def compute_pvalue(self):
difference = smooth_difference(self.random_frequencies, self.data_x, self.data_y)
return mahalanobis_distance(difference, 2 * self.num_random_features)
def compute_pvalue(self):
_, dimension = numpy.shape(self.data_x)
obs = self.vector_of_differences(dimension)
return mahalanobis_distance(obs, self.number_of_frequencies)
