Principal Component Analysis

medium· Machine Learningruns: 0

Implement PCA for dimensionality reduction. Center the data, compute the covariance matrix, find its eigenvectors, sort them by eigenvalue in descending order, and use the top k as the projection. Provide fit, transform, and fit_transform.

wpm 0acc 100%time 0:000 / 689

import numpy as np

class PCA:
    def __init__(self, n_components: int):
        self.n_components = n_components
        self.components = None
        self.mean = None

    def fit(self, X):
        self.mean = X.mean(axis=0)
        centered = X - self.mean
        cov = np.cov(centered, rowvar=False)
        eigenvalues, eigenvectors = np.linalg.eigh(cov)
        order = np.argsort(eigenvalues)[::-1]
        eigenvectors = eigenvectors[:, order]
        self.components = eigenvectors[:, : self.n_components]
        return self

    def transform(self, X):
        return (X - self.mean) @ self.components

    def fit_transform(self, X):
        return self.fit(X).transform(X)

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