예제 #1
0
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))
max_gen=max(max(generated))
plot_real=empty(0)
plot_est=empty(0)
예제 #2
0
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

plot_real=empty(0)
예제 #3
0
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

plot_real = empty(0)
plot_est = empty(0)
예제 #4
0
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()