vampire.amath.pca#

vampire.amath.pca(A, method=None)[source]#

Principal component analysis of matrix A.

Returns loadings, principal components, and explained variance.

Parameters:

Andarray

Matrix with shape (m, n), where n features are in columns, and m measurements are in rows.

methodNone or str, optional

Algorithm used to compute PCA:

None

If m >= n, use eigen-decomposition algorithm.

If m < n, use singular value decomposition algorithm.

'eig'

Eigen-decomposition algorithm.

'svd'

Singular value decomposition algorithm.

Returns:

Vndarray: Loadings, weights, principal directions, principal axes, eigenvector of covariance matrix of mean-subtracted A, with shape (n, n).
Tndarray: PC score, principal components, coordinates of mean-subtracted A in its principal directions, with shape (m, n).
dndarray: Explained variance, eigenvalues of covariance matrix of mean-subtracted A, with size n.