hyperspy.learn.svd_pca module

hyperspy.learn.svd_pca.svd_pca(data, fast=False, output_dimension=None, centre=None, auto_transpose=True)

Perform PCA using SVD.

  • data (numpy array) – MxN array of input data (M variables, N trials)
  • fast (bool) – Wheter to use randomized svd estimation to estimate a limited number of componentes given by output_dimension
  • output_dimension (int) – Number of components to estimate when fast is True
  • centre (None | 'variables' | 'trials') – If None no centring is applied. If ‘variable’ the centring will be performed in the variable axis. If ‘trials’, the centring will be performed in the ‘trials’ axis.
  • auto_transpose (bool) – If True, automatically transposes the data to boost performance

  • factors (numpy array)
  • loadings (numpy array)
  • explained_variance (numpy array)
  • mean (numpy array or None (if center is None))