print(sample)
# Print samples from single variable
x_sample = x.get_sample(10)
print(x_sample)
# Print samples conditional on an input
in_sample = model.get_sample(10, input_values={mu: 100.})
print(in_sample)
# # Generate data
data = x_real._get_sample(number_samples=50)
# Observe data
x.observe(data[x_real][:, 0, :])
# Variational model
Qnu = LogNormalVariable(0., 1., "nu", learnable=True)
Qmu = NormalVariable(0., 1., "mu", learnable=True)
model.set_posterior_model(ProbabilisticModel([Qmu, Qnu]))
# Inference
inference.stochastic_variational_inference(model,
number_iterations=100,
number_samples=50,
optimizer=chainer.optimizers.Adam(0.1))
loss_list = model.diagnostics["loss curve"]
# print posterior sample
post_samples = model.get_posterior_sample(10)
print(post_samples)
#Generate data
data = y.get_sample(20, input_values={z1: 1, z2: 0})
data.hist(bins=20)
plt.show()
#Observe data
y.observe(data)
#Variational Model
Qz1 = BernulliVariable(logits=0., name="z1", learnable=True)
Qz2 = BernulliVariable(logits=0., name="z2", learnable=True)
variational_model = ProbabilisticModel([Qz1, Qz2])
model.set_posterior_model(variational_model)
# Joint-contrastive inference
inference.perform_inference(
model,
inference_method=ReverseKL(gradient_estimator=Taylor1Estimator),
number_iterations=600,
number_samples=20,
optimizer="SGD",
lr=0.001)
loss_list = model.diagnostics["loss curve"]
#Plot results
plt.plot(loss_list)
plt.show()
#Plot posterior
model.get_posterior_sample(200).hist(bins=20)
plt.show()
# Observe data
noise_level = 0.2
data = np.sin(2 * np.pi * 0.4 * x_range) + noise_level * np.random.normal(
0., 1., (1, num_datapoints))
y.observe(data)
#Variational Model
Qf = Normal(loc=np.zeros((num_datapoints, )),
scale=2.,
name="f(x)",
learnable=True)
variational_model = ProbabilisticModel([Qf])
model.set_posterior_model(variational_model)
# Inference
inference.perform_inference(model,
number_iterations=2000,
number_samples=20,
optimizer='SGD',
lr=0.00001)
loss_list = model.diagnostics["loss curve"]
plt.plot(loss_list)
plt.show()
# Posterior
posterior_samples = model.get_posterior_sample(8000)["f(x)"]
posterior_mean = posterior_samples.mean()
plt.plot(x_range, posterior_mean)
plt.scatter(x_range, data, color="k")
plt.show()
