Machine Learning, Clustering, Expectation Maximization, Gaussian Mixture Model, Image Clustering.
= _get_means(data, resp, counts) covariances = _get_covariances(data, resp, counts, means) l1_new = get_log_likelihood(data, weights, means, covariances) l1_list.append(l1_new) if abs(l1_new – l1) < thresh : break l1 = l1_new param = {'weights': weights, 'means': means, 'covariances': covariances,