#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
min_gen=min(min(generated))
max_gen=max(max(generated))
plot_real=empty(0)
plot_est=empty(0)
for i in arange(min_gen, max_gen, 0.001):
plot_real=append(plot_real, array([exp(real_gmm.cluster(array([i]))[3])]))
plot_est=append(plot_est, array([exp(est_gmm.cluster(array([i]))[3])]))
real_plot=plot(arange(min_gen, max_gen, 0.001), plot_real, "b")
est_plot=plot(arange(min_gen, max_gen, 0.001), plot_est, "r")
real_hist=hist(generated.transpose(), bins=50, normed=True, fc="gray")
legend(("Real GMM", "Estimated GMM"))
connect('key_press_event', util.quit)
show()
print est_em_gmm.train_em(min_cov, max_em_iter, min_change)
#plot real GMM, data and both estimated GMMs
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_smem = empty(0)
plot_est_em = empty(0)
for i in arange(min_x_gen, max_x_gen, 0.05):
for j in arange(min_y_gen, max_y_gen, 0.05):
plot_real = append(plot_real,
array([exp(real_gmm.cluster(array([i, j]))[3])]))
plot_est_smem = append(
plot_est_smem, array([exp(est_smem_gmm.cluster(array([i,
j]))[3])]))
plot_est_em = append(
plot_est_em, array([exp(est_em_gmm.cluster(array([i, j]))[3])]))
plot_real = reshape(plot_real, (arange(min_x_gen, max_x_gen, 0.05).shape[0],
arange(min_y_gen, max_y_gen, 0.05).shape[0]))
plot_est_smem = reshape(plot_est_smem,
(arange(min_x_gen, max_x_gen, 0.05).shape[0],
arange(min_y_gen, max_y_gen, 0.05).shape[0]))
plot_est_em = reshape(plot_est_em,
(arange(min_x_gen, max_x_gen, 0.05).shape[0],
arange(min_y_gen, max_y_gen, 0.05).shape[0]))
#train GMM using SMEM and print log-likelihood
est_smem_gmm = GMM(3)
est_smem_gmm.train(feat_train)
print est_smem_gmm.train_smem(max_iter, max_cand, min_cov, max_em_iter,
min_change)
#train GMM using EM and print log-likelihood
est_em_gmm = GMM(3)
est_em_gmm.train(feat_train)
print est_em_gmm.train_em(min_cov, max_em_iter, min_change)
#plot real GMM, data and both estimated GMMs
min_gen = min(min(generated))
max_gen = max(max(generated))
plot_real = empty(0)
plot_est_smem = empty(0)
plot_est_em = empty(0)
for i in arange(min_gen, max_gen, 0.001):
plot_real = append(plot_real,
array([exp(real_gmm.cluster(array([i]))[3])]))
plot_est_smem = append(plot_est_smem,
array([exp(est_smem_gmm.cluster(array([i]))[3])]))
plot_est_em = append(plot_est_em,
array([exp(est_em_gmm.cluster(array([i]))[3])]))
real_plot = plot(arange(min_gen, max_gen, 0.001), plot_real, "b")
est_em_plot = plot(arange(min_gen, max_gen, 0.001), plot_est_em, "g")
est_smem_plot = plot(arange(min_gen, max_gen, 0.001), plot_est_smem, "r")
real_hist = hist(generated.transpose(), bins=50, normed=True, fc="gray")
legend(("Real GMM", "Estimated EM GMM", "Estimated SMEM GMM"))
connect('key_press_event', util.quit)
show()
print est_em_gmm.train_em(min_cov, max_em_iter, min_change)
#plot real GMM, data and both estimated GMMs
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_smem=empty(0)
plot_est_em=empty(0)
for i in arange(min_x_gen, max_x_gen, 0.05):
for j in arange(min_y_gen, max_y_gen, 0.05):
plot_real=append(plot_real, array([exp(real_gmm.cluster(array([i, j]))[3])]))
plot_est_smem=append(plot_est_smem, array([exp(est_smem_gmm.cluster(array([i, j]))[3])]))
plot_est_em=append(plot_est_em, array([exp(est_em_gmm.cluster(array([i, j]))[3])]))
plot_real=reshape(plot_real, (arange(min_x_gen, max_x_gen, 0.05).shape[0], arange(min_y_gen, max_y_gen, 0.05).shape[0]))
plot_est_smem=reshape(plot_est_smem, (arange(min_x_gen, max_x_gen, 0.05).shape[0], arange(min_y_gen, max_y_gen, 0.05).shape[0]))
plot_est_em=reshape(plot_est_em, (arange(min_x_gen, max_x_gen, 0.05).shape[0], arange(min_y_gen, max_y_gen, 0.05).shape[0]))
real_plot=contour(arange(min_x_gen, max_x_gen, 0.05), arange(min_y_gen, max_y_gen, 0.05), plot_real.transpose(), colors="b")
est_smem_plot=contour(arange(min_x_gen, max_x_gen, 0.05), arange(min_y_gen, max_y_gen, 0.05), plot_est_smem.transpose(), colors="r")
est_em_plot=contour(arange(min_x_gen, max_x_gen, 0.05), arange(min_y_gen, max_y_gen, 0.05), plot_est_em.transpose(), colors="g")
real_scatter=scatter(generated[[0]], generated[[1]], c="gray")
legend((real_plot.collections[0], est_em_plot.collections[0], est_smem_plot.collections[0]), ("Real GMM", "Estimated EM GMM", "Estimated SMEM GMM"))
connect('key_press_event', util.quit)
show()
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
min_gen = min(min(generated))
max_gen = max(max(generated))
plot_real = empty(0)
plot_est = empty(0)
for i in arange(min_gen, max_gen, 0.001):
plot_real = append(plot_real,
array([exp(real_gmm.cluster(array([i]))[3])]))
plot_est = append(plot_est, array([exp(est_gmm.cluster(array([i]))[3])]))
real_plot = plot(arange(min_gen, max_gen, 0.001), plot_real, "b")
est_plot = plot(arange(min_gen, max_gen, 0.001), plot_est, "r")
real_hist = hist(generated.transpose(), bins=50, normed=True, fc="gray")
legend(("Real GMM", "Estimated GMM"))
connect('key_press_event', util.quit)
show()
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)
plot_est = empty(0)
for i in arange(min_x_gen, max_x_gen, 0.05):
for j in arange(min_y_gen, max_y_gen, 0.05):
plot_real = append(plot_real,
array([exp(real_gmm.cluster(array([i, j]))[2])]))
plot_est = append(plot_est,
array([exp(est_gmm.cluster(array([i, j]))[2])]))
plot_real = reshape(plot_real, (arange(min_x_gen, max_x_gen, 0.05).shape[0],
arange(min_y_gen, max_y_gen, 0.05).shape[0]))
plot_est = reshape(plot_est, (arange(min_x_gen, max_x_gen, 0.05).shape[0],
arange(min_y_gen, max_y_gen, 0.05).shape[0]))
real_plot = contour(arange(min_x_gen, max_x_gen, 0.05),
arange(min_y_gen, max_y_gen, 0.05),
plot_real.transpose(),
colors="b")
est_plot = contour(arange(min_x_gen, max_x_gen, 0.05),
arange(min_y_gen, max_y_gen, 0.05),
plot_est.transpose(),
