Module « scipy.cluster.vq »

Fonction vq - module scipy.cluster.vq

Signature de la fonction vq

def vq(obs, code_book, check_finite=True) 

Description

vq.__doc__

    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
    --------
    >>> from numpy import array
    >>> from scipy.cluster.vq import vq
    >>> code_book = array([[1.,1.,1.],
    ...                    [2.,2.,2.]])
    >>> features  = 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],'i'), array([ 0.43588989,  0.73484692,  0.83066239]))