Vectorized Covariance Matrix Calculation

import numpy as np

def fast_covariance(X):
    # X is an (n_samples, n_features) matrix
    n = X.shape[0]
    X_centered = X - X.mean(axis=0)
    # Using the dot product is significantly faster than np.cov for large matrices
    return (X_centered.T @ X_centered) / (n - 1)

Why it matters: Center once, multiply once. Large feature banks compute faster when you skip Python loops and lean on vectorized math.