generated=array([real_gmm.sample()]) for i in range(199): generated=append(generated, array([real_gmm.sample()]), axis=1) feat_train=RealFeatures(generated) est_gmm=GMM(3) est_gmm.train(feat_train) est_gmm.train_em(min_cov, max_iter, min_change) est_mean1=est_gmm.get_nth_mean(0) est_mean2=est_gmm.get_nth_mean(1) est_mean3=est_gmm.get_nth_mean(2) est_cov1=est_gmm.get_nth_cov(0) est_cov2=est_gmm.get_nth_cov(1) est_cov3=est_gmm.get_nth_cov(2) est_coef=est_gmm.get_coef() print est_mean1 print est_cov1 print est_mean2 print est_cov2 print est_mean3 print est_cov3 print est_coef min_gen=min(min(generated)) max_gen=max(max(generated)) plot_real=empty(0) plot_est=empty(0) for i in arange(min_gen, max_gen, 0.001): plot_real=append(plot_real, array([real_gmm.cluster(array([i]))[3]])) plot_est=append(plot_est, array([est_gmm.cluster(array([i]))[3]]))
import numpy as np import os from shogun.Features import RealFeatures from shogun.Distribution import GMM from shogun.Library import Math_init_random # Load the data. f = open(os.path.dirname(__file__) + '../data/mvnrnd.data') data = np.fromfile(f, dtype=np.float64, sep=' ') data = data.reshape(-1, 2) f.close() Math_init_random(5) feat = RealFeatures(data.T) # Calculate mixture of Gaussians. gmm = GMM(2, 0) gmm.set_features(feat) gmm.train_em() # Vector of covariances; one for each Gaussian. print gmm.get_nth_cov(0) print gmm.get_nth_cov(1) # The vector of means. print gmm.get_nth_mean(0) print gmm.get_nth_mean(1) # The a priori weights of each Gaussian. print gmm.get_coef()