pymor.algorithms.pca

Module Contents

pymor.algorithms.pca.pca(A, product=None, modes=None, rtol=None, atol=None, l2_err=None, method=None, orth_tol=None, return_reduced_coefficients=False, copy=True)[source]

Principal component analysis (PCA) using pod.

The principal components of A are the pod modes of A centered around the mean.

Parameters:

  • A – See pod.

  • product – See pod.

  • modes – See pod.

  • rtol – See pod.

  • atol – See pod.

  • l2_err – See pod.

  • method – See pod.

  • orth_tol – See pod.

  • return_reduced_coefficients – See pod.

  • copy – If True (default) do not modify A. If False subtract the mean from A in-place.

Returns:

  • mean – VectorArray containing the empirical mean of the input A.

  • principal_components – VectorArray of principal components.

  • svals – One-dimensional NumPy array of singular values.

  • coeffs – If return_reduced_coefficients is True, a NumPy array of right singular vectors as conjugated rows.