def testReproducibility(self):
def target_log_prob_fn(event):
return tfd.Normal(loc=0., scale=1.).log_prob(event)
tf.set_random_seed(4)
xs = no_u_turn_sampler.kernel(target_log_prob_fn=target_log_prob_fn,
current_state=[0.],
step_size=[0.3],
seed=3)
tf.set_random_seed(4)
ys = no_u_turn_sampler.kernel(target_log_prob_fn=target_log_prob_fn,
current_state=[0.],
step_size=[0.3],
seed=3)
for x, y in zip(xs, ys):
self.assertAllEqual(x, y)
def testMultivariateNormalNd(self, event_size, num_samples):
def target_log_prob_fn(event):
return tfd.MultivariateNormalFullCovariance(
loc=tf.zeros(event_size),
covariance_matrix=tf.eye(event_size)).log_prob(event)
state = tf.zeros(event_size)
samples = []
for seed in range(num_samples):
[state], _, _ = no_u_turn_sampler.kernel(
target_log_prob_fn=target_log_prob_fn,
current_state=[state],
step_size=[0.3],
seed=seed)
npstate = state.numpy()
samples.append([npstate[0], npstate[1]])
samples = np.array(samples)
plt.scatter(samples[:, 0], samples[:, 1])
savefig("projection_chain_{}d_normal_{}_steps.png".format(
event_size, num_samples))
plt.close()
target_samples = tfd.MultivariateNormalFullCovariance(
loc=tf.zeros(event_size),
covariance_matrix=tf.eye(event_size)).sample(num_samples,
seed=4).numpy()
plt.scatter(target_samples[:, 0], target_samples[:, 1])
savefig("projection_independent_{}d_normal_{}_samples.png".format(
event_size, num_samples))
plt.close()
def testSkewedMultivariateNormal2d(self):
def target_log_prob_fn(event):
return tfd.MultivariateNormalFullCovariance(
loc=tf.zeros(2),
covariance_matrix=tf.diag([1., 10.])).log_prob(event)
rng = np.random.RandomState(seed=7)
states = tf.cast(rng.normal(scale=[1.0, 10.0], size=[10, 2]),
tf.float32)
plt.scatter(states[:, 0], states[:, 1])
savefig("skewed_start_positions_2d.png")
plt.close()
samples = []
for seed, state in enumerate(states):
[state], _, _ = no_u_turn_sampler.kernel(
target_log_prob_fn=target_log_prob_fn,
current_state=[state],
step_size=[0.3],
seed=seed)
samples.append(state)
samples = tf.stack(samples).numpy()
plt.scatter(samples[:, 0], samples[:, 1])
savefig("one_step_skewed_posterior_conservation_2d.png")
plt.close()
plot_with_expectation(
samples[:, 0],
dist=scipy.stats.norm(0, 1),
suffix="one_step_skewed_posterior_conservation_2d_dim_0.png")
plot_with_expectation(
samples[:, 1],
dist=scipy.stats.norm(0, 10),
suffix="one_step_skewed_posterior_conservation_2d_dim_1.png")
def testLogitBeta(self):
def target_log_prob_fn(event):
return tfd.TransformedDistribution(
distribution=tfd.Beta(concentration0=1.0, concentration1=3.0),
bijector=tfb.Invert(tfb.Sigmoid())).log_prob(event)
states = tfd.TransformedDistribution(
distribution=tfd.Beta(concentration0=1.0, concentration1=3.0),
bijector=tfb.Invert(tfb.Sigmoid())).sample(10, seed=7)
plt.hist(states.numpy(), bins=30)
savefig("logit_beta_start_positions.png")
plt.close()
samples = []
for seed, state in enumerate(states):
[state], _, _ = no_u_turn_sampler.kernel(
target_log_prob_fn=target_log_prob_fn,
current_state=[state],
step_size=[0.3],
seed=seed)
samples.append(state)
samples = np.array(samples)
plt.hist(samples, bins=30)
savefig("one_step_logit_beta_posterior_conservation.png")
plt.close()
_ = scipy.stats.ks_2samp(samples.flatten(), states.numpy().flatten())
def testOneStepFromOrigin(self):
def target_log_prob_fn(event):
return tfd.Normal(loc=0., scale=1.).log_prob(event)
samples = []
for seed in range(10):
[state], _, _ = no_u_turn_sampler.kernel(
target_log_prob_fn=target_log_prob_fn,
current_state=[0.],
step_size=[0.3],
seed=seed)
samples.append(state)
samples = np.array(samples)
plt.hist(samples, bins=30)
savefig("one_step_from_origin.png")
plt.close()
def testNormal(self):
def target_log_prob_fn(event):
return tfd.Normal(loc=0., scale=1.).log_prob(event)
rng = np.random.RandomState(seed=7)
states = tf.cast(rng.normal(size=10), dtype=tf.float32)
tf.set_random_seed(2)
samples = []
for seed, state in enumerate(states):
[state], _, _ = no_u_turn_sampler.kernel(
target_log_prob_fn=target_log_prob_fn,
current_state=[state],
step_size=[0.3],
seed=seed)
samples.append(state)
samples = np.array(samples)
plot_with_expectation(samples,
dist=scipy.stats.norm(0, 1),
suffix="one_step_posterior_conservation_normal.png")