ax.plot(cur_pts[:, 0], cur_pts[:, 1], '-', alpha=alpha)
def plot_gaussian_mixture(params, ax):
for log_proportion, mean, cov_sqrt in zip(*unpack_gmm_params(params)):
alpha = np.minimum(1.0, np.exp(log_proportion) * 10)
plot_ellipse(ax, mean, cov_sqrt, alpha)
if __name__ == '__main__':
init_params = init_gmm_params(num_components=10, D=2, scale=0.1)
data = make_pinwheel(radial_std=0.3,
tangential_std=0.05,
num_classes=3,
num_per_class=100,
rate=0.4)
def objective(params):
return -gmm_log_likelihood(params, data)
flattened_obj, unflatten, flattened_init_params =\
flatten_func(objective, init_params)
fig = plt.figure(figsize=(12, 8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.show(block=False)
def callback(flattened_params):
params = unflatten(flattened_params)
def plot_ellipse(ax, mean, cov_sqrt, alpha, num_points=100):
angles = np.linspace(0, 2*np.pi, num_points)
circle_pts = np.vstack([np.cos(angles), np.sin(angles)]).T * 2.0
cur_pts = mean + np.dot(circle_pts, cov_sqrt)
ax.plot(cur_pts[:, 0], cur_pts[:, 1], '-', alpha=alpha)
def plot_gaussian_mixture(params, ax):
for log_proportion, mean, cov_sqrt in zip(*unpack_gmm_params(params)):
alpha = np.minimum(1.0, np.exp(log_proportion) * 10)
plot_ellipse(ax, mean, cov_sqrt, alpha)
if __name__ == '__main__':
init_params = init_gmm_params(num_components=10, D=2, scale=0.1)
data = make_pinwheel(radial_std=0.3, tangential_std=0.05, num_classes=3,
num_per_class=100, rate=0.4)
def objective(params):
return -gmm_log_likelihood(params, data)
flattened_obj, unflatten, flattened_init_params =\
flatten_func(objective, init_params)
fig = plt.figure(figsize=(12,8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.show(block=False)
def callback(flattened_params):
params = unflatten(flattened_params)
print("Log likelihood {}".format(-objective(params)))
ax.cla()