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src/m/i/milk-0.5.3/milk/unsupervised/gaussianmixture.py   milk(Download)
from numpy import log, pi, array
from numpy.linalg import det, inv
from .kmeans import residual_sum_squares, centroid_errors
 
__all__ = [
        return -N/2.*log(2*pi*Rss/N)-N/2
    elif model == 'diagonal_covariance':
        errors = centroid_errors(fmatrix,assignments,centroids)
        errors *= errors
        errors = errors.sum(1)

src/m/i/milk-HEAD/milk/unsupervised/gaussianmixture.py   milk(Download)
from numpy import log, pi, array
from numpy.linalg import det, inv
from .kmeans import residual_sum_squares, centroid_errors
 
__all__ = [
        return -N/2.*log(2*pi*Rss/N)-N/2
    elif model == 'diagonal_covariance':
        errors = centroid_errors(fmatrix,assignments,centroids)
        errors *= errors
        errors = errors.sum(1)