mnist.py

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('./csv/mnist_train_small.csv')
print(df.shape)
print(df.columns)
df.head(n=5)
data = df.values
print(data.shape)
print(type(data))

X = data[:,1:]
Y = data[:,0]

print(X.shape,Y.shape)

split = int(0.8*X.shape[0])
print(split)

X_train = X[:split,:]
Y_train = Y[:split]

X_test = X[split:,:]
Y_test = Y[split:]

print(X_train.shape,Y_train.shape)
print(X_test.shape,Y_test.shape)


# Visualise SOme Samples

def drawImg(sample):
    img = sample.reshape((28, 28))
    plt.imshow(img, cmap='gray')
    plt.show()


drawImg(X_train[64])
print(Y_train[64])


def dist(x1, x2):
    return np.sqrt(sum((x1 - x2) ** 2))


def knn(X, Y, queryPoint, k=5):
    vals = []
    m = X.shape[0]

    for i in range(m):
        d = dist(queryPoint, X[i])
        vals.append((d, Y[i]))

    vals = sorted(vals)
    # Nearest/First K points
    vals = vals[:k]

    vals = np.array(vals)

    # print(vals)

    new_vals = np.unique(vals[:, 1], return_counts=True)
    # print(new_vals)

    index = new_vals[1].argmax()
    pred = new_vals[0][index]

    return pred


pred = knn(X_train,Y_train,X_test[1])

print(int(pred))


drawImg(X_test[63])
print(Y_test[63])