`scipy.cluster.vq._vq_impl:whiten`

source: build-install/usr/lib/python3.14/site-packages/scipy/cluster/vq/_vq_impl.py:26

Signature

def whiten(obs, check_finite = None)

Summary

Normalize a group of observations on a per feature basis.

Extended Summary

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 for eager backends and False for lazy ones.

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([[4.17944, 2.69811, 7.21249],
       [3.29956, 2.93273, 9.33381],
       [1.75977, 0.70386, 7.21249]])

Aliases

scipy.cluster.vq.whiten