Module « scipy.cluster.vq »

Fonction whiten - module scipy.cluster.vq

Signature de la fonction whiten

def whiten(obs, check_finite=True) 

Description

whiten.__doc__

    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
    --------
    >>> 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]])