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# Which function of scipy.cluster.vq module is used to normalize observations on each feature dimension?

Before implementing k-means algorithms, it is always beneficial to rescale each feature dimension of the observation set. The function **scipy.cluster.vq.whiten(obs, check_finite = True)**is used for this purpose. To give it unit variance, it divides each feature dimension of the observation by its standard deviation (SD).

## Parameters

Below are given the parameters of the function scipy.cluster.vq.whiten(obs, check_finite = True) −

**obs**− ndarray

It is an array, to be rescaled, where each row is an observation, and the columns are the features seen during each observation. The example is given below −

obs = [[ 1., 1., 1.], [ 2., 2., 2.], [ 3., 3., 3.], [ 4., 4., 4.]]

**check_finite**− bool,optional

This parameter is used to check whether the input matrices contain only finite numbers. Disabling this parameter may give you a performance gain but it may also result in some problems like crashes or non-termination if the observations do contain infinites. The default value of this parameter is **True**.

## Returns

It returns an array which contains the values in obs scaled by the SD of each column.

## Example

import numpy as np from scipy.cluster.vq import whiten observations = np.array([[2.9, 1.3, 1.9], [1.7, 3.2, 1.1], [1.0, 0.2, 1.7,]]) whiten(observations)

## Output

array([[3.69627581, 1.04908478, 5.58930985], [2.16678237, 2.58236253, 3.23591623], [1.27457787, 0.16139766, 5.00096145]])

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