 и 5.5 должны были быть уникальными для каждого студента, поэтому в ресурсах есть готовый код только для части задач.","keywords":["индивидуальные","5.3","5.4","5.5","pdf","procedural-programming","алгоритмы","были","быть","варианты","варианты.","видно"],"topics":["algorithms"]},{"id":"chunk-1","heading":"5.1 «Алгоритм Евклида»","text":"Как устроено решение. Сохранённый код идёт не через классический цикл Евклида с остатком, а через перебор делителей от min(a, b) вниз до 1 . Как только находится общий делитель, программа печатает его и завершает работу.","keywords":["евклида","через","5.1","min","procedural-programming","алгоритм","варианты","вниз","делителей","делитель","дз","до"],"topics":["oop","algorithms"]},{"id":"chunk-2","heading":"5.1 «Алгоритм Евклида»","text":"cpp title=\"resources/procedural programming/home work 5/51 DONE.cpp\" include include using namespace std; int main(){ int a, b; cin a b; for (int i = min(a, b); i = 1; i ) { if (a % i == 0 and b % i == 0) { cout << i; return 0; } } }","keywords":["int","include","5.1","51","cin","cout","cpp","done.cpp","home","if","main","min"],"topics":["ai","algorithms"]},{"id":"chunk-3","heading":"5.2 «Решето Эратосфена»","text":"Как устроено решение. Файл из ресурсов проверяет каждое число j на делимость всеми меньшими числами и печатает его, если делителей нет.","keywords":["5.2","procedural-programming","варианты","всеми","делимость","делителей","дз","домашнее","его","если","задание","индивидуальные"],"topics":[]},{"id":"chunk-4","heading":"5.2 «Решето Эратосфена»","text":"Что учесть. Это решение реализует не решето Эратосфена в чистом виде, а прямую проверку на простоту. Кроме того, 2 здесь не выводится, потому что цикл начинается с 3 .","keywords":["решето","эратосфена","5.2","procedural-programming","варианты","виде","выводится","дз","домашнее","задание","здесь","индивидуальные"],"topics":[]},{"id":"chunk-5","heading":"5.2 «Решето Эратосфена»","text":"cpp title=\"resources/procedural programming/home work 5/52 DONE.cpp\" include include using namespace std; int main() { int a, b; cin a; for (int j = 3; j 1; i ) { if (j % i == 0) { b = 1; } } if (b == 0) { cout << j << \" \"; } } }","keywords":["int","if","include","5.2","52","cin","cout","cpp","done.cpp","home","main","namespace"],"topics":["ai"]},{"id":"chunk-6","heading":"5.3 «Обработка текстовых файлов»","text":"Условие. В PDF сказано, что это индивидуальный вариант. В ресурсах сохранён вариант, где программа читает файл и ищет наиболее часто встречающуюся согласную букву из заданного набора.","keywords":["5.3","pdf","procedural-programming","букву","вариант","вариант.","варианты","встречающуюся","дз","домашнее","задание","заданного"],"topics":[]},{"id":"chunk-7","heading":"5.3 «Обработка текстовых файлов»","text":"Как устроено решение. Код открывает файл 123.txt , считает частоты для заранее заданного массива букв, затем выбирает индекс максимального значения и печатает количество вхождений вместе с самой буквой.","keywords":["123.txt","5.3","procedural-programming","букв","буквой.","варианты","вместе","вхождений","выбирает","дз","домашнее","задание"],"topics":[]},{"id":"chunk-8","heading":"5.3 «Обработка текстовых файлов»","text":"```cpp title=\"resources/procedural programming/home work 5/5.3.cpp\" include include include using namespace std;","keywords":["include","5.3","5.3.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std","title"],"topics":[]},{"id":"chunk-9","heading":"5.3 «Обработка текстовых файлов»","text":"ifstream ifile; char bukvi[20]{ 'b', 'd', 'c', 'f', 'g', 'h', 'j', 'k', 'l', 'm', 'n', 'p', 'q', 'r', 's', 't', 'v', 'w', 'x', 'z' }; int kol[20]{};","keywords":["20","5.3","bukvi","char","ifile","ifstream","int","kol","procedural-programming","варианты","дз","домашнее"],"topics":[]},{"id":"chunk-10","heading":"5.3 «Обработка текстовых файлов»","text":"int main() { int max = 0, num; char c, a = 'a'; ifile.open(\"123.txt\"); if (ifile.is open()) { cout << \"okey...\"; getch();","keywords":["int","123.txt","5.3","char","cout","getch","if","ifile.is","ifile.open","main","max","num"],"topics":["ai"]},{"id":"chunk-11","heading":"5.3 «Обработка текстовых файлов»","text":"while (ifile.get(c)) { if (c == 'b') { kol[0] += 1; } else if (c == 'd') { kol[1] += 1; } else if (c == 'c') { kol[2] += 1; } else if (c == 'f') { kol[3] += 1; } else if (c == 'g') { kol[4] += 1; } else if (c == 'h') { kol[5] += 1; } else if (c == 'j') { kol[6] += 1; } else if (c == 'k') { kol[7] += 1; } else if (c == 'l') { kol[8] += 1; } else if (c == 'm') { kol[9] += 1; } else if (c == 'n') { kol[10] += 1","keywords":["if","kol","else","10","5.3","ifile.get","procedural-programming","while","варианты","дз","домашнее","задание"],"topics":[]},{"id":"chunk-12","heading":"5.3 «Обработка текстовых файлов»","text":"; } else if (c == 'p') { kol[11] += 1; } else if (c == 'q') { kol[12] += 1; } else if (c == 'r') { kol[13] += 1; } else if (c == 's') { kol[14] += 1; } else if (c == 't') { kol[15] += 1; } else if (c == 'v') { kol[16] += 1; } else if (c == 'w') { kol[17] += 1; } else if (c == 'x') { kol[18] += 1; } else if (c == 'z') { kol[19] += 1; } }","keywords":["else","if","kol","11","12","13","14","15","16","17","18","19"],"topics":[]},{"id":"chunk-13","heading":"5.3 «Обработка текстовых файлов»","text":"if (num == 0) { a = 'b'; } else if (num == 1) { a = 'd'; } else if (num == 2) { a = 'c'; } else if (num == 3) { a = 'f'; } else if (num == 4) { a = 'g'; } else if (num == 5) { a = 'h'; } else if (num == 6) { a = 'j'; } else if (num == 7) { a = 'k'; } else if (num == 8) { a = 'l'; } else if (num == 9) { a = 'm'; } else if (num == 10) { a = 'n'; } else if (num == 11) { a = 'p'; } else if (num == 12) { a = 'q'","keywords":["if","num","else","10","11","12","5.3","procedural-programming","варианты","дз","домашнее","задание"],"topics":[]},{"id":"chunk-14","heading":"5.3 «Обработка текстовых файлов»","text":"; } else if (num == 13) { a = 'r'; } else if (num == 14) { a = 's'; } else if (num == 15) { a = 't'; } else if (num == 16) { a = 'v'; } else if (num == 17) { a = 'w'; } else if (num == 18) { a = 'x'; } else if (num == 19) { a = 'z'; }","keywords":["else","if","num","13","14","15","16","17","18","19","5.3","procedural-programming"],"topics":[]},{"id":"chunk-15","heading":"5.3 «Обработка текстовых файлов»","text":"cout << \"\\n\" << max << \" [\" << a << \"]\"; } else { cout << \"ne okey...\"; getch(); exit(0); } } ```","keywords":["cout","5.3","else","exit","getch","max","ne","okey...","procedural-programming","варианты","дз","домашнее"],"topics":[]},{"id":"chunk-16","heading":"5.4 «Ряды»","text":"Что известно по PDF. В формулировке указано только то, что это индивидуальный вариант на человека.","keywords":["5.4","pdf.","procedural-programming","вариант","варианты","дз","домашнее","задание","известно","индивидуальные","индивидуальный","нод"],"topics":[]},{"id":"chunk-17","heading":"5.4 «Ряды»","text":"Что учесть. В resources/procedural programming нет отдельного условия или cpp файла для конкретного варианта, поэтому в вики нельзя восстановить точную постановку без исходного личного задания.","keywords":["5.4","cpp","procedural","procedural-programming","programming","resources","без","варианта","варианты","вики","восстановить","дз"],"topics":[]},{"id":"chunk-18","heading":"5.5 «Файлы»","text":"Что известно по PDF. Здесь ситуация такая же, как и в пункте 5.4 : в PDF сказано, что вариант индивидуальный.","keywords":["5.4","5.5","pdf","pdf.","procedural-programming","вариант","варианты","дз","домашнее","же","задание","здесь"],"topics":[]},{"id":"chunk-19","heading":"5.5 «Файлы»","text":"Что учесть. Без отдельного листа варианта или готового исходника точную задачу восстановить нельзя.","keywords":["5.5","procedural-programming","без","варианта","варианты","восстановить","готового","дз","домашнее","задание","задачу","индивидуальные"],"topics":[]},{"id":"chunk-20","heading":"5.6 «Шарики»","text":"Условие. Из урны последовательно достают пронумерованные шарики. Нужно посчитать число перестановок, в которых номер хотя бы одного шарика совпадает с номером шага.","keywords":["шарики","5.6","procedural-programming","бы","варианты","дз","домашнее","достают","задание","индивидуальные","которых","нод"],"topics":[]},{"id":"chunk-21","heading":"5.6 «Шарики»","text":"Как устроено решение. Ниже приведён аккуратный эталонный вариант, который соответствует алгоритму из PDF: строятся все перестановки, а на листе рекурсии проверяется наличие хотя бы одного совпадения balls[i] == i + 1 .","keywords":["шарики","5.6","balls","pdf","procedural-programming","аккуратный","алгоритму","бы","вариант","варианты","дз","домашнее"],"topics":["algorithms"]},{"id":"chunk-22","heading":"5.6 «Шарики»","text":"Что учесть. Это уже полноценная рекурсивная задача на перебор перестановок. Решение сохраняет идею из формулировки и работает для произвольного n , хотя на больших значениях факториальный рост быстро делает перебор тяжёлым.","keywords":["перебор","перестановок.","шарики","5.6","procedural-programming","больших","быстро","варианты","делает","дз","домашнее","задание"],"topics":[]},{"id":"chunk-23","heading":"5.6 «Шарики»","text":"```cpp title=\"resources/procedural programming/home work 5/5.6.cpp\" include include using namespace std;","keywords":["include","шарики","5.6","5.6.cpp","cpp","home","namespace","procedural","procedural-programming","programming","resources","std"],"topics":[]},{"id":"chunk-24","heading":"5.6 «Шарики»","text":"bool hasFixedPoint(const vector & balls) { for (int i = 0; i (balls.size()); ++i) { if (balls[i] == i + 1) { return true; } } return false; }","keywords":["balls","return","шарики","++i","5.6","balls.size","bool","const","false","hasfixedpoint","if","int"],"topics":[]},{"id":"chunk-25","heading":"5.6 «Шарики»","text":"void generatePermutations(int position, vector & balls, long long& count) { if (position == static cast (balls.size())) { if (hasFixedPoint(balls)) { ++count; } return; }","keywords":["balls","if","long","position","шарики","++count","5.6","balls.size","cast","count","generatepermutations","hasfixedpoint"],"topics":["python"]},{"id":"chunk-26","heading":"5.6 «Шарики»","text":"for (int i = position; i (balls.size()); ++i) { swap(balls[position], balls[i]); generatePermutations(position + 1, balls, count); swap(balls[position], balls[i]); } }","keywords":["balls","position","swap","шарики","++i","5.6","balls.size","count","generatepermutations","int","procedural-programming","варианты"],"topics":[]},{"id":"chunk-27","heading":"5.6 «Шарики»","text":"long long count = 0; generatePermutations(0, balls, count); cout << count; return 0; } ```","keywords":["count","long","шарики","5.6","balls","cout","generatepermutations","procedural-programming","return","варианты","дз","домашнее"],"topics":[]}]},{"id":"internet-of-things","href":"/docs/internet-of-things","slug":["internet-of-things"],"section":"internet-of-things","sectionTitle":"Интернет вещей","title":"Интернет вещей","description":"Материалы по IoT: устройства, протоколы обмена и интеграции.","preview":"О разделе Раздел Интернет вещей добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["вещей","интернет","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","iot","задач","интеграции.","кода","конспекты","материалы","можно","обмена"],"topics":[],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Интернет вещей добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["вещей","интернет","internet-of-things","iot","stackmirea","готов","добавлен","интеграции.","материалами.","материалы","наполнению","обмена"],"topics":[]}]},{"id":"configuration-management","href":"/docs/configuration-management","slug":["configuration-management"],"section":"configuration-management","sectionTitle":"Конфигурационное управление","title":"Конфигурационное управление","description":"Практики по управлению конфигурацией, версиям и средам разработки.","preview":"О разделе Раздел Конфигурационное управление добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["конфигурационное","управление","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","версиям","задач","кода","конспекты","конфигурацией","можно","практики","практические"],"topics":[],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Конфигурационное управление добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["конфигурационное","управление","configuration-management","stackmirea","версиям","готов","добавлен","конфигурацией","материалами.","наполнению","практики","раздел"],"topics":[]}]},{"id":"business-process-modeling","href":"/docs/business-process-modeling","slug":["business-process-modeling"],"section":"business-process-modeling","sectionTitle":"Моделирование бизнес процессов","title":"Моделирование бизнес процессов","description":"Подходы и инструменты для анализа и моделирования бизнес-процессов.","preview":"О разделе Раздел Моделирование бизнес процессов добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["бизнес","моделирование","процессов","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","анализа","бизнес-процессов.","задач","инструменты","кода","конспекты","моделирования"],"topics":[],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Моделирование бизнес процессов добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["бизнес","моделирование","процессов","business-process-modeling","stackmirea","анализа","бизнес-процессов.","готов","добавлен","инструменты","материалами.","моделирования"],"topics":[]}]},{"id":"object-oriented-programming","href":"/docs/object-oriented-programming","slug":["object-oriented-programming"],"section":"object-oriented-programming","sectionTitle":"Объектно ориентированное программирование","title":"Объектно ориентированное программирование","description":"Практики по ООП: классы, наследование, полиморфизм и архитектура.","preview":"О разделе Раздел Объектно ориентированное программирование добавлен в StackMIREA и готов к наполнению учебными материалами. Что можно публиковать Практические работы Конспекты и чеклисты Разборы задач и примеры кода","keywords":["объектно","ориентированное","программирование","stackmirea","готов","добавлен","материалами.","наполнению","раздел","разделе","учебными","архитектура.","задач","классы","кода","конспекты","можно","наследование"],"topics":["oop"],"chunks":[{"id":"chunk-0","heading":"О разделе","text":"Раздел Объектно ориентированное программирование добавлен в StackMIREA и готов к наполнению учебными материалами.","keywords":["объектно","ориентированное","программирование","object","oriented-programming","stackmirea","архитектура.","готов","добавлен","классы","материалами.","наполнению"],"topics":["oop"]}]},{"id":"bigdata/practice-01-introduction","href":"/docs/bigdata/practice-01-introduction","slug":["bigdata","practice-01-introduction"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 1. Введение в BigData","description":"Базовые операции и знакомство с форматом работы.","preview":"Wiki версия Практическая работа №1 1. Установить Python, если это не было сделано ранее. 2. Написать программу, которая вычисляет площадь фигуры, параметры которой подаются на вход. Фигуры, которые подаются на вход: треугольник, прямоугольник, круг. Результато","keywords":["non","null","dtype","column","count","значения","print","ok","чтение","..","none","входного","52.0","height","20640","operation","bhnqaptmp","float64"],"topics":["python","ai","pandas","numpy"],"chunks":[{"id":"chunk-0","heading":"Практическая работа №1","text":"2. Написать программу, которая вычисляет площадь фигуры, параметры которой подаются на вход. Фигуры, которые подаются на вход: треугольник, прямоугольник, круг. Результатом работы является словарь, где ключ – это название фигуры, а значение – это площадь.","keywords":["bigdata","фигуры","подаются","1.","2.","базовые","введение","вход","вход.","вычисляет","знакомство","значение"],"topics":[]},{"id":"chunk-1","heading":"Практическая работа №1","text":"def calculate areas(figures): \"\"\" figures — список словарей с параметрами фигур, пример: [ {'type': 'треугольник', 'base': 10, 'height': 5}, {'type': 'прямоугольник', 'width': 4, 'height': 6}, {'type': 'круг', 'radius': 3} ]","keywords":["bigdata","type","figures","height","1.","10","areas","base","calculate","def","radius","width"],"topics":[]},{"id":"chunk-2","heading":"Практическая работа №1","text":"if shape == 'треугольник': base = fig.get('base') height = fig.get('height') if base is not None and height is not None: area = 0.5 base height areas['треугольник'] = area","keywords":["base","height","bigdata","area","fig.get","if","none","not","треугольник","0.5","1.","areas"],"topics":[]},{"id":"chunk-3","heading":"Практическая работа №1","text":"elif shape == 'прямоугольник': width = fig.get('width') height = fig.get('height') if width is not None and height is not None: area = width height areas['прямоугольник'] = area","keywords":["height","width","bigdata","area","fig.get","none","not","прямоугольник","1.","areas","elif","if"],"topics":[]},{"id":"chunk-4","heading":"Практическая работа №1","text":"elif shape == 'круг': radius = fig.get('radius') if radius is not None: area = math.pi radius 2 areas['круг'] = area","keywords":["radius","bigdata","area","круг","1.","areas","elif","fig.get","if","math.pi","none","not"],"topics":[]},{"id":"chunk-5","heading":"Пример использования:","text":"figures = [ {'type': 'треугольник', 'base': 10, 'height': 5}, {'type': 'прямоугольник', 'width': 4, 'height': 6}, {'type': 'круг', 'radius': 3} ]","keywords":["bigdata","type","height","1.","10","base","figures","radius","width","базовые","введение","знакомство"],"topics":[]},{"id":"chunk-6","heading":"Вывод: {'треугольник': 25.0, 'прямоугольник': 24, 'круг': 28.274333882308138}","text":"3. Написать программу, которая на вход получает два числа и операцию, которую к ним нужно применить. Должны быть реализованы следующие операции: +, , /, //, abs – модуль, pow или – возведение в степень.","keywords":["bigdata","операции","1.","24","25.0","28.274333882308138","3.","abs","pow","базовые","быть","введение"],"topics":[]},{"id":"chunk-7","heading":"Вывод: {'треугольник': 25.0, 'прямоугольник': 24, 'круг': 28.274333882308138}","text":"```python def calculator(a, b, operation): if operation == '+': return a + b elif operation == ' ': return a b elif operation == '/': if b == 0: return \"Ошибка: деление на ноль\" return a / b elif operation == '//': if b == 0: return \"Ошибка: деление на ноль\" return a // b elif operation == 'abs':","keywords":["operation","return","elif","bigdata","if","деление","ноль","ошибка","1.","24","25.0","28.274333882308138"],"topics":["python"]},{"id":"chunk-8","heading":"Игнорируем b, возвращаем модуль a","text":"return abs(a) elif operation == 'pow' or operation == ' ': return a b else: return \"Ошибка: неизвестная операция\"","keywords":["bigdata","return","operation","1.","abs","elif","else","pow","базовые","введение","возвращаем","знакомство"],"topics":[]},{"id":"chunk-9","heading":"Пример использования:","text":"a = float(input(\"Введите первое число: \")) b = float(input(\"Введите второе число: \")) operation = input(\"Введите операцию (+, , /, //, abs, pow, ): \")","keywords":["bigdata","input","введите","float","число","1.","abs","operation","pow","базовые","введение","второе"],"topics":["numpy"]},{"id":"chunk-10","heading":"Пример использования:","text":"text Введите первое число: 5 Введите второе число: 4 Введите операцию (+, , /, //, abs, pow, ): // Результат: 1.0","keywords":["bigdata","введите","число","1.","1.0","abs","pow","text","базовые","введение","второе","знакомство"],"topics":[]},{"id":"chunk-11","heading":"Пример использования:","text":"4. Напишите программу, которая считывает с консоли числа (по одному в строке) до тех пор, пока сумма введённых чисел не будет равна 0 и после этого выводит сумму квадратов всех считанных чисел.","keywords":["bigdata","1.","4.","базовые","будет","введение","введенных","всех","выводит","до","знакомство","использования"],"topics":[]},{"id":"chunk-12","heading":"Пример использования:","text":"while True: try: num = float(input(\"Введите число: \")) except ValueError: print(\"Ошибка: введите корректное число.\") continue","keywords":["bigdata","введите","1.","continue","except","float","input","num","print","true","try","valueerror"],"topics":["numpy"]},{"id":"chunk-13","heading":"Пример использования:","text":"sum of squares = sum(x 2 for x in numbers) print(\"Сумма квадратов введённых чисел:\", sum of squares)","keywords":["bigdata","sum","squares","1.","numbers","print","базовые","введение","введенных","знакомство","использования","квадратов"],"topics":[]},{"id":"chunk-14","heading":"Запуск функции","text":"5. Напишите программу, которая выводит последовательность чисел, длинною N, где каждое число повторяется столько раз, чему оно равно. На вход программе передаётся неотрицательное целое число N. Например, если N = 7, то программа должна вывести 1 2 2 3 3 3 4. Вывод элементов списка через пробел – print( list).","keywords":["bigdata","число","1.","4.","5.","list","n.","print","базовые","введение","вход","вывести"],"topics":[]},{"id":"chunk-15","heading":"Запуск функции","text":"```python def generate sequence(N): result = [] num = 1 while len(result) < N: result.extend([num] num) num += 1","keywords":["num","bigdata","result","1.","def","generate","len","python","result.extend","sequence","while","базовые"],"topics":["python"]},{"id":"chunk-16","heading":"Чтение входного значения","text":"Создать словарь, в котором ключи – это содержимое списка В, а значения для ключей словаря – это сумма всех элементов списка А в соответствии с буквой, содержащийся на той же позиции в списке В.","keywords":["bigdata","значения","списка","1.","базовые","буквой","в.","введение","всех","входного","же","знакомство"],"topics":[]},{"id":"chunk-17","heading":"Чтение входного значения","text":"```python A = [1, 2, 3, 4, 2, 1, 3, 4, 5, 6, 5, 4, 3, 2] B = ['a', 'b', 'c', 'c', 'c', 'b', 'a', 'c', 'a', 'a', 'b', 'c', 'b', 'a']","keywords":["bigdata","1.","python","базовые","введение","входного","знакомство","значения","операции","практика","работы.","форматом"],"topics":["python"]},{"id":"chunk-18","heading":"Чтение входного значения","text":"result = {} for key, value in zip(B, A): result[key] = result.get(key, 0) + value","keywords":["bigdata","key","result","value","1.","result.get","zip","базовые","введение","входного","знакомство","значения"],"topics":[]},{"id":"chunk-19","heading":"Чтение входного значения","text":"Скачать и загрузить данные о стоимости домов в калифорнии, используя библиотеку sklearn.","keywords":["bigdata","1.","sklearn.","базовые","библиотеку","введение","входного","данные","домов","загрузить","знакомство","значения"],"topics":[]},{"id":"chunk-20","heading":"Чтение входного значения","text":"10. Вывести записи, где средний возраст домов в районе более 50 лет и население более 2500 человек, используя метод loc().","keywords":["bigdata","более","1.","10.","2500","50","loc","базовые","введение","возраст","входного","вывести"],"topics":[]},{"id":"chunk-21","heading":"Чтение входного значения","text":"12. Используя метод apply(), вывести на экран название признака и его среднее значение.","keywords":["bigdata","1.","12.","apply","базовые","введение","входного","вывести","его","знакомство","значение.","значения"],"topics":[]},{"id":"chunk-22","heading":"Чтение входного значения","text":"13. Составить отчет о проделанной работе. В отчете должен быть представлен код и результаты его выполнения с выводами.","keywords":["bigdata","1.","13.","базовые","быть","введение","входного","выводами.","выполнения","должен","его","знакомство"],"topics":[]},{"id":"chunk-23","heading":"Чтение входного значения","text":"Данные о стоимости домов в Калифорнии успешно загружены с использованием sklearn.datasets.fetch california housing с параметром as frame=True для удобной работы с pandas DataFrame.","keywords":["bigdata","1.","california","dataframe.","frame","housing","pandas","sklearn.datasets.fetch","true","базовые","введение","входного"],"topics":["pandas"]},{"id":"chunk-24","heading":"Чтение входного значения","text":"Метод info() предоставил сведения о структуре данных: количество строк (20640), типы столбцов и отсутствие пропущенных значений.","keywords":["bigdata","1.","20640","info","базовые","введение","входного","данных","знакомство","значений.","значения","количество"],"topics":[]},{"id":"chunk-25","heading":"Чтение входного значения","text":"Проверка на пропущенные значения с помощью isna().sum() подтвердила, что данные полные, без отсутствующих значений.","keywords":["bigdata","значения",".sum","1.","isna","базовые","без","введение","входного","данные","знакомство","значений."],"topics":[]},{"id":"chunk-26","heading":"Чтение входного значения","text":"Отфильтрованы записи районов с домами старше 50 лет и населением свыше 2500 человек, что позволяет выделить специфические участки для дополнительного анализа.","keywords":["bigdata","1.","2500","50","анализа.","базовые","введение","входного","выделить","домами","дополнительного","записи"],"topics":[]},{"id":"chunk-27","heading":"Чтение входного значения","text":"Найдены максимальное и минимальное значение целевой переменной — медианной стоимости дома, что дает ориентировку по разбросу цен.","keywords":["bigdata","1.","базовые","введение","входного","дает","дома","знакомство","значение","значения","максимальное","медианной"],"topics":[]},{"id":"chunk-28","heading":"Чтение входного значения","text":"Использование apply() помогло быстро вывести средние значения всех признаков для общего понимания их распределения.","keywords":["bigdata","значения","1.","apply","базовые","быстро","введение","всех","входного","вывести","знакомство","использование"],"topics":[]},{"id":"chunk-29","heading":"Чтение входного значения","text":"Эти шаги создают прочную основу для дальнейшего анализа с использованием методов машинного обучения или углубленной статистики.","keywords":["bigdata","1.","анализа","базовые","введение","входного","дальнейшего","знакомство","значения","использованием","машинного","методов"],"topics":[]},{"id":"chunk-30","heading":"Загрузка данных с признаком as frame=True для получения DataFrame сразу","text":"housing = fetch california housing(as frame=True) df = housing.frame pandas DataFrame с признаками и целевой переменной","keywords":["bigdata","dataframe","frame","housing","true","1.","california","df","fetch","housing.frame","pandas","базовые"],"topics":["pandas"]},{"id":"chunk-31","heading":"9. Узнать, есть ли пропущенные значения (NaN)","text":"print(\"Сумма пропущенных значений по столбцам:\") print(df.isna().sum()) print(\"\\n\")","keywords":["bigdata","print",".sum","1.","9.","df.isna","nan","базовые","введение","знакомство","значений","значения"],"topics":[]},{"id":"chunk-32","heading":"10. Вывести записи, где средний возраст домов 50 лет и население 2500 человек","text":"print(\"Записи с HouseAge 50 и Population 2500:\") filtered = df.loc[(df['HouseAge'] 50) & (df['Population'] 2500)] print(filtered) print(\"\\n\")","keywords":["2500","50","bigdata","print","df","filtered","houseage","population","записи","1.","10.","df.loc"],"topics":[]},{"id":"chunk-33","heading":"11. Максимальное и минимальное значение медианной стоимости дома","text":"max price = df['MedHouseVal'].max() min price = df['MedHouseVal'].min() print(f\"Максимальная медианная стоимость дома: {max price}\") print(f\"Минимальная медианная стоимость дома: {min price}\") print(\"\\n\")","keywords":["price","bigdata","print","дома","df","max","medhouseval","min","медианная","стоимость",".max",".min"],"topics":[]},{"id":"chunk-34","heading":"12. С использованием apply() вывести название признака и его среднее значение","text":"print(\"Средние значения признаков:\") def print mean(column): print(f\"{column.name}: {column.mean():.3f}\")","keywords":["bigdata","print",".3f","1.","12.","apply","column","column.mean","column.name","def","mean","базовые"],"topics":[]},{"id":"chunk-35","heading":"12. С использованием apply() вывести название признака и его среднее значение","text":"```text Информация о DataFrame: RangeIndex: 20640 entries, 0 to 20639 Data columns (total 9 columns):","keywords":["bigdata","columns","1.","12.","20639","20640","apply","data","dataframe","entries","rangeindex","text"],"topics":["pandas"]},{"id":"chunk-36","heading":"Column Non Null Count Dtype","text":"0 MedInc 20640 non null float64 1 HouseAge 20640 non null float64 2 AveRooms 20640 non null float64 3 AveBedrms 20640 non null float64 4 Population 20640 non null float64 5 AveOccup 20640 non null float64 6 Latitude 20640 non null float64 7 Longitude 20640 non null float64 8 MedHouseVal 20640 non null float64 dtypes: float64(9) memory usage: 1.4 MB","keywords":["float64","non","null","20640","bigdata","1.","1.4","avebedrms","aveoccup","averooms","column","count"],"topics":[]},{"id":"chunk-37","heading":"Column Non Null Count Dtype","text":"Сумма пропущенных значений по столбцам: MedInc 0 HouseAge 0 AveRooms 0 AveBedrms 0 Population 0 AveOccup 0 Latitude 0 Longitude 0 MedHouseVal 0 dtype: int64","keywords":["bigdata","dtype","1.","avebedrms","aveoccup","averooms","column","count","houseage","int64","latitude","longitude"],"topics":[]},{"id":"chunk-38","heading":"Column Non Null Count Dtype","text":"Записи с HouseAge 50 и Population 2500: MedInc HouseAge AveRooms AveBedrms Population AveOccup \\ 460 1.4012 52.0 3.105714 1.060000 3337.0 9.534286 4131 3.5349 52.0 4.646119 1.047945 2589.0 5.910959 4440 2.6806 52.0 4.806283 1.057592 3062.0 4.007853 5986 1.8750 52.0 4.500000 1.206349 2688.0 21.333333 7369 3.1901 52.0 4.730942 1.017937 3731.0 4.182735 8227 2.3305 52.0 3.488860 1.170380 3018.0 3.955439 13034 6.1359 52.0","keywords":["52.0","bigdata","houseage","population","1.","1.017937","1.047945","1.057592","1.060000","1.170380","1.206349","1.4012"],"topics":[]},{"id":"chunk-39","heading":"Column Non Null Count Dtype","text":"8.275862 1.517241 6675.0 230.172414 15634 1.8295 52.0 2.628169 1.053521 2957.0 4.164789 15652 0.9000 52.0 2.237474 1.053535 3260.0 2.237474 15657 2.5166 52.0 2.839075 1.184049 3436.0 1.621520 15659 1.7240 52.0 2.278566 1.082348 4518.0 1.780142 15795 2.5755 52.0 3.402576 1.058776 2619.0 2.108696 15868 2.8135 52.0 4.584329 1.041169 2987.0 3.966799","keywords":["52.0","bigdata","2.237474","0.9000","1.","1.041169","1.053521","1.053535","1.058776","1.082348","1.184049","1.517241"],"topics":[]},{"id":"chunk-40","heading":"Column Non Null Count Dtype","text":"Latitude Longitude MedHouseVal 460 37.87 122.26 1.75000 4131 34.13 118.20 1.93600 4440 34.08 118.21 1.53000 5986 34.10 117.71 2.12500 7369 33.97 118.21 1.67600 8227 33.78 118.20 1.62500 13034 38.69 121.15 2.25000 15634 37.80 122.41 2.43800 15652 37.80 122.41 5.00001 15657 37.79 122.41 2.75000 15659 37.79 122.41 2.25000 15795 37.77 122.42 3.25000 15868 37.76 122.41 2.60300","keywords":["122.41","bigdata","118.20","118.21","2.25000","37.79","37.80","1.","1.53000","1.62500","1.67600","1.75000"],"topics":[]},{"id":"chunk-41","heading":"Column Non Null Count Dtype","text":"Максимальная медианная стоимость дома: 5.00001 Минимальная медианная стоимость дома: 0.14999","keywords":["bigdata","дома","медианная","стоимость","0.14999","1.","5.00001","column","count","dtype","non","null"],"topics":[]},{"id":"chunk-42","heading":"Column Non Null Count Dtype","text":"Средние значения признаков: MedInc: 3.871 HouseAge: 28.639 AveRooms: 5.429 AveBedrms: 1.097 Population: 1425.477 AveOccup: 3.071 Latitude: 35.632 Longitude: 119.570 MedHouseVal: 2.069","keywords":["bigdata","1.","1.097","119.570","1425.477","2.069","28.639","3.071","3.871","35.632","5.429","avebedrms"],"topics":[]},{"id":"chunk-43","heading":"Column Non Null Count Dtype","text":"MedInc None HouseAge None AveRooms None AveBedrms None Population None AveOccup None Latitude None Longitude None MedHouseVal None dtype: object ```","keywords":["none","bigdata","dtype","1.","avebedrms","aveoccup","averooms","column","count","houseage","latitude","longitude"],"topics":[]},{"id":"chunk-44","heading":"Column Non Null Count Dtype","text":"1. Дан текст на английском языке. Необходимо закодировать его с помощью азбуки Морзе, где каждой букве соответствует последовательность точек и тире. Например, буква «g» превратится в строку « .». В переменной morze для удобства хранится словарь соответствия латинских букв коду Морзе.","keywords":["bigdata","1.","column","count","dtype","morze","non","null","азбуки","английском","базовые","букв"],"topics":[]},{"id":"chunk-45","heading":"Column Non Null Count Dtype","text":"morze = {'a': '. ', 'b': ' …', 'c': ' . .', 'd': ' ..', 'e': '.', 'f': '.. .', 'g': ' .', 'h': '….', 'i': '..', 'j': '. ', 'k': ' . ', 'l': '. ..', 'm': ' ', 'n': ' .', 'o': ' ', 'p': '. .', 'q': ' . ', 'r': '. .', 's': '…', 't': ' ', 'u': '.. ', 'v': '… ', 'w': '. ', 'x': ' .. ', 'y': ' . ', 'z': ' ..'}","keywords":["..","bigdata","1.","column","count","dtype","morze","non","null","базовые","введение","знакомство"],"topics":[]},{"id":"chunk-46","heading":"Column Non Null Count Dtype","text":"На входе: В одной строке вам дан текст, который состоит из латинских букв и пробелов.","keywords":["bigdata","1.","column","count","dtype","non","null","базовые","букв","вам","введение","входе"],"topics":[]},{"id":"chunk-47","heading":"Column Non Null Count Dtype","text":"Выведите каждое слово исходного текста, закодированное азбукой Морзе. Количество строк в ответе должно совпадать с количеством слов в исходном тексте. Между закодированными буквами ставится ровно один пробел. Например, слово «Help» превратится в «.... . . .. . .» . Строчные и заглавные буквы кодируются одинаково.","keywords":["bigdata","слово","..","....","1.","column","count","dtype","help","non","null","азбукой"],"topics":[]},{"id":"chunk-48","heading":"Column Non Null Count Dtype","text":"```python morze = { 'a': '. ', 'b': ' ...', 'c': ' . .', 'd': ' ..', 'e': '.', 'f': '.. .', 'g': ' .', 'h': '....', 'i': '..', 'j': '. ', 'k': ' . ', 'l': '. ..', 'm': ' ', 'n': ' .', 'o': ' ', 'p': '. .', 'q': ' . ', 'r': '. .', 's': '...', 't': ' ', 'u': '.. ', 'v': '... ', 'w': '. ', 'x': ' .. ', 'y': ' . ', 'z': ' ..' }","keywords":["..","...","bigdata","....","1.","column","count","dtype","morze","non","null","python"],"topics":["python"]},{"id":"chunk-49","heading":"Column Non Null Count Dtype","text":"for word in words: encoded letters = [morze[char.lower()] for char in word if char.lower() in morze] print(' '.join(encoded letters)) ```","keywords":["bigdata","char.lower","encoded","letters","morze","word",".join","1.","char","column","count","dtype"],"topics":[]},{"id":"chunk-50","heading":"Column Non Null Count Dtype","text":"Регистрация должна работать следующим образом: если новый пользователь хочет зарегистрироваться на сайте, то он должен послать системе запрос name со своим именем. Система должна определить, существует ли уже такое имя в базе данных. Если такого имени не существует, то оно заносится в базу данных системы и пользователю возвращается ответ \"ОК\", подтверждающий успешную регистрацию","keywords":["bigdata","должна","если","существует","то","1.","column","count","dtype","name","non","null"],"topics":[]},{"id":"chunk-51","heading":"Column Non Null Count Dtype","text":". А если пользователь с таким именем уже существует, то система должна сформировать новое имя и выдать его пользователю в качестве подсказки, при этом сама подсказка также добавляется в базу данных. Новое имя формируется следующим образом: к name последовательно приписываются числа, начиная с 1 (name1, name2 и так далее), и среди них находят такое наименьшее i, что namei еще не содержится в системе.","keywords":["bigdata","имя","новое","1.","column","count","dtype","name","name1","name2","namei","non"],"topics":[]},{"id":"chunk-52","heading":"Column Non Null Count Dtype","text":"Следующие n строк содержат запросы к системе. Каждый запрос представляет собой непустую строку длиной не более 32 символов, состоящую только из строчных букв латинского алфавита.","keywords":["bigdata","1.","32","column","count","dtype","non","null","алфавита.","базовые","более","букв"],"topics":[]},{"id":"chunk-53","heading":"Column Non Null Count Dtype","text":"В выходных данных должно содержаться n строк – ответы системы на запросы: \"OK\" в случае успешной регистрации, или подсказка с новым именем, если запрашиваемое уже занято.","keywords":["bigdata","1.","column","count","dtype","non","null","ok","базовые","введение","выходных","данных"],"topics":[]},{"id":"chunk-54","heading":"Column Non Null Count Dtype","text":"Вход: Выход: 3 b b b OK b1 b2 10 bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp bhnqaptmp OK bhnqaptmp1 bhnqaptmp2 bhnqaptmp3 bhnqaptmp4 bhnqaptmp5 bhnqaptmp6 bhnqaptmp7 bhnqaptmp8 bhnqaptmp9 10 fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh fpqhfouqdldravpjttarh jmvplnrmba fpqhfouqdldravpjttarh jmvplnrmba","keywords":["bhnqaptmp","fpqhfouqdldravpjttarh","bigdata","10","jmvplnrmba","ok","1.","b1","b2","bhnqaptmp1","bhnqaptmp2","bhnqaptmp3"],"topics":[]},{"id":"chunk-55","heading":"Column Non Null Count Dtype","text":"fpqhfouqdldravpjttarh OK fpqhfouqdldravpjttarh1 fpqhfouqdldravpjttarh2 fpqhfouqdldravpjttarh3 fpqhfouqdldravpjttarh4 fpqhfouqdldravpjttarh5 OK fpqhfouqdldravpjttarh6 jmvplnrmba1 fpqhfouqdldravpjttarh7","keywords":["bigdata","ok","1.","column","count","dtype","fpqhfouqdldravpjttarh","fpqhfouqdldravpjttarh1","fpqhfouqdldravpjttarh2","fpqhfouqdldravpjttarh3","fpqhfouqdldravpjttarh4","fpqhfouqdldravpjttarh5"],"topics":[]},{"id":"chunk-56","heading":"Далее убеждаемся, что new name тоже уникально","text":"while new name in database: database[name] += 1 new name = name + str(database[name]) database[new name] = 0 print(new name)","keywords":["name","new","database","bigdata","1.","print","str","while","базовые","введение","далее","знакомство"],"topics":[]},{"id":"chunk-57","heading":"Вызов функции","text":"Необходимо создать программу обработки запросов пользователей к файловой системе компьютера. Над каждым файлом можно производить следующие действия: запись – w (\"write\") , чтение – r (\"read\") , запуск – x (\"execute\") .","keywords":["bigdata","1.","execute","read","write","базовые","введение","вызов","действия","запись","запросов","запуск"],"topics":[]},{"id":"chunk-58","heading":"Вызов функции","text":"На вход программе подаются следующие параметры: число n – количество файлов в файловой системе. В следующих n строках содержится информация с именами файлов и допустимыми действиями (w, x, r), разделенных пробелами. Далее идет число m – количество запросов к файлам вида «операция файл» (обозначение операции: \"write\" , \"read\" , \"execute\" ).","keywords":["bigdata","количество","операции","файлов","число","1.","execute","read","write","базовые","введение","вида"],"topics":[]},{"id":"chunk-59","heading":"Вызов функции","text":"Для каждого допустимого запроса программа должна возвращать OK, для недопустимого – Access denied .","keywords":["bigdata","1.","access","denied","ok","базовые","введение","возвращать","вызов","должна","допустимого","запроса"],"topics":[]},{"id":"chunk-60","heading":"Вызов функции","text":"Вход: Выход: 3 python.exe x book.txt r w notebook.exe r w x 5 read python.exe read book.txt write notebook.exe execute notebook.exe write book.txt Access denied OK OK OK OK 3 root.html r w x main.py x login.txt w r 4 read root.html write main.py execute main.py execute login.txt OK Access denied OK Access denied 2 1.txt 2.txt 2 write 1.txt execute 2.txt Access denied Access denied","keywords":["ok","access","denied","execute","write","bigdata","book.txt","main.py","notebook.exe","read","1.txt","2.txt"],"topics":["python","ai"]},{"id":"chunk-61","heading":"Чтение информации о файлах и их правах доступа","text":"for in range(n): line = input().strip().split() filename = line[0] allowed = set(line[1:]) if len(line) 1 else set() permissions[filename] = allowed","keywords":["line","bigdata","allowed","filename","set",".split",".strip","1.","else","if","input","len"],"topics":["numpy"]},{"id":"chunk-62","heading":"Обработка запросов","text":"for in range(m): operation, filename = input().split() op code = op map.get(operation) if op code and filename in permissions and op code in permissions[filename]: print(\"OK\") else: print(\"Access denied\")","keywords":["op","bigdata","code","filename","operation","permissions","print",".split","1.","access","denied","else"],"topics":["numpy"]},{"id":"chunk-63","heading":"Обработка запросов","text":"text 3 python.exe x book.txt r w notebook.exe r w x 5 read python.exe Access denied read book.txt OK write notebook.exe OK execute notebook.exe OK write book.txt OK","keywords":["ok","bigdata","book.txt","notebook.exe","python.exe","read","write","1.","access","denied","execute","text"],"topics":["python"]}]},{"id":"bigdata/practice-02-data-analysis","href":"/docs/bigdata/practice-02-data-analysis","slug":["bigdata","practice-02-data-analysis"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 2. Аналитика данных","description":"Обработка данных и исследовательский анализ в Jupyter Notebook.","preview":"Wiki версия Минькин Александр Дмитривеич ИКБО 25 22 Практическая работа №2 Визуализация в языке программирования Python Цель: ознакомится с различными библиотеками визуализации данных ( matplotlib , plotly , TSNE , UMAP ) и особенностями работы с ними в среде","keywords":["non","null","quality","1599","float64","dict","count","df","sne","0m","dtype","umap","use","column","dist","fontsize","grouped","data"],"topics":["python","ai","pandas","numpy","testing","algorithms"],"chunks":[{"id":"chunk-0","heading":"Визуализация в языке программирования Python","text":"Цель: ознакомится с различными библиотеками визуализации данных ( matplotlib , plotly , TSNE , UMAP ) и особенностями работы с ними в среде программирования Python.","keywords":["данных","bigdata","программирования","2.","jupyter","matplotlib","notebook.","plotly","python","python.","tsne","umap"],"topics":["python"]},{"id":"chunk-1","heading":"1. Задание","text":"Найти и выгрузить многомерные данные (с большим количеством признаков – столбцов) с использованием библиотеки pandas. В отчёте описать найденные данные.","keywords":["bigdata","данных","1.","2.","jupyter","notebook.","pandas.","анализ","аналитика","библиотеки","большим","выгрузить"],"topics":["pandas"]},{"id":"chunk-2","heading":"Датасет доступен на UCI","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\"","keywords":["bigdata","данных","2.","archive.ics.uci.edu","databases","https","jupyter","learning","machine","ml","notebook.","quality"],"topics":["ai"]},{"id":"chunk-3","heading":"Общая информация","text":"```text fixed acidity volatile acidity citric acid residual sugar chlorides \\ 0 7.4 0.70 0.00 1.9 0.076 1 7.8 0.88 0.00 2.6 0.098 2 7.8 0.76 0.04 2.3 0.092 3 11.2 0.28 0.56 1.9 0.075 4 7.4 0.70 0.00 1.9 0.076","keywords":["0.00","1.9","0.076","0.70","7.4","7.8","acidity","bigdata","данных","0.04","0.075","0.092"],"topics":[]},{"id":"chunk-4","heading":"Общая информация","text":"free sulfur dioxide total sulfur dioxide density pH sulphates \\ 0 11.0 34.0 0.9978 3.51 0.56 1 25.0 67.0 0.9968 3.20 0.68 2 15.0 54.0 0.9970 3.26 0.65 3 17.0 60.0 0.9980 3.16 0.58 4 11.0 34.0 0.9978 3.51 0.56","keywords":["0.56","0.9978","11.0","3.51","34.0","bigdata","dioxide","sulfur","данных","0.58","0.65","0.68"],"topics":[]},{"id":"chunk-5","heading":"Общая информация","text":"alcohol quality 0 9.4 5 1 9.8 5 2 9.8 5 3 9.8 6 4 9.4 5 Размерность: (1599, 12) RangeIndex: 1599 entries, 0 to 1598 Data columns (total 12 columns):","keywords":["9.8","12","1599","9.4","bigdata","columns","данных","1598","2.","alcohol","data","entries"],"topics":[]},{"id":"chunk-6","heading":"Column Non Null Count Dtype","text":"0 fixed acidity 1599 non null float64 1 volatile acidity 1599 non null float64 2 citric acid 1599 non null float64 3 residual sugar 1599 non null float64 4 chlorides 1599 non null float64 5 free sulfur dioxide 1599 non null float64 6 total sulfur dioxide 1599 non null float64 7 density 1599 non null float64 8 pH 1599 non null float64 9 sulphates 1599 non null float64 10 alcohol 1599 non null float64 11 quality 1599","keywords":["1599","non","null","float64","acidity","bigdata","dioxide","sulfur","данных","10","11","2."],"topics":[]},{"id":"chunk-7","heading":"Column Non Null Count Dtype","text":"non null int64 dtypes: float64(11), int64(1) memory usage: 150.0 KB None ```","keywords":["bigdata","int64","non","null","данных","11","150.0","2.","column","count","dtype","dtypes"],"topics":[]},{"id":"chunk-8","heading":"2. Вывести информацию о данных при помощи методов .info() , .head() .","text":"Проверить данные на наличие пустых значений. В случае их наличия удалить данные строки или интерполировать пропущенные значения. При необходимости дополнительно предобработать данные для дальнейшей работы с ними.","keywords":["данные","данных","2.","bigdata","при",".head",".info","jupyter","notebook.","анализ","аналитика","вывести"],"topics":[]},{"id":"chunk-9","heading":"Загружаем датасет (красное вино)","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-10","heading":"либо интерполировать (заполнить средним значением):","text":"df = df.interpolate() print(\"\\nПропуски были, выполнена интерполяция.\") else: print(\"\\nПропусков не найдено.\")","keywords":["bigdata","print","данных","2.","df","df.interpolate","else","jupyter","notebook.","nпропуски","nпропусков","анализ"],"topics":[]},{"id":"chunk-11","heading":"Проверим дубликаты","text":"duplicates = df.duplicated().sum() print(f\"\\nКоличество дубликатов: {duplicates}\")","keywords":["bigdata","duplicates","данных",".sum","2.","df.duplicated","jupyter","notebook.","nколичество","print","анализ","аналитика"],"topics":[]},{"id":"chunk-12","heading":"Финальные размеры набора данных","text":"```text === Информация о датасете === RangeIndex: 1599 entries, 0 to 1598 Data columns (total 12 columns):","keywords":["данных","bigdata","columns","12","1598","1599","2.","data","entries","jupyter","notebook.","rangeindex"],"topics":[]},{"id":"chunk-13","heading":"Column Non Null Count Dtype","text":"0 fixed acidity 1599 non null float64 1 volatile acidity 1599 non null float64 2 citric acid 1599 non null float64 3 residual sugar 1599 non null float64 4 chlorides 1599 non null float64 5 free sulfur dioxide 1599 non null float64 6 total sulfur dioxide 1599 non null float64 7 density 1599 non null float64 8 pH 1599 non null float64 9 sulphates 1599 non null float64 10 alcohol 1599 non null float64 11 quality 1599","keywords":["1599","non","null","float64","acidity","bigdata","dioxide","sulfur","данных","10","11","2."],"topics":[]},{"id":"chunk-14","heading":"Column Non Null Count Dtype","text":"non null int64 dtypes: float64(11), int64(1) memory usage: 150.0 KB None","keywords":["bigdata","int64","non","null","данных","11","150.0","2.","column","count","dtype","dtypes"],"topics":[]},{"id":"chunk-15","heading":"Column Non Null Count Dtype","text":"=== Первые 5 строк === fixed acidity volatile acidity citric acid residual sugar chlorides \\ 0 7.4 0.70 0.00 1.9 0.076 1 7.8 0.88 0.00 2.6 0.098 2 7.8 0.76 0.04 2.3 0.092 3 11.2 0.28 0.56 1.9 0.075 4 7.4 0.70 0.00 1.9 0.076","keywords":["0.00","1.9","0.076","0.70","7.4","7.8","acidity","bigdata","данных","0.04","0.075","0.092"],"topics":[]},{"id":"chunk-16","heading":"Column Non Null Count Dtype","text":"free sulfur dioxide total sulfur dioxide density pH sulphates \\ 0 11.0 34.0 0.9978 3.51 0.56 1 25.0 67.0 0.9968 3.20 0.68 2 15.0 54.0 0.9970 3.26 0.65 3 17.0 60.0 0.9980 3.16 0.58 4 11.0 34.0 0.9978 3.51 0.56","keywords":["0.56","0.9978","11.0","3.51","34.0","bigdata","dioxide","sulfur","данных","0.58","0.65","0.68"],"topics":[]},{"id":"chunk-17","heading":"Column Non Null Count Dtype","text":"=== Проверка на пропущенные значения === fixed acidity 0 volatile acidity 0 citric acid 0 residual sugar 0 chlorides 0 free sulfur dioxide 0 total sulfur dioxide 0 density 0 pH 0 sulphates 0 alcohol 0 quality 0 dtype: int64","keywords":["acidity","bigdata","dioxide","dtype","sulfur","данных","2.","acid","alcohol","chlorides","citric","column"],"topics":[]},{"id":"chunk-18","heading":"Column Non Null Count Dtype","text":"3. Построить столбчатую диаграмму (.bar) с использованием модуля graph objs из библиотеки Plotly со следующими параметрами:","keywords":["bigdata","данных",".bar","2.","3.","column","count","dtype","graph","jupyter","non","notebook."],"topics":[]},{"id":"chunk-19","heading":"Column Non Null Count Dtype","text":"3.1. По оси Х указать дату или название, по оси У указать количественный показатель.","keywords":["bigdata","данных","оси","указать","2.","3.1.","column","count","dtype","jupyter","non","notebook."],"topics":[]},{"id":"chunk-20","heading":"Column Non Null Count Dtype","text":"3.2. Сделать так, чтобы столбец принимал цвет в зависимости от значения показателя ( marker=dict(color=признак, coloraxis=\"coloraxis\") ).","keywords":["bigdata","coloraxis","данных","2.","3.2.","color","column","count","dict","dtype","jupyter","marker"],"topics":[]},{"id":"chunk-21","heading":"Column Non Null Count Dtype","text":"3.3. Сделать так, чтобы границы каждого столбца были выделены чёрной линией с толщиной равной 2 .","keywords":["bigdata","данных","2.","3.3.","column","count","dtype","jupyter","non","notebook.","null","анализ"],"topics":[]},{"id":"chunk-22","heading":"Column Non Null Count Dtype","text":"3.4. Отобразить заголовок диаграммы, разместив его по центру сверху, с 20 размером текста.","keywords":["bigdata","данных","2.","20","3.4.","column","count","dtype","jupyter","non","notebook.","null"],"topics":[]},{"id":"chunk-23","heading":"Column Non Null Count Dtype","text":"3.5. Добавить подписи для осей X и Y с размером текста, равным 16 . Для оси абсцисс развернуть метки так, чтобы они читались под углом, равным 315 .","keywords":["bigdata","данных","равным","16","2.","3.5.","315","column","count","dtype","jupyter","non"],"topics":[]},{"id":"chunk-24","heading":"Column Non Null Count Dtype","text":"3.7. Расположить график во всю ширину рабочей области и присвоить высоту, равную 700 пикселей.","keywords":["bigdata","данных","2.","3.7.","700","column","count","dtype","jupyter","non","notebook.","null"],"topics":[]},{"id":"chunk-25","heading":"Column Non Null Count Dtype","text":"3.8. Добавить сетку на график, сделать её цвет 'ivory' и толщину равную 2 . (Можно сделать это при настройке осей с помощью gridwidth=2 , gridcolor='ivory' )","keywords":["bigdata","ivory","данных","сделать","2.","3.8.","column","count","dtype","gridcolor","gridwidth","jupyter"],"topics":[]},{"id":"chunk-26","heading":"Загружаем датасет","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-27","heading":"по оси Y – среднее качество (quality)","text":"data grouped = df.groupby(df[\"alcohol\"].round())[\"quality\"].mean().reset index()","keywords":["bigdata","quality","данных",".mean",".reset",".round","2.","alcohol","data","df","df.groupby","grouped"],"topics":[]},{"id":"chunk-28","heading":"Создаём диаграмму","text":"fig = go.Figure( data=[ go.Bar( x=data grouped[\"alcohol\"], y=data grouped[\"quality\"], marker=dict( color=data grouped[\"quality\"], цвет в зависимости от значения coloraxis=\"coloraxis\", line=dict(color=\"black\", width=2) границы столбцов ) ) ] )","keywords":["data","grouped","bigdata","color","coloraxis","dict","quality","данных","2.","alcohol","black","fig"],"topics":[]},{"id":"chunk-29","heading":"Настройки оформления","text":"fig.update layout( title=dict( text=\"Среднее качество вина в зависимости от содержания алкоголя\", x=0.5, по центру xanchor=\"center\", yanchor=\"top\", font=dict(size=20) ), xaxis=dict( title=dict(text=\"Содержание алкоголя (%)\", font=dict(size=16)), tickangle=315, tickfont=dict(size=14), showgrid=True, gridwidth=2, gridcolor=\"ivory\" ), yaxis=dict( title=dict(text=\"Качество вина (среднее)\", font=dict(size=16)),","keywords":["dict","size","font","text","title","16","bigdata","алкоголя","вина","данных","качество","среднее"],"topics":[]},{"id":"chunk-30","heading":"Настройки оформления","text":"tickfont=dict(size=14), showgrid=True, gridwidth=2, gridcolor=\"ivory\" ), coloraxis=dict(colorscale=\"Viridis\"), height=700, autosize=True, margin=dict(l=0, r=0, t=50, b=0) убираем лишние отступы )","keywords":["dict","bigdata","true","данных","14","2.","50","700","autosize","coloraxis","colorscale","gridcolor"],"topics":[]},{"id":"chunk-31","heading":"Отображаем график","text":"4. Построить круговую диаграмму ( go.Pie ), использовав данные и стиль оформления из предыдущего графика. Сделать так, чтобы границы каждой доли были выделены чёрной линией с толщиной, равной 2 и категории круговой диаграммы были читаемы (к примеру, объединить часть объектов)","keywords":["bigdata","были","данных","2.","4.","go.pie","jupyter","notebook.","анализ","аналитика","выделены","границы"],"topics":[]},{"id":"chunk-32","heading":"Загружаем датасет","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-33","heading":"Считаем количество вин каждого качества","text":"quality counts = df[\"quality\"].value counts().reset index() quality counts.columns = [\"quality\", \"count\"]","keywords":["quality","bigdata","counts","данных",".reset",".value","2.","count","counts.columns","df","index","jupyter"],"topics":[]},{"id":"chunk-34","heading":"Объединим редкие категории (менее 50 объектов) в \"Other\"","text":"quality counts[\"quality\"] = quality counts[\"quality\"].astype(str) quality counts.loc[quality counts[\"count\"] < 50, \"quality\"] = \"Other\"","keywords":["quality","counts","50","bigdata","other","данных",".astype","2.","count","counts.loc","jupyter","notebook."],"topics":["python"]},{"id":"chunk-35","heading":"Создаём круговую диаграмму","text":"fig = go.Figure( data=[ go.Pie( labels=quality counts[\"quality\"], values=quality counts[\"count\"], marker=dict( line=dict(color=\"black\", width=2) границы долей ), textinfo=\"label+percent\", показываем категории и проценты insidetextorientation=\"radial\" ) ] )","keywords":["quality","bigdata","counts","dict","данных","2.","black","color","count","data","fig","go.figure"],"topics":[]},{"id":"chunk-36","heading":"Настройки оформления (стиль как у предыдущего графика)","text":"fig.update layout( title=dict( text=\"Распределение качества вин\", x=0.5, xanchor=\"center\", yanchor=\"top\", font=dict(size=20) ), height=700, autosize=True, margin=dict(l=0, r=0, t=50, b=0) убираем лишние отступы )","keywords":["dict","bigdata","данных","0.5","2.","20","50","700","autosize","center","fig.update","font"],"topics":[]},{"id":"chunk-37","heading":"Отображаем","text":"5. Построить линейные графики, взять один из параметров и определить зависимость между другими несколькими (от 2 до 5) показателями с использованием библиотеки matplotlib . Сделать вывод.","keywords":["bigdata","данных","2.","5.","jupyter","matplotlib","notebook.","анализ","аналитика","библиотеки","взять","вывод."],"topics":[]},{"id":"chunk-38","heading":"Загружаем датасет","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-39","heading":"Строим линейные графики","text":"plt.plot(df grouped[\"alcohol rounded\"], df grouped[\"quality\"], label=\"Качество\", marker=\"o\") plt.plot(df grouped[\"alcohol rounded\"], df grouped[\"sulphates\"], label=\"Сульфаты\", marker=\"s\") plt.plot(df grouped[\"alcohol rounded\"], df grouped[\"volatile acidity\"], label=\"Летучая кислотность\", marker=\"^\") plt.plot(df grouped[\"alcohol rounded\"], df grouped[\"density\"], label=\"Плотность\", marker=\"d\")","keywords":["df","grouped","alcohol","label","marker","plt.plot","rounded","bigdata","данных","2.","acidity","density"],"topics":[]},{"id":"chunk-40","heading":"Настройки графика","text":"plt.title(\"Зависимость показателей вина от содержания алкоголя\", fontsize=18) plt.xlabel(\"Содержание алкоголя (%)\", fontsize=14) plt.ylabel(\"Средние значения показателей\", fontsize=14) plt.xticks(rotation=45, fontsize=12) plt.yticks(fontsize=12) plt.grid(True, linestyle=\" \", alpha=0.7) plt.legend(fontsize=12) plt.tight layout()","keywords":["fontsize","12","14","bigdata","алкоголя","данных","показателей","0.7","18","2.","45","alpha"],"topics":[]},{"id":"chunk-41","heading":"Показ графика","text":"1. Качество вина в среднем растёт с увеличением содержания алкоголя — вина с высоким алкоголем чаще получают более высокие оценки.","keywords":["bigdata","вина","данных","1.","2.","jupyter","notebook.","алкоголем","алкоголя","анализ","аналитика","более"],"topics":[]},{"id":"chunk-42","heading":"Показ графика","text":"2. Сульфаты также постепенно увеличиваются вместе с алкоголем, что может указывать на технологические особенности производства.","keywords":["2.","bigdata","данных","jupyter","notebook.","алкоголем","анализ","аналитика","вместе","графика","исследовательский","может"],"topics":[]},{"id":"chunk-43","heading":"Показ графика","text":"3. Летучая кислотность наоборот снижается при увеличении алкоголя — вина с низкой кислотностью воспринимаются как более качественные.","keywords":["bigdata","данных","2.","3.","jupyter","notebook.","алкоголя","анализ","аналитика","более","вина","воспринимаются"],"topics":[]},{"id":"chunk-44","heading":"Показ графика","text":"4. Плотность имеет обратную зависимость: чем больше алкоголя, тем ниже плотность вина.","keywords":["bigdata","данных","плотность","2.","4.","jupyter","notebook.","алкоголя","анализ","аналитика","больше","вина."],"topics":[]},{"id":"chunk-45","heading":"Показ графика","text":"Таким образом, можно сделать вывод: алкогольное содержание оказывает значительное влияние на качество вина и связано с изменением химических свойств напитка (кислотность, плотность, уровень сульфатов).","keywords":["bigdata","данных","2.","jupyter","notebook.","алкогольное","анализ","аналитика","вина","влияние","вывод","графика"],"topics":[]},{"id":"chunk-46","heading":"Показ графика","text":"5.1. Сделать график с линиями и маркерами, цвет линии 'crimson' , цвет точек 'white' , цвет границ точек 'black' , толщина границ точек равна 2 .","keywords":["точек","цвет","bigdata","границ","данных","2.","5.1.","black","crimson","jupyter","notebook.","white"],"topics":[]},{"id":"chunk-47","heading":"Показ графика","text":"5.2. Добавить сетку на график, сделать её цвет 'mistyrose' и толщину равную 2 . (Можно сделать это при настройке осей с помощью linewidth=2 , color='mistyrose' ).","keywords":["bigdata","mistyrose","данных","сделать","2.","5.2.","color","jupyter","linewidth","notebook.","анализ","аналитика"],"topics":[]},{"id":"chunk-48","heading":"Загружаем датасет","text":"url = \"https://archive.ics.uci.edu/ml/machine learning databases/wine quality/winequality red.csv\" df = pd.read csv(url, sep=\";\")","keywords":["bigdata","url","данных","2.","archive.ics.uci.edu","csv","databases","df","https","jupyter","learning","machine"],"topics":["ai"]},{"id":"chunk-49","heading":"Строим график","text":"plt.plot( df grouped[\"alcohol rounded\"], df grouped[\"quality\"], color=\"crimson\", цвет линии marker=\"o\", маркер markerfacecolor=\"white\", цвет точки (заливка) markeredgecolor=\"black\", цвет границы точки markeredgewidth=2, толщина границы точки linewidth=2, толщина линии label=\"Качество вина\" )","keywords":["точки","цвет","bigdata","df","grouped","границы","данных","линии","толщина","2.","alcohol","black"],"topics":[]},{"id":"chunk-50","heading":"Настройки графика","text":"plt.title(\"Зависимость качества вина от содержания алкоголя\", fontsize=18) plt.xlabel(\"Содержание алкоголя (%)\", fontsize=14) plt.ylabel(\"Среднее качество вина\", fontsize=14) plt.xticks(fontsize=12) plt.yticks(fontsize=12)","keywords":["fontsize","12","14","bigdata","алкоголя","вина","данных","18","2.","jupyter","notebook.","plt.title"],"topics":[]},{"id":"chunk-51","heading":"Показ графика","text":"6. Выполнить визуализацию многомерных данных, используя t SNE . Необходимо использовать набор данных MNIST или fashion MNIST (можно использовать и другие готовые наборы данных, где можно наблюдать разделение объектов по кластерам).","keywords":["данных","bigdata","mnist","использовать","можно","2.","6.","fashion","jupyter","notebook.","sne","анализ"],"topics":[]},{"id":"chunk-52","heading":"load dataset (fallback to digits)","text":"try: from tensorflow.keras.datasets import fashion mnist as fm (x train, y train), (x test, y test) = fm.load data() X = np.vstack([x train, x test]) y = np.hstack([y train, y test]) dataset name = \"Fashion MNIST (28x28 grayscale)\" X = X.reshape((X.shape[0], 1)).astype(np.float32) / 255.0 except Exception: from sklearn.datasets import load digits digits = load digits() X = digits.data.astype(np.float32) y =","keywords":["digits","test","train","load","bigdata","dataset","fashion","import","mnist","np.float32","данных",".astype"],"topics":["python","ai","numpy","testing"]},{"id":"chunk-53","heading":"load dataset (fallback to digits)","text":"digits.target dataset name = \"sklearn digits (8x8 grayscale) fallback\"","keywords":["bigdata","dataset","digits","fallback","данных","2.","8x8","digits.target","grayscale","jupyter","load","name"],"topics":[]},{"id":"chunk-54","heading":"load dataset (fallback to digits)","text":"print(\"Dataset:\", dataset name) print(\"Samples:\", X.shape[0], \"Features:\", X.shape[1])","keywords":["dataset","bigdata","print","x.shape","данных","2.","digits","fallback","features","jupyter","load","name"],"topics":[]},{"id":"chunk-55","heading":"use a small subset for reliability","text":"max samples = 300 if X.shape[0] max samples: rng = np.random.default rng(42) idx = rng.choice(X.shape[0], size=max samples, replace=False) X sub = X[idx] y sub = y[idx] else: X sub = X.copy() y sub = y.copy()","keywords":["sub","idx","max","samples","bigdata","rng","x.shape","данных","2.","300","42","else"],"topics":["numpy"]},{"id":"chunk-56","heading":"use a small subset for reliability","text":"from sklearn.decomposition import PCA n pca = min(20, X sub.shape[0] 1, X sub.shape[1]) pca = PCA(n components=n pca, random state=42) X pca = pca.fit transform(X sub) print(\"PCA reduced to\", X pca.shape[1], \"components\")","keywords":["pca","bigdata","components","sub.shape","данных","2.","20","42","import","jupyter","min","notebook."],"topics":[]},{"id":"chunk-57","heading":"use a small subset for reliability","text":"from sklearn.manifold import TSNE perplexities = [5, 30] compare two perplexities tsne results = {}","keywords":["bigdata","perplexities","tsne","данных","2.","30","compare","import","jupyter","notebook.","reliability","results"],"topics":[]},{"id":"chunk-58","heading":"use a small subset for reliability","text":"for perp in perplexities: print(f\"Running t SNE with perplexity={perp} ...\") tsne = TSNE(n components=2, perplexity=perp, init='pca', random state=42, learning rate='auto', n iter=300) X emb = tsne.fit transform(X pca) tsne results[perp] = X emb print(f\"Done perplexity={perp}\")","keywords":["perp","perplexity","tsne","bigdata","emb","pca","print","данных","...","2.","300","42"],"topics":[]},{"id":"chunk-59","heading":"use a small subset for reliability","text":"for perp, X emb in tsne results.items(): plt.figure(figsize=(10,8)) sc = plt.scatter(X emb[:,0], X emb[:,1], c=y sub, cmap='tab10', s=20, alpha=0.9) plt.title(f\"t SNE (perplexity={perp}) — {dataset name}\", fontsize=16) plt.xlabel(\"t SNE 1\", fontsize=12) plt.ylabel(\"t SNE 2\", fontsize=12) plt.grid(True, linewidth=1, color='lightgray', linestyle=' ') cbar = plt.colorbar(sc, ticks=np.unique(y sub)) cbar.ax.tick","keywords":["emb","fontsize","sne","12","bigdata","perp","sc","sub","данных","0.9","10","16"],"topics":["numpy"]},{"id":"chunk-60","heading":"use a small subset for reliability","text":"params(labelsize=10) plt.tight layout() plt.show()","keywords":["bigdata","данных","10","2.","jupyter","labelsize","layout","notebook.","params","plt.show","plt.tight","reliability"],"topics":[]},{"id":"chunk-61","heading":"use a small subset for reliability","text":"```text Downloading data from https://storage.googleapis.com/tensorflow/tf keras datasets/train labels idx1 ubyte.gz \u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf keras datasets/train images idx3 ubyte.gz \u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step Downloading data from","keywords":["0m","data","downloading","0us","32m","37m","bigdata","datasets","https","keras","step","storage.googleapis.com"],"topics":["ai"]},{"id":"chunk-62","heading":"use a small subset for reliability","text":"https://storage.googleapis.com/tensorflow/tf keras datasets/t10k labels idx1 ubyte.gz \u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step Downloading data from https://storage.googleapis.com/tensorflow/tf keras datasets/t10k images idx3 ubyte.gz \u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step Dataset: Fashion MNIST (28x28 grayscale) Samples: 70000 Features:","keywords":["0m","0us","32m","37m","bigdata","datasets","https","keras","step","storage.googleapis.com","t10k","tensorflow"],"topics":[]},{"id":"chunk-63","heading":"use a small subset for reliability","text":"784 PCA reduced to 20 components Running t SNE with perplexity=5 ...","keywords":["bigdata","данных","...","2.","20","784","components","jupyter","notebook.","pca","perplexity","reduced"],"topics":[]},{"id":"chunk-64","heading":"use a small subset for reliability","text":"/usr/local/lib/python3.12/dist packages/sklearn/manifold/ t sne.py:1164: FutureWarning:","keywords":["bigdata","данных","1164","2.","dist","futurewarning","jupyter","lib","local","manifold","notebook.","packages"],"topics":["python"]},{"id":"chunk-65","heading":"use a small subset for reliability","text":"/usr/local/lib/python3.12/dist packages/sklearn/manifold/ t sne.py:1164: FutureWarning:","keywords":["bigdata","данных","1164","2.","dist","futurewarning","jupyter","lib","local","manifold","notebook.","packages"],"topics":["python"]},{"id":"chunk-66","heading":"use a small subset for reliability","text":"7. Выполнить визуализацию многомерных данных, используя UMAP с различными параметрами n neighbors и min dist . Рассчитать время работы алгоритма с помощью библиотеки time и сравнить его с временем работы t SNE .","keywords":["данных","bigdata","работы","2.","7.","dist","jupyter","min","neighbors","notebook.","reliability","small"],"topics":["algorithms"]},{"id":"chunk-67","heading":"use a small subset for reliability","text":"```python from sklearn.datasets import load digits from sklearn.manifold import TSNE","keywords":["bigdata","import","данных","2.","digits","jupyter","load","notebook.","python","reliability","sklearn.datasets","sklearn.manifold"],"topics":["python"]},{"id":"chunk-68","heading":"Список параметров для UMAP","text":"params = [ {\"n neighbors\": 5, \"min dist\": 0.1}, {\"n neighbors\": 15, \"min dist\": 0.1}, {\"n neighbors\": 30, \"min dist\": 0.3}, ]","keywords":["dist","min","neighbors","0.1","bigdata","данных","0.3","15","2.","30","jupyter","notebook."],"topics":[]},{"id":"chunk-69","heading":"UMAP с разными параметрами","text":"for i, p in enumerate(params, 1): start = time.time() reducer = umap.UMAP(n neighbors=p[\"n neighbors\"], min dist=p[\"min dist\"], random state=42) X umap = reducer.fit transform(X) duration = time.time() start results[f\"UMAP {p}\"] = duration","keywords":["umap","bigdata","dist","duration","min","neighbors","start","time.time","данных","2.","42","enumerate"],"topics":[]},{"id":"chunk-70","heading":"UMAP с разными параметрами","text":"plt.figure(figsize=(5,4)) plt.scatter(X umap[:,0], X umap[:,1], c=y, cmap=\"Spectral\", s=5) plt.title(f\"UMAP (n neighbors={p['n neighbors']}, min dist={p['min dist']})\\nВремя: {duration:.2f} сек\") plt.show()","keywords":["umap","bigdata","dist","min","neighbors","данных",".2f","2.","cmap","duration","figsize","jupyter"],"topics":[]},{"id":"chunk-71","heading":"t SNE","text":"start = time.time() X tsne = TSNE(n components=2, random state=42, init=\"pca\", perplexity=30).fit transform(X) duration = time.time() start results[\"t SNE\"] = duration","keywords":["bigdata","duration","sne","start","time.time","tsne","данных",".fit","2.","30","42","components"],"topics":[]},{"id":"chunk-72","heading":"t SNE","text":"plt.figure(figsize=(5,4)) plt.scatter(X tsne[:,0], X tsne[:,1], c=y, cmap=\"Spectral\", s=5) plt.title(f\"t SNE\\nВремя: {duration:.2f} сек\") plt.show()","keywords":["bigdata","sne","tsne","данных",".2f","2.","cmap","duration","figsize","jupyter","notebook.","nвремя"],"topics":[]},{"id":"chunk-73","heading":"t SNE","text":"```text /usr/local/lib/python3.12/dist packages/umap/umap .py:1952: UserWarning:","keywords":["bigdata","umap","данных",".py","1952","2.","dist","jupyter","lib","local","notebook.","packages"],"topics":["python"]},{"id":"chunk-74","heading":"t SNE","text":"n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism.","keywords":["bigdata","данных","2.","jobs","jupyter","no","notebook.","overridden","parallelism.","random","seed","setting"],"topics":[]},{"id":"chunk-75","heading":"t SNE","text":"n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism.","keywords":["bigdata","данных","2.","jobs","jupyter","no","notebook.","overridden","parallelism.","random","seed","setting"],"topics":[]},{"id":"chunk-76","heading":"t SNE","text":"n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism.","keywords":["bigdata","данных","2.","jobs","jupyter","no","notebook.","overridden","parallelism.","random","seed","setting"],"topics":[]},{"id":"chunk-77","heading":"t SNE","text":"{\"UMAP {'n neighbors': 5, 'min dist': 0.1}\": 14.975495338439941, \"UMAP {'n neighbors': 15, 'min dist': 0.1}\": 7.100525617599487, \"UMAP {'n neighbors': 30, 'min dist': 0.3}\": 7.385646104812622, 't SNE': 21.6993350982666} ```","keywords":["dist","min","neighbors","umap","0.1","bigdata","sne","данных","0.3","14.975495338439941","15","2."],"topics":[]}]},{"id":"bigdata/practice-03-regression-and-datasets","href":"/docs/bigdata/practice-03-regression-and-datasets","slug":["bigdata","practice-03-regression-and-datasets"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 3. Регрессия и наборы данных","description":"Работа с несколькими датасетами и построение моделей.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитривеич ИКБО 25 22 Практическая работа №3 Статистика на Python Генеральная совокупность – это множество абсолютно всех объектов, которые испол","keywords":["print","df","nan","4.","bmi","col","cols","3.",".2f","charges","mean","true","95","test","построение","0.000000","вывод","количество"],"topics":["python","pandas","numpy","testing","regression"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","3.","wiki","версия","данных","датасетами","документацию.","исходный","материал","моделей.","наборы","несколькими"],"topics":[]},{"id":"chunk-1","heading":"Статистика на Python","text":"Генеральная совокупность – это множество абсолютно всех объектов, которые используются для исследования. Выборка","keywords":["bigdata","3.","python","абсолютно","всех","выборка","генеральная","данных","датасетами","используются","исследования.","которые"],"topics":["python"]},{"id":"chunk-2","heading":"Статистика на Python","text":"1. Загрузить данные из файла “insurance.csv”. 2. С помощью метода describe() посмотреть статистику по данным. Сделать выводы.","keywords":["bigdata","1.","2.","3.","describe","insurance.csv","python","выводы.","данные","данным.","данных","датасетами"],"topics":["python"]},{"id":"chunk-3","heading":"4. Статистическое описание","text":"print(\"Статистическое описание данных:\") print(df.describe(include='all'), \"\\n\") ```","keywords":["bigdata","print","данных","описание","статистическое","3.","4.","all","df.describe","include","датасетами","моделей."],"topics":[]},{"id":"chunk-4","heading":"4. Статистическое описание","text":"```text Первые 5 строк датасета: age sex bmi children smoker region charges 0 19 female 27.900 0 yes southwest 16884.92400 1 18 male 33.770 1 no southeast 1725.55230 2 28 male 33.000 3 no southeast 4449.46200 3 33 male 22.705 0 no northwest 21984.47061 4 32 male 28.880 0 no northwest 3866.85520","keywords":["male","no","bigdata","northwest","southeast","16884.92400","1725.55230","18","19","21984.47061","22.705","27.900"],"topics":["python"]},{"id":"chunk-5","heading":"4. Статистическое описание","text":"Информация о данных: RangeIndex: 1338 entries, 0 to 1337 Data columns (total 7 columns):","keywords":["bigdata","columns","данных","1337","1338","3.","4.","data","entries","rangeindex","total","датасетами"],"topics":[]},{"id":"chunk-6","heading":"Column Non Null Count Dtype","text":"0 age 1338 non null int64 1 sex 1338 non null object 2 bmi 1338 non null float64 3 children 1338 non null int64 4 smoker 1338 non null object 5 region 1338 non null object 6 charges 1338 non null float64 dtypes: float64(2), int64(2), object(3) memory usage: 73.3+ KB None","keywords":["non","null","1338","object","float64","int64","bigdata","3.","73.3+","age","bmi","charges"],"topics":[]},{"id":"chunk-7","heading":"Column Non Null Count Dtype","text":"Статистическое описание данных: age sex bmi children smoker region \\ count 1338.000000 1338 1338.000000 1338.000000 1338 1338 unique NaN 2 NaN NaN 2 4 top NaN male NaN NaN no southeast freq NaN 676 NaN NaN 1064 364 mean 39.207025 NaN 30.663397 1.094918 NaN NaN std 14.049960 NaN 6.098187 1.205493 NaN NaN min 18.000000 NaN 15.960000 0.000000 NaN NaN 25% 27.000000 NaN 26.296250 0.000000 NaN NaN 50% 39.000000 NaN","keywords":["nan","1338","1338.000000","0.000000","bigdata","count","данных","1.094918","1.205493","1064","14.049960","15.960000"],"topics":["python"]},{"id":"chunk-8","heading":"Column Non Null Count Dtype","text":"30.400000 1.000000 NaN NaN 75% 51.000000 NaN 34.693750 2.000000 NaN NaN max 64.000000 NaN 53.130000 5.000000 NaN NaN","keywords":["nan","bigdata","1.000000","2.000000","3.","30.400000","34.693750","5.000000","51.000000","53.130000","64.000000","75"],"topics":[]},{"id":"chunk-9","heading":"Column Non Null Count Dtype","text":"charges count 1338.000000 unique NaN top NaN freq NaN mean 13270.422265 std 12110.011237 min 1121.873900 25% 4740.287150 50% 9382.033000 75% 16639.912515 max 63770.428010 ```","keywords":["nan","bigdata","count","1121.873900","12110.011237","13270.422265","1338.000000","16639.912515","25","3.","4740.287150","50"],"topics":[]},{"id":"chunk-10","heading":"Column Non Null Count Dtype","text":"Выводы: Возраст: от 18 до 64 лет, средний — около 39 лет. Пол: примерно поровну мужчин и женщин. Средний BMI 30.7, что указывает на избыточный вес. Среднее количество детей — 1. Курильщиков 20%. Средние страховые выплаты около 13 270, но разброс большой (до 63 770).","keywords":["bigdata","до","около","средний","1.","13","18","20","270","3.","30.7","39"],"topics":[]},{"id":"chunk-11","heading":"2. Просмотр числовых столбцов","text":"numeric cols = df.select dtypes(include=['int64', 'float64']).columns print(\"Числовые признаки:\", list(numeric cols), \"\\n\")","keywords":["bigdata","cols","numeric",".columns","2.","3.","df.select","dtypes","float64","include","int64","list"],"topics":[]},{"id":"chunk-12","heading":"3. Построение гистограмм","text":"df[numeric cols].hist(bins=20, figsize=(12, 8), color='skyblue', edgecolor='black') plt.suptitle(\"Гистограммы числовых признаков страховых данных\", fontsize=16) plt.show() ```","keywords":["3.","bigdata","данных","построение",".hist","12","16","20","bins","black","color","cols"],"topics":[]},{"id":"chunk-13","heading":"3. Построение гистограмм","text":"Выводы: Возраст (age): распределение равномерное от 18 до 64 лет. Индекс массы тела (bmi): большинство людей имеют BMI в диапазоне 25–35 (избыточный вес). Количество детей (children): большинство клиентов не имеют детей или имеют 1–2 ребёнка. Страховые выплаты (charges): распределение сильно скошено вправо — у большинства низкие выплаты, у меньшинства очень высокие.","keywords":["имеют","3.","bigdata","bmi","большинство","выплаты","детей","построение","распределение","18","25","35"],"topics":[]},{"id":"chunk-14","heading":"3. Построение гистограмм","text":"4. Найти меры центральной тенденции и меры разброса для индекса массы тела (bmi) и расходов (charges). Отобразить результаты в виде текста и на гистограммах (3 вертикальные линии). Добавить легенду на графики.","keywords":["3.","bigdata","меры","построение","4.","bmi","charges","вертикальные","виде","гистограмм","гистограммах","графики."],"topics":[]},{"id":"chunk-15","heading":"2. Расчёт статистических мер","text":"for col in ['bmi', 'charges']: mean = df[col].mean() median = df[col].median() mode = df[col].mode()[0] std = df[col].std() min val = df[col].min() max val = df[col].max()","keywords":["col","df","bigdata","val",".max",".mean",".median",".min",".mode",".std","2.","3."],"topics":[]},{"id":"chunk-16","heading":"3. Текстовый вывод","text":"print(f\"===== {col.upper()} =====\") print(f\"Среднее значение: {mean:.2f}\") print(f\"Медиана: {median:.2f}\") print(f\"Мода: {mode:.2f}\") print(f\"Стандартное отклонение: {std:.2f}\") print(f\"Минимум: {min val:.2f}\") print(f\"Максимум: {max val:.2f}\") print()","keywords":["print",".2f","3.","bigdata","val","col.upper","max","mean","median","min","mode","std"],"topics":[]},{"id":"chunk-17","heading":"4. Построение гистограммы с линиями","text":"plt.figure(figsize=(8, 5)) plt.hist(df[col], bins=30, color='skyblue', edgecolor='black') plt.axvline(mean, color='red', linestyle=' ', linewidth=2, label=f'Среднее ({mean:.2f})') plt.axvline(median, color='green', linestyle=' ', linewidth=2, label=f'Медиана ({median:.2f})') plt.axvline(mode, color='orange', linestyle=' .', linewidth=2, label=f'Мода ({mode:.2f})') plt.title(f\"Распределение {col}\") plt.xlabel(col)","keywords":["color",".2f","col","label","linestyle","linewidth","plt.axvline","bigdata","mean","median","mode","построение"],"topics":[]},{"id":"chunk-18","heading":"4. Построение гистограммы с линиями","text":"plt.ylabel(\"Частота\") plt.legend() plt.grid(True, linestyle=' ', alpha=0.6) plt.show() ```","keywords":["bigdata","построение","0.6","3.","4.","alpha","linestyle","plt.grid","plt.legend","plt.show","plt.ylabel","true"],"topics":[]},{"id":"chunk-19","heading":"4. Построение гистограммы с линиями","text":"```text ===== BMI ===== Среднее значение: 30.66 Медиана: 30.40 Мода: 32.30 Стандартное отклонение: 6.10 Минимум: 15.96 Максимум: 53.13","keywords":["bigdata","построение","15.96","3.","30.40","30.66","32.30","4.","53.13","6.10","bmi","text"],"topics":[]},{"id":"chunk-20","heading":"4. Построение гистограммы с линиями","text":"===== CHARGES ===== Среднее значение: 13270.42 Медиана: 9382.03 Мода: 1639.56 Стандартное отклонение: 12110.01 Минимум: 1121.87 Максимум: 63770.43","keywords":["bigdata","построение","1121.87","12110.01","13270.42","1639.56","3.","4.","63770.43","9382.03","charges","гистограммы"],"topics":[]},{"id":"chunk-21","heading":"4. Построение гистограммы с линиями","text":"5. Построить box plot для числовых показателей. Названия графиков должны соответствовать названиям признаков. Сделать выводы.","keywords":["bigdata","построение","3.","4.","5.","box","plot","выводы.","гистограммы","графиков","данных","датасетами"],"topics":[]},{"id":"chunk-22","heading":"3. Построим boxplot для каждого числового признака","text":"for i, col in enumerate(numeric cols, 1): plt.subplot(2, 2, i) 2 строки, 2 столбца plt.boxplot(df[col], patch artist=True, boxprops=dict(facecolor='skyblue')) plt.title(col) plt.ylabel(\"Значение\") plt.grid(True, linestyle=' ', alpha=0.6)","keywords":["col","3.","bigdata","true","0.6","alpha","artist","boxplot","boxprops","cols","df","dict"],"topics":[]},{"id":"chunk-23","heading":"3. Построим boxplot для каждого числового признака","text":"plt.suptitle(\"Box plot для числовых признаков\", fontsize=16) plt.tight layout(rect=[0, 0, 1, 0.96]) plt.show() ```","keywords":["3.","bigdata","0.96","16","box","boxplot","fontsize","layout","plot","plt.show","plt.suptitle","plt.tight"],"topics":[]},{"id":"chunk-24","heading":"3. Построим boxplot для каждого числового признака","text":"6. Используя признак charges или imb, проверить, выполняется ли центральная предельная теорема. Использовать различные длины выборок n. Количество выборок = 300. Вывести результат в виде гистограмм. Найти стандартное отклонение и среднее для полученных распределений. Сделать выводы.","keywords":["3.","bigdata","выборок","300.","6.","boxplot","charges","imb","n.","виде","вывести","выводы."],"topics":[]},{"id":"chunk-25","heading":"создаем 300 выборок размера n и считаем среднее каждой","text":"for in range(num samples): sample = np.random.choice(data, size=n, replace=True) sample means.append(np.mean(sample))","keywords":["sample","bigdata","3.","300","data","means.append","np.mean","np.random.choice","num","range","replace","samples"],"topics":["numpy"]},{"id":"chunk-26","heading":"5. Строим гистограммы распределений средних","text":"plt.subplot(1, 3, i) plt.hist(sample means, bins=20, color='skyblue', edgecolor='black') plt.title(f'n = {n}\\nСреднее: {sample means.mean():.2f}, σ: {sample means.std():.2f}') plt.xlabel(\"Среднее значение выборки\") plt.ylabel(\"Частота\") plt.grid(True, linestyle=' ', alpha=0.6)","keywords":["sample",".2f","bigdata","0.6","20","3.","5.","alpha","bins","black","color","edgecolor"],"topics":[]},{"id":"chunk-27","heading":"5. Строим гистограммы распределений средних","text":"plt.suptitle(\"Проверка Центральной предельной теоремы (ЦПТ) для признака 'charges'\", fontsize=14) plt.tight layout(rect=[0, 0, 1, 0.95]) plt.show() ```","keywords":["bigdata","0.95","14","3.","5.","charges","fontsize","layout","plt.show","plt.suptitle","plt.tight","rect"],"topics":[]},{"id":"chunk-28","heading":"5. Строим гистограммы распределений средних","text":"7. Построить 95% и 99% доверительный интервал для среднего значения расходов и среднего значения индекса массы тела.","keywords":["bigdata","значения","среднего","3.","5.","7.","95","99","гистограммы","данных","датасетами","доверительный"],"topics":[]},{"id":"chunk-29","heading":"2. Функция для вычисления доверительного интервала","text":"def confidence interval(data, confidence=0.95): n = len(data) mean = np.mean(data) std err = st.sem(data) стандартная ошибка среднего h = std err st.t.ppf((1 + confidence) / 2, n 1) return mean, mean h, mean + h","keywords":["data","mean","confidence","bigdata","err","std","0.95","2.","3.","def","interval","len"],"topics":["numpy"]},{"id":"chunk-30","heading":"3. Расчёт интервалов для 'charges' и 'bmi'","text":"for col in ['charges', 'bmi']: mean 95, lower 95, upper 95 = confidence interval(df[col], 0.95) mean 99, lower 99, upper 99 = confidence interval(df[col], 0.99)","keywords":["95","99","col","3.","bigdata","bmi","charges","confidence","df","interval","lower","mean"],"topics":[]},{"id":"chunk-31","heading":"3. Расчёт интервалов для 'charges' и 'bmi'","text":"print(f\"===== {col.upper()} =====\") print(f\"Среднее значение: {mean 95:.2f}\") print(f\"95% доверительный интервал: [{lower 95:.2f}, {upper 95:.2f}]\") print(f\"99% доверительный интервал: [{lower 99:.2f}, {upper 99:.2f}]\") print()","keywords":[".2f","print","95","99","3.","bigdata","lower","upper","доверительный","интервал","bmi","charges"],"topics":[]},{"id":"chunk-32","heading":"4. (Дополнительно) Визуализация интервалов","text":"cols = ['charges', 'bmi'] conf 95 = [confidence interval(df[c], 0.95) for c in cols] conf 99 = [confidence interval(df[c], 0.99) for c in cols]","keywords":["cols","bigdata","conf","confidence","df","interval","0.95","0.99","3.","4.","95","99"],"topics":[]},{"id":"chunk-33","heading":"Построение графика","text":"plt.errorbar(x, means, yerr=err 95, fmt='o', color='blue', capsize=5, label='95% ДИ') plt.errorbar(x, means, yerr=err 99, fmt='o', color='red', capsize=8, label='99% ДИ') plt.xticks(x, cols) plt.ylabel(\"Среднее значение\") plt.title(\"Доверительные интервалы для средних значений (charges и bmi)\") plt.legend() plt.grid(True, linestyle=' ', alpha=0.6) plt.show() ```","keywords":["95","99","bigdata","capsize","color","err","fmt","label","means","plt.errorbar","yerr","ди"],"topics":[]},{"id":"chunk-34","heading":"Построение графика","text":"```text ===== CHARGES ===== Среднее значение: 13270.42 95% доверительный интервал: [12620.95, 13919.89] 99% доверительный интервал: [12416.43, 14124.41]","keywords":["bigdata","доверительный","интервал","построение","12416.43","12620.95","13270.42","13919.89","14124.41","3.","95","99"],"topics":[]},{"id":"chunk-35","heading":"Построение графика","text":"===== BMI ===== Среднее значение: 30.66 95% доверительный интервал: [30.34, 30.99] 99% доверительный интервал: [30.23, 31.09]","keywords":["bigdata","доверительный","интервал","построение","3.","30.23","30.34","30.66","30.99","31.09","95","99"],"topics":[]},{"id":"chunk-36","heading":"Построение графика","text":"8. Проверить распределения следующих признаков на нормальность: индекс массы тела, расходы. Сформулировать нулевую и альтернативную гипотезы. Для каждого признака использовать KS тест и q q plot. Сделать выводы на основе полученных p значений.","keywords":["bigdata","построение","3.","8.","ks","plot.","альтернативную","выводы","гипотезы.","графика","данных","датасетами"],"topics":[]},{"id":"chunk-37","heading":"KS тест","text":"print(f\"===== Признак: {col.upper()} =====\") print(\"Нулевая гипотеза H₀: данные распределены нормально.\") print(\"Альтернативная гипотеза H₁: данные НЕ распределены нормально.\") print(f\"Статистика теста: {ks stat:.4f}\") print(f\"p значение: {p value:.4f}\")","keywords":["print",".4f","bigdata","ks","гипотеза","данные","нормально.","распределены","3.","col.upper","h₀","h₁"],"topics":[]},{"id":"chunk-38","heading":"KS тест","text":"if p value < 0.05: print(\"→ Отклоняем H₀: распределение статистически отличается от нормального.\\n\") else: print(\"→ Не отклоняем H₀: нет оснований считать распределение ненормальным.\\n\")","keywords":["bigdata","h₀","print","отклоняем","распределение","0.05","3.","else","if","ks","value","данных"],"topics":[]},{"id":"chunk-39","heading":"Q Q plot","text":"plt.figure(figsize=(6, 5)) sm.qqplot(data, line='s', markerfacecolor='blue', alpha=0.5) plt.title(f\"Q Q Plot для признака '{col}'\") plt.grid(True, linestyle=' ', alpha=0.6) plt.show() ```","keywords":["alpha","bigdata","plot","0.5","0.6","3.","blue","col","data","figsize","line","linestyle"],"topics":[]},{"id":"chunk-40","heading":"Q Q plot","text":"```text ===== Признак: BMI ===== Нулевая гипотеза H₀: данные распределены нормально. Альтернативная гипотеза H₁: данные НЕ распределены нормально. Статистика теста: 0.0261 p значение: 0.3145 → Не отклоняем H₀: нет оснований считать распределение ненормальным.","keywords":["bigdata","h₀","гипотеза","данные","нормально.","распределены","0.0261","0.3145","3.","bmi","h₁","plot"],"topics":[]},{"id":"chunk-41","heading":"Q Q plot","text":"===== Признак: CHARGES ===== Нулевая гипотеза H₀: данные распределены нормально. Альтернативная гипотеза H₁: данные НЕ распределены нормально. Статистика теста: 0.1885 p значение: 0.0000 → Отклоняем H₀: распределение статистически отличается от нормального.","keywords":["bigdata","h₀","гипотеза","данные","нормально.","распределены","0.0000","0.1885","3.","charges","h₁","plot"],"topics":[]},{"id":"chunk-42","heading":"Q Q plot","text":"10. Проверить в данных наличие пропущенных значений. Вывести количество пропущенных значений в процентах. Удалить два признака, в которых больше всех пропущенных значений. Для оставшихся признаков обработать пропуски: для категориального признака использовать заполнение значением по умолчанию (например, «other»), для числового признака использовать заполнение медианным значением","keywords":["признака","пропущенных","bigdata","данных","заполнение","значением","значений.","использовать","10.","3.","other","plot"],"topics":[]},{"id":"chunk-43","heading":"Q Q plot","text":". Показать, что пропусков больше в данных нет.","keywords":["bigdata","данных","3.","plot","больше","датасетами","моделей.","наборы","несколькими","нет.","показать","построение"],"topics":[]},{"id":"chunk-44","heading":"1. Проверяем наличие пропущенных значений","text":"missing counts = df.isna().sum() missing percent = (missing counts / len(df)) 100 print(\"Пропущенные значения в процентах:\\n\", missing percent)","keywords":["missing","bigdata","counts","percent",".sum","1.","100","3.","df","df.isna","len","print"],"topics":[]},{"id":"chunk-45","heading":"2. Находим два признака с наибольшим количеством пропусков и удаляем их","text":"top2 missing cols = missing counts.sort values(ascending=False).head(2).index df = df.drop(columns=top2 missing cols) print(\"\\nУдалены признаки с наибольшим количеством пропусков:\", list(top2 missing cols))","keywords":["missing","cols","top2","bigdata","количеством","наибольшим","пропусков",".head",".index","2.","3.","ascending"],"topics":[]},{"id":"chunk-46","heading":"Для категориальных признаков заполняем значением \"other\"","text":"categorical cols = df.select dtypes(include=['object']).columns df[categorical cols] = df[categorical cols].fillna(\"other\")","keywords":["categorical","cols","bigdata","df","other",".columns",".fillna","3.","df.select","dtypes","include","object"],"topics":[]},{"id":"chunk-47","heading":"Для числовых признаков заполняем медианой","text":"numeric cols = df.select dtypes(include=['number']).columns df[numeric cols] = df[numeric cols].fillna(df[numeric cols].median())","keywords":["cols","numeric","df","bigdata",".columns",".fillna",".median","3.","df.select","dtypes","include","number"],"topics":[]},{"id":"chunk-48","heading":"4. Проверяем, что пропусков больше нет","text":"print(\"\\nКоличество пропущенных значений после обработки:\\n\", df.isna().sum()) ```","keywords":["bigdata",".sum","3.","4.","df.isna","nколичество","print","больше","данных","датасетами","значений","моделей."],"topics":[]},{"id":"chunk-49","heading":"4. Проверяем, что пропусков больше нет","text":"```text Пропущенные значения в процентах: dateRep 0.000000 day 0.000000 month 0.000000 year 0.000000 cases 0.000000 deaths 0.000000 countriesAndTerritories 0.000000 geoId 0.444236 countryterritoryCode 0.198695 popData2019 0.198695 continentExp 0.000000 Cumulative number for 14 days of COVID 19 cases per 100000 4.650750 dtype: float64","keywords":["0.000000","0.198695","bigdata","cases","0.444236","100000","14","19","3.","4.","4.650750","continentexp"],"topics":[]},{"id":"chunk-50","heading":"4. Проверяем, что пропусков больше нет","text":"Удалены признаки с наибольшим количеством пропусков: ['Cumulative number for 14 days of COVID 19 cases per 100000', 'geoId']","keywords":["bigdata","пропусков","100000","14","19","3.","4.","cases","covid","cumulative","days","geoid"],"topics":[]},{"id":"chunk-51","heading":"4. Проверяем, что пропусков больше нет","text":"Количество пропущенных значений после обработки: dateRep 0 day 0 month 0 year 0 cases 0 deaths 0 countriesAndTerritories 0 countryterritoryCode 0 popData2019 0 continentExp 0 dtype: int64 ```","keywords":["bigdata","3.","4.","cases","continentexp","countriesandterritories","countryterritorycode","daterep","day","deaths","dtype","int64"],"topics":[]},{"id":"chunk-52","heading":"4. Проверяем, что пропусков больше нет","text":"11. Посмотреть статистику по данным, используя describe(). Сделать выводы о том, какие признаки содержат выбросы. Посмотреть, для каких стран количество смертей в день превысило 3000 и сколько таких дней было.","keywords":["bigdata","посмотреть","11.","3.","3000","4.","describe","больше","было.","выбросы.","выводы","данным"],"topics":[]},{"id":"chunk-53","heading":"Обычно выбросы проявляются как экстремальные значения в 'max' и 'min'","text":"numeric cols = df.select dtypes(include=['number']).columns for col in numeric cols: q1 = df[col].quantile(0.25) q3 = df[col].quantile(0.75) iqr = q3 q1 lower bound = q1 1.5 iqr upper bound = q3 + 1.5 iqr outliers = df[(df[col] upper bound)] if not outliers.empty: print(f\"\\nПризнак '{col}' содержит выбросы.\") print(f\"Количество выбросов: {len(outliers)}\")","keywords":["col","df","bound","iqr","q1","q3",".quantile","1.5","bigdata","cols","numeric","outliers"],"topics":[]},{"id":"chunk-54","heading":"3. Смотрим, для каких стран количество смертей в день превышало 3000","text":"high deaths = df[df['deaths'] 3000] print(\"\\nДни с количеством смертей 3000:\") print(high deaths[['countriesAndTerritories', 'dateRep', 'deaths']])","keywords":["deaths","3000","3.","bigdata","df","high","print","смертей","countriesandterritories","daterep","nдни","данных"],"topics":[]},{"id":"chunk-55","heading":"4. Сколько таких дней было","text":"num high death days = len(high deaths) print(f\"\\nКоличество дней с количеством смертей 3000: {num high death days}\") ```","keywords":["high","bigdata","days","death","num","дней","3.","3000","4.","deaths","len","nколичество"],"topics":[]},{"id":"chunk-56","heading":"4. Сколько таких дней было","text":"day month year cases deaths \\ count 61904.000000 61904.000000 61904.000000 61904.000000 61904.000000 mean 15.629232 7.067104 2019.998918 1155.079026 26.053987 std 8.841624 2.954816 0.032881 6779.010824 131.222948 min 1.000000 1.000000 2019.000000 8261.000000 1918.000000 25% 8.000000 5.000000 2020.000000 0.000000 0.000000 50% 15.000000 7.000000 2020.000000 15.000000 0.000000 75% 23.000000 10.000000 2020.000000","keywords":["61904.000000","0.000000","2020.000000","1.000000","15.000000","bigdata","0.032881","10.000000","1155.079026","131.222948","15.629232","1918.000000"],"topics":[]},{"id":"chunk-57","heading":"4. Сколько таких дней было","text":"273.000000 4.000000 max 31.000000 12.000000 2020.000000 234633.000000 4928.000000","keywords":["bigdata","12.000000","2020.000000","234633.000000","273.000000","3.","31.000000","4.","4.000000","4928.000000","max","было"],"topics":[]},{"id":"chunk-58","heading":"4. Сколько таких дней было","text":"popData2019 \\ count 6.178100e+04 mean 4.098628e+07 std 1.531246e+08 min 8.150000e+02 25% 1.293120e+06 50% 7.169456e+06 75% 2.851583e+07 max 1.433784e+09","keywords":["bigdata","1.293120e+06","1.433784e+09","1.531246e+08","2.851583e+07","25","3.","4.","4.098628e+07","50","6.178100e+04","7.169456e+06"],"topics":[]},{"id":"chunk-59","heading":"4. Сколько таких дней было","text":"Cumulative number for 14 days of COVID 19 cases per 100000 count 59025.000000 mean 66.316369 std 162.324550 min 147.419587 25% 0.757526 50% 6.724045 75% 52.561206 max 1900.836210","keywords":["bigdata","0.757526","100000","14","147.419587","162.324550","19","1900.836210","25","3.","4.","50"],"topics":[]},{"id":"chunk-60","heading":"4. Сколько таких дней было","text":"Признак 'Cumulative number for 14 days of COVID 19 cases per 100000' содержит выбросы. Количество выбросов: 8073","keywords":["bigdata","100000","14","19","3.","4.","8073","cases","covid","cumulative","days","number"],"topics":[]},{"id":"chunk-61","heading":"4. Сколько таких дней было","text":"Дни с количеством смертей 3000: countriesAndTerritories dateRep deaths 2118 Argentina 02/10/2020 3351 16908 Ecuador 07/09/2020 3800 37038 Mexico 09/10/2020 3013 44888 Peru 14/08/2020 3935 44909 Peru 24/07/2020 3887 59007 United States of America 12/12/2020 3343 59009 United States of America 10/12/2020 3124 59016 United States of America 03/12/2020 3190 59239 United States of America 24/04/2020 3179 59245 United","keywords":["2020","united","12","america","states","10","07","09","24","bigdata","peru","02"],"topics":["testing"]},{"id":"chunk-62","heading":"4. Сколько таких дней было","text":"States of America 18/04/2020 3770 59247 United States of America 16/04/2020 4928","keywords":["04","2020","america","bigdata","states","16","18","3.","3770","4.","4928","59247"],"topics":["testing"]},{"id":"chunk-63","heading":"1. Проверяем наличие дубликатов","text":"num duplicates = df.duplicated().sum() print(f\"Количество дубликатов: {num duplicates}\")","keywords":["bigdata","duplicates","num","дубликатов",".sum","1.","3.","df.duplicated","print","данных","датасетами","количество"],"topics":[]},{"id":"chunk-64","heading":"2. Удаляем дубликаты","text":"df = df.drop duplicates() print(f\"Количество строк после удаления дубликатов: {len(df)}\") ```","keywords":["bigdata","df","2.","3.","df.drop","duplicates","len","print","данных","датасетами","дубликатов","дубликаты"],"topics":[]},{"id":"chunk-65","heading":"2. Удаляем дубликаты","text":"Взять оттуда две выборки. Одна выборка – это индекс массы тела людей c региона northwest, вторая выборка – это индекс массы тела людей с региона southwest. Сравнить средние значения этих выборок, используя t критерий Стьюдента. Предварительно проверить выборки на нормальность (критерий Шопиро Уилка) и на гомогенность дисперсии (критерий Бартлетта).","keywords":["критерий","bigdata","выборка","индекс","людей","массы","региона","тела","2.","3.","northwest","southwest."],"topics":[]},{"id":"chunk-66","heading":"2. Создаем две выборки по регионам","text":"bmi nw = df[df['region'] == 'northwest']['bmi'] bmi sw = df[df['region'] == 'southwest']['bmi']","keywords":["bmi","df","bigdata","region","2.","3.","northwest","nw","southwest","sw","выборки","данных"],"topics":[]},{"id":"chunk-67","heading":"3. Проверка на нормальность (Shapiro Wilk test)","text":"print(\"Shapiro Wilk test для northwest:\", shapiro nw) print(\"Shapiro Wilk test для southwest:\", shapiro sw)","keywords":["shapiro","test","wilk","3.","bigdata","print","northwest","nw","southwest","sw","данных","датасетами"],"topics":["testing"]},{"id":"chunk-68","heading":"4. Проверка на гомогенность дисперсий (Bartlett test)","text":"bartlett test = bartlett(bmi nw, bmi sw) print(\"\\nBartlett test:\", bartlett test)","keywords":["bartlett","test","bigdata","bmi","3.","4.","nbartlett","nw","print","sw","гомогенность","данных"],"topics":["testing"]},{"id":"chunk-69","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"equal var = bartlett test.pvalue 0.05 t test = ttest ind(bmi nw, bmi sw, equal var=equal var)","keywords":["equal","var","bigdata","bmi","0.05","3.","bartlett","false","ind","nw","sw","test"],"topics":["testing"]},{"id":"chunk-70","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"print(\"\\nT test Стьюдента:\", t test) print(\"\\nСредние значения BMI по выборкам:\") print(\"Northwest:\", bmi nw.mean()) print(\"Southwest:\", bmi sw.mean()) ```","keywords":["print","bmi","bigdata","test","3.","equal","false","northwest","nt","nw.mean","nсредние","southwest"],"topics":["testing"]},{"id":"chunk-71","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"```text Shapiro Wilk test для northwest: ShapiroResult(statistic=np.float64(0.995464981663833), pvalue=np.float64(0.4655897798883668)) Shapiro Wilk test для southwest: ShapiroResult(statistic=np.float64(0.9949269360950754), pvalue=np.float64(0.36296471144790743))","keywords":["np.float64","bigdata","pvalue","shapiro","shapiroresult","statistic","test","wilk","0.36296471144790743","0.4655897798883668","0.9949269360950754","0.995464981663833"],"topics":["numpy","testing"]},{"id":"chunk-72","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"Bartlett test: BartlettResult(statistic=np.float64(3.4000745256459286), pvalue=np.float64(0.06519347353581818))","keywords":["bigdata","np.float64","0.06519347353581818","3.","3.4000745256459286","bartlett","bartlettresult","equal","false","pvalue","statistic","test"],"topics":["numpy","testing"]},{"id":"chunk-73","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"T test Стьюдента: TtestResult(statistic=np.float64( 3.2844171500398582), pvalue=np.float64(0.001076958496307695), df=np.float64(648.0))","keywords":["np.float64","bigdata","0.001076958496307695","3.","3.2844171500398582","648.0","df","equal","false","pvalue","statistic","test"],"topics":["numpy","testing"]},{"id":"chunk-74","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"Средние значения BMI по выборкам: Northwest: 29.199784615384615 Southwest: 30.59661538461538 ```","keywords":["bigdata","29.199784615384615","3.","30.59661538461538","bmi","equal","false","northwest","southwest","true","var","выборкам"],"topics":[]},{"id":"chunk-75","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"14. Кубик бросили 600 раз, получили следующие результаты: N Количество выпадений 1 97 2 98 3 109 4 95 5 97 6 104","keywords":["97","bigdata","104","109","14.","3.","600","95","98","equal","false","true"],"topics":[]},{"id":"chunk-76","heading":"Если дисперсии гомогенные, equal var=True, иначе False","text":"С помощью критерия Хи квадрат проверить, является ли полученное распределение равномерным. scipy.stats.chisquare().","keywords":["bigdata","3.","equal","false","scipy.stats.chisquare","true","var","гомогенные","данных","датасетами","дисперсии","если"],"topics":[]},{"id":"chunk-77","heading":"2. Ожидаемое количество выпадений для равномерного распределения","text":"total rolls = sum(observed) expected = [total rolls / 6] 6 равные вероятности для 6 граней","keywords":["bigdata","rolls","total","2.","3.","expected","observed","sum","вероятности","выпадений","граней","данных"],"topics":[]},{"id":"chunk-78","heading":"4. Вывод о равномерности","text":"alpha = 0.05 if p value < alpha: print(\"Распределение не равномерное (отвергаем H0)\") else: print(\"Распределение можно считать равномерным (не отвергаем H0)\") ```","keywords":["alpha","bigdata","h0","print","отвергаем","распределение","0.05","3.","4.","else","if","value"],"topics":[]},{"id":"chunk-79","heading":"4. Вывод о равномерности","text":"text Chi square statistic: 1.44 p value: 0.9198882077437889 Распределение можно считать равномерным (не отвергаем H0)","keywords":["bigdata","0.9198882077437889","1.44","3.","4.","chi","h0","square","statistic","text","value","вывод"],"topics":[]},{"id":"chunk-80","heading":"4. Вывод о равномерности","text":"15. С помощью критерия Хи квадрат проверить, являются ли переменные зависимыми. Создать датафрейм, используя следующий код:","keywords":["bigdata","15.","3.","4.","вывод","данных","датасетами","датафрейм","зависимыми.","используя","квадрат","код"],"topics":["pandas"]},{"id":"chunk-81","heading":"4. Вывод о равномерности","text":"data = pd.DataFrame({'Женат': [89,17,11,43,22,1], 'Гражданский брак': [80,22,20,35,6,4], 'Не состоит в отношениях': [35,44,35,6,8,22]}) data.index = ['Полный рабочий день','Частичная занятость','Временно не работает','На домохозяйстве','На пенсии','Учёба']","keywords":["22","35","bigdata","11","17","20","3.","4.","43","44","80","89"],"topics":["pandas"]},{"id":"chunk-82","heading":"4. Вывод о равномерности","text":"Использовать функцию scipy.stats.chi2 contingency() . Влияет ли семейное положение на занятость?","keywords":["bigdata","3.","4.","contingency","scipy.stats.chi2","влияет","вывод","данных","датасетами","занятость","использовать","ли"],"topics":[]},{"id":"chunk-83","heading":"1. Создаем датафрейм","text":"data = pd.DataFrame({ 'Женат': [89, 17, 11, 43, 22, 1], 'Гражданский брак': [80, 22, 20, 35, 6, 4], 'Не состоит в отношениях': [35, 44, 35, 6, 8, 22] }) data.index = ['Полный рабочий день','Частичная занятость','Временно не работает', 'На домохозяйстве','На пенсии','Учёба']","keywords":["22","35","bigdata","1.","11","17","20","3.","43","44","80","89"],"topics":["pandas"]},{"id":"chunk-84","heading":"2. Применяем критерий Хи квадрат для таблицы сопряженности","text":"print(\"\\nChi square statistic:\", chi2 stat) print(\"p value:\", p value) print(\"Степени свободы:\", dof) print(\"\\nОжидаемые частоты:\\n\", pd.DataFrame(expected, index=data.index, columns=data.columns))","keywords":["print","bigdata","value","2.","3.","chi2","columns","data.columns","data.index","dof","expected","index"],"topics":["pandas"]},{"id":"chunk-85","heading":"3. Вывод о зависимости","text":"alpha = 0.05 if p value < alpha: print(\"\\nСемейное положение и занятость зависимы (отвергаем H0)\") else: print(\"\\nСемейное положение и занятость независимы (не отвергаем H0)\") ```","keywords":["3.","alpha","bigdata","h0","nсемейное","print","занятость","отвергаем","положение","0.05","else","if"],"topics":[]},{"id":"chunk-86","heading":"3. Вывод о зависимости","text":"Женат Гражданский брак Не состоит в отношениях Полный рабочий день 89 80 35 Частичная занятость 17 22 44 Временно не работает 11 20 35 На домохозяйстве 43 35 6 На пенсии 22 6 8 Учёба 1 4 22","keywords":["22","35","3.","bigdata","11","17","20","43","44","80","89","брак"],"topics":[]},{"id":"chunk-87","heading":"3. Вывод о зависимости","text":"Chi square statistic: 122.29654948595365 p value: 1.7291616900960234e 21 Степени свободы: 10","keywords":["3.","bigdata","1.7291616900960234e","10","122.29654948595365","21","chi","square","statistic","value","вывод","данных"],"topics":[]},{"id":"chunk-88","heading":"3. Вывод о зависимости","text":"Ожидаемые частоты: Женат Гражданский брак Не состоит в отношениях Полный рабочий день 74.664 68.136 61.2 Частичная занятость 30.378 27.722 24.9 Временно не работает 24.156 22.044 19.8 На домохозяйстве 30.744 28.056 25.2 На пенсии 13.176 12.024 10.8 Учёба 9.882 9.018 8.1","keywords":["3.","bigdata","10.8","12.024","13.176","19.8","22.044","24.156","24.9","25.2","27.722","28.056"],"topics":[]},{"id":"chunk-89","heading":"3. Вывод о зависимости","text":"В ходе выполнения заданий был использован комплекс технологий анализа данных: библиотеки pandas, scipy, matplotlib и seaborn.","keywords":["3.","bigdata","данных","matplotlib","pandas","scipy","seaborn.","анализа","библиотеки","был","вывод","выполнения"],"topics":["pandas"]},{"id":"chunk-90","heading":"3. Вывод о зависимости","text":"Получен опыт работы с реальными наборами данных — от первичной загрузки и проверки на пропуски и дубликаты до статистического анализа и визуализации: расчет описательных статистик, построение гистограмм и box plot, проверка нормальности и гомогенности дисперсий, применение t теста и χ² теста для проверки гипотез.","keywords":["3.","bigdata","данных","построение","проверки","теста","box","plot","χ²","анализа","визуализации","вывод"],"topics":[]},{"id":"chunk-91","heading":"3. Вывод о зависимости","text":"В заданиях столкнулся с обработкой медицинских и эпидемиологических данных, анализом распределений, проверкой равномерности событий и исследованием зависимостей между категориальными переменными, что позволило закрепить практические навыки статистического анализа и работы с данными.","keywords":["3.","bigdata","данных","анализа","анализом","вывод","данными.","датасетами","зависимостей","зависимости","заданиях","закрепить"],"topics":[]}]},{"id":"python/practice-4-oop","href":"/docs/python/practice-4-oop","slug":["python","practice-4-oop"],"section":"python","sectionTitle":"Python","title":"Практика 4 — OOP","description":"Классы, объекты и деревья выражений из рабочей тетради №4.","preview":"Источник: pr Python/pr Python/pract4.ipynb Выполнение рабочей тетради № 4 1. Некоторые операции с классами и объектами 1.1. (уровень сложности: простейший) Напишите код, который выведет на экране все имена полей объекта произвольного пользовательского класса,","keywords":["self","сложности","уровень","def","key","средний","return","value","dict","my","age","node","node.left.accept","node.right.accept","self.key","visit","простейший","3.3."],"topics":["python","ai","oop","testing","algorithms"],"chunks":[{"id":"chunk-0","heading":"1.1. (уровень сложности: простейший)","text":"Напишите код, который выведет на экране все имена полей объекта произвольного пользовательского класса, кроме служебных имен.","keywords":["python","1.1.","4.","oop","выведет","выражений","деревья","имен.","имена","класса","классы","код"],"topics":["oop"]},{"id":"chunk-1","heading":"1.1. (уровень сложности: простейший)","text":"````python class Body(object): def init (self,age, len): self.age = age self.len = len","keywords":["python","age","len","1.1.","4.","body","class","def","init","object","oop","self"],"topics":["python"]},{"id":"chunk-2","heading":"1.2. (уровень сложности: простейший)","text":"Напишите код, который по имени метода, заданному строкой, вызовет этот метод в объекте некоторого пользовательского класса.","keywords":["python","1.2.","4.","oop","вызовет","выражений","деревья","заданному","имени","класса.","классы","код"],"topics":["oop"]},{"id":"chunk-3","heading":"1.2. (уровень сложности: простейший)","text":"````python class Body(object): def init (self,age, len): self.age = age self.len = len","keywords":["python","age","len","1.2.","4.","body","class","def","init","object","oop","self"],"topics":["python"]},{"id":"chunk-4","heading":"1.3. (уровень сложности: простейший)","text":"С кодом ниже что то не так. Что именно неправильно и как это исправить? ``` class A: pass","keywords":["python","1.3.","4.","class","oop","pass","выражений","деревья","именно","исправить","классы","кодом"],"topics":[]},{"id":"chunk-5","heading":"1.4. (уровень сложности: низкий)","text":"Напишите функцию однострочник get inheritance для вывода строки, отражающей иерархию наследования для входного класса.","keywords":["python","1.4.","4.","get","inheritance","oop","входного","вывода","выражений","деревья","иерархию","класса."],"topics":["oop"]},{"id":"chunk-6","heading":"1.4. (уровень сложности: низкий)","text":"def getinheritance(classname): return f\"{classname. name } {', '.join([base. name for base in classname. bases ])}\"","keywords":["classname.","name","python",".join","1.4.","4.","base","base.","bases","classname","def","getinheritance"],"topics":[]},{"id":"chunk-7","heading":"2. Своя реализация структуры данных","text":"Примените тестирование на случайных данных с использованием assert и оригинального dict.","keywords":["python","данных","2.","4.","assert","dict.","oop","выражений","деревья","использованием","классы","объекты"],"topics":["testing","algorithms"]},{"id":"chunk-8","heading":"2.2. (уровень сложности: низкий)","text":"Реализуйте для методов своей хэш таблицы тот же интерфейс, что и в dict, включая перегрузку операций.","keywords":["python","2.2.","4.","dict","oop","включая","выражений","деревья","же","интерфейс","классы","методов"],"topics":[]},{"id":"chunk-9","heading":"2.3. (уровень сложности: средний)","text":"Реализуйте поддержку итератора для цикла for. Обязательно протестируйте код на примерах с вложенными циклами!","keywords":["python","2.3.","4.","for.","oop","вложенными","выражений","деревья","итератора","классы","код","объекты"],"topics":[]},{"id":"chunk-10","heading":"2.3. (уровень сложности: средний)","text":"def getitem (self, key): for k, v in self.key value pairs: if k == key: return v raise KeyError(f\"Key '{key}' not found\")","keywords":["key","python","2.3.","4.","def","found","getitem","if","keyerror","not","oop","pairs"],"topics":["ai"]},{"id":"chunk-11","heading":"2.3. (уровень сложности: средний)","text":"def setitem (self, key, value): for i, (k, v) in enumerate(self.key value pairs): if k == key: self.key value pairs[i] = (key, value) return self.key value pairs.append((key, value))","keywords":["value","key","self.key","pairs","python","2.3.","4.","def","enumerate","if","oop","pairs.append"],"topics":["ai"]},{"id":"chunk-12","heading":"2.3. (уровень сложности: средний)","text":"def delitem (self, key): for i, (k, v) in enumerate(self.key value pairs): if k == key: del self.key value pairs[i] return raise KeyError(f\"Key '{key}' not found\")","keywords":["key","pairs","python","self.key","value","2.3.","4.","def","del","delitem","enumerate","found"],"topics":["ai"]},{"id":"chunk-13","heading":"Тестирование пользовательской хэш таблицы","text":"my dict = MyHashTable() my dict[\"name\"] = \"Alice\" my dict[\"age\"] = 30 my dict[\"city\"] = \"Wonderland\"","keywords":["dict","my","python","30","4.","age","alice","city","myhashtable","name","oop","wonderland"],"topics":["testing"]},{"id":"chunk-14","heading":"Получение значений","text":"print(my dict[\"name\"]) Вывод: Alice print(my dict[\"age\"]) Вывод: 30 print(my dict[\"city\"]) Вывод: Wonderland","keywords":["dict","my","print","вывод","python","30","4.","age","alice","city","name","oop"],"topics":[]},{"id":"chunk-15","heading":"3. Деревья выражений","text":"Требуется реализовать операции для печати, вычисления арифметических выражений и компиляции выражений в код стековой машины.","keywords":["выражений","python","деревья","3.","4.","oop","арифметических","вычисления","классы","код","компиляции","машины."],"topics":[]},{"id":"chunk-16","heading":"3.1. (уровень сложности: низкий)","text":"Реализовать классы узлов дерева: Num, Add и Mul. Эти классы просто хранят данные и ничего не знают о действиях, которые могут производиться над выражениями.","keywords":["классы","python","3.1.","4.","add","mul.","num","oop","выражений","выражениями.","данные","действиях"],"topics":["oop"]},{"id":"chunk-17","heading":"3.1. (уровень сложности: низкий)","text":"Пример, который будет использоваться далее: ast = Add(Num(7), Mul(Num(3), Num(2)))","keywords":["num","python","3.1.","4.","add","ast","mul","oop","будет","выражений","далее","деревья"],"topics":["python"]},{"id":"chunk-18","heading":"3.2. (уровень сложности: средний)","text":"Реализовать класс посетитель PrintVisitor для печати выражения. Обойтись без перегрузки repr и str, а также без операторов ветвления!","keywords":["python","без","3.2.","4.","oop","printvisitor","repr","str","ветвления","выражений","выражения.","деревья"],"topics":["oop"]},{"id":"chunk-19","heading":"3.2. (уровень сложности: средний)","text":"def visit add(self, node): return f\"({node.left.accept(self)} + {node.right.accept(self)})\"","keywords":["self","python","3.2.","4.","add","def","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-20","heading":"3.2. (уровень сложности: средний)","text":"def visit mul(self, node): return f\"({node.left.accept(self)} {node.right.accept(self)})\"","keywords":["self","python","3.2.","4.","def","mul","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-21","heading":"3.3. (уровень сложности: средний)","text":"Реализовать класс посетитель CalcVisitor для вычисления выражения. Обойтись без eval с exec, а также без операторов ветвления!","keywords":["python","без","3.3.","4.","calcvisitor","eval","exec","oop","ветвления","выражений","выражения.","вычисления"],"topics":["oop"]},{"id":"chunk-22","heading":"3.3. (уровень сложности: средний)","text":"def visit add(self, node): return f\"({node.left.accept(self)} + {node.right.accept(self)})\"","keywords":["self","python","3.3.","4.","add","def","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-23","heading":"3.3. (уровень сложности: средний)","text":"def visit mul(self, node): return f\"({node.left.accept(self)} {node.right.accept(self)})\"","keywords":["self","python","3.3.","4.","def","mul","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-24","heading":"3.3. (уровень сложности: средний)","text":"def visit add(self, node): return node.left.accept(self) + node.right.accept(self)","keywords":["self","python","3.3.","4.","add","def","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]},{"id":"chunk-25","heading":"3.3. (уровень сложности: средний)","text":"def visit mul(self, node): return node.left.accept(self) node.right.accept(self)","keywords":["self","python","3.3.","4.","def","mul","node","node.left.accept","node.right.accept","oop","return","visit"],"topics":[]}]},{"id":"bigdata/practice-04-insurance-analysis","href":"/docs/bigdata/practice-04-insurance-analysis","slug":["bigdata","practice-04-insurance-analysis"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 4. Анализ данных страхования","description":"Практика с датасетом по страхованию и подготовкой признаков.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитривеич ИКБО 25 22 Практическая работа №4 Корреляция, линейная регрессия и дисперсионный анализ 1. Определить два вектора, представляющие собо","keywords":["non","null","schools","136","136.000000","count","int64","bmi","df","girls","mean","boys","northeast","northwest","southeast","southwest","75","region"],"topics":["python","ai","numpy","testing","regression"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","практика","4.","wiki","анализ","версия","данных","датасетом","документацию.","исходный","материал","ноутбук"],"topics":[]},{"id":"chunk-1","heading":"Корреляция, линейная регрессия и дисперсионный анализ","text":"1. Определить два вектора, представляющие собой число автомобилей, припаркованных в течении 5 рабочих дней у бизнес центра на уличной стоянке и в подземном гараже.","keywords":["bigdata","анализ","практика","1.","4.","автомобилей","бизнес","вектора","гараже.","данных","датасетом","два"],"topics":["regression"]},{"id":"chunk-2","heading":"Корреляция, линейная регрессия и дисперсионный анализ","text":"День Улица Гараж Понедельник 80 100 Вторник 98 82 Среда 75 105 Четверг 91 89 Пятница 78 102","keywords":["bigdata","анализ","практика","100","102","105","4.","75","78","80","82","89"],"topics":["regression"]},{"id":"chunk-3","heading":"Выводим данные","text":"text Понедельник Улица: 80, Гараж: 100 Вторник Улица: 98, Гараж: 82 Среда Улица: 75, Гараж: 105 Четверг Улица: 91, Гараж: 89 Пятница Улица: 78, Гараж: 102","keywords":["гараж","улица","bigdata","практика","100","102","105","4.","75","78","80","82"],"topics":[]},{"id":"chunk-4","heading":"Выводим данные","text":"1.1. Найти и интерпретировать корреляцию между переменными «Улица» и «Гараж» (подсчитать корреляцию по Пирсону).","keywords":["bigdata","корреляцию","практика","1.1.","4.","анализ","выводим","гараж","данные","данных","датасетом","интерпретировать"],"topics":[]},{"id":"chunk-5","heading":"Данные","text":"улица = np.array([80, 98, 75, 91, 78]) гараж = np.array([100, 82, 105, 89, 102])","keywords":["bigdata","np.array","практика","100","102","105","4.","75","78","80","82","89"],"topics":["numpy"]},{"id":"chunk-6","heading":"Вычисляем корреляцию Пирсона","text":"print(f\"Коэффициент корреляции Пирсона между улицей и гаражом: {корреляция:.2f}\") ```","keywords":["bigdata","пирсона","практика",".2f","4.","print","анализ","вычисляем","гаражом","данных","датасетом","корреляции"],"topics":[]},{"id":"chunk-7","heading":"Данные","text":"улица = np.array([80, 98, 75, 91, 78]) гараж = np.array([100, 82, 105, 89, 102]) дни = [\"Понедельник\", \"Вторник\", \"Среда\", \"Четверг\", \"Пятница\"]","keywords":["bigdata","np.array","практика","100","102","105","4.","75","78","80","82","89"],"topics":["numpy"]},{"id":"chunk-8","heading":"Добавляем подписи к точкам","text":"for i, день in enumerate(дни): plt.text(улица[i]+0.5, гараж[i]+0.5, день, fontsize=9)","keywords":["+0.5","bigdata","день","практика","4.","enumerate","fontsize","plt.text","анализ","гараж","данных","датасетом"],"topics":[]},{"id":"chunk-9","heading":"Подписи осей и заголовок","text":"plt.xlabel(\"Улица\") plt.ylabel(\"Гараж\") plt.title(\"Диаграмма рассеяния: Улица vs Гараж\") plt.grid(True) plt.show() ```","keywords":["bigdata","гараж","практика","улица","4.","plt.grid","plt.show","plt.title","plt.xlabel","plt.ylabel","true","vs"],"topics":[]},{"id":"chunk-10","heading":"Подписи осей и заголовок","text":"2. Найти и выгрузить данные. Вывести, провести предобработку и описать признаки.","keywords":["bigdata","практика","2.","4.","анализ","вывести","выгрузить","данные.","данных","датасетом","заголовок","найти"],"topics":[]},{"id":"chunk-11","heading":"Подписи осей и заголовок","text":"2.1. Построить корреляционную матрицу по одной целевой переменной. Определить наиболее коррелирующую переменную, продолжить с ней работу в следующем пункте.","keywords":["bigdata","практика","2.1.","4.","анализ","данных","датасетом","заголовок","коррелирующую","корреляционную","матрицу","наиболее"],"topics":[]},{"id":"chunk-12","heading":"4. Описательная статистика","text":"```text Первые строки данных: sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","4.","bigdata","данных","практика","115635","161594","178958","2023","272506","27808"],"topics":["python"]},{"id":"chunk-13","heading":"4. Описательная статистика","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","4.","bigdata","практика","121821","15153","2.79","2.83","2017","211201","265676","29510"],"topics":[]},{"id":"chunk-14","heading":"4. Описательная статистика","text":"primary girls schools secondary boys schools secondary girls schools \\ 0 107 24 13 1 84 23 18 2 177 45 46 3 124 46 29 4 94 24 31","keywords":["schools","24","4.","46","bigdata","girls","secondary","практика","107","124","13","177"],"topics":[]},{"id":"chunk-15","heading":"4. Описательная статистика","text":"high boys schools high girls schools Intermediate boys schools \\ 0 29 10 2 1 27 17 1 2 32 21 6 3 27 11 4 4 18 18 2","keywords":["schools","18","27","4.","bigdata","boys","high","практика","10","11","17","21"],"topics":[]},{"id":"chunk-16","heading":"4. Описательная статистика","text":"Intermediate girls schools total boys schools total girls schools \\ 0 2 249 132 1 1 269 120 2 1 569 245 3 4 507 168 4 0 268 143","keywords":["schools","4.","bigdata","girls","total","практика","120","132","143","168","245","249"],"topics":[]},{"id":"chunk-17","heading":"4. Описательная статистика","text":"Информация о данных: RangeIndex: 136 entries, 0 to 135 Data columns (total 22 columns):","keywords":["4.","bigdata","columns","данных","практика","135","136","22","data","entries","rangeindex","total"],"topics":[]},{"id":"chunk-18","heading":"Column Non Null Count Dtype","text":"0 sr.no. 136 non null int64 1 province 136 non null object 2 division 136 non null object 3 district 136 non null object 4 households 136 non null int64 5 population 2023 136 non null int64 6 average household size 136 non null float64 7 population 2017 136 non null int64 8 growth rate 136 non null float64 9 area(km²) 136 non null int64 10 density 2023(people/km²) 136 non null float64 11 primary boys schools 136 non","keywords":["non","136","null","int64","float64","object","2023","bigdata","km²","population","практика","10"],"topics":[]},{"id":"chunk-19","heading":"Column Non Null Count Dtype","text":"null int64 12 primary girls schools 136 non null int64 13 secondary boys schools 136 non null int64 14 secondary girls schools 136 non null int64 15 high boys schools 136 non null int64 16 high girls schools 136 non null int64 17 Intermediate boys schools 136 non null int64 18 Intermediate girls schools 136 non null int64 19 total boys schools 136 non null int64 20 total girls schools 136 non null int64 21 total","keywords":["null","int64","non","136","schools","girls","boys","total","bigdata","high","intermediate","secondary"],"topics":[]},{"id":"chunk-20","heading":"Column Non Null Count Dtype","text":"schools 136 non null int64 dtypes: float64(3), int64(16), object(3) memory usage: 23.5+ KB None","keywords":["bigdata","int64","non","null","практика","136","16","23.5+","4.","column","count","dtype"],"topics":[]},{"id":"chunk-21","heading":"Column Non Null Count Dtype","text":"Описательные статистики: sr.no. households population 2023 average household size \\ count 136.000000 1.360000e+02 1.360000e+02 136.000000 mean 68.500000 2.819159e+05 1.775731e+06 6.451618 std 39.403892 2.789400e+05 1.791408e+06 0.944771 min 1.000000 1.640000e+04 1.275710e+05 4.180000 25% 34.750000 7.052575e+04 4.999090e+05 5.852500 50% 68.500000 2.187750e+05 1.370099e+06 6.390000 75% 102.250000 3.838998e+05","keywords":["1.360000e+02","136.000000","68.500000","bigdata","count","практика","0.944771","1.000000","1.275710e+05","1.370099e+06","1.640000e+04","1.775731e+06"],"topics":[]},{"id":"chunk-22","heading":"Column Non Null Count Dtype","text":"2.338289e+06 6.960000 max 136.000000 2.012526e+06 1.300414e+07 9.110000","keywords":["bigdata","практика","1.300414e+07","136.000000","2.012526e+06","2.338289e+06","4.","6.960000","9.110000","column","count","dtype"],"topics":[]},{"id":"chunk-23","heading":"Column Non Null Count Dtype","text":"population 2017 growth rate area(km²) density 2023(people/km²) \\ count 1.360000e+02 136.000000 136.000000 136.000000 mean 1.527093e+06 2.827574 6036.323529 1521.740000 std 1.540987e+06 1.542254 6802.861419 6036.440643 min 9.705200e+04 0.230000 69.000000 6.020000 25% 4.326818e+05 2.077500 2289.000000 122.947500 50% 1.216293e+06 2.460000 3709.500000 331.580000 75% 1.942269e+06 2.875000 7492.000000 772.712500 max","keywords":["136.000000","bigdata","count","km²","практика","0.230000","1.216293e+06","1.360000e+02","1.527093e+06","1.540987e+06","1.542254","1.942269e+06"],"topics":[]},{"id":"chunk-24","heading":"Column Non Null Count Dtype","text":"1.111998e+07 9.510000 44748.000000 55396.010000","keywords":["bigdata","практика","1.111998e+07","4.","44748.000000","55396.010000","9.510000","column","count","dtype","non","null"],"topics":[]},{"id":"chunk-25","heading":"Column Non Null Count Dtype","text":"primary boys schools primary girls schools secondary boys schools \\ count 136.000000 136.000000 136.000000 mean 579.588235 264.397059 51.860294 std 591.909008 207.461492 34.935321 min 0.000000 0.000000 0.000000 25% 239.250000 93.750000 24.750000 50% 378.000000 206.000000 46.000000 75% 641.750000 395.250000 72.500000 max 3375.000000 1068.000000 241.000000","keywords":["0.000000","136.000000","schools","bigdata","boys","count","primary","практика","1068.000000","206.000000","207.461492","239.250000"],"topics":[]},{"id":"chunk-26","heading":"Column Non Null Count Dtype","text":"secondary girls schools high boys schools high girls schools \\ count 136.000000 136.000000 136.000000 mean 50.786765 64.617647 44.029412 std 51.258879 53.103959 51.681781 min 0.000000 0.000000 0.000000 25% 16.750000 27.000000 10.000000 50% 31.000000 53.000000 23.500000 75% 72.500000 84.250000 55.250000 max 281.000000 345.000000 300.000000","keywords":["0.000000","136.000000","schools","bigdata","count","girls","high","практика","10.000000","16.750000","23.500000","25"],"topics":[]},{"id":"chunk-27","heading":"Column Non Null Count Dtype","text":"Intermediate boys schools Intermediate girls schools \\ count 136.000000 136.000000 mean 10.647059 6.389706 std 20.981660 8.071140 min 0.000000 0.000000 25% 3.000000 1.000000 50% 6.500000 3.000000 75% 12.000000 9.250000 max 233.000000 51.000000","keywords":["0.000000","136.000000","3.000000","bigdata","count","intermediate","schools","практика","1.000000","10.647059","12.000000","20.981660"],"topics":[]},{"id":"chunk-28","heading":"Column Non Null Count Dtype","text":"total boys schools total girls schools total schools count 136.000000 136.000000 136.000000 mean 706.713235 365.602941 1072.316176 std 621.938128 292.229090 753.412205 min 0.000000 0.000000 0.000000 25% 321.250000 120.000000 469.750000 50% 540.500000 268.000000 864.000000 75% 788.000000 582.750000 1439.250000 max 3684.000000 1367.000000 4269.000000 ```","keywords":["0.000000","136.000000","schools","total","bigdata","count","практика","1072.316176","120.000000","1367.000000","1439.250000","25"],"topics":[]},{"id":"chunk-29","heading":"Данные (например, наиболее коррелирующая переменная с целевой)","text":"X = np.array([80, 98, 75, 91, 78]) независимая переменная y = np.array([100, 82, 105, 89, 102]) целевая переменная","keywords":["переменная","bigdata","np.array","практика","100","102","105","4.","75","78","80","82"],"topics":["numpy"]},{"id":"chunk-30","heading":"Данные","text":"X = np.array([80, 98, 75, 91, 78]) независимая переменная y = np.array([100, 82, 105, 89, 102]) целевая переменная","keywords":["bigdata","np.array","переменная","практика","100","102","105","4.","75","78","80","82"],"topics":["numpy"]},{"id":"chunk-31","heading":"Линейная регрессия вручную","text":"X mean = np.mean(X) y mean = np.mean(y) w = np.sum((X X mean) (y y mean)) / np.sum((X X mean) 2) b = y mean w X mean","keywords":["mean","bigdata","np.mean","np.sum","практика","4.","анализ","вручную","данных","датасетом","линейная","подготовкой"],"topics":["numpy","regression"]},{"id":"chunk-32","heading":"Построение графика","text":"plt.figure(figsize=(8,5)) plt.scatter(X, y, color='blue', label='Данные') plt.plot(X, y pred, color='red', label=f'Регрессия: y = {w:.2f}x + {b:.2f}') plt.xlabel(\"X (наиболее коррелирующая переменная)\") plt.ylabel(\"y (Целевая переменная)\") plt.title(\"Линейная регрессия вручную\") plt.legend() plt.grid(True) plt.show() ```","keywords":[".2f","bigdata","color","label","переменная","практика","регрессия","4.","blue","figsize","plt.figure","plt.grid"],"topics":["regression"]},{"id":"chunk-33","heading":"Построение графика","text":"3. Загрузить данные: 'insurance.csv'. Вывести и провести предобработку. Вывести список уникальных регионов.","keywords":["bigdata","вывести","практика","3.","4.","insurance.csv","анализ","графика","данные","данных","датасетом","загрузить"],"topics":[]},{"id":"chunk-34","heading":"Построение графика","text":"3.1. Выполнить однофакторный ANOVA тест, чтобы проверить влияние региона на индекс массы тела (BMI), используя первый способ, через библиотеку Scipy.","keywords":["bigdata","практика","3.1.","4.","anova","bmi","scipy.","анализ","библиотеку","влияние","выполнить","графика"],"topics":[]},{"id":"chunk-35","heading":"Группируем данные по регионам и извлекаем BMI для каждой группы","text":"groups = [df[df['region'] == region]['bmi'] for region in df['region'].unique()]","keywords":["region","df","bigdata","bmi","практика",".unique","4.","groups","анализ","группируем","группы","данные"],"topics":[]},{"id":"chunk-36","heading":"Интерпретация","text":"if p value < 0.05: print(\"Результат значимый: средние значения BMI различаются между регионами.\") else: print(\"Результат не значимый: средние значения BMI не различаются между регионами.\") ```","keywords":["bigdata","bmi","print","значения","значимый","между","практика","различаются","регионами.","результат","средние","0.05"],"topics":[]},{"id":"chunk-37","heading":"Интерпретация","text":"text Уникальные регионы: ['southwest' 'southeast' 'northwest' 'northeast'] F статистика: 39.495 P значение: 0.000 Результат значимый: средние значения BMI различаются между регионами.","keywords":["bigdata","практика","0.000","39.495","4.","bmi","northeast","northwest","southeast","southwest","text","анализ"],"topics":["python"]},{"id":"chunk-38","heading":"Интерпретация","text":"3.2. Выполнить однофакторный ANOVA тест, чтобы проверить влияние региона на индекс массы тела (BMI), используя второй способ, с помощью функции anova lm() из библиотеки statsmodels.","keywords":["anova","bigdata","практика","3.2.","4.","bmi","lm","statsmodels.","анализ","библиотеки","влияние","второй"],"topics":[]},{"id":"chunk-39","heading":"Интерпретация","text":"```python from statsmodels.formula.api import ols from statsmodels.stats.anova import anova lm","keywords":["bigdata","import","практика","4.","anova","lm","ols","python","statsmodels.formula.api","statsmodels.stats.anova","анализ","данных"],"topics":["python"]},{"id":"chunk-40","heading":"Вывод результатов","text":"text Уникальные регионы: ['southwest' 'southeast' 'northwest' 'northeast'] df sum sq mean sq F PR( F) C(region) 3.0 4055.880631 1351.960210 39.495057 1.881839e 24 Residual 1334.0 45664.319755 34.231124 NaN NaN","keywords":["bigdata","nan","sq","практика","1.881839e","1334.0","1351.960210","24","3.0","34.231124","39.495057","4."],"topics":["python"]},{"id":"chunk-41","heading":"Вывод результатов","text":"3.3. С помощью t критерия Стьюдента перебрать все пары. Определить поправку Бонферрони. Сделать выводы.","keywords":["bigdata","практика","3.3.","4.","анализ","бонферрони.","вывод","выводы.","данных","датасетом","критерия","определить"],"topics":[]},{"id":"chunk-42","heading":"Выполняем t тест для каждой пары","text":"for r1, r2 in pairs: group1 = df[df['region'] == r1]['bmi'] group2 = df[df['region'] == r2]['bmi'] t stat, p value = ttest ind(group1, group2, equal var=False) Welch t test significant = \"Да\" if p value < bonf alpha else \"Нет\" print(f\"{r1} vs {r2}: t = {t stat:.3f}, p = {p value:.4f}, значимо после Bonferroni? {significant}\") ```","keywords":["df","r1","r2","value","bigdata","bmi","group1","group2","region","significant","stat","практика"],"topics":["ai","testing"]},{"id":"chunk-43","heading":"Выполняем t тест для каждой пары","text":"text Уникальные регионы: ['southwest' 'southeast' 'northwest' 'northeast'] Поправка Бонферрони: 0.0083 southwest vs southeast: t = 5.952, p = 0.0000, значимо после Bonferroni? Да southwest vs northwest: t = 3.284, p = 0.0011, значимо после Bonferroni? Да southwest vs northeast: t = 3.117, p = 0.0019, значимо после Bonferroni? Да southeast vs northwest: t = 9.377, p = 0.0000, значимо после Bonferroni","keywords":["bonferroni","southwest","vs","значимо","после","northwest","southeast","да","0.0000","bigdata","northeast","практика"],"topics":["python"]},{"id":"chunk-44","heading":"Выполняем t тест для каждой пары","text":"? Да southeast vs northeast: t = 8.835, p = 0.0000, значимо после Bonferroni? Да northwest vs northeast: t = 0.060, p = 0.9519, значимо после Bonferroni? Нет","keywords":["bigdata","bonferroni","northeast","vs","да","значимо","после","практика","0.0000","0.060","0.9519","4."],"topics":["python"]},{"id":"chunk-45","heading":"Пост хок тест Тьюки","text":"tukey = pairwise tukeyhsd(endog=df['bmi'], зависимая переменная groups=df['region'], группирующая переменная alpha=0.05)","keywords":["bigdata","df","переменная","практика","0.05","4.","alpha","bmi","endog","groups","pairwise","region"],"topics":["ai"]},{"id":"chunk-46","heading":"Визуализация результатов","text":"tukey.plot simultaneous(comparison name=df['region'].unique()[0]) выбираем первый регион для базовой линии plt.title(\"Пост хок тест Тьюки для BMI по регионам\") plt.xlabel(\"Разница средних BMI\") plt.show() ```","keywords":["bigdata","bmi","практика",".unique","4.","comparison","df","name","plt.show","plt.title","plt.xlabel","region"],"topics":[]},{"id":"chunk-47","heading":"Визуализация результатов","text":"```text Multiple Comparison of Means Tukey HSD, FWER=0.05 ========================================================== group1 group2 meandiff p adj lower upper reject northeast northwest 0.0263 0.9999 1.1552 1.2078 False northeast southeast 4.1825 0.0 3.033 5.332 True northeast southwest 1.4231 0.0107 0.2416 2.6046 True northwest southeast 4.1562 0.0 3.0077 5.3047 True northwest southwest 1.3968 0.0127 0.2162 2.5774","keywords":["northeast","northwest","true","0.0","bigdata","southeast","southwest","практика","0.0107","0.0127","0.0263","0.05"],"topics":["python"]},{"id":"chunk-48","heading":"Визуализация результатов","text":"True southeast southwest 2.7594 0.0 3.9079 1.6108 True","keywords":["bigdata","true","практика","0.0","1.6108","2.7594","3.9079","4.","southeast","southwest","анализ","визуализация"],"topics":["python"]},{"id":"chunk-49","heading":"Визуализация результатов","text":"3.5. Выполнить двухфакторный ANOVA тест, чтобы проверить влияние региона и пола на индекс массы тела (BMI), используя функцию anova lm() из библиотеки statsmodels.","keywords":["anova","bigdata","практика","3.5.","4.","bmi","lm","statsmodels.","анализ","библиотеки","визуализация","влияние"],"topics":[]},{"id":"chunk-50","heading":"Визуализация результатов","text":"```python from statsmodels.formula.api import ols from statsmodels.stats.anova import anova lm","keywords":["bigdata","import","практика","4.","anova","lm","ols","python","statsmodels.formula.api","statsmodels.stats.anova","анализ","визуализация"],"topics":["python"]},{"id":"chunk-51","heading":"Проверим уникальные значения регионов и пола","text":"print(\"Уникальные регионы:\", df['region'].unique()) print(\"Уникальные значения пола:\", df['sex'].unique())","keywords":["уникальные",".unique","bigdata","df","print","значения","пола","практика","4.","region","sex","анализ"],"topics":[]},{"id":"chunk-52","heading":"Вывод результатов","text":"text Уникальные регионы: ['southwest' 'southeast' 'northwest' 'northeast'] Уникальные значения пола: ['female' 'male'] df sum sq mean sq F PR( F) C(region) 3.0 4055.880631 1351.960210 39.539923 1.773031e 24 C(sex) 1.0 86.007035 86.007035 2.515393 1.129767e 01 Residual 1333.0 45578.312720 34.192283 NaN NaN","keywords":["86.007035","bigdata","nan","sq","практика","уникальные","01","1.0","1.129767e","1.773031e","1333.0","1351.960210"],"topics":["python"]},{"id":"chunk-53","heading":"Вывод результатов","text":"```python from statsmodels.stats.multicomp import pairwise tukeyhsd from statsmodels.formula.api import ols","keywords":["bigdata","import","практика","4.","ols","pairwise","python","statsmodels.formula.api","statsmodels.stats.multicomp","tukeyhsd","анализ","вывод"],"topics":["python","ai"]},{"id":"chunk-54","heading":"Визуализация","text":"tukey.plot simultaneous(comparison name=df['region'].unique()[0]) базовый регион plt.title(\"Пост хок тест Тьюки для BMI по регионам\") plt.xlabel(\"Разница средних BMI\") plt.show() ```","keywords":["bigdata","bmi","практика",".unique","4.","comparison","df","name","plt.show","plt.title","plt.xlabel","region"],"topics":[]},{"id":"chunk-55","heading":"Визуализация","text":"```text Multiple Comparison of Means Tukey HSD, FWER=0.05 ========================================================== group1 group2 meandiff p adj lower upper reject northeast northwest 0.0263 0.9999 1.1552 1.2078 False northeast southeast 4.1825 0.0 3.033 5.332 True northeast southwest 1.4231 0.0107 0.2416 2.6046 True northwest southeast 4.1562 0.0 3.0077 5.3047 True northwest southwest 1.3968 0.0127 0.2162 2.5774","keywords":["northeast","northwest","true","0.0","bigdata","southeast","southwest","практика","0.0107","0.0127","0.0263","0.05"],"topics":["python"]},{"id":"chunk-56","heading":"Визуализация","text":"True southeast southwest 2.7594 0.0 3.9079 1.6108 True","keywords":["bigdata","true","практика","0.0","1.6108","2.7594","3.9079","4.","southeast","southwest","анализ","визуализация"],"topics":["python"]},{"id":"chunk-57","heading":"Визуализация","text":"В ходе работы были проведены статистические анализы данных о здоровье и парковках: выполнена загрузка и предобработка наборов данных, рассчитаны корреляции Пирсона, построены диаграммы рассеяния и реализована линейная регрессия вручную с оценкой наклона, сдвига и MSE .","keywords":["данных","bigdata","практика","4.","mse","анализ","анализы","были","визуализация","вручную","выполнена","датасетом"],"topics":["regression"]},{"id":"chunk-58","heading":"Визуализация","text":"Для данных insurance.csv проведен однофакторный и двухфакторный ANOVA анализ с использованием библиотек Scipy и Statsmodels , выполнены пост хок тесты Тьюки, применена поправка Бонферрони и визуализированы результаты различий между группами. Работа показала применение методов статистики и Python для анализа, визуализации и интерпретации данных.","keywords":["bigdata","анализ","данных","практика","4.","anova","insurance.csv","python","scipy","statsmodels","анализа","библиотек"],"topics":["python"]}]},{"id":"python/practice-5-test-automation","href":"/docs/python/practice-5-test-automation","slug":["python","practice-5-test-automation"],"section":"python","sectionTitle":"Python","title":"Практика 5 — Тестовая автоматизация","description":"Практикум по pytest, coverage, мутационному и property-based тестированию.","preview":"Источник: pr Python/pr Python/pract5.ipynb Практическое занятие №5 Многие задачи из этого блокнота решать удобнее в парном составе: один студент занимается написанием кода программы и пытается внести в код такие ошибки, которые будет сложно выявить с помощью т","keywords":["test","def","number","сложности","уровень","return","assert","средний","account","bytes","balance","self.account","программы","amount","rle","self","тестирование","divide"],"topics":["python","ai","numpy","oop","sql","testing"],"chunks":[{"id":"chunk-0","heading":"Практическое занятие №5","text":"Многие задачи из этого блокнота решать удобнее в парном составе: один студент занимается написанием кода программы и пытается внести в код такие ошибки, которые будет сложно выявить с помощью тестов другому студенту.","keywords":["python","coverage","property-based","pytest","автоматизация","блокнота","будет","внести","выявить","другому","задачи","занимается"],"topics":[]},{"id":"chunk-1","heading":"Практическое занятие №5","text":"Далее потребуется несколько программ, содержащих ошибки. Если в задаче явно не указана программа для тестирования, то используется одна из следующих программ:","keywords":["python","программ","coverage","property-based","pytest","автоматизация","далее","если","задаче","занятие","используется","мутационному"],"topics":[]},{"id":"chunk-2","heading":"2. Бинарный поиск.","text":"python def binary search(arr, x): left = 0 right = len(arr) while left <= right: mid = round((left + right) / 2) if arr[mid] == x: return mid if arr[mid] < x: left = mid + 1 else: right = mid return 1","keywords":["mid","arr","left","right","python","if","return","2.","binary","coverage","def","else"],"topics":["python"]},{"id":"chunk-3","heading":"4. Определение типа треугольника.","text":"python def triangle type(x1, y1, x2, y2, x3, y3): a = distance(x1, y1, x2, y2) b = distance(x2, y2, x3, y3) c = distance(x3, y3, x1, y1) if a == b == c: return \"равнобедренный\" elif a == b or a == c or b == c: return \"равносторонний\" elif a != b != c: return \"разносторонний\"","keywords":["distance","python","return","x1","x2","x3","y1","y2","y3","elif","4.","coverage"],"topics":["python"]},{"id":"chunk-4","heading":"5. Сжатие и расжатие данных по методу RLE.","text":"````python def encode rle(data): encoded = bytes() count = 0 last char = data[ 1] for i in range(1, len(data) + 1): if data[i] == last char: count += 1 else: encoded.append(data[i]) encoded.append(count) count = 1 last char = data[i] encoded.append(count) encoded.append(last char) return bytes(encoded)","keywords":["data","count","char","encoded.append","last","python","bytes","encoded","5.","coverage","def","else"],"topics":["python"]},{"id":"chunk-5","heading":"5. Сжатие и расжатие данных по методу RLE.","text":"def decode rle(data): decoded = bytes() i = 1 while i < len(data): count = data[i 1] char = data[i] decoded.extend([char] count) i += 1 return bytes(decoded) ````","keywords":["data","bytes","char","count","decoded","python","5.","coverage","decode","decoded.extend","def","len"],"topics":[]},{"id":"chunk-6","heading":"6. Банковский счет.","text":"````python class BankAccount: def init (self, account number, balance=0): self.account number = account number self.balance = balance","keywords":["number","python","account","balance","6.","bankaccount","class","coverage","def","init","property-based","pytest"],"topics":["python"]},{"id":"chunk-7","heading":"6. Банковский счет.","text":"def deposit(self, amount): self.balance += amount return f\"{amount} средств успешно зачислены на счет {self.account number}\"","keywords":["amount","python","6.","coverage","def","deposit","number","property-based","pytest","return","self","self.account"],"topics":[]},{"id":"chunk-8","heading":"6. Банковский счет.","text":"def withdraw(self, amount): self.balance = amount return f\"{amount} средств успешно сняты с счета {self.account number}\"","keywords":["amount","python","6.","coverage","def","number","property-based","pytest","return","self","self.account","self.balance"],"topics":[]},{"id":"chunk-9","heading":"6. Банковский счет.","text":"def check balance(self): return f\"Баланс счета {self.account number}: {self.balance}\" ````","keywords":["python","6.","balance","check","coverage","def","number","property-based","pytest","return","self","self.account"],"topics":[]},{"id":"chunk-10","heading":"7. Банковский счет с использованием базы данных.","text":"class BankAccount: def init (self, account number): self.account number = account number self.conn = sqlite3.connect('bank.db') self.cursor = self.conn.cursor() self.cursor.execute( \"CREATE TABLE IF NOT EXISTS accounts (account number INTEGER PRIMARY KEY, balance REAL)\") self.conn.commit()","keywords":["number","account","python","7.","accounts","balance","bank.db","bankaccount","class","coverage","create","def"],"topics":["sql"]},{"id":"chunk-11","heading":"7. Банковский счет с использованием базы данных.","text":"def deposit(self, amount): self.cursor.execute( \"UPDATE accounts SET balance = balance + ? WHERE account number = ?\", (amount, self.account number)) self.conn.commit() return f\"{amount} средств успешно зачислены на счет {self.account number}\"","keywords":["amount","number","balance","python","self.account","счет","7.","account","accounts","coverage","def","deposit"],"topics":["sql"]},{"id":"chunk-12","heading":"7. Банковский счет с использованием базы данных.","text":"def withdraw(self, amount): self.cursor.execute( \"SELECT balance FROM accounts WHERE account number = ?\", (self.account number,)) balance = self.cursor.fetchone()[0] self.cursor.execute( \"UPDATE accounts SET balance = balance ? WHERE account number = ?\", (amount, self.account number)) self.conn.commit() return f\"{amount} средств успешно сняты с счета {self.account number}\"","keywords":["number","balance","amount","self.account","account","accounts","python","self.cursor.execute","where","7.","coverage","def"],"topics":["sql"]},{"id":"chunk-13","heading":"7. Банковский счет с использованием базы данных.","text":"def check balance(self): self.cursor.execute( \"SELECT balance FROM accounts WHERE account number = ?\", (self.account number,)) balance = self.cursor.fetchone()[0] return f\"Баланс счета {self.account number}: {balance}\"","keywords":["balance","number","python","self.account","7.","account","accounts","check","coverage","def","property-based","pytest"],"topics":["sql"]},{"id":"chunk-14","heading":"7. Банковский счет с использованием базы данных.","text":"def close account(self): self.cursor.execute( \"DELETE FROM accounts WHERE account number = ?\", (self.account number,)) self.conn.commit() return f\"Счет {self.account number} закрыт\"","keywords":["number","account","python","self.account","счет","7.","accounts","close","coverage","def","delete","property-based"],"topics":["sql"]},{"id":"chunk-15","heading":"7. Банковский счет с использованием базы данных.","text":"def create account(self, balance): self.cursor.execute( \"INSERT INTO accounts (account number, balance) VALUES (?, ?)\", (self.account number, balance)) self.conn.commit() return f\"Счет {self.account number} успешно создан\" ````","keywords":["balance","number","account","python","self.account","счет","7.","accounts","coverage","create","def","insert"],"topics":["sql"]},{"id":"chunk-16","heading":"1.1. (уровень сложности: низкий)","text":"Исправьте функцию distance. Добавьте документацию к функции в виде docstring строки. Укажите примеры в формате doctest. Примеры должны охватывать граничные случаи. Протестируйте программу с помощью вызова модуля doctest. Перенесите примеры в отдельный файл и снова протестируйте программу.","keywords":["примеры","doctest.","python","протестируйте","1.1.","coverage","distance.","docstring","property-based","pytest","автоматизация","виде"],"topics":["testing"]},{"id":"chunk-17","heading":"1.1. (уровень сложности: низкий)","text":"````python def distance(x1, y1, x2, y2): \"\"\" Calculate the Euclidean distance between two points in a 2D plane.","keywords":["python","distance","1.1.","2d","between","calculate","coverage","def","euclidean","plane.","points","property-based"],"topics":["python"]},{"id":"chunk-18","heading":"1.1. (уровень сложности: низкий)","text":"Arguments: x1 x coordinate of the first point y1 y coordinate of the first point x2 x coordinate of the second point y2 y coordinate of the second point","keywords":["coordinate","point","first","python","second","1.1.","arguments","coverage","property-based","pytest","x1","x2"],"topics":[]},{"id":"chunk-19","heading":"1.1. (уровень сложности: низкий)","text":"Example: distance(0, 0, 3, 4) 3 4 5 right triangle 5.0 distance(0, 0, 1, 1) distance is sqrt(2) 1.4142135623730951 distance(0, 0, 0, 0) distance between the same point 0.0 \"\"\" return ((x2 x1) 2 + (y2 y1) 2) 0.5","keywords":["distance","python","0.0","0.5","1.1.","1.4142135623730951","5.0","between","coverage","example","point","property-based"],"topics":[]},{"id":"chunk-20","heading":"1.2.(уровень сложности: высокий)","text":"Добавьте к функции сортировки тестирование на случайных данных. Исправьте ошибки в функции.","keywords":["python","1.2.","coverage","property-based","pytest","автоматизация","высокий","данных.","добавьте","исправьте","мутационному","ошибки"],"topics":["testing"]},{"id":"chunk-21","heading":"1.2.(уровень сложности: высокий)","text":"Напишите к функции сортировки отдельную функцию спецификацию в виде набора assert'ов. Спецификация должна исчерпывающим образом описывать задачу сортировки (без привлечения готовых функций сортировки), иными словами – для общего случая нельзя придумать такое искажение кода сортировки, которое будет принято спецификацией.","keywords":["сортировки","python","1.2.","assert","coverage","property-based","pytest","автоматизация","без","будет","виде","высокий"],"topics":[]},{"id":"chunk-22","heading":"Проверяем кодирование","text":"assert encode rle(bytes([1, 1, 1, 2, 2, 3, 3, 3, 3])) == bytes([1, 3, 2, 2, 3, 4])","keywords":["bytes","python","assert","coverage","encode","property-based","pytest","rle","автоматизация","кодирование","мутационному","практика"],"topics":[]},{"id":"chunk-23","heading":"Проверяем декодирование","text":"assert decode rle(bytes([1, 3, 2, 2, 3, 4])) == bytes([1, 1, 1, 2, 2, 3, 3, 3, 3])","keywords":["bytes","python","assert","coverage","decode","property-based","pytest","rle","автоматизация","декодирование","мутационному","практика"],"topics":[]},{"id":"chunk-24","heading":"Проверяем крайний случай с одним элементом","text":"assert encode rle(bytes([5])) == bytes([5, 1]) assert decode rle(bytes([5, 1])) == bytes([5])","keywords":["bytes","assert","python","rle","coverage","decode","encode","property-based","pytest","автоматизация","крайний","мутационному"],"topics":[]},{"id":"chunk-25","heading":"Проверяем пустую последовательность","text":"assert encode rle(bytes([])) == bytes([]) assert decode rle(bytes([])) == bytes([])","keywords":["bytes","assert","python","rle","coverage","decode","encode","property-based","pytest","автоматизация","мутационному","последовательность"],"topics":[]},{"id":"chunk-26","heading":"Проверяем последовательность с одним повторяющимся элементом","text":"assert encode rle(bytes([1, 1, 1, 1, 1])) == bytes([1, 5]) assert decode rle(bytes([1, 5])) == bytes([1, 1, 1, 1, 1])","keywords":["bytes","assert","python","rle","coverage","decode","encode","property-based","pytest","автоматизация","мутационному","одним"],"topics":[]},{"id":"chunk-27","heading":"Проверяем последовательность с различными элементами","text":"assert encode rle(bytes([4, 4, 1, 1, 2, 2, 2, 5, 6, 6, 6, 3, 3])) == bytes([4, 2, 1, 2, 2, 3, 5, 6, 3, 3]) assert decode rle(bytes([4, 2, 1, 2, 2, 3, 5, 6, 3, 3])) == bytes([4, 4, 1, 1, 2, 2, 2, 5, 6, 6, 6, 3, 3]) ````","keywords":["bytes","assert","python","rle","coverage","decode","encode","property-based","pytest","автоматизация","мутационному","последовательность"],"topics":[]},{"id":"chunk-28","heading":"1.3. (уровень сложности: средний)","text":"Реализуйте конструкцию raises с помощью менеджера контекста в духе таковой из pytest.","keywords":["python","1.3.","coverage","property-based","pytest","pytest.","raises","автоматизация","духе","конструкцию","контекста","менеджера"],"topics":["python","ai","testing"]},{"id":"chunk-29","heading":"1.3. (уровень сложности: средний)","text":"def exit (self, exc type, exc value, traceback): if exc type is None: raise AssertionError(f\"{self.exception. name } was not raised\") if issubclass(exc type, self.exception): return True","keywords":["exc","type","if","python","1.3.","assertionerror","coverage","def","exit","issubclass","name","none"],"topics":["ai"]},{"id":"chunk-30","heading":"1.3. (уровень сложности: средний)","text":"raise AssertionError(f\"Expected {self.exception. name }, but got {exc type. name }\")","keywords":["name","python","1.3.","assertionerror","but","coverage","exc","expected","got","property-based","pytest","raise"],"topics":["ai"]},{"id":"chunk-31","heading":"1.3. (уровень сложности: средний)","text":"````python with raises(MealyError) as e: raise MealyError(\"This error was explicitly raised\")","keywords":["python","mealyerror","1.3.","coverage","error","explicitly","property-based","pytest","raise","raised","raises","this"],"topics":["python","ai"]},{"id":"chunk-32","heading":"2.1. (уровень сложности: средний)","text":"Научитесь работать с модулем pytest. Выберите одну из программ, содержащих ошибки. Создайте отдельный файл для тестирования, в который поместите тестирующие функции (не менее двух). Упростите код с помощью добавления fixture функций. Добавьте параметризацию.","keywords":["python","2.1.","coverage","fixture","property-based","pytest","pytest.","автоматизация","выберите","двух","добавления","добавьте"],"topics":["python","testing"]},{"id":"chunk-33","heading":"Тестирование программы с помощью pytest (test program.py)","text":"def test divide numbers(numbers): a, b = numbers assert divide numbers(a, b) == 5","keywords":["numbers","divide","pytest","python","test","assert","coverage","def","program.py","property-based","автоматизация","мутационному"],"topics":["python","testing"]},{"id":"chunk-34","heading":"Тестирование программы с помощью pytest (test program.py)","text":"@pytest.mark.parametrize(\"test input, expected\", [(\"hello\", \"HELLO\"), (\"world\", \"WORLD\")]) def test uppercase string(test input, expected): assert uppercase string(test input) == expected ````","keywords":["test","expected","input","hello","pytest","python","string","uppercase","world","assert","coverage","def"],"topics":["python","numpy","testing"]},{"id":"chunk-35","heading":"2.2. (уровень сложности: средний)","text":"Выберите одну из программ, содержащих ошибки. Добавьте туда ввод со стороны пользователя. Добавьте макетный код для тестирования, с учетом такого ввода.","keywords":["python","добавьте","2.2.","coverage","property-based","pytest","автоматизация","ввод","ввода.","выберите","код","макетный"],"topics":[]},{"id":"chunk-36","heading":"Программа с ошибкой и вводом от пользователя (program with error.py)","text":"def divide numbers(): try: a = float(input(\"Enter the first number: \")) b = float(input(\"Enter the second number: \")) result = a / b return result except ZeroDivisionError: return \"Error: Division by zero\" except ValueError: return \"Error: Invalid input\"","keywords":["input","return","enter","error","except","float","number","python","result","coverage","def","divide"],"topics":["numpy"]},{"id":"chunk-37","heading":"Программа с ошибкой и вводом от пользователя (program with error.py)","text":"Теперь давайте создадим файл для тестирования с использованием pytest, учитывая ввод от пользователя:","keywords":["pytest","python","от","пользователя","coverage","error.py","program","property-based","автоматизация","ввод","вводом","давайте"],"topics":["python","testing"]},{"id":"chunk-38","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"from program with error import divide numbers, uppercase string from io import StringIO","keywords":["import","program","python","coverage","divide","error","error.py","io","numbers","property-based","pytest","string"],"topics":["testing"]},{"id":"chunk-39","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"@pytest.fixture def numbers(monkeypatch): monkeypatch.setattr('sys.stdin', StringIO(\"10\\n2\\n\")) return 10, 2","keywords":["10","python","coverage","def","error.py","monkeypatch","monkeypatch.setattr","n2","numbers","program","property-based","pytest"],"topics":["python","testing"]},{"id":"chunk-40","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"@pytest.mark.parametrize(\"test input, expected\", [(\"hello\\n\", \"HELLO\"), (\"world\\n\", \"WORLD\")]) def test uppercase string(test input, expected, monkeypatch): monkeypatch.setattr('sys.stdin', StringIO(test input)) assert uppercase string() == expected ````","keywords":["test","expected","input","hello","python","string","uppercase","world","assert","coverage","def","error.py"],"topics":["python","numpy","testing"]},{"id":"chunk-41","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"В этом файле мы также использовали @pytest.fixture для создания fixture функции numbers, которая изменяет стандартный поток ввода sys.stdin на заданные значения. Это позволяет нам имитировать ввод от пользователя в тестах.","keywords":["python","от","пользователя","coverage","error.py","fixture","numbers","program","property-based","pytest","pytest.fixture","sys.stdin"],"topics":["python","testing"]},{"id":"chunk-42","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"Тестирующие функции test divide numbers и test uppercase string теперь также учитывают ввод от пользователя, благодаря использованию pytest.mark.parametrize и изменению потока ввода для каждого теста.","keywords":["test","python","от","пользователя","coverage","divide","error.py","numbers","program","property-based","pytest","pytest.mark.parametrize"],"topics":["python","testing"]},{"id":"chunk-43","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"Для запуска тестов также используйте команду pytest в терминале, указав файл для тестирования:","keywords":["pytest","python","coverage","error.py","program","property-based","test","автоматизация","вводом","запуска","используйте","команду"],"topics":["python","testing"]},{"id":"chunk-44","heading":"Тестирование программы с ошибкой и вводом от пользователя (test program with error.py)","text":"Теперь программа с ошибкой и вводом от пользователя готова для тестирования с помощью pytest.","keywords":["python","вводом","от","ошибкой","пользователя","coverage","error.py","program","property-based","pytest","pytest.","test"],"topics":["python","testing"]},{"id":"chunk-45","heading":"2.2. (уровень сложности: средний)","text":"Научитесь работать с модулем coverage. Выберите одну из программ, содержащих ошибки. Получите статистику по покрытию операторов. Получите статистику по покрытию ветвей. Найдите случай, когда покрытие ветвей отличается от покрытия операторов.","keywords":["python","операторов.","покрытию","получите","статистику","2.2.","coverage","coverage.","property-based","pytest","автоматизация","ветвей"],"topics":[]},{"id":"chunk-46","heading":"2.2. (уровень сложности: средний)","text":"Постарайтесь изменить код исходной программы так, чтобы затруднить получение 100% покрытия. Найдите простой пример ошибки в выбранной программе, при полученном 100% покрытии.","keywords":["100","python","2.2.","coverage","property-based","pytest","автоматизация","выбранной","затруднить","изменить","исходной","код"],"topics":[]},{"id":"chunk-47","heading":"2.2. (уровень сложности: средний)","text":"Реализуйте вывод статистики о покрытии в HTML представлении с демонстрацией покрытия по строкам программы.","keywords":["python","2.2.","coverage","html","property-based","pytest","автоматизация","вывод","демонстрацией","мутационному","покрытии","покрытия"],"topics":["ai"]},{"id":"chunk-48","heading":"2.2. (уровень сложности: средний)","text":"Для примера, давайте выберем программу с ошибкой \"programwitherror.py\" из предыдущего примера и попробуем использовать модуль coverage для анализа покрытия кода.","keywords":["coverage","python","примера","2.2.","programwitherror.py","property-based","pytest","автоматизация","анализа","выберем","давайте","использовать"],"topics":[]},{"id":"chunk-49","heading":"2.2. (уровень сложности: средний)","text":"Сначала установим модуль coverage, если он еще не установлен, с помощью команды: pip install coverage","keywords":["coverage","python","2.2.","install","pip","property-based","pytest","автоматизация","если","еще","команды","модуль"],"topics":[]},{"id":"chunk-50","heading":"2.2. (уровень сложности: средний)","text":"Теперь создадим скрипт, который запускает тесты с использованием coverage и собирает статистику покрытия: coverage run m pytest test program with error.py","keywords":["coverage","pytest","python","2.2.","error.py","program","property-based","run","test","автоматизация","запускает","использованием"],"topics":["python","testing"]},{"id":"chunk-51","heading":"2.2. (уровень сложности: средний)","text":"После того как тесты успешно выполнены, мы можем получить статистику по покрытию операторов: coverage report m","keywords":["coverage","python","2.2.","property-based","pytest","report","автоматизация","выполнены","можем","мутационному","мы","операторов"],"topics":[]},{"id":"chunk-52","heading":"2.2. (уровень сложности: средний)","text":"Это покажет покрытие по каждому файлу, каждой функции и оператору внутри функции.","keywords":["python","2.2.","coverage","property-based","pytest","автоматизация","внутри","каждой","каждому","мутационному","оператору","покажет"],"topics":[]},{"id":"chunk-53","heading":"2.2. (уровень сложности: средний)","text":"Далее, для анализа покрытия ветвей, воспользуемся командой: coverage report m include=program with error.py","keywords":["coverage","python","2.2.","error.py","include","program","property-based","pytest","report","автоматизация","анализа","ветвей"],"topics":[]},{"id":"chunk-54","heading":"2.2. (уровень сложности: средний)","text":"Теперь давайте внесем изменения в программу, чтобы создать ситуацию, когда покрытие ветвей отличается от покрытия операторов:","keywords":["python","2.2.","coverage","property-based","pytest","автоматизация","ветвей","внесем","давайте","изменения","когда","мутационному"],"topics":[]},{"id":"chunk-55","heading":"Изменим программу, чтобы создать ошибку при 100% покрытии","text":"def divide numbers(a, b): if b == 0: return \"Error: Division by zero\" result = a / b return result","keywords":["python","result","return","100","coverage","def","divide","division","error","if","numbers","property-based"],"topics":[]},{"id":"chunk-56","heading":"Изменим программу, чтобы создать ошибку при 100% покрытии","text":"def uppercase string(s): if not s: return \"Error: Empty input\" return s.upper() ````","keywords":["python","return","100","coverage","def","empty","error","if","input","not","property-based","pytest"],"topics":["numpy"]},{"id":"chunk-57","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"def test divide numbers(): assert divide numbers(10, 0) == \"Error: Division by zero\" assert divide numbers(10, 2) == 5","keywords":["divide","numbers","10","assert","error","python","test","coverage","def","division","program","property-based"],"topics":["testing"]},{"id":"chunk-58","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"def test uppercase string(): assert uppercase string(\"\") == \"Error: Empty input\" assert uppercase string(\"hello\") == \"HELLO\" ````","keywords":["string","uppercase","assert","error","hello","python","test","coverage","def","empty","input","program"],"topics":["numpy","testing"]},{"id":"chunk-59","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"И, наконец, выведем статистику покрытия в HTML представлении с помощью команды: coverage html","keywords":["coverage","html","python","error","program","property-based","pytest","test","v2.py","автоматизация","версии","выведем"],"topics":["ai","testing"]},{"id":"chunk-60","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"После выполнения этой команды, вы найдете папку \"htmlcov\" в текущей директории, в которой содержится детальная информация о покрытии операторов и ветвей, включая подсвеченный исходный код с указанием покрытых и непокрытых строк.","keywords":["python","coverage","error","htmlcov","program","property-based","pytest","test","v2.py","автоматизация","версии","ветвей"],"topics":["ai","testing"]},{"id":"chunk-61","heading":"Изменения в тестах для новой версии программы с ошибкой (test program with error v2.py)","text":"Теперь вы можете исследовать различия между покрытием операторов и ветвей, а также обнаружить пример ошибки, проявляющейся при 100% покрытии.","keywords":["python","100","coverage","error","program","property-based","pytest","test","v2.py","автоматизация","версии","ветвей"],"topics":["testing"]},{"id":"chunk-62","heading":"3. Мутационное тестирование","text":"Прототип системы мутационного тестирования приведен ниже. Попробуйте разобраться в том, как работает этот код. Вам поможет документация к модулю ast.","keywords":["python","3.","ast.","coverage","property-based","pytest","автоматизация","вам","документация","код.","модулю","мутационного"],"topics":["python","testing"]},{"id":"chunk-63","heading":"3. Мутационное тестирование","text":"Функция mut test принимает на вход тестируемую функцию и функцию, осуществляющую тестирование с помощью assert.","keywords":["python","тестирование","функцию","3.","assert.","coverage","mut","property-based","pytest","test","автоматизация","вход"],"topics":["testing"]},{"id":"chunk-64","heading":"TODO","text":"def mutate code(src): tree = ast.parse(src) Mutator().visit(tree) return ast.unparse(tree)","keywords":["tree","python","src",".visit","ast.parse","ast.unparse","code","coverage","def","mutate","mutator","property-based"],"topics":["python","numpy"]},{"id":"chunk-65","heading":"TODO","text":"def make mutants(func, size): mutant = src = ast.unparse(ast.parse(inspect.getsource(func))) mutants = [src] while len(mutants) < size + 1: while mutant in mutants: mutant = mutate code(src) mutants.append(mutant) return mutants[1:]","keywords":["mutants","mutant","src","func","python","size","while","ast.parse","ast.unparse","code","coverage","def"],"topics":["python","numpy"]},{"id":"chunk-66","heading":"TODO","text":"def mut test(func, test, size=20): survived = [] mutants = make mutants(func, size) for mutant in mutants: try: exec(mutant, globals()) test() survived.append(mutant) except: pass return survived ````","keywords":["mutant","mutants","test","func","python","size","survived","20","coverage","def","except","exec"],"topics":["testing"]},{"id":"chunk-67","heading":"3.1. (уровень сложности: средний)","text":"Выберите одну из программ, содержащих ошибки. Доработайте код мутационного тестирования так, чтобы генерировались программы мутанты со случайными константами. Покажите, что при 100% покрытии тестами мутационное тестирование в состоянии находить ошибки.","keywords":["python","ошибки.","100","3.1.","coverage","property-based","pytest","автоматизация","выберите","генерировались","доработайте","код"],"topics":["testing"]},{"id":"chunk-68","heading":"3.1. (уровень сложности: средний)","text":"class Mutator(ast.NodeTransformer): def visit Constant(self, node): if isinstance(node.value, int) or isinstance(node.value, float): if random.random() < 0.5: 50% вероятность мутации константы mutated value = random.randint( 100, 100) случайное целое число от 100 до 100 return ast.copy location(ast.Constant(value=mutated value), node) return node","keywords":["100","node","value","if","isinstance","mutated","node.value","python","return","0.5","3.1.","50"],"topics":["python"]},{"id":"chunk-69","heading":"3.1. (уровень сложности: средний)","text":"def test mutant divide by two(): assert divide by two(42) == 21 Неверное ожидаемое значение для успешного тестирования","keywords":["divide","python","two","21","3.1.","42","assert","coverage","def","mutant","property-based","pytest"],"topics":["testing"]},{"id":"chunk-70","heading":"3.1. (уровень сложности: средний)","text":"def main(): survivors = mut test(divide by two, test divide by two) print(\"Survived mutants:\", len(survivors))","keywords":["divide","python","survivors","test","two","3.1.","coverage","def","len","main","mut","mutants"],"topics":["ai","testing"]},{"id":"chunk-71","heading":"3.2. (уровень сложности: средний)","text":"Добавьте к системе мутационного тестирования генерацию случайных бинарных операций. Проверьте результат на сортировке. Постарайтесь генерировать программы мутанты, которые «выживают» после большинства assert'ов.","keywords":["python","3.2.","assert","coverage","property-based","pytest","автоматизация","бинарных","большинства","выживают","генерацию","генерировать"],"topics":[]},{"id":"chunk-72","heading":"3.2. (уровень сложности: средний)","text":"class Mutator(ast.NodeTransformer): def visit BinOp(self, node): if random.random() < 0.5: 50% вероятность мутации бинарной операции operators = [ast.Add(), ast.Sub(), ast.Mult(), ast.Div()] возможные операторы mutated op = random.choice(operators) return ast.copy location(ast.BinOp(left=node.left, op=mutated op, right=node.right), node) return node","keywords":["node","op","mutated","operators","python","return","0.5","3.2.","50","ast.add","ast.binop","ast.copy"],"topics":["python"]},{"id":"chunk-73","heading":"3.2. (уровень сложности: средний)","text":"def bubble sort(arr): n = len(arr) for i in range(n): for j in range(0, n i 1): if arr[j] arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] return arr","keywords":["arr","j+1","python","range","3.2.","bubble","coverage","def","if","len","property-based","pytest"],"topics":[]},{"id":"chunk-74","heading":"3.2. (уровень сложности: средний)","text":"def test mutant bubble sort(): assert bubble sort([42, 3, 15]) == [3, 15, 42] Неверное ожидаемое значение для успешного тестирования","keywords":["15","42","bubble","python","sort","3.2.","assert","coverage","def","mutant","property-based","pytest"],"topics":["testing"]},{"id":"chunk-75","heading":"3.2. (уровень сложности: средний)","text":"def main(): survivors = mut test(bubble sort, test bubble sort) print(\"Survived mutants:\", len(survivors))","keywords":["bubble","python","sort","survivors","test","3.2.","coverage","def","len","main","mut","mutants"],"topics":["ai","testing"]},{"id":"chunk-76","heading":"4.1. (уровень сложности: средний)","text":"Изучите работу с модулем deal. Для тестирования контрактов используйте pytest. Выберите одну из программ, содержащих ошибки. Добавьте к программе контракты pre, post, ensure, raises, reason, has.","keywords":["python","4.1.","coverage","deal.","ensure","has.","post","pre","property-based","pytest","pytest.","raises"],"topics":["python","ai","testing"]},{"id":"chunk-77","heading":"pip install deal pytest","text":"def test divide(): assert divide(10, 2) == 5 assert divide(5, 0) == 0 Ошибка: деление на ноль","keywords":["divide","assert","pytest","python","10","coverage","deal","def","install","pip","property-based","test"],"topics":["python","testing"]},{"id":"chunk-78","heading":"","text":"@deal.pre(lambda a, b: b != 0, \"Делитель не должен быть равен нулю\") @deal.post(lambda , , result: result != float('inf'), \"Результат не должен быть бесконечностью\") @deal.ensure(lambda , , result, old result: old result b == a, \"Корректность деления\") @deal.raises(ZeroDivisionError) def divide(a, b): return a / b","keywords":["result","lambda","old","python","быть","должен","coverage","deal.ensure","deal.post","deal.pre","deal.raises","def"],"topics":["ai"]},{"id":"chunk-79","heading":"4.2. (уровень сложности: средний)","text":"Перепишите класс банковского счета (6) с использованием контрактного программирования и, в частности, инвариантов класса. Продемонстрируйте, что реализованные инварианты класса действительно позволяют выявлять ошибки.","keywords":["python","4.2.","coverage","property-based","pytest","автоматизация","банковского","выявлять","действительно","инвариантов","инварианты","использованием"],"topics":["oop"]},{"id":"chunk-80","heading":"pip install deal","text":"@deal.inv(lambda self: self.balance = 0, \"Баланс счета не может быть отрицательным\") class BankAccount: def init (self, account number, balance=0): self.account number = account number self.balance = balance","keywords":["number","account","balance","python","self","self.balance","bankaccount","class","coverage","deal","deal.inv","def"],"topics":[]},{"id":"chunk-81","heading":"pip install deal","text":"@deal.post(lambda , result: result 0, \"Сумма депозита должна быть положительной\") def deposit(self, amount): self.balance += amount return f\"{amount} средств успешно зачислены на счет {self.account number}\"","keywords":["amount","python","result","coverage","deal","deal.post","def","deposit","install","lambda","number","pip"],"topics":[]},{"id":"chunk-82","heading":"pip install deal","text":"@deal.post(lambda , result: result 0, \"Сумма списания должна быть положительной\") def withdraw(self, amount): self.balance = amount return f\"{amount} средств успешно сняты с счета {self.account number}\"","keywords":["amount","python","result","coverage","deal","deal.post","def","install","lambda","number","pip","property-based"],"topics":[]},{"id":"chunk-83","heading":"pip install deal","text":"def check balance(self): return f\"Баланс счета {self.account number}: {self.balance}\"","keywords":["python","balance","check","coverage","deal","def","install","number","pip","property-based","pytest","return"],"topics":[]},{"id":"chunk-84","heading":"pip install deal","text":"account = BankAccount(12345, 100) print(account.withdraw(150)) Пытаемся снять больше, чем на счету","keywords":["python","100","12345","150","account","account.withdraw","bankaccount","coverage","deal","install","pip","print"],"topics":[]},{"id":"chunk-85","heading":"4.3. (уровень сложности: высокий)","text":"Реализуйте контракт, выполнение которого deal проверяет статически. Какие ограничения имеют статически проверяемые контракты?","keywords":["python","4.3.","coverage","deal","property-based","pytest","автоматизация","выполнение","высокий","имеют","какие","контракт"],"topics":[]},{"id":"chunk-86","heading":"4.3. (уровень сложности: высокий)","text":"@deal.pure def check positive number(number: int) int: if number <= 0: raise ValueError(\"Число должно быть положительным\") return number","keywords":["number","int","python","4.3.","check","coverage","deal.pure","def","if","positive","property-based","pytest"],"topics":["ai"]},{"id":"chunk-87","heading":"4.3. (уровень сложности: высокий)","text":"number = 10 checked number = check positive number(number) print(checked number) ````","keywords":["number","checked","python","10","4.3.","check","coverage","positive","print","property-based","pytest","автоматизация"],"topics":[]},{"id":"chunk-88","heading":"4.3. (уровень сложности: высокий)","text":"В этом примере мы объявляем функцию check positive number, которая принимает число типа int и возвращает также int. Мы добавляем контракт, который проверяет, что число положительное. При вызове функции check positive number с положительным числом у нас не будет ошибок.","keywords":["check","number","positive","python","мы","число","4.3.","coverage","int","int.","property-based","pytest"],"topics":[]},{"id":"chunk-89","heading":"4.3. (уровень сложности: высокий)","text":"1. Ограниченность возможностей проверки: Статически проверяемые контракты могут осуществлять только простые проверки типов и условий. Они не могут осуществлять сложные или динамические проверки.","keywords":["python","могут","осуществлять","проверки","1.","4.3.","coverage","property-based","pytest","автоматизация","возможностей","высокий"],"topics":[]},{"id":"chunk-90","heading":"4.3. (уровень сложности: высокий)","text":"2. Ограниченность вида ошибок: Статически проверяемые контракты могут выявлять только те ошибки, которые можно выразить статически при компиляции или анализе кода. Они могут не покрывать все возможные сценарии ошибок.","keywords":["python","могут","статически","2.","4.3.","coverage","property-based","pytest","автоматизация","анализе","вида","возможные"],"topics":[]},{"id":"chunk-91","heading":"4.3. (уровень сложности: высокий)","text":"3. Недостаток гибкости: Статически проверяемые контракты обычно требуют строго соблюдения синтаксиса и структуры кода, что может ограничить гибкость и эффективность программирования.","keywords":["python","3.","4.3.","coverage","property-based","pytest","автоматизация","высокий","гибкости","гибкость","кода","контракты"],"topics":[]},{"id":"chunk-92","heading":"4.3. (уровень сложности: высокий)","text":"Несмотря на ограничения, статически проверяемые контракты могут быть полезными для раннего выявления ошибок и повышения надежности программного обеспечения. Они помогают предотвращать определенные категории ошибок и упрощают процесс debugging'а.","keywords":["python","ошибок","4.3.","coverage","debugging","property-based","pytest","а.","автоматизация","быть","высокий","выявления"],"topics":[]},{"id":"chunk-93","heading":"Задаем стратегию генерации данных для тестирования","text":"@given(st.floats(), st.floats(), st.floats(), st.floats()) def test distance(x1, y1, x2, y2): result = distance(x1, y1, x2, y2) assert isinstance(result, float) assert result = 0","keywords":["st.floats","result","assert","distance","python","x1","x2","y1","y2","coverage","def","float"],"topics":["testing"]},{"id":"chunk-94","heading":"Тестирование функции encode rle","text":"def test encode rle(): assert encode rle(b'AAAABBBCCDAA') == b'\\x04A\\x03B\\x02C\\x01D\\x02A'","keywords":["encode","rle","python","aaaabbbccdaa","assert","coverage","def","property-based","pytest","test","x01d","x02a"],"topics":["testing"]},{"id":"chunk-95","heading":"Тестирование функции decode rle","text":"def test decode rle(): assert decode rle(b'\\x04A\\x03B\\x02C\\x01D\\x02A') == b'AAAABBBCCDAA'","keywords":["decode","rle","python","aaaabbbccdaa","assert","coverage","def","property-based","pytest","test","x01d","x02a"],"topics":["testing"]},{"id":"chunk-96","heading":"5.3. (уровень сложности: высокий)","text":"Реализуйте тестирование для деревьев выражений из предыдущей практики, для одного из «посетителей».","keywords":["python","5.3.","coverage","property-based","pytest","автоматизация","выражений","высокий","деревьев","мутационному","одного","посетителей"],"topics":["testing"]},{"id":"chunk-97","heading":"5.4. (уровень сложности: высокий)","text":"Используйте тестирование по модели для проверки реализации банковского счета (7).","keywords":["python","5.4.","coverage","property-based","pytest","автоматизация","банковского","высокий","используйте","модели","мутационному","практика"],"topics":["testing"]},{"id":"chunk-98","heading":"файл test bank account.py с тестами","text":"@pytest.fixture def bank account(): account = BankAccount(12345) account.create account(1000) return account","keywords":["account","bank","python","1000","12345","account.create","account.py","bankaccount","coverage","def","property-based","pytest"],"topics":["python","testing"]},{"id":"chunk-99","heading":"файл test bank account.py с тестами","text":"def test deposit(bank account): assert bank account.deposit(500) == \"500 средств успешно зачислены на счет 12345\"","keywords":["bank","500","python","test","12345","account","account.deposit","account.py","assert","coverage","def","deposit"],"topics":["testing"]},{"id":"chunk-100","heading":"файл test bank account.py с тестами","text":"def test withdraw(bank account): assert bank account.withdraw(200) == \"200 средств успешно сняты с счета 12345\" assert bank account.withdraw(900) == \"Недостаточно средств на счете\"","keywords":["bank","200","account.withdraw","assert","python","test","средств","12345","900","account","account.py","coverage"],"topics":["testing"]},{"id":"chunk-101","heading":"файл test bank account.py с тестами","text":"def test check balance(bank account): assert bank account.check balance() == \"Баланс счета 12345: 800\"","keywords":["bank","balance","python","test","12345","800","account","account.check","account.py","assert","check","coverage"],"topics":["testing"]},{"id":"chunk-102","heading":"файл test bank account.py с тестами","text":"def test close account(bank account): assert bank account.close account() == \"Счет 12345 закрыт\"","keywords":["account","bank","python","test","12345","account.close","account.py","assert","close","coverage","def","property-based"],"topics":["testing"]}]},{"id":"bigdata/practice-05-classification","href":"/docs/bigdata/practice-05-classification","slug":["bigdata","practice-05-classification"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 5. Классификация","description":"Построение и оценка моделей классификации.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитривеич ИКБО 25 22 Практическая работа №5 Классификация 1. Найти данные для классификации. Данные в группе повторяться не должны. Предобработа","keywords":["test","train","print","import","accuracy","name","класс","knn","svm","preds","0.71","encoded","iter","logistic","regression","split","встречается","выводы"],"topics":["python","ai","knn","oop","testing","algorithms","regression"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","5.","wiki","версия","документацию.","исходный","классификации.","классификация","материал","моделей","ноутбук","оценка"],"topics":[]},{"id":"chunk-1","heading":"Классификация","text":"1. Найти данные для классификации. Данные в группе повторяться не должны. Предобработать данные, если это необходимо.","keywords":["данные","bigdata","классификации.","классификация","1.","5.","группе","должны.","если","моделей","найти","необходимо."],"topics":["oop"]},{"id":"chunk-2","heading":"Строим гистограмму","text":"plt.figure(figsize=(8, 5)) counts.plot(kind='bar') plt.title(f'Баланс классов по признаку \"{class column}\"') plt.xlabel('Класс') plt.ylabel('Количество объектов') plt.grid(axis='y', linestyle=' ', alpha=0.5) plt.tight layout() plt.show()","keywords":["bigdata","0.5","5.","alpha","axis","bar","class","column","counts.plot","figsize","kind","layout"],"topics":["oop"]},{"id":"chunk-3","heading":"Выводы","text":"print(\"=== Выводы ===\") print(counts) print(\"\\nСамый редкий класс:\", counts.idxmin()) print(\"Самый частый класс:\", counts.idxmax()) ```","keywords":["print","bigdata","выводы","класс","5.","counts","counts.idxmax","counts.idxmin","nсамый","классификации.","классификация","моделей"],"topics":["oop"]},{"id":"chunk-4","heading":"Выводы","text":"=== Выводы === province Punjab 36 KP 35 Balochistan 34 Sindh 30 Capital territory 1 Name: count, dtype: int64","keywords":["bigdata","выводы","30","34","35","36","5.","balochistan","capital","count","dtype","int64"],"topics":[]},{"id":"chunk-5","heading":"Выводы","text":"3. Разбить выборку на тренировочную и тестовую. Тренировочная для обучения модели, тестовая для проверки ее качества.","keywords":["bigdata","3.","5.","выборку","выводы","ее","качества.","классификации.","классификация","моделей","модели","обучения"],"topics":[]},{"id":"chunk-6","heading":"Выводы","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import LabelEncoder","keywords":["bigdata","import","5.","labelencoder","python","selection","sklearn.model","sklearn.preprocessing","split","test","train","выводы"],"topics":["python","ai","testing"]},{"id":"chunk-7","heading":"удаляем класс, который встречается только один раз","text":"y = df filtered['province'] X = df filtered.select dtypes(include=['int64', 'float64'])","keywords":["bigdata","df","5.","dtypes","filtered","filtered.select","float64","include","int64","province","встречается","класс"],"topics":["oop"]},{"id":"chunk-8","heading":"удаляем класс, который встречается только один раз","text":"X train, X test, y train, y test = train test split( X, y encoded, test size=0.2, random state=42, stratify=y encoded )","keywords":["test","train","bigdata","encoded","0.2","42","5.","random","size","split","state","stratify"],"topics":["ai","oop","testing"]},{"id":"chunk-9","heading":"удаляем класс, который встречается только один раз","text":"print(\"Train:\", X train.shape) print(\"Test:\", X test.shape) print(\"Классы:\", le.classes ) ```","keywords":["print","bigdata","5.","le.classes","test","test.shape","train","train.shape","встречается","класс","классификации.","классификация"],"topics":["ai","oop","testing"]},{"id":"chunk-10","heading":"удаляем класс, который встречается только один раз","text":"text Train: (108, 19) Test: (27, 19) Классы: ['Balochistan' 'KP' 'Punjab' 'Sindh']","keywords":["19","bigdata","108","27","5.","balochistan","kp","punjab","sindh","test","text","train"],"topics":["ai","oop","testing"]},{"id":"chunk-11","heading":"удаляем класс, который встречается только один раз","text":"4. Применить алгоритмы классификации: логистическая регрессия, SVM, KNN. Построить матрицу ошибок по результатам работы моделей (использовать confusion matrix из sklearn.metrics).","keywords":["bigdata","моделей","4.","5.","confusion","knn.","matrix","sklearn.metrics","svm","алгоритмы","встречается","использовать"],"topics":["knn","oop","algorithms","regression"]},{"id":"chunk-12","heading":"удаляем класс, который встречается только один раз","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import LabelEncoder from sklearn.metrics import confusion matrix from sklearn.linear model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier","keywords":["import","bigdata","5.","confusion","kneighborsclassifier","labelencoder","logisticregression","matrix","model","python","selection","sklearn.linear"],"topics":["python","ai","oop","testing","regression"]},{"id":"chunk-13","heading":"","text":"X train, X test, y train, y test = train test split( X, y encoded, test size=0.2, random state=42, stratify=y encoded )","keywords":["test","train","bigdata","encoded","0.2","42","5.","random","size","split","state","stratify"],"topics":["ai","testing"]},{"id":"chunk-14","heading":"Логистическая регрессия","text":"log reg = LogisticRegression(max iter=500) log reg.fit(X train, y train) pred log = log reg.predict(X test)","keywords":["log","bigdata","train","5.","500","iter","logisticregression","max","pred","reg","reg.fit","reg.predict"],"topics":["ai","testing","regression"]},{"id":"chunk-15","heading":"KNN","text":"knn = KNeighborsClassifier(n neighbors=5) knn.fit(X train, y train) pred knn = knn.predict(X test)","keywords":["knn","bigdata","train","5.","kneighborsclassifier","knn.fit","knn.predict","neighbors","pred","test","классификации.","классификация"],"topics":["ai","knn","testing"]},{"id":"chunk-16","heading":"","text":"for name, preds in models.items(): cm = confusion matrix(y test, preds) plt.figure(figsize=(6, 4)) sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=le.classes , yticklabels=le.classes ) plt.title(f\"Confusion Matrix — {name}\") plt.xlabel(\"Predicted\") plt.ylabel(\"Actual\") plt.show()","keywords":["bigdata","cm","confusion","le.classes","matrix","name","preds","5.","actual","annot","blues","cmap"],"topics":["testing"]},{"id":"chunk-17","heading":"","text":"print(\"=== Accuracy ===\") print(\"Logistic Regression:\", accuracy score(y test, pred log)) print(\"SVM:\", accuracy score(y test, pred svm)) print(\"KNN:\", accuracy score(y test, pred knn)) ```","keywords":["accuracy","print","pred","score","test","bigdata","knn","svm","5.","log","logistic","regression"],"topics":["knn","testing","regression"]},{"id":"chunk-18","heading":"","text":"```text /usr/local/lib/python3.12/dist packages/sklearn/linear model/ logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.","keywords":["bigdata","465","5.","converge","convergencewarning","dist","failed","iterations","lbfgs","lib","limit.","linear"],"topics":["python","ai"]},{"id":"chunk-19","heading":"","text":"Increase the number of iterations (max iter) or scale the data as shown in: https://scikit learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit learn.org/stable/modules/linear model.html logistic regression n iter i = check optimize result(","keywords":["bigdata","https","iter","learn.org","modules","scikit","stable","5.","also","alternative","check","data"],"topics":["ai","regression"]},{"id":"chunk-20","heading":"","text":"=== Accuracy === Logistic Regression: 0.7407407407407407 SVM: 0.5555555555555556 KNN: 0.5555555555555556 ```","keywords":["0.5555555555555556","bigdata","0.7407407407407407","5.","accuracy","knn","logistic","regression","svm","классификации.","классификация","моделей"],"topics":["knn","regression"]},{"id":"chunk-21","heading":"","text":"5. Сравнить результаты классификации, используя accuracy, precision, recall и f1 меру (можно использовать classification report из sklearn.metrics). Сделать выводы.","keywords":["5.","bigdata","accuracy","classification","f1","precision","recall","report","sklearn.metrics","выводы.","использовать","используя"],"topics":["oop"]},{"id":"chunk-22","heading":"","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import LabelEncoder from sklearn.metrics import classification report, accuracy score from sklearn.linear model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier","keywords":["import","bigdata","5.","accuracy","classification","kneighborsclassifier","labelencoder","logisticregression","model","python","report","score"],"topics":["python","ai","testing","regression"]},{"id":"chunk-23","heading":"","text":"X train, X test, y train, y test = train test split( X, y encoded, test size=0.2, random state=42, stratify=y encoded )","keywords":["test","train","bigdata","encoded","0.2","42","5.","random","size","split","state","stratify"],"topics":["ai","testing"]},{"id":"chunk-24","heading":"","text":"models = { \"Logistic Regression\": LogisticRegression(max iter=500), \"SVM\": SVC(), \"KNN\": KNeighborsClassifier(n neighbors=5) }","keywords":["bigdata","5.","500","iter","kneighborsclassifier","knn","logistic","logisticregression","max","models","neighbors","regression"],"topics":["knn","regression"]},{"id":"chunk-25","heading":"","text":"for name, model in models.items(): model.fit(X train, y train) preds = model.predict(X test) predictions[name] = preds reports[name] = classification report(y test, preds, target names=le.classes ) print(f\"\\n===== {name} =====\") print(reports[name])","keywords":["name","preds","bigdata","print","reports","test","train","5.","classification","le.classes","model","model.fit"],"topics":["ai","testing"]},{"id":"chunk-26","heading":"","text":"print(\"\\n=== Accuracy сравнение ===\") for name, preds in predictions.items(): print(f\"{name}: {accuracy score(y test, preds)}\") ```","keywords":["accuracy","bigdata","name","preds","print","5.","predictions.items","score","test","классификации.","классификация","моделей"],"topics":["testing"]},{"id":"chunk-27","heading":"","text":"Balochistan 0.75 0.86 0.80 7 KP 0.57 0.57 0.57 7 Punjab 0.71 0.71 0.71 7 Sindh 1.00 0.83 0.91 6","keywords":["0.57","0.71","bigdata","0.75","0.80","0.83","0.86","0.91","1.00","5.","balochistan","kp"],"topics":[]},{"id":"chunk-28","heading":"","text":"Balochistan 0.60 0.86 0.71 7 KP 0.40 0.29 0.33 7 Punjab 0.62 0.71 0.67 7 Sindh 0.50 0.33 0.40 6","keywords":["0.33","0.40","0.71","bigdata","0.29","0.50","0.60","0.62","0.67","0.86","5.","balochistan"],"topics":[]},{"id":"chunk-29","heading":"","text":"Balochistan 0.75 0.86 0.80 7 KP 0.33 0.43 0.38 7 Punjab 0.62 0.71 0.67 7 Sindh 0.50 0.17 0.25 6","keywords":["bigdata","0.17","0.25","0.33","0.38","0.43","0.50","0.62","0.67","0.71","0.75","0.80"],"topics":[]},{"id":"chunk-30","heading":"","text":"=== Accuracy сравнение === Logistic Regression: 0.7407407407407407 SVM: 0.5555555555555556 KNN: 0.5555555555555556","keywords":["0.5555555555555556","bigdata","0.7407407407407407","5.","accuracy","knn","logistic","regression","svm","классификации.","классификация","моделей"],"topics":["knn","regression"]},{"id":"chunk-31","heading":"","text":"/usr/local/lib/python3.12/dist packages/sklearn/linear model/ logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.","keywords":["bigdata","465","5.","converge","convergencewarning","dist","failed","iterations","lbfgs","lib","limit.","linear"],"topics":["python","ai"]},{"id":"chunk-32","heading":"","text":"Increase the number of iterations (max iter) or scale the data as shown in: https://scikit learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit learn.org/stable/modules/linear model.html logistic regression n iter i = check optimize result( ```","keywords":["bigdata","https","iter","learn.org","modules","scikit","stable","5.","also","alternative","check","data"],"topics":["ai","regression"]},{"id":"chunk-33","heading":"","text":"В ходе выполнения работы был проведён полный цикл решения задачи классификации: выполнена загрузка и предобработка данных, построена гистограмма, продемонстрировавшая заметный дисбаланс классов из за наличия редкой категории, в связи с чем было принято решение исключить класс с единственным объектом","keywords":["bigdata","5.","был","было","выполнена","выполнения","гистограмма","данных","дисбаланс","единственным","за","загрузка"],"topics":["oop"]},{"id":"chunk-34","heading":"","text":". После этого данные были корректно разделены на тренировочную и тестовую выборки, а затем обучены три модели классификации: логистическая регрессия, SVM и KNN. Сравнение результатов с использованием accuracy, precision, recall и F1 меры показало, что наибольшее качество демонстрирует модель SVM, наиболее устойчиво справляясь с разделением классов и обеспечивая лучшие значения ключевых метрик","keywords":["bigdata","svm","5.","accuracy","f1","knn.","precision","recall","были","выборки","данные","демонстрирует"],"topics":["knn","oop","regression"]},{"id":"chunk-35","heading":"","text":". Логистическая регрессия выступила надёжной базовой моделью со стабильными, но более скромными результатами, в то время как KNN оказался менее эффективным из за чувствительности к структуре и масштабу признаков. Таким образом, наиболее подходящей моделью для классификации на основании данного набора данных является SVM, обеспечивающая оптимальный баланс качества и устойчивости.","keywords":["bigdata","моделью","5.","knn","svm","базовой","баланс","более","время","выступила","данного","данных"],"topics":["knn","oop","regression"]}]},{"id":"python/practice-6-regex-combinators","href":"/docs/python/practice-6-regex-combinators","slug":["python","practice-6-regex-combinators"],"section":"python","sectionTitle":"Python","title":"Практика 6 — Комбинаторы и функциональный стиль","description":"Функции высшего порядка, декораторы и функциональные структуры данных.","preview":"Источник: pr Python/pr Python/pract6.ipynb 1.1. (уровень сложности: низкий) Напишите функцию deriv для приближенного вычисления производной в заданной точке. Пример работы: deriv(lambda x: x 3)(5) 75.00014999664018 python def deriv(f, h=0.0001): def fprime(x):","keywords":["node","использования","current","list","def","lambda","value","примеры","return","my","pair","print","age","name","none","p1","if","next"],"topics":["python","ai","numpy","oop","algorithms"],"chunks":[{"id":"chunk-0","heading":"","text":"Напишите функцию deriv для приближенного вычисления производной в заданной точке.","keywords":["python","deriv","высшего","вычисления","данных.","декораторы","заданной","комбинаторы","напишите","порядка","практика","приближенного"],"topics":[]},{"id":"chunk-1","heading":"","text":"````python def deriv(f, h=0.0001): def fprime(x): return (f(x + h) f(x)) / h return fprime","keywords":["python","def","fprime","return","0.0001","deriv","высшего","данных.","декораторы","комбинаторы","порядка","практика"],"topics":["python"]},{"id":"chunk-2","heading":"","text":"Создайте вариант именованного кортежа с помощью ФВП. Классы и готовые структуры данных (словари, кортежи и так далее) использовать нельзя.","keywords":["python","структуры","вариант","высшего","готовые","далее","данных","данных.","декораторы","именованного","использовать","классы"],"topics":["oop","algorithms"]},{"id":"chunk-3","heading":"","text":"p1 = person(name='Иван', age=20) p2 = replace(replace(p1, 'name', 'Алексей'), 'age', 21) get(p1, 'name'), get(p1, 'age') ('Иван', 20) get(p2, 'name'), get(p2, 'age') ('Алексей', 21)","keywords":["age","get","name","p1","p2","20","21","python","replace","алексей","иван","person"],"topics":[]},{"id":"chunk-4","heading":"","text":"````python def person( kwargs): def get(obj, key): return obj[\"data\"][key] if key in obj[\"data\"] else None","keywords":["key","obj","python","data","def","else","get","if","kwargs","none","person","return"],"topics":["python"]},{"id":"chunk-5","heading":"Примеры использования","text":"p1 = person(name=\"Иван\", age=20) p2 = person(replace(p1, 'name', 'Алексей'), 'age', 21)","keywords":["age","name","p1","person","python","20","21","p2","replace","алексей","высшего","данных."],"topics":[]},{"id":"chunk-6","heading":"Примеры использования","text":"print(person.get(p1, \"name\"), person.get(p1, \"age\")) print(person.get(p2, \"name\"), person.get(p2, \"age\")) ````","keywords":["person.get","age","name","p1","p2","print","python","высшего","данных.","декораторы","использования","комбинаторы"],"topics":[]},{"id":"chunk-7","heading":"Примеры использования","text":"Реализуйте рекурсивное вычисление факториала в виде выражения. Необходимо это сделать без использования именованных функций, переменных (в том числе без имени факториала) и присваиваний.","keywords":["python","без","использования","факториала","виде","выражения.","высшего","вычисление","данных.","декораторы","имени","именованных"],"topics":[]},{"id":"chunk-8","heading":"Примеры использования","text":"python (lambda f: (lambda x: f(lambda k: x(x)(k)))(lambda x: f(lambda k: x(x)(k)))(lambda f: lambda n: 1 if n == 0 else n f(f)(n 1))(10)","keywords":["lambda","python","10","else","if","высшего","данных.","декораторы","использования","комбинаторы","порядка","практика"],"topics":["python"]},{"id":"chunk-9","heading":"Примеры использования","text":"Создайте декоратор io, который задает функции для получения входных аргументов и вовращения результата.","keywords":["python","функции","io","аргументов","вовращения","входных","высшего","данных.","декоратор","декораторы","задает","использования"],"topics":[]},{"id":"chunk-10","heading":"Примеры использования","text":"f1() one two three onetwothree @io(lambda: random.random(), lambda: random.random(), lambda x: x) def f2(x, y): return x y","keywords":["lambda","python","random.random","def","f1","f2","io","one","onetwothree","return","three","two"],"topics":[]},{"id":"chunk-11","heading":"Примеры использования","text":"def io( inputs and output): def decorator(func): def wrapper( args): input values = [input func() for input func in inputs and output[: 1]] result = func( input values) output func = inputs and output[ 1] output func(result) return wrapper return decorator","keywords":["func","output","input","def","inputs","decorator","python","result","return","values","wrapper","args"],"topics":["numpy"]},{"id":"chunk-12","heading":"Примеры использования","text":"@io(lambda: random.random(), lambda: random.random(), lambda x: x) def f2(x, y): return x y ````","keywords":["lambda","python","random.random","def","f2","io","return","высшего","данных.","декораторы","использования","комбинаторы"],"topics":[]},{"id":"chunk-13","heading":"Примеры использования","text":"Создайте декоратор класса @collect, который собирает все создаваемые объекты класса в единый список. К классу добавляется метод get objects, который выдает этот список.","keywords":["python","класса","который","список.","collect","get","objects","выдает","высшего","данных.","декоратор","декораторы"],"topics":["oop"]},{"id":"chunk-14","heading":"Примеры использования","text":"def wrapper( args, kwargs): obj = cls. new (cls) obj. init ( args, kwargs) cls. objects.append(obj) return obj","keywords":["obj","args","cls.","kwargs","python","cls","def","init","new","obj.","objects.append","return"],"topics":[]},{"id":"chunk-15","heading":"Примеры использования","text":"2. Работа со списками в функциональном стиле Создайте тип данных односвязный список с помощью ФВП. При создании списка нельзя использовать классы, готовые списки, кортежи и так далее.","keywords":["python","2.","высшего","готовые","далее.","данных","данных.","декораторы","использования","использовать","классы","комбинаторы"],"topics":["oop"]},{"id":"chunk-16","heading":"Примеры использования","text":"def prepend(node, value): new node = create node(value) new node[\"next\"] = node return new node","keywords":["node","new","python","value","create","def","next","prepend","return","высшего","данных.","декораторы"],"topics":[]},{"id":"chunk-17","heading":"Примеры использования","text":"def find(node, value): current = node while current is not None: if current[\"value\"] == value: return current current = current[\"next\"] return None","keywords":["current","value","node","none","python","return","def","find","if","next","not","while"],"topics":[]},{"id":"chunk-18","heading":"Примеры использования","text":"def print list(node): current = node while current is not None: print(current[\"value\"], end=\" \") current = current[\"next\"] print()","keywords":["current","print","node","python","def","end","list","next","none","not","value","while"],"topics":[]},{"id":"chunk-19","heading":"Пример использования","text":"my list = None my list = prepend(my list, 3) my list = prepend(my list, 2) my list = append(my list, 4) my list = append(my list, 5) print list(my list)","keywords":["list","my","append","prepend","python","none","print","высшего","данных.","декораторы","использования","комбинаторы"],"topics":[]},{"id":"chunk-20","heading":"Поиск элемента","text":"element = find(my list, 4) if element: print(\"Found:\", element[\"value\"]) else: print(\"Not Found\") ````","keywords":["element","found","print","python","else","find","if","list","my","not","value","высшего"],"topics":[]},{"id":"chunk-21","heading":"Поиск элемента","text":"Создайте функцию pair(head, tail), которая порождает элемент списка. Не используйте ветвления. Создайте также функции head(lst) (возвращает значение головы списка) и tail(lst) (возвращает хвост списка).","keywords":["head","lst","python","tail","возвращает","создайте","списка","функции","pair","ветвления.","высшего","головы"],"topics":["ai"]},{"id":"chunk-22","heading":"Функция pair создает элемент списка","text":"def pair(value, next node=None): return lambda selector: value if selector == \"value\" else next node","keywords":["value","next","node","pair","python","selector","def","else","if","lambda","none","return"],"topics":["ai"]},{"id":"chunk-23","heading":"Пример использования","text":"list node = pair(1, pair(2, pair(3, pair(4)))) print(\"Head node value:\", head(list node))","keywords":["pair","node","head","list","python","print","value","высшего","данных.","декораторы","использования","комбинаторы"],"topics":["ai"]},{"id":"chunk-24","heading":"Пример использования","text":"def pair(value, next node=None): return lambda selector: value if selector == \"value\" else next node","keywords":["value","next","node","python","selector","def","else","if","lambda","none","pair","return"],"topics":["ai"]},{"id":"chunk-25","heading":"Пример использования","text":"for arg in reversed(args): if list node is None: list node = pair(arg) current node = list node else: new node = pair(arg, current node) current node = new node","keywords":["node","arg","current","list","new","pair","python","args","else","if","none","reversed"],"topics":["ai"]},{"id":"chunk-26","heading":"Пример использования","text":"while current node: print(current node(\"value\")) current node = current node(\"tail\") ````","keywords":["current","node","python","print","tail","value","while","высшего","данных.","декораторы","использования","комбинаторы"],"topics":["ai"]}]},{"id":"bigdata/practice-06-clustering","href":"/docs/bigdata/practice-06-clustering","slug":["bigdata","practice-06-clustering"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 6. Кластеризация","description":"Неподконтрольное обучение и анализ кластеров.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитриевич ИКБО 25 22 Практическая работа №6 Задача кластеризации 1. Найти данные для кластеризации. Данные в группе не должны повторяться. Если","keywords":["schools","кластерами","первых","просмотр","строк","...","kalat","balochistan","cluster","dbscan","clusters","boys","girls","method","import","umap","кластеров","kmeans"],"topics":["python","ai","numpy","algorithms","clustering"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","6.","wiki","анализ","версия","документацию.","исходный","кластеризация","кластеров.","материал","неподконтрольное","ноутбук"],"topics":[]},{"id":"chunk-1","heading":"Задача кластеризации","text":"1. Найти данные для кластеризации. Данные в группе не должны повторяться. Если признаки в данных имеют очень сильно разные масштабы, то необходимо данные предварительно нормализовать.","keywords":["данные","bigdata","1.","6.","анализ","группе","данных","должны","если","задача","имеют","кластеризации"],"topics":[]},{"id":"chunk-2","heading":"Задача кластеризации","text":"2. Провести кластеризацию данных с помощью алгоритма k means. Использовать «правило локтя» и коэффициент силуэта для поиска оптимального количества кластеров.","keywords":["bigdata","кластеров.","2.","6.","means.","алгоритма","анализ","данных","задача","использовать","кластеризации","кластеризацию"],"topics":["algorithms"]},{"id":"chunk-3","heading":"Импортируем необходимые библиотеки","text":"from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler from sklearn.metrics import silhouette score","keywords":["import","bigdata","6.","kmeans","score","silhouette","sklearn.cluster","sklearn.metrics","sklearn.preprocessing","standardscaler","анализ","библиотеки"],"topics":["clustering"]},{"id":"chunk-4","heading":"Используем все числовые столбцы для кластеризации","text":"numerical cols = data.select dtypes(include=['float64', 'int64']).columns X = data[numerical cols]","keywords":["bigdata","cols","numerical",".columns","6.","data","data.select","dtypes","float64","include","int64","анализ"],"topics":[]},{"id":"chunk-5","heading":"Поиск оптимального числа кластеров с помощью \"правила локтя\"","text":"inertia = [] K range = range(2, 11) Проверим от 2 до 10 кластеров for k in K range: kmeans = KMeans(n clusters=k, random state=42) kmeans.fit(X scaled) inertia.append(kmeans.inertia )","keywords":["range","bigdata","kmeans","кластеров","10","11","42","6.","clusters","inertia","inertia.append","kmeans.fit"],"topics":["clustering"]},{"id":"chunk-6","heading":"Визуализация правила локтя","text":"plt.figure(figsize=(8,5)) plt.plot(K range, inertia, marker='o') plt.title(\"Elbow Method\") plt.xlabel(\"Number of clusters\") plt.ylabel(\"Inertia (Sum of Squared Distances)\") plt.show()","keywords":["bigdata","inertia","6.","clusters","distances","elbow","figsize","marker","method","number","plt.figure","plt.plot"],"topics":["clustering"]},{"id":"chunk-7","heading":"Расчет коэффициента силуэта для каждого числа кластеров","text":"silhouette scores = [] for k in K range: kmeans = KMeans(n clusters=k, random state=42) labels = kmeans.fit predict(X scaled) score = silhouette score(X scaled, labels) silhouette scores.append(score)","keywords":["score","silhouette","bigdata","kmeans","labels","scaled","42","6.","clusters","kmeans.fit","predict","random"],"topics":["clustering"]},{"id":"chunk-8","heading":"Визуализация коэффициента силуэта","text":"plt.figure(figsize=(8,5)) plt.plot(K range, silhouette scores, marker='o', color='orange') plt.title(\"Silhouette Score\") plt.xlabel(\"Number of clusters\") plt.ylabel(\"Silhouette Score\") plt.show()","keywords":["silhouette","bigdata","score","6.","clusters","color","figsize","marker","number","orange","plt.figure","plt.plot"],"topics":["clustering"]},{"id":"chunk-9","heading":"Выбираем оптимальное число кластеров по максимальному силуэту","text":"optimal k = K range[silhouette scores.index(max(silhouette scores))] print(f\"Оптимальное число кластеров: {optimal k}\")","keywords":["bigdata","optimal","silhouette","кластеров","оптимальное","число","6.","max","print","range","scores","scores.index"],"topics":[]},{"id":"chunk-10","heading":"Финальная кластеризация с оптимальным числом кластеров","text":"final kmeans = KMeans(n clusters=optimal k, random state=42) data['cluster'] = final kmeans.fit predict(X scaled)","keywords":["bigdata","final","kmeans","кластеризация","42","6.","cluster","clusters","data","kmeans.fit","optimal","predict"],"topics":["clustering"]},{"id":"chunk-11","heading":"Просмотр первых строк с кластерами","text":"sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","115635","161594","178958","2023","272506","27808","313271","33415","43736"],"topics":["python"]},{"id":"chunk-12","heading":"Просмотр первых строк с кластерами","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","121821","15153","2.79","2.83","2017","211201","265676","29510","3.78","35380"],"topics":[]},{"id":"chunk-13","heading":"Просмотр первых строк с кластерами","text":"secondary boys schools secondary girls schools high boys schools \\ 0 24 13 29 1 23 18 27 2 45 46 32 3 46 29 27 4 24 31 18","keywords":["schools","18","24","27","29","46","bigdata","boys","secondary","13","23","31"],"topics":[]},{"id":"chunk-14","heading":"Просмотр первых строк с кластерами","text":"high girls schools Intermediate boys schools Intermediate girls schools \\ 0 10 2 2 1 17 1 1 2 21 6 1 3 11 4 4 4 18 2 0","keywords":["schools","bigdata","girls","intermediate","10","11","17","18","21","6.","boys","high"],"topics":[]},{"id":"chunk-15","heading":"Просмотр первых строк с кластерами","text":"total boys schools total girls schools total schools cluster 0 249 132 381 0 1 269 120 389 0 2 569 245 814 0 3 507 168 675 0 4 268 143 411 0","keywords":["schools","total","bigdata","120","132","143","168","245","249","268","269","381"],"topics":["clustering"]},{"id":"chunk-16","heading":"Просмотр первых строк с кластерами","text":"3. Провести кластеризацию данных с помощью алгоритма иерархической кластеризации.","keywords":["bigdata","3.","6.","алгоритма","анализ","данных","иерархической","кластерами","кластеризации.","кластеризацию","кластеризация","кластеров."],"topics":["algorithms"]},{"id":"chunk-17","heading":"Импортируем библиотеки","text":"from sklearn.preprocessing import StandardScaler from scipy.cluster.hierarchy import dendrogram, linkage, fcluster","keywords":["bigdata","import","6.","dendrogram","fcluster","linkage","scipy.cluster.hierarchy","sklearn.preprocessing","standardscaler","анализ","библиотеки","импортируем"],"topics":["clustering"]},{"id":"chunk-18","heading":"Используем числовые столбцы","text":"numerical cols = data.select dtypes(include=['float64', 'int64']).columns X = data[numerical cols]","keywords":["bigdata","cols","numerical",".columns","6.","data","data.select","dtypes","float64","include","int64","анализ"],"topics":[]},{"id":"chunk-19","heading":"Визуализация дендрограммы","text":"plt.figure(figsize=(10, 6)) dendrogram(linked, orientation='top', distance sort='ascending', show leaf counts=True) plt.title(\"Дендрограмма иерархической кластеризации\") plt.xlabel(\"Объекты\") plt.ylabel(\"Расстояние\") plt.show()","keywords":["bigdata","10","6.","ascending","counts","dendrogram","distance","figsize","leaf","linked","orientation","plt.figure"],"topics":[]},{"id":"chunk-20","heading":"Здесь можно задать число кластеров, например 3","text":"num clusters = 3 clusters = fcluster(linked, num clusters, criterion='maxclust')","keywords":["clusters","bigdata","num","6.","criterion","fcluster","linked","maxclust","анализ","задать","здесь","кластеризация"],"topics":["clustering"]},{"id":"chunk-21","heading":"Просмотр первых строк с кластерами","text":"sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","115635","161594","178958","2023","272506","27808","313271","33415","43736"],"topics":["python"]},{"id":"chunk-22","heading":"Просмотр первых строк с кластерами","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","121821","15153","2.79","2.83","2017","211201","265676","29510","3.78","35380"],"topics":[]},{"id":"chunk-23","heading":"Просмотр первых строк с кластерами","text":"secondary boys schools secondary girls schools high boys schools \\ 0 24 13 29 1 23 18 27 2 45 46 32 3 46 29 27 4 24 31 18","keywords":["schools","18","24","27","29","46","bigdata","boys","secondary","13","23","31"],"topics":[]},{"id":"chunk-24","heading":"Просмотр первых строк с кластерами","text":"high girls schools Intermediate boys schools Intermediate girls schools \\ 0 10 2 2 1 17 1 1 2 21 6 1 3 11 4 4 4 18 2 0","keywords":["schools","bigdata","girls","intermediate","10","11","17","18","21","6.","boys","high"],"topics":[]},{"id":"chunk-25","heading":"Просмотр первых строк с кластерами","text":"total boys schools total girls schools total schools \\ 0 249 132 381 1 269 120 389 2 569 245 814 3 507 168 675 4 268 143 411","keywords":["schools","total","bigdata","120","132","143","168","245","249","268","269","381"],"topics":[]},{"id":"chunk-26","heading":"Импортируем библиотеки","text":"from sklearn.preprocessing import StandardScaler from sklearn.cluster import DBSCAN from sklearn.neighbors import NearestNeighbors","keywords":["import","bigdata","6.","dbscan","nearestneighbors","sklearn.cluster","sklearn.neighbors","sklearn.preprocessing","standardscaler","анализ","библиотеки","импортируем"],"topics":["clustering"]},{"id":"chunk-27","heading":"Выбираем числовые столбцы","text":"numerical cols = data.select dtypes(include=['float64', 'int64']).columns X = data[numerical cols]","keywords":["bigdata","cols","numerical",".columns","6.","data","data.select","dtypes","float64","include","int64","анализ"],"topics":[]},{"id":"chunk-28","heading":"Подбор eps с помощью графика расстояний до k ближайшего соседа","text":"neighbors = NearestNeighbors(n neighbors=5) k = min samples 1, обычно 4 neighbors fit = neighbors.fit(X scaled) distances, indices = neighbors fit.kneighbors(X scaled) distances = np.sort(distances[:, 4]) 4 й сосед plt.figure(figsize=(8,5)) plt.plot(distances) plt.title(\"k distance график для подбора eps\") plt.xlabel(\"Объекты, отсортированные по расстоянию\") plt.ylabel(\"Расстояние до 5 го ближайшего соседа\")","keywords":["distances","neighbors","bigdata","eps","scaled","ближайшего","до","соседа","6.","distance","figsize","fit"],"topics":["numpy"]},{"id":"chunk-29","heading":"Подбор eps с помощью графика расстояний до k ближайшего соседа","text":"plt.show()","keywords":["bigdata","6.","eps","plt.show","анализ","ближайшего","графика","до","кластеризация","кластеров.","неподконтрольное","обучение"],"topics":[]},{"id":"chunk-30","heading":"eps нужно подобрать визуально из графика k distance","text":"dbscan = DBSCAN(eps=1.5, min samples=5) примерные значения dbscan labels = dbscan.fit predict(X scaled)","keywords":["dbscan","bigdata","eps","1.5","6.","dbscan.fit","distance","labels","min","predict","samples","scaled"],"topics":[]},{"id":"chunk-31","heading":"Выводим количество кластеров и количество шумовых точек","text":"n clusters = len(set(dbscan labels)) (1 if 1 in dbscan labels else 0) n noise = list(dbscan labels).count( 1) print(f\"Количество кластеров: {n clusters}\") print(f\"Количество шумовых точек: {n noise}\")","keywords":["количество","dbscan","labels","bigdata","clusters","noise","print","кластеров","точек","шумовых",".count","6."],"topics":["clustering"]},{"id":"chunk-32","heading":"Просмотр первых строк с кластерами","text":"sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","115635","161594","178958","2023","272506","27808","313271","33415","43736"],"topics":["python"]},{"id":"chunk-33","heading":"Просмотр первых строк с кластерами","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","121821","15153","2.79","2.83","2017","211201","265676","29510","3.78","35380"],"topics":[]},{"id":"chunk-34","heading":"Просмотр первых строк с кластерами","text":"secondary boys schools secondary girls schools high boys schools \\ 0 24 13 29 1 23 18 27 2 45 46 32 3 46 29 27 4 24 31 18","keywords":["schools","18","24","27","29","46","bigdata","boys","secondary","13","23","31"],"topics":[]},{"id":"chunk-35","heading":"Просмотр первых строк с кластерами","text":"high girls schools Intermediate boys schools Intermediate girls schools \\ 0 10 2 2 1 17 1 1 2 21 6 1 3 11 4 4 4 18 2 0","keywords":["schools","bigdata","girls","intermediate","10","11","17","18","21","6.","boys","high"],"topics":[]},{"id":"chunk-36","heading":"Просмотр первых строк с кластерами","text":"total boys schools total girls schools total schools dbscan cluster 0 249 132 381 1 1 269 120 389 0 2 569 245 814 1 3 507 168 675 0 4 268 143 411 0","keywords":["schools","total","bigdata","120","132","143","168","245","249","268","269","381"],"topics":["clustering"]},{"id":"chunk-37","heading":"Просмотр первых строк с кластерами","text":"5. Визуализировать кластеризованные данные с помощью t SNE или UMAP, если необходимо. Если данные трехмерные, то можно использовать трехмерный точечный график.","keywords":["bigdata","данные","если","5.","6.","sne","umap","анализ","визуализировать","график.","использовать","кластерами"],"topics":[]},{"id":"chunk-38","heading":"Импорт библиотек","text":"from sklearn.preprocessing import StandardScaler from sklearn.cluster import KMeans, DBSCAN from sklearn.metrics import silhouette score from scipy.cluster.hierarchy import linkage, fcluster from sklearn.manifold import TSNE from mpl toolkits.mplot3d import Axes3D","keywords":["import","bigdata","6.","axes3d","dbscan","fcluster","kmeans","linkage","mpl","scipy.cluster.hierarchy","score","silhouette"],"topics":["clustering"]},{"id":"chunk-39","heading":"1. Загрузка данных","text":"data = pd.read csv(\"data population2023.csv\") numerical cols = data.select dtypes(include=['float64', 'int64']).columns X = data[numerical cols]","keywords":["data","bigdata","cols","numerical",".columns","1.","6.","csv","data.select","dtypes","float64","include"],"topics":[]},{"id":"chunk-40","heading":"2. K means кластеризация","text":"optimal k = 3 пример, можно изменить после анализа локтя/силуэта kmeans = KMeans(n clusters=optimal k, random state=42) data['kmeans cluster'] = kmeans.fit predict(X scaled)","keywords":["kmeans","bigdata","optimal","кластеризация","2.","42","6.","cluster","clusters","data","kmeans.fit","means"],"topics":["clustering"]},{"id":"chunk-41","heading":"3. Иерархическая кластеризация","text":"linked = linkage(X scaled, method='ward') num clusters = 3 пример, можно подбирать по дендрограмме data['hierarchical cluster'] = fcluster(linked, num clusters, criterion='maxclust')","keywords":["bigdata","clusters","linked","num","кластеризация","3.","6.","cluster","criterion","data","fcluster","hierarchical"],"topics":["clustering"]},{"id":"chunk-42","heading":"4. DBSCAN","text":"dbscan = DBSCAN(eps=1.5, min samples=5) eps можно подбирать через k distance график data['dbscan cluster'] = dbscan.fit predict(X scaled)","keywords":["dbscan","bigdata","eps","1.5","4.","6.","cluster","data","dbscan.fit","distance","min","predict"],"topics":["clustering"]},{"id":"chunk-43","heading":"5. Визуализация кластеров через t SNE и UMAP","text":"def visualize clusters(X scaled, df, cluster col, method='t SNE', dims=2): if method == 't SNE': reducer = TSNE(n components=dims, random state=42) elif method == 'UMAP': reducer = umap.UMAP(n components=dims, random state=42) else: raise ValueError(\"method must be 't SNE' or 'UMAP'\")","keywords":["method","sne","dims","umap","42","bigdata","components","random","reducer","state","5.","6."],"topics":["ai","clustering"]},{"id":"chunk-44","heading":"5. Визуализация кластеров через t SNE и UMAP","text":"if dims == 2: plt.figure(figsize=(8,6)) plt.scatter(X reduced[:,0], X reduced[:,1], c=df[cluster col], cmap='tab10', s=50) plt.title(f\"{method} визуализация ({cluster col})\") plt.xlabel(f\"{method} 1\") plt.ylabel(f\"{method} 2\") plt.colorbar(label='Cluster') plt.show() elif dims == 3: fig = plt.figure(figsize=(8,6)) ax = fig.add subplot(111, projection='3d') scatter = ax.scatter(X reduced[:,0], X reduced[:,1], X","keywords":["reduced","cluster","method","bigdata","col","dims","figsize","plt.figure","визуализация","111","3d","5."],"topics":["clustering"]},{"id":"chunk-45","heading":"5. Визуализация кластеров через t SNE и UMAP","text":"reduced[:,2], c=df[cluster col], cmap='tab10', s=50) ax.set title(f\"3D {method} визуализация ({cluster col})\") ax.set xlabel(f\"{method} 1\") ax.set ylabel(f\"{method} 2\") ax.set zlabel(f\"{method} 3\") fig.colorbar(scatter, ax=ax, label='Cluster') plt.show()","keywords":["ax.set","method","cluster","ax","bigdata","col","визуализация","3d","5.","50","6.","cmap"],"topics":["clustering"]},{"id":"chunk-46","heading":"Визуализируем все кластеризации (2D)","text":"for col in ['kmeans cluster', 'hierarchical cluster', 'dbscan cluster']: visualize clusters(X scaled, data, col, method='t SNE', dims=2) visualize clusters(X scaled, data, col, method='UMAP', dims=2)","keywords":["cluster","col","bigdata","clusters","data","dims","method","scaled","visualize","2d","6.","dbscan"],"topics":["clustering"]},{"id":"chunk-47","heading":"Просмотр первых строк с кластерами","text":"/usr/local/lib/python3.12/dist packages/umap/umap .py:1952: UserWarning: n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism. warn(","keywords":["bigdata","umap",".py","1952","6.","dist","jobs","lib","local","no","overridden","packages"],"topics":["python"]},{"id":"chunk-48","heading":"Просмотр первых строк с кластерами","text":"/usr/local/lib/python3.12/dist packages/umap/umap .py:1952: UserWarning: n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism. warn(","keywords":["bigdata","umap",".py","1952","6.","dist","jobs","lib","local","no","overridden","packages"],"topics":["python"]},{"id":"chunk-49","heading":"Просмотр первых строк с кластерами","text":"/usr/local/lib/python3.12/dist packages/umap/umap .py:1952: UserWarning: n jobs value 1 overridden to 1 by setting random state. Use no seed for parallelism. warn(","keywords":["bigdata","umap",".py","1952","6.","dist","jobs","lib","local","no","overridden","packages"],"topics":["python"]},{"id":"chunk-50","heading":"Просмотр первых строк с кластерами","text":"sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","115635","161594","178958","2023","272506","27808","313271","33415","43736"],"topics":["python"]},{"id":"chunk-51","heading":"Просмотр первых строк с кластерами","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","121821","15153","2.79","2.83","2017","211201","265676","29510","3.78","35380"],"topics":[]},{"id":"chunk-52","heading":"Просмотр первых строк с кластерами","text":"high boys schools high girls schools Intermediate boys schools \\ 0 29 10 2 1 27 17 1 2 32 21 6 3 27 11 4 4 18 18 2","keywords":["schools","18","27","bigdata","boys","high","10","11","17","21","29","32"],"topics":[]},{"id":"chunk-53","heading":"Просмотр первых строк с кластерами","text":"Intermediate girls schools total boys schools total girls schools \\ 0 2 249 132 1 1 269 120 2 1 569 245 3 4 507 168 4 0 268 143","keywords":["schools","bigdata","girls","total","120","132","143","168","245","249","268","269"],"topics":[]},{"id":"chunk-54","heading":"Просмотр первых строк с кластерами","text":"total schools kmeans cluster hierarchical cluster dbscan cluster 0 381 2 1 1 1 389 2 1 0 2 814 2 1 1 3 675 2 1 0 4 411 2 1 0","keywords":["cluster","bigdata","381","389","411","6.","675","814","dbscan","hierarchical","kmeans","schools"],"topics":["clustering"]},{"id":"chunk-55","heading":"Просмотр первых строк с кластерами","text":"В ходе выполнения заданий 2–5 была проведена комплексная кластеризация данных с использованием различных алгоритмов: K means, иерархической кластеризации и DBSCAN, с последующим анализом качества кластеризации через правило локтя, коэффициент силуэта и визуальный подбор параметров","keywords":["bigdata","кластеризации","кластеризация","6.","dbscan","means","алгоритмов","анализ","анализом","была","визуальный","выполнения"],"topics":["algorithms","clustering"]},{"id":"chunk-56","heading":"Просмотр первых строк с кластерами","text":". K means позволил выявить оптимальное число кластеров на основе максимального силуэта, и объекты были распределены по четко различимым группам. Иерархическая кластеризация обеспечила наглядное представление структуры данных через дендрограмму и позволила формировать кластеры на разных уровнях разбиения. DBSCAN выявил как плотные кластеры, так и шумовые точки, что особенно полезно при наличии выбросов","keywords":["bigdata","кластеризация","кластеры","6.","dbscan","means","анализ","были","выбросов","выявил","выявить","группам."],"topics":["algorithms","clustering"]},{"id":"chunk-57","heading":"Просмотр первых строк с кластерами","text":". Для визуализации результатов была применена методика снижения размерности (t SNE и UMAP), которая позволила на двумерной или трехмерной плоскости наглядно оценить распределение кластеров и взаимное расстояние между ними, подтвердив внутреннюю структуру и качество выделенных групп.","keywords":["bigdata","6.","sne","umap","анализ","была","взаимное","визуализации","внутреннюю","выделенных","групп.","двумерной"],"topics":[]}]},{"id":"python/practice-6-2-regex-combinators","href":"/docs/python/practice-6-2-regex-combinators","slug":["python","practice-6-2-regex-combinators"],"section":"python","sectionTitle":"Python","title":"Практика 6.2 — Расширенные комбинаторы regex","description":"Дополнительные задания по комбинаторному и функциональному подходу.","preview":"Источник: pr Python/pr Python/pract6 2.ipynb 4. Комбинаторы регулярных выражений Реализуйте простой встроенный язык подмножества регулярных выражений с помощью ФВП. Комбинаторы имеют следующий общий вид: def comb(param, ...): def parse(text, ...): ... return n","keywords":["pos","text","hex","return","digit","def","sym","parse","сложности","уровень","высокий","seq","none","if","new","range","color","parsers"],"topics":["python","ai"],"chunks":[{"id":"chunk-0","heading":"4. Комбинаторы регулярных выражений","text":"Реализуйте простой встроенный язык подмножества регулярных выражений с помощью ФВП. Комбинаторы имеют следующий общий вид: def comb(param, ...): def parse(text, ...): ... return new text, ... return parse","keywords":["...","комбинаторы","def","parse","python","return","text","выражений","регулярных","4.","6.2","comb"],"topics":["python"]},{"id":"chunk-1","heading":"4. Комбинаторы регулярных выражений","text":"````python def char(c): def parse(text, pos): if pos = len(text) or text[pos] != c: return text, pos return text, pos + 1 return parse","keywords":["pos","text","python","return","def","parse","комбинаторы","4.","6.2","char","if","len"],"topics":["python"]},{"id":"chunk-2","heading":"4. Комбинаторы регулярных выражений","text":"def seq( parsers): def parse(text, pos): for p in parsers: text, pos = p(text, pos) if pos == 1: return text, pos return text, pos return parse","keywords":["pos","text","return","def","parse","parsers","python","комбинаторы","4.","6.2","if","regex"],"topics":["python"]},{"id":"chunk-3","heading":"4. Комбинаторы регулярных выражений","text":"def alt( parsers): def parse(text, pos): for p in parsers: new text, new pos = p(text, pos) if new pos != 1: return new text, new pos return text, 1 return parse","keywords":["new","pos","text","return","def","parse","parsers","python","комбинаторы","4.","6.2","alt"],"topics":["python"]},{"id":"chunk-4","heading":"4. Комбинаторы регулярных выражений","text":"def rep(p): def parse(text, pos): while pos < len(text): text, pos = p(text, pos) return text, pos return parse","keywords":["pos","text","def","parse","python","return","комбинаторы","4.","6.2","len","regex","rep"],"topics":["python"]},{"id":"chunk-5","heading":"4. Комбинаторы регулярных выражений","text":"def opt(p): def parse(text, pos): new text, new pos = p(text, pos) return new text, new pos if new pos != 1 else pos return parse","keywords":["pos","new","text","def","parse","python","return","комбинаторы","4.","6.2","else","if"],"topics":["python"]},{"id":"chunk-6","heading":"4. Комбинаторы регулярных выражений","text":"result = parser(\"aaabbaa\", 0) if result[1] == 1: print(\"Failed to parse\") else: print(\"Parsed successfully\") ````","keywords":["print","python","result","комбинаторы","4.","6.2","aaabbaa","else","failed","if","parse","parsed"],"topics":["python","ai"]},{"id":"chunk-7","heading":"4.1. (уровень сложности: высокий)","text":"Реализуйте комбинаторы sym (разобрать символ) и seq (разобрать последовательность).","keywords":["python","комбинаторы","разобрать","4.1.","6.2","regex","seq","sym","высокий","дополнительные","задания","комбинаторному"],"topics":["python"]},{"id":"chunk-8","heading":"4.1. (уровень сложности: высокий)","text":"``` text = 'abc' re1 = seq(sym('a'), sym('b'), sym('c')) re2 = seq(sym('a'), sym('z'), sym('c'))","keywords":["sym","python","seq","4.1.","6.2","abc","re1","re2","regex","text","высокий","дополнительные"],"topics":[]},{"id":"chunk-9","heading":"4.1. (уровень сложности: высокий)","text":"````python def sym(c): def parse(text, pos): if pos < len(text) and text[pos] == c: return pos + 1 return None return parse","keywords":["pos","python","return","text","def","parse","4.1.","6.2","if","len","none","regex"],"topics":["python"]},{"id":"chunk-10","heading":"4.1. (уровень сложности: высокий)","text":"def seq( parsers): def parse(text, pos): for p in parsers: pos = p(text, pos) if pos is None: return None return pos return parse","keywords":["pos","return","def","none","parse","parsers","python","text","4.1.","6.2","if","regex"],"topics":[]},{"id":"chunk-11","heading":"4.1. (уровень сложности: высокий)","text":"text = 'abc' re1 = seq(sym('a'), sym('b'), sym('c')) re2 = seq(sym('a'), sym('z'), sym('c'))","keywords":["sym","python","seq","4.1.","6.2","abc","re1","re2","regex","text","высокий","дополнительные"],"topics":[]},{"id":"chunk-12","heading":"4.2. (уровень сложности: средний)","text":"````python def range of(start, end): def parse(text, pos): if pos < len(text) and start <= text[pos] <= end: return pos + 1 return None return parse","keywords":["pos","python","return","text","def","end","parse","start","4.2.","6.2","if","len"],"topics":["python"]},{"id":"chunk-13","heading":"4.2. (уровень сложности: средний)","text":"def seq( parsers): def parse(text, pos): for p in parsers: pos = p(text, pos) if pos is None: return None return pos return parse","keywords":["pos","return","def","none","parse","parsers","python","text","4.2.","6.2","if","regex"],"topics":[]},{"id":"chunk-14","heading":"4.3. (уровень сложности: высокий)","text":"``` digit = range of('0', '9') hex digit = alt(digit, range of('a', 'f'), range of('A', 'F')) space = alt(sym(' '), sym('\\n'), sym('\\t')) hex color = seq(sym(' '), hex digit, hex digit, hex digit, hex digit, hex digit, hex digit)","keywords":["digit","hex","sym","range","alt","python","4.3.","6.2","color","regex","seq","space"],"topics":[]},{"id":"chunk-15","heading":"4.3. (уровень сложности: высокий)","text":"print(hex color(' ffaa43') is not None) print(hex color(' xxxxxx') is not None) ```","keywords":["color","hex","none","not","print","python","4.3.","6.2","ffaa43","regex","xxxxxx","высокий"],"topics":[]},{"id":"chunk-16","heading":"4.3. (уровень сложности: высокий)","text":"````python def alt( parsers): def parse(text, pos): for p in parsers: new pos = p(text, pos) if new pos is not None: return new pos return None return parse","keywords":["pos","new","python","return","def","none","parse","parsers","text","4.3.","6.2","alt"],"topics":["python"]},{"id":"chunk-17","heading":"4.3. (уровень сложности: высокий)","text":"def range of(start, end): def parse(text, pos): if pos < len(text) and start <= text[pos] <= end: return pos + 1 return None return parse","keywords":["pos","return","text","def","end","parse","python","start","4.3.","6.2","if","len"],"topics":[]},{"id":"chunk-18","heading":"4.3. (уровень сложности: высокий)","text":"def sym(char): def parse(text, pos): if pos < len(text) and text[pos] == char: return pos + 1 return None return parse","keywords":["pos","return","text","char","def","parse","python","4.3.","6.2","if","len","none"],"topics":[]},{"id":"chunk-19","heading":"4.3. (уровень сложности: высокий)","text":"def seq( parsers): def parse(text, pos): for p in parsers: pos = p(text, pos) if pos is None: return None return pos return parse","keywords":["pos","return","def","none","parse","parsers","python","text","4.3.","6.2","if","regex"],"topics":[]},{"id":"chunk-20","heading":"4.3. (уровень сложности: высокий)","text":"digit = range of('0', '9') hex digit = alt(digit, range of('a', 'f'), range of('A', 'F')) space = alt(sym(' '), sym('\\n'), sym('\\t')) hex color = seq(sym(' '), hex digit, hex digit, hex digit, hex digit, hex digit, hex digit)","keywords":["digit","hex","sym","range","alt","python","4.3.","6.2","color","regex","seq","space"],"topics":[]},{"id":"chunk-21","heading":"4.3. (уровень сложности: высокий)","text":"print(hex color(' ffaa43') is not None) print(hex color(' xxxxxx') is not None) ````","keywords":["color","hex","none","not","print","python","4.3.","6.2","ffaa43","regex","xxxxxx","высокий"],"topics":[]},{"id":"chunk-22","heading":"4.4. (уровень сложности: высокий)","text":"Реализуйте комбинатор group (группировка) для извлечения разобранных фрагментов.","keywords":["python","4.4.","6.2","group","regex","высокий","группировка","дополнительные","задания","извлечения","комбинатор","комбинаторному"],"topics":["python"]},{"id":"chunk-23","heading":"4.4. (уровень сложности: высокий)","text":"``` digit = range of('0', '9') hex digit = alt(digit, range of('a', 'f'), range of('A', 'F')) space = alt(sym(' '), sym('\\n'), sym('\\t')) hex color = seq(sym(' '), group(seq(hex digit, hex digit, hex digit, hex digit, hex digit, hex digit))) hex colors = seq(hex color, sym(' '), hex color)","keywords":["hex","digit","sym","color","range","seq","alt","python","4.4.","6.2","colors","group"],"topics":[]},{"id":"chunk-24","heading":"4.4. (уровень сложности: высокий)","text":"````python def group(rule): def inner(text, start): result, end = rule(text, start) if result: return ([result], end) return (result, end) return inner","keywords":["result","end","python","return","def","inner","rule","start","text","4.4.","6.2","group"],"topics":["python"]},{"id":"chunk-25","heading":"4.4. (уровень сложности: высокий)","text":"digit = range of('0', '9') hex digit = alt(digit, range of('a', 'f'), range of('A', 'F')) space = alt(sym(' '), sym('\\n'), sym('\\t')) hex color = seq(sym(' '), group(seq(hex digit, hex digit, hex digit, hex digit, hex digit, hex digit))) hex colors = seq(hex color, sym(' '), hex color)","keywords":["hex","digit","sym","color","range","seq","alt","python","4.4.","6.2","colors","group"],"topics":[]},{"id":"chunk-26","heading":"4.4. (уровень сложности: высокий)","text":"def parse hex colors(text, start): result, end = hex colors(text, start) return ('', result)","keywords":["colors","hex","python","result","start","text","4.4.","6.2","def","end","parse","regex"],"topics":[]}]},{"id":"bigdata/practice-07-ensemble-learning","href":"/docs/bigdata/practice-07-ensemble-learning","slug":["bigdata","practice-07-ensemble-learning"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 7. Ансамблевое обучение","description":"Комбинирование моделей для повышения качества предсказаний.","preview":"Wiki версия Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория. Минькин Александр Дмитриевич ИКБО 25 22 Практическая работа №7 Ансамблевое обучение // Stacking // Bagging // Boosting 1. Найти данные для кластеризации. Данные в","keywords":["boosting","bagging","ансамблевое","обучение","stacking","schools","test","girls","random","train","mse","boys","rf","gbr","r2","...","forest","import"],"topics":["python","ai","pandas","testing","algorithms"],"chunks":[{"id":"chunk-0","heading":"Wiki версия","text":"Материал полностью перенесен в документацию. Исходный ноутбук удален из репозитория.","keywords":["bigdata","7.","wiki","ансамблевое","версия","документацию.","исходный","качества","комбинирование","материал","моделей","ноутбук"],"topics":[]},{"id":"chunk-1","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"1. Найти данные для кластеризации. Данные в группе не должны повторяться. Если признаки в данных имеют очень сильно разные масштабы, то необходимо данные предварительно нормализовать.","keywords":["данные","bigdata","ансамблевое","обучение","1.","7.","bagging","boosting","stacking","группе","данных","должны"],"topics":[]},{"id":"chunk-2","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```python from sklearn.model selection import train test split from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean squared error, r2 score from sklearn.tree import plot tree","keywords":["import","bigdata","ансамблевое","обучение","7.","bagging","boosting","error","mean","plot","python","r2"],"topics":["python","ai","testing"]},{"id":"chunk-3","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"features = ['households', 'average household size', 'population 2017', 'growth rate', 'area(km²)', 'density 2023(people/km²)', 'primary boys schools', 'primary girls schools', 'secondary boys schools', 'secondary girls schools', 'high boys schools', 'high girls schools', 'Intermediate boys schools', 'Intermediate girls schools', 'total boys schools', 'total girls schools', 'total schools']","keywords":["schools","boys","girls","total","bigdata","high","intermediate","km²","primary","secondary","ансамблевое","обучение"],"topics":[]},{"id":"chunk-4","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"X train, X test, y train, y test = train test split(X, y, test size=0.2, random state=42)","keywords":["test","train","bigdata","ансамблевое","обучение","0.2","42","7.","bagging","boosting","random","size"],"topics":["ai","testing"]},{"id":"chunk-5","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"rf model = RandomForestRegressor( n estimators=100, max depth=None, min samples leaf=1, random state=42 )","keywords":["bigdata","ансамблевое","обучение","100","42","7.","bagging","boosting","depth","estimators","leaf","max"],"topics":[]},{"id":"chunk-6","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(f'\\nMean Squared Error (MSE): {mse:.2f}') print(f'Коэффициент детерминации (R²): {r2:.2f}')","keywords":[".2f","bigdata","mse","print","ансамблевое","обучение","7.","bagging","boosting","error","nmean","r2"],"topics":[]},{"id":"chunk-7","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"feature importances = pd.Series(rf model.feature importances , index=features).sort values(ascending=False)","keywords":["bigdata","importances","ансамблевое","обучение",".sort","7.","ascending","bagging","boosting","false","feature","features"],"topics":["pandas"]},{"id":"chunk-8","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"plt.figure(figsize=(12,6)) sns.barplot(x=feature importances.values, y=feature importances.index) plt.title('Важность признаков (Feature Importance) в Random Forest') plt.show()","keywords":["feature","bigdata","ансамблевое","обучение","12","7.","bagging","boosting","figsize","forest","importance","importances.index"],"topics":[]},{"id":"chunk-9","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"plt.figure(figsize=(20,10)) plot tree(tree, feature names=features, filled=True, rounded=True, fontsize=10) plt.title(\"Пример одного дерева из Random Forest\") plt.show() ```","keywords":["10","bigdata","tree","true","ансамблевое","обучение","20","7.","bagging","boosting","feature","features"],"topics":[]},{"id":"chunk-10","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```text Первые 5 строк датасета: sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","ансамблевое","обучение","115635","161594","178958","2023","272506","27808","313271"],"topics":["python"]},{"id":"chunk-11","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","ансамблевое","обучение","121821","15153","2.79","2.83","2017","211201","265676","29510"],"topics":[]},{"id":"chunk-12","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"primary girls schools secondary boys schools secondary girls schools \\ 0 107 24 13 1 84 23 18 2 177 45 46 3 124 46 29 4 94 24 31","keywords":["schools","24","46","bigdata","girls","secondary","ансамблевое","обучение","107","124","13","177"],"topics":[]},{"id":"chunk-13","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"high boys schools high girls schools Intermediate boys schools \\ 0 29 10 2 1 27 17 1 2 32 21 6 3 27 11 4 4 18 18 2","keywords":["schools","18","27","bigdata","boys","high","ансамблевое","обучение","10","11","17","21"],"topics":[]},{"id":"chunk-14","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Intermediate girls schools total boys schools total girls schools \\ 0 2 249 132 1 1 269 120 2 1 569 245 3 4 507 168 4 0 268 143","keywords":["schools","bigdata","girls","total","ансамблевое","обучение","120","132","143","168","245","249"],"topics":[]},{"id":"chunk-15","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.metrics import mean squared error, r2 score","keywords":["import","bigdata","ансамблевое","обучение","7.","bagging","boosting","error","gradientboostingregressor","mean","python","r2"],"topics":["python","ai","testing"]},{"id":"chunk-16","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"features = data.drop(columns=['sr.no.', 'population 2023', 'province', 'division', 'district']) target = data['population 2023']","keywords":["2023","bigdata","population","ансамблевое","обучение","7.","bagging","boosting","columns","data","data.drop","district"],"topics":[]},{"id":"chunk-17","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"X train, X test, y train, y test = train test split(features, target, test size=0.2, random state=42)","keywords":["test","train","bigdata","ансамблевое","обучение","0.2","42","7.","bagging","boosting","features","random"],"topics":["ai","testing"]},{"id":"chunk-18","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"scaler = StandardScaler() X train scaled = scaler.fit transform(X train) X test scaled = scaler.transform(X test) rf = RandomForestRegressor( n estimators=100, max depth=10, min samples leaf=5, random state=42 )","keywords":["bigdata","scaled","test","train","ансамблевое","обучение","10","100","42","7.","bagging","boosting"],"topics":["ai","testing"]},{"id":"chunk-19","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(\"Random Forest R^2:\", r2 score(y test, y pred rf)) print(\"Random Forest MSE:\", mean squared error(y test, y pred rf))","keywords":["bigdata","forest","pred","print","random","rf","test","ансамблевое","обучение","7.","bagging","boosting"],"topics":["testing"]},{"id":"chunk-20","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"gbr = GradientBoostingRegressor( n estimators=200, learning rate=0.1, max depth=3, min samples leaf=5, random state=42 )","keywords":["bigdata","ансамблевое","обучение","0.1","200","42","7.","bagging","boosting","depth","estimators","gbr"],"topics":[]},{"id":"chunk-21","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(\"Gradient Boosting R^2:\", r2 score(y test, y pred gbr)) print(\"Gradient Boosting MSE:\", mean squared error(y test, y pred gbr)) print(\"R^2 Random Forest:\", r2 score(y test, y pred rf)) print(\"R^2 Gradient Boosting:\", r2 score(y test, y pred gbr)) ```","keywords":["boosting","pred","print","test","gbr","gradient","r2","score","bigdata","ансамблевое","обучение","7."],"topics":["testing"]},{"id":"chunk-22","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"text Random Forest R^2: 0.9902127230594779 Random Forest MSE: 8824200825.067791 Gradient Boosting R^2: 0.9680482164967752 Gradient Boosting MSE: 28807701678.92102 R^2 Random Forest: 0.9902127230594779 R^2 Gradient Boosting: 0.9680482164967752","keywords":["boosting","forest","gradient","random","0.9680482164967752","0.9902127230594779","bigdata","mse","ансамблевое","обучение","28807701678.92102","7."],"topics":[]},{"id":"chunk-23","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"4) Сравнить результаты работы алгоритмов (время работы и качество моделей). Сделать выводы.","keywords":["bigdata","ансамблевое","моделей","обучение","работы","7.","bagging","boosting","stacking","алгоритмов","время","выводы."],"topics":["algorithms"]},{"id":"chunk-24","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```python from sklearn.model selection import train test split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.metrics import mean squared error, r2 score","keywords":["import","bigdata","ансамблевое","обучение","7.","bagging","boosting","error","gradientboostingregressor","mean","python","r2"],"topics":["python","ai","testing"]},{"id":"chunk-25","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"features = data.drop(columns=['sr.no.', 'population 2023', 'province', 'division', 'district']) target = data['population 2023']","keywords":["2023","bigdata","population","ансамблевое","обучение","7.","bagging","boosting","columns","data","data.drop","district"],"topics":[]},{"id":"chunk-26","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"X train, X test, y train, y test = train test split(features, target, test size=0.2, random state=42)","keywords":["test","train","bigdata","ансамблевое","обучение","0.2","42","7.","bagging","boosting","features","random"],"topics":["ai","testing"]},{"id":"chunk-27","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"scaler = StandardScaler() X train scaled = scaler.fit transform(X train) X test scaled = scaler.transform(X test)","keywords":["bigdata","scaled","test","train","ансамблевое","обучение","7.","bagging","boosting","scaler","scaler.fit","scaler.transform"],"topics":["ai","testing"]},{"id":"chunk-28","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"rf = RandomForestRegressor( n estimators=100, max depth=10, min samples leaf=5, random state=42 )","keywords":["bigdata","ансамблевое","обучение","10","100","42","7.","bagging","boosting","depth","estimators","leaf"],"topics":[]},{"id":"chunk-29","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"start time = time.time() rf.fit(X train, y train) rf time = time.time() start time","keywords":["time","bigdata","start","time.time","train","ансамблевое","обучение","7.","bagging","boosting","rf","rf.fit"],"topics":["ai"]},{"id":"chunk-30","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"rf r2 = r2 score(y test, y pred rf) rf mse = mean squared error(y test, y pred rf)","keywords":["rf","bigdata","pred","r2","test","ансамблевое","обучение","7.","bagging","boosting","error","mean"],"topics":["testing"]},{"id":"chunk-31","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(f\"Random Forest: R^2 = {rf r2:.4f}, MSE = {rf mse:.2f}, Время = {rf time:.2f} сек\")","keywords":["rf",".2f","bigdata","mse","ансамблевое","обучение",".4f","7.","bagging","boosting","forest","print"],"topics":[]},{"id":"chunk-32","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"gbr = GradientBoostingRegressor( n estimators=200, learning rate=0.1, max depth=3, min samples leaf=5, random state=42 )","keywords":["bigdata","ансамблевое","обучение","0.1","200","42","7.","bagging","boosting","depth","estimators","gbr"],"topics":[]},{"id":"chunk-33","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"start time = time.time() gbr.fit(X train scaled, y train) gbr time = time.time() start time","keywords":["time","bigdata","start","time.time","train","ансамблевое","обучение","7.","bagging","boosting","gbr","gbr.fit"],"topics":["ai"]},{"id":"chunk-34","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"gbr r2 = r2 score(y test, y pred gbr) gbr mse = mean squared error(y test, y pred gbr)","keywords":["gbr","bigdata","pred","r2","test","ансамблевое","обучение","7.","bagging","boosting","error","mean"],"topics":["testing"]},{"id":"chunk-35","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"print(f\"Gradient Boosting: R^2 = {gbr r2:.4f}, MSE = {gbr mse:.2f}, Время = {gbr time:.2f} сек\")","keywords":["gbr",".2f","bigdata","boosting","mse","ансамблевое","обучение",".4f","7.","bagging","gradient","print"],"topics":[]},{"id":"chunk-36","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"comparison = pd.DataFrame({ 'Модель': ['Random Forest (Bagging)', 'Gradient Boosting'], 'R^2': [rf r2, gbr r2], 'MSE': [rf mse, gbr mse], 'Время обучения (сек)': [rf time, gbr time] })","keywords":["gbr","mse","rf","bagging","bigdata","boosting","r2","time","ансамблевое","обучение","7.","comparison"],"topics":["pandas"]},{"id":"chunk-37","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"```text sr.no. province division district households population 2023 \\ 0 1 Balochistan Kalat Awaran 27808 178958 1 2 Balochistan Kalat Kalat 33415 272506 2 3 Balochistan Kalat Khuzdar 161594 997214 3 4 Balochistan Kalat Lasbela 115635 680977 4 5 Balochistan Kalat Mastung 43736 313271","keywords":["kalat","balochistan","bigdata","ансамблевое","обучение","115635","161594","178958","2023","272506","27808","313271"],"topics":["python"]},{"id":"chunk-38","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"average household size population 2017 growth rate area(km²) ... \\ 0 6.44 121821 6.64 29510 ... 1 8.16 211201 4.35 6622 ... 2 6.17 798896 3.78 35380 ... 3 5.89 576271 2.83 15153 ... 4 7.16 265676 2.79 5896 ...","keywords":["...","bigdata","ансамблевое","обучение","121821","15153","2.79","2.83","2017","211201","265676","29510"],"topics":[]},{"id":"chunk-39","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"primary girls schools secondary boys schools secondary girls schools \\ 0 107 24 13 1 84 23 18 2 177 45 46 3 124 46 29 4 94 24 31","keywords":["schools","24","46","bigdata","girls","secondary","ансамблевое","обучение","107","124","13","177"],"topics":[]},{"id":"chunk-40","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"high boys schools high girls schools Intermediate boys schools \\ 0 29 10 2 1 27 17 1 2 32 21 6 3 27 11 4 4 18 18 2","keywords":["schools","18","27","bigdata","boys","high","ансамблевое","обучение","10","11","17","21"],"topics":[]},{"id":"chunk-41","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Intermediate girls schools total boys schools total girls schools \\ 0 2 249 132 1 1 269 120 2 1 569 245 3 4 507 168 4 0 268 143","keywords":["schools","bigdata","girls","total","ансамблевое","обучение","120","132","143","168","245","249"],"topics":[]},{"id":"chunk-42","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"[5 rows x 22 columns] Index(['sr.no.', 'province', 'division', 'district', 'households', 'population 2023', 'average household size', 'population 2017', 'growth rate', 'area(km²)', 'density 2023(people/km²)', 'primary boys schools', 'primary girls schools', 'secondary boys schools', 'secondary girls schools', 'high boys schools', 'high girls schools', 'Intermediate boys schools', 'Intermediate girls schools', 'total","keywords":["schools","boys","girls","2023","bigdata","high","intermediate","km²","population","primary","secondary","ансамблевое"],"topics":[]},{"id":"chunk-43","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"boys schools', 'total girls schools', 'total schools'], dtype='object') Random Forest: R^2 = 0.9902, MSE = 8824200825.07, Время = 0.34 сек Gradient Boosting: R^2 = 0.9680, MSE = 28807701678.92, Время = 0.38 сек Модель R^2 MSE Время обучения (сек) 0 Random Forest (Bagging) 0.990213 8.824201e+09 0.341938 1 Gradient Boosting 0.968048 2.880770e+10 0.379149 ```","keywords":["boosting","mse","schools","время","сек","bagging","bigdata","forest","gradient","random","total","ансамблевое"],"topics":[]},{"id":"chunk-44","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"В ходе практической работы была выполнена задача регрессии на данных по населению и школам провинций.","keywords":["bigdata","ансамблевое","обучение","7.","bagging","boosting","stacking","была","выполнена","данных","задача","качества"],"topics":[]},{"id":"chunk-45","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Сначала реализован бэггинг с использованием случайного леса, который показал высокую стабильность предсказаний и относительно быстрое обучение.","keywords":["bigdata","ансамблевое","обучение","7.","bagging","boosting","stacking","быстрое","бэггинг","высокую","использованием","качества"],"topics":[]},{"id":"chunk-46","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Затем на тех же данных применён градиентный бустинг, который продемонстрировал чуть более высокое качество прогнозов (выше R² и ниже MSE), но требовал большего времени на обучение.","keywords":["bigdata","ансамблевое","обучение","7.","bagging","boosting","mse","r²","stacking","более","большего","бустинг"],"topics":[]},{"id":"chunk-47","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Сравнение моделей показало, что случайный лес удобен для быстрой и устойчивой оценки, а бустинг эффективен для повышения точности предсказаний.","keywords":["bigdata","ансамблевое","моделей","обучение","повышения","предсказаний.","7.","bagging","boosting","stacking","бустинг","быстрой"],"topics":[]},{"id":"chunk-48","heading":"Ансамблевое обучение // Stacking // Bagging // Boosting","text":"Результаты подтверждают преимущества ансамблевых методов в уменьшении дисперсии и смещения моделей.","keywords":["bigdata","ансамблевое","обучение","7.","bagging","boosting","stacking","ансамблевых","дисперсии","качества","комбинирование","методов"],"topics":[]}]},{"id":"bigdata/practice-08-final-report","href":"/docs/bigdata/practice-08-final-report","slug":["bigdata","practice-08-final-report"],"section":"bigdata","sectionTitle":"BigData","title":"Практика 8. Итоговая работа","description":"Финальный отчет и итоговые материалы по модулю BigData.","preview":"Wiki версия требуется доработка 1. Настроить виртуальную машину Ubuntu Server 22.04.5 LTS в системе виртуализации операционных систем, а также настроить Docker контейнеры PostgreSQL, Arenadata Cluster Manager совместно с бандлами для кластера и мониторинга (Pr","keywords":["требуется","wiki","версия","доработка","заказов","данных","категории","количество","нужно","уникальных","доставки","каждой","оплаты","товарных","postgresql","виде","каждого","настроить"],"topics":["sql","clustering"],"chunks":[{"id":"chunk-0","heading":"Wiki версия требуется доработка","text":"1. Настроить виртуальную машину Ubuntu Server 22.04.5 LTS в системе виртуализации операционных систем, а также настроить Docker контейнеры PostgreSQL, Arenadata Cluster Manager совместно с бандлами для кластера и мониторинга (Prometheus, Grafana). (1 балл)","keywords":["bigdata","настроить","1.","22.04.5","8.","arenadata","bigdata.","cluster","docker","grafana","lts","manager"],"topics":["sql","clustering"]},{"id":"chunk-1","heading":"Wiki версия требуется доработка","text":"В отчет прикрепить скриншот с интерфейса ADCM на странице Cluster Overview где показано, что все сервисы подняты и работают в штатном режиме.","keywords":["bigdata","отчет","8.","adcm","bigdata.","cluster","overview","wiki","версия","доработка","интерфейса","итоговая"],"topics":["clustering"]},{"id":"chunk-2","heading":"Wiki версия требуется доработка","text":"2. Создать базу данных ecommerce и развернуть public схему таблиц, на основе приложенного скрипта, для хранения данных транзакций в PostgreSQL посредством postgresql client (DBeaver или любой другой админ баз данных). Для каждой таблицы сделать импорт данных посредством импорта из CSV файла в следующем порядке (1 балл):","keywords":["данных","bigdata","postgresql","посредством","2.","8.","bigdata.","client","csv","dbeaver","ecommerce","public"],"topics":["sql"]},{"id":"chunk-3","heading":"Wiki версия требуется доработка","text":"В отчет прикрепить визуализацию схемы таблиц, полученную в консоли командной строки или в виде диаграммы в админе баз данных (например DBeaver).","keywords":["bigdata","отчет","8.","bigdata.","dbeaver","wiki","админе","баз","версия","виде","визуализацию","данных"],"topics":[]},{"id":"chunk-4","heading":"Wiki версия требуется доработка","text":"3. Написать запросы на языке SQL для базы данных ecommerce для получения данных по следующим заданиям:","keywords":["bigdata","данных","3.","8.","bigdata.","ecommerce","sql","wiki","базы","версия","доработка","заданиям"],"topics":["sql"]},{"id":"chunk-5","heading":"Wiki версия требуется доработка","text":"Нужно определить десять товарных категорий с наибольшим оборотом продаж. Для каждой категории требуется посчитать сумму цен всех проданных позиций (это и есть выручка/GMV категории), количество уникальных заказов, в которых встречались товары этой категории, и общее число товарных позиций. Дополнительно к исходному названию категории нужно вывести её англоязычный перевод из словаря переводов","keywords":["категории","bigdata","нужно","товарных","требуется","8.","bigdata.","gmv","wiki","англоязычный","версия","всех"],"topics":[]},{"id":"chunk-6","heading":"Wiki версия требуется доработка","text":". Итоговый набор данных должен быть отсортирован по выручке по убыванию и ограничен первыми десятью строками.","keywords":["bigdata","8.","bigdata.","wiki","быть","версия","выручке","данных","десятью","должен","доработка","итоговая"],"topics":[]},{"id":"chunk-7","heading":"Wiki версия требуется доработка","text":"Нужно сравнить используемые способы оплаты. По каждому типу оплаты требуется вывести количество зарегистрированных платежей, среднее число рассрочек (если применимо), средний размер платежа и долю заказов, в которых встречается данный способ оплаты.","keywords":["bigdata","оплаты.","требуется","8.","bigdata.","wiki","версия","встречается","вывести","данный","долю","доработка"],"topics":[]},{"id":"chunk-8","heading":"Wiki версия требуется доработка","text":"Под долей заказов понимается отношение числа уникальных заказов, где зафиксирован хотя бы один платеж данного типа, к общему числу уникальных заказов, по которым известны записи об оплате.","keywords":["заказов","bigdata","уникальных","8.","bigdata.","wiki","бы","версия","данного","долей","доработка","записи"],"topics":[]},{"id":"chunk-9","heading":"Wiki версия требуется доработка","text":"Следует учитывать, что у одного заказа может быть несколько платежей. Доля считается на уровне «есть/нет» для типа оплаты в заказе.","keywords":["bigdata","8.","bigdata.","wiki","быть","версия","доля","доработка","заказа","заказе.","итоговая","итоговые"],"topics":[]},{"id":"chunk-10","heading":"Wiki версия требуется доработка","text":"Нужно изучить, как длительность доставки коррелирует с оценками в отзывах. Для каждого заказа с отзывом и известной фактической датой доставки рассчитывается длительность доставки в днях как разница между датой доставки клиенту и датой покупки.","keywords":["доставки","датой","bigdata","длительность","8.","bigdata.","wiki","версия","днях","доработка","заказа","известной"],"topics":[]},{"id":"chunk-11","heading":"Wiki версия требуется доработка","text":"Для каждого интервала требуется посчитать количество заказов и среднюю оценку из отзывов. Итог должен содержать три строки — по одному агрегату на интервал — в логическом порядке от самого быстрого интервала к самому длительному.","keywords":["bigdata","интервала","требуется","8.","bigdata.","wiki","агрегату","быстрого","версия","длительному.","должен","доработка"],"topics":[]},{"id":"chunk-12","heading":"Wiki версия требуется доработка","text":"Нужно получить рейтинг продавцов по активности и базовым бизнес показателям. Для каждого продавца считается количество уникальных заказов, в которых он участвовал, число уникальных клиентов, которым он продал хотя бы одну позицию, общее число проданных товарных позиций, совокупный оборот по этим позициям (сумма цен) и средняя стоимость доставки на позицию (среднее от значений стоимости фрахта по позициям)","keywords":["bigdata","он","позицию","позициям","уникальных","число","8.","bigdata.","wiki","активности","базовым","бизнес"],"topics":[]},{"id":"chunk-13","heading":"Wiki версия требуется доработка","text":". 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