MachineLearning/pca library
📐 Principal Component Analysis (PCA)
A minimalist PCA implementation using the covariance method and power-iteration for the top components. Focused on clarity and good docstring quality for engineering usage. Returns projected data and principal components.
Contract:
- Input: X (n x m), desired number of components
d. - Output: projected matrix (n x d) and components (d x m).
- Error: throws ArgumentError for invalid shapes.
Time Complexity: O(n * m * d * iters) Space Complexity: O(m*m)