def _log_wishart_norm(degrees_of_freedom, log_det_precisions_chol, n_features):
 return (- (((degrees_of_freedom * log_det_precisions_chol) + (((degrees_of_freedom * n_features) * 0.5) * math.log(2.0))) + np.sum(gammaln((0.5 * (degrees_of_freedom - np.arange(n_features)[:, np.newaxis]))), 0)))

def _log_wishart_norm ( degrees_of_freedom , log_det_precisions_chol , n_features ) : return ( - ( ( ( degrees_of_freedom * log_det_precisions_chol ) + ( ( ( degrees_of_freedom * n_features ) * 0.5 ) * math . log ( 2.0 ) ) ) + np . sum ( gammaln ( ( 0.5 * ( degrees_of_freedom - np . arange ( n_features ) [ : , np . newaxis ] ) ) ) , 0 ) ) )
compute the log of the wishart distribution normalization term .