e = Ellipse(xy=center, width=width, height=height,
angle=angle, color='m', alpha=0.5, clip_box = ax.bbox)
ax.add_artist(e)
ax1_min, ax1_max, ax2_min, ax2_max = plt.axis()
plt.xlim((x1_min, x1_max))
plt.ylim((x2_min, x2_max))
plt.title(u'GMM', fontsize=20)
plt.grid(True)
# DPGMM
n_components = 3
dpgmm = DPGMM(n_components=n_components, alpha=1, covariance_type='full', random_state=0)
dpgmm.fit(x)
centers = dpgmm.means_
covs = dpgmm._get_covars()
print 'DPGMM均值 = \n', centers
print 'DPGMM方差 = \n', covs
y_hat = dpgmm.predict(x)
# print y_hat
ax = plt.subplot(212)
grid_hat = dpgmm.predict(grid_test)
grid_hat = grid_hat.reshape(x1.shape)
plt.pcolormesh(x1, x2, grid_hat, cmap=cm)
plt.scatter(x[:, 0], x[:, 1], s=30, c=y, cmap=cm, marker='o')
for i, cc in enumerate(zip(centers, covs)):
if i not in y_hat:
continue
center, cov = cc
ax1_min, ax1_max, ax2_min, ax2_max = plt.axis()
plt.xlim((x1_min, x1_max))
plt.ylim((x2_min, x2_max))
plt.title(u'GMM', fontsize=20)
plt.grid(True)
# DPGMM
n_components = 3
dpgmm = DPGMM(n_components=n_components,
alpha=1,
covariance_type='full',
random_state=0)
dpgmm.fit(x)
centers = dpgmm.means_
covs = dpgmm._get_covars()
print 'DPGMM均值 = \n', centers
print 'DPGMM方差 = \n', covs
y_hat = dpgmm.predict(x)
# print y_hat
ax = plt.subplot(212)
grid_hat = dpgmm.predict(grid_test)
grid_hat = grid_hat.reshape(x1.shape)
plt.pcolormesh(x1, x2, grid_hat, cmap=cm)
plt.scatter(x[:, 0], x[:, 1], s=30, c=y, cmap=cm, marker='o')
for i, cc in enumerate(zip(centers, covs)):
if i not in y_hat:
continue
center, cov = cc
