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Module « scipy.cluster.vq »

Fonction vq - module scipy.cluster.vq

Signature de la fonction vq

def vq(obs, code_book, check_finite=True) 

Description

help(scipy.cluster.vq.vq)

Assign codes from a code book to observations.

Assigns a code from a code book to each observation. Each
observation vector in the 'M' by 'N' `obs` array is compared with the
centroids in the code book and assigned the code of the closest
centroid.

The features in `obs` should have unit variance, which can be
achieved by passing them through the whiten function. The code
book can be created with the k-means algorithm or a different
encoding algorithm.

Parameters
----------
obs : ndarray
    Each row of the 'M' x 'N' array is an observation. The columns are
    the "features" seen during each observation. The features must be
    whitened first using the whiten function or something equivalent.
code_book : ndarray
    The code book is usually generated using the k-means algorithm.
    Each row of the array holds a different code, and the columns are
    the features of the code.

     >>> #              f0    f1    f2   f3
     >>> code_book = [
     ...             [  1.,   2.,   3.,   4.],  #c0
     ...             [  1.,   2.,   3.,   4.],  #c1
     ...             [  1.,   2.,   3.,   4.]]  #c2

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
-------
code : ndarray
    A length M array holding the code book index for each observation.
dist : ndarray
    The distortion (distance) between the observation and its nearest
    code.

Examples
--------
>>> import numpy as np
>>> from scipy.cluster.vq import vq
>>> code_book = np.array([[1., 1., 1.],
...                       [2., 2., 2.]])
>>> features  = np.array([[1.9, 2.3, 1.7],
...                       [1.5, 2.5, 2.2],
...                       [0.8, 0.6, 1.7]])
>>> vq(features, code_book)
(array([1, 1, 0], dtype=int32), array([0.43588989, 0.73484692, 0.83066239]))

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