# scipy.cluster.vq.whiten

scipy.cluster.vq.whiten(obs, check_finite=True)

  Normalize a group of observations on a per feature basis.

  Before running k-means, it is beneficial to rescale each feature dimension of the observation set by its standard deviation (i.e. “whiten” it - as in “white noise” where each frequency has equal power). Each feature is divided by its standard deviation across all observations to give it unit variance.

  Parameters:
    obs ndarray

      Each row of the array is an observation. The columns are the features seen during each observation.

          >>> #         f0    f1    f2
          >>> obs = [[  1.,   1.,   1.],  #o0
          ...        [  2.,   2.,   2.],  #o1
          ...        [  3.,   3.,   3.],  #o2
          ...        [  4.,   4.,   4.]]  #o3

    check_finite bool, optional

      Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Default: True

  Returns:
    result ndarray

      Contains the values in obs scaled by the standard deviation of each column.

  Examples

      >>> import numpy as np
      >>> from scipy.cluster.vq import whiten
      >>> features  = np.array([[1.9, 2.3, 1.7],
      ...                       [1.5, 2.5, 2.2],
      ...                       [0.8, 0.6, 1.7,]])
      >>> whiten(features)
      array([[ 4.17944278,  2.69811351,  7.21248917],
             [ 3.29956009,  2.93273208,  9.33380951],
             [ 1.75976538,  0.7038557 ,  7.21248917]])