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Module « scipy.cluster.vq »

Fonction whiten - module scipy.cluster.vq

Signature de la fonction whiten

def whiten(obs, check_finite=True) 

Description

help(scipy.cluster.vq.whiten)

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]])

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