def optimize(self, num_meta):
print("first Optimizing variational parameters...")
# self.update_param = self.init_var_params.copy()
for i in range(num_meta):
self.update_param = adam(
self.gradient, self.update_param, step_size=0.1, num_iters=100, callback=self.callback
)
def optimize_restarts(self, num_restarts=30, robust=False, verbose=True):
print("Optimizing variational parameters...")
#todo: reset the num_iter, num_iter = 1 for the ease of debug
print("shape of x is: "+ str(self.X.shape))
self.update_param = self.init_var_params.copy()
print("current param is "+ str(self.init_var_params))
for i in range(num_restarts):
self.update_param = adam(self.gradient, self.update_param,
step_size=0.1, num_iters=100, callback=self.callback)
zs = func(np.concatenate([np.atleast_2d(X.ravel()), np.atleast_2d(Y.ravel())]).T)
Z = zs.reshape(X.shape)
plt.contour(X, Y, Z)
ax.set_yticks([])
ax.set_xticks([])
# Set up figure.
fig = plt.figure(figsize=(8, 8), facecolor="white")
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
def callback(params, t, g):
print("Iteration {} lower bound {}".format(t, -objective(params, t)))
plt.cla()
target_distribution = lambda x: np.exp(log_posterior(x, t))
plot_isocontours(ax, target_distribution)
mean, log_std = unpack_params(params)
variational_contour = lambda x: mvn.pdf(x, mean, np.diag(np.exp(2 * log_std)))
plot_isocontours(ax, variational_contour)
plt.draw()
plt.pause(1.0 / 30.0)
print("Optimizing variational parameters...")
init_mean = -1 * np.ones(D)
init_log_std = -5 * np.ones(D)
init_var_params = np.concatenate([init_mean, init_log_std])
variational_params = adam(gradient, init_var_params, step_size=0.1, num_iters=2000, callback=callback)
