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Commit 8826ad3

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feat: Implement Principal Component Analysis (PCA) (#12596)
- Added PCA implementation with dataset standardization. - Used Singular Value Decomposition (SVD) for computing principal components. - Fixed import sorting to comply with PEP 8 (Ruff I001). - Ensured type hints and docstrings for better readability. - Added doctests to validate correctness. - Passed all Ruff checks and automated tests.
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‎DIRECTORY.md

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* [Minimum Tickets Cost](dynamic_programming/minimum_tickets_cost.py)
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* [Optimal Binary Search Tree](dynamic_programming/optimal_binary_search_tree.py)
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* [Palindrome Partitioning](dynamic_programming/palindrome_partitioning.py)
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* [Range Sum Query](dynamic_programming/range_sum_query.py)
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* [Regex Match](dynamic_programming/regex_match.py)
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* [Rod Cutting](dynamic_programming/rod_cutting.py)
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* [Smith Waterman](dynamic_programming/smith_waterman.py)
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* [Mfcc](machine_learning/mfcc.py)
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* [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
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* [Polynomial Regression](machine_learning/polynomial_regression.py)
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* [Principle Component Analysis](machine_learning/principle_component_analysis.py)
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* [Scoring Functions](machine_learning/scoring_functions.py)
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* [Self Organizing Map](machine_learning/self_organizing_map.py)
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* [Sequential Minimum Optimization](machine_learning/sequential_minimum_optimization.py)
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"""
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Principal Component Analysis (PCA) is a dimensionality reduction technique
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used in machine learning. It transforms high-dimensional data into a lower-dimensional
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representation while retaining as much variance as possible.
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This implementation follows best practices, including:
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- Standardizing the dataset.
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- Computing principal components using Singular Value Decomposition (SVD).
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- Returning transformed data and explained variance ratio.
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"""
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import doctest
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import numpy as np
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from sklearn.datasets import load_iris
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from sklearn.decomposition import PCA
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from sklearn.preprocessing import StandardScaler
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def collect_dataset() -> tuple[np.ndarray, np.ndarray]:
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"""
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Collects the dataset (Iris dataset) and returns feature matrix and target values.
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:return: Tuple containing feature matrix (X) and target labels (y)
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Example:
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>>> X, y = collect_dataset()
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>>> X.shape
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(150, 4)
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>>> y.shape
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(150,)
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"""
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data = load_iris()
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return np.array(data.data), np.array(data.target)
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def apply_pca(data_x: np.ndarray, n_components: int) -> tuple[np.ndarray, np.ndarray]:
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"""
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Applies Principal Component Analysis (PCA) to reduce dimensionality.
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:param data_x: Original dataset (features)
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:param n_components: Number of principal components to retain
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:return: Tuple containing transformed dataset and explained variance ratio
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Example:
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>>> X, _ = collect_dataset()
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>>> transformed_X, variance = apply_pca(X, 2)
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>>> transformed_X.shape
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(150, 2)
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>>> len(variance) == 2
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True
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"""
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# Standardizing the dataset
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scaler = StandardScaler()
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data_x_scaled = scaler.fit_transform(data_x)
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# Applying PCA
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pca = PCA(n_components=n_components)
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principal_components = pca.fit_transform(data_x_scaled)
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return principal_components, pca.explained_variance_ratio_
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def main() -> None:
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"""
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Driver function to execute PCA and display results.
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"""
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data_x, data_y = collect_dataset()
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# Number of principal components to retain
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n_components = 2
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# Apply PCA
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transformed_data, variance_ratio = apply_pca(data_x, n_components)
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print("Transformed Dataset (First 5 rows):")
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print(transformed_data[:5])
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print("\nExplained Variance Ratio:")
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print(variance_ratio)
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if __name__ == "__main__":
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doctest.testmod()
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main()

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