# -*- coding: utf-8 -*-

import numpy as np

## helper function: principal component analysis (pca)

#

# @param V: features for all train observations (dimension iNumFeatures x iNumObservations)

#

# @return U_pc: transformed features 'score' (dimension see V)

# @return T: transformation matrix 'loading' (dimension iNumFeatures x iNumFeatures )

# @return eigenvalues: 'latent' (length iNumFeatures)

def ToolPca(V):

return U_pc, T, eigenvalues