scipy.cluster.vq.whiten#
- scipy.cluster.vq.whiten(obs, check_finite=True)[source]#
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
- obsndarray
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_finitebool, 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
- resultndarray
Contains the values in obs scaled by the standard deviation of each column.
Examples
>>> 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]])