Add Python implementation using numpy and scipy

[Copilot]

Rewrites the MATLAB ALPUD algorithm in Python while preserving the original MATLAB files for backward compatibility.

Implementation

• alpud.py: Core module with two functions using scipy.stats.gaussian_kde (equivalent to MATLAB's ksdensity)

  • compute_informativeness_with_known_prior() - implements equation 8 with known prior P
  • compute_informativeness_with_unknown_prior() - computes expected value over uniform prior distribution
  • Added epsilon to denominator to prevent division by zero

• Package configuration: setup.py, requirements.txt (numpy>=1.19.0, scipy>=1.5.0)

• Testing: test_alpud.py validates 1D and 2D data, example.py demonstrates usage

• Documentation: Updated README with Python installation and usage

Usage

import numpy as np
from alpud import compute_informativeness_with_known_prior

positive_data = np.array([1.0, 1.2, 1.1, 1.3, 1.15])
unlabeled_data = np.array([0.5, 2.0, 1.5, 0.8, 2.5])
query_samples = np.array([0.9, 1.0, 1.5, 2.0])

informativeness = compute_informativeness_with_known_prior(
    positive_data, unlabeled_data, P=0.3, query_samples=query_samples
)

Original prompt

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This section details on the original issue you should resolve

<issue_title>Rewrite in Python</issue_title>
<issue_description>Rewrite the original MATLAB code in Python, using numpy and possibly scipy and scikit-learn as dependencies. </issue_description>

Comments on the Issue (you are @copilot in this section)

• Fixes Rewrite in Python #1

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