Normalization.py 文件源码

def compute_svd_normalization(samples, Ndiscard, max_rescale):
    # S is list of singular values in descending order
    # Each row of V is a list of the weights of the features in a given principal component
    centered_samples = samples - samples.mean(axis=0) # subtract columnwise means
    U, S, V = np.linalg.svd(centered_samples, full_matrices=False)
    Nsamp = samples.shape[0]
    component_stddevs = S / math.sqrt(Nsamp)

    print "singular values ="
    print S
    print "component standard deviations ="
    print component_stddevs
    print "V matrix ="
    print V

    Nfeat = samples.shape[1]
    rescaling_factors = np.minimum(np.reciprocal(component_stddevs[:Nfeat-Ndiscard]), max_rescale)
    whitening_matrix = np.dot(V[:Nfeat-Ndiscard].T, np.diag(rescaling_factors))

    print "Ndiscard =", Ndiscard
    print "max_rescale =", max_rescale
    print "rescaling_factors ="
    print rescaling_factors
    print "whitening_matrix ="
    print repr(whitening_matrix)