Python GMM.get_nth_mean 예제들

예제 #1

파일: em_1d_gmm.py 프로젝트: 42MachineLearning/shogun

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
print est_cov3
print est_coef

#plot real GMM, data and estimated GMM

예제 #2

파일: em_1d_gmm.py 프로젝트: manantomar/test

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
print est_cov3
print est_coef

#plot real GMM, data and estimated GMM

예제 #3

파일: em_2d_gmm.py 프로젝트: 42MachineLearning/shogun

#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

예제 #4

#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