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
