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]))
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))
#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 EM
est_gmm=GMM(2, cov_type)
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_cov1=est_gmm.get_nth_cov(0)
est_cov2=est_gmm.get_nth_cov(1)
est_coef=est_gmm.get_coef()
print est_mean1
print est_cov1
print est_mean2
print est_cov2
print est_coef
#plot real GMM, data and estimated GMM
min_x_gen=min(min(generated[[0]]))-0.1
max_x_gen=max(max(generated[[0]]))+0.1
min_y_gen=min(min(generated[[1]]))-0.1
max_y_gen=max(max(generated[[1]]))+0.1
real_gmm.set_nth_cov(array([[1.0, 0.2], [0.2, 0.1]]), 0)
real_gmm.set_nth_cov(array([[0.3, 0.1], [0.1, 1.0]]), 1)
real_gmm.set_coef(array([0.3, 0.7]))
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)
est_gmm = GMM(2, cov_type)
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_cov1 = est_gmm.get_nth_cov(0)
est_cov2 = est_gmm.get_nth_cov(1)
est_coef = est_gmm.get_coef()
print est_mean1
print est_cov1
print est_mean2
print est_cov2
print est_coef
min_x_gen = min(min(generated[[0]])) - 0.1
max_x_gen = max(max(generated[[0]])) + 0.1
min_y_gen = min(min(generated[[1]])) - 0.1
max_y_gen = max(max(generated[[1]])) + 0.1
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()
