real_gmm.set_nth_cov(array([[0.3]]), 0) real_gmm.set_nth_cov(array([[0.1]]), 1) real_gmm.set_nth_cov(array([[0.2]]), 2) real_gmm.set_coef(array([0.3, 0.5, 0.2])) #generate training set from real GMM generated=array([real_gmm.sample()]) for i in range(199): generated=append(generated, array([real_gmm.sample()]), axis=1) feat_train=RealFeatures(generated) #train GMM using EM est_gmm=GMM(3) est_gmm.train(feat_train) est_gmm.train_em(min_cov, max_iter, min_change) #get and print estimated means and covariances 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
real_gmm.set_nth_cov(array([[0.2, 0.1],[0.1, 0.5]]), 1) real_gmm.set_nth_cov(array([[0.3, -0.2],[-0.2, 0.8]]), 2) real_gmm.set_coef(array([0.3, 0.4, 0.3])) #generate training set from real GMM generated=array([real_gmm.sample()]) for i in range(199): generated=append(generated, array([real_gmm.sample()]), axis=0) generated=generated.transpose() feat_train=RealFeatures(generated) #train GMM using SMEM and print log-likelihood est_smem_gmm=GMM(3, cov_type) est_smem_gmm.train(feat_train) est_smem_gmm.set_nth_mean(array([2.0, 0.0]), 0) est_smem_gmm.set_nth_mean(array([-2.0, -2.0]), 1) est_smem_gmm.set_nth_mean(array([-3.0, -3.0]), 2) est_smem_gmm.set_nth_cov(array([[1.0, 0.0],[0.0, 1.0]]), 0) est_smem_gmm.set_nth_cov(array([[1.0, 0.0],[0.0, 1.0]]), 1) est_smem_gmm.set_nth_cov(array([[1.0, 0.0],[0.0, 1.0]]), 2) est_smem_gmm.set_coef(array([0.3333, 0.3333, 0.3334])) print est_smem_gmm.train_smem(max_iter, max_cand, min_cov, max_em_iter, min_change) #train GMM using EM and bad initial conditions and print log-likelihood est_em_gmm=GMM(3, cov_type)
real_gmm.set_nth_cov(array([[0.2, 0.1], [0.1, 0.5]]), 1) real_gmm.set_nth_cov(array([[0.3, -0.2], [-0.2, 0.8]]), 2) real_gmm.set_coef(array([0.3, 0.4, 0.3])) #generate training set from real GMM generated = array([real_gmm.sample()]) for i in range(199): generated = append(generated, array([real_gmm.sample()]), axis=0) generated = generated.transpose() feat_train = RealFeatures(generated) #train GMM using SMEM and print log-likelihood est_smem_gmm = GMM(3, cov_type) est_smem_gmm.train(feat_train) est_smem_gmm.set_nth_mean(array([2.0, 0.0]), 0) est_smem_gmm.set_nth_mean(array([-2.0, -2.0]), 1) est_smem_gmm.set_nth_mean(array([-3.0, -3.0]), 2) est_smem_gmm.set_nth_cov(array([[1.0, 0.0], [0.0, 1.0]]), 0) est_smem_gmm.set_nth_cov(array([[1.0, 0.0], [0.0, 1.0]]), 1) est_smem_gmm.set_nth_cov(array([[1.0, 0.0], [0.0, 1.0]]), 2) est_smem_gmm.set_coef(array([0.3333, 0.3333, 0.3334])) print est_smem_gmm.train_smem(max_iter, max_cand, min_cov, max_em_iter, min_change) #train GMM using EM and bad initial conditions and print log-likelihood
real_gmm.set_nth_cov(array([[0.3]]), 0) real_gmm.set_nth_cov(array([[0.1]]), 1) real_gmm.set_nth_cov(array([[0.2]]), 2) real_gmm.set_coef(array([0.3, 0.5, 0.2])) #generate training set from real GMM generated = array([real_gmm.sample()]) for i in range(199): generated = append(generated, array([real_gmm.sample()]), axis=1) feat_train = RealFeatures(generated) #train GMM using EM est_gmm = GMM(3) est_gmm.train(feat_train) est_gmm.train_em(min_cov, max_iter, min_change) #get and print estimated means and covariances 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