def test_model_remote_branching_importance_sampling(self):
observation = 6
posterior_correct = util.empirical_to_categorical(
self.true_posterior(observation), max_val=40)
posterior = util.empirical_to_categorical(
self._model.posterior_distribution(samples,
observation=observation),
max_val=40)
posterior_probs = util.to_numpy(posterior._probs[0])
posterior_probs_correct = util.to_numpy(posterior_correct._probs[0])
kl_divergence = float(
util.kl_divergence_categorical(posterior_correct, posterior))
util.debug('samples', 'posterior_probs', 'posterior_probs_correct',
'kl_divergence')
self.assertLess(kl_divergence, 0.25)
def test_model_remote_hmm_posterior_importance_sampling(self):
observation = self._observation
posterior_mean_correct = self._posterior_mean_correct
posterior = self._model.posterior_distribution(samples,
observation=observation)
posterior_mean_unweighted = posterior.unweighted().mean
posterior_mean = posterior.mean
l2_distance = float(
F.pairwise_distance(posterior_mean, posterior_mean_correct).sum())
kl_divergence = float(
sum([
util.kl_divergence_categorical(Categorical(i), Categorical(j))
for (i, j) in zip(posterior_mean, posterior_mean_correct)
]))
util.debug('samples', 'posterior_mean_unweighted', 'posterior_mean',
'posterior_mean_correct', 'l2_distance', 'kl_divergence')
self.assertLess(l2_distance, 10)
def test_model_remote_branching_random_walk_metropolis_hastings(self):
observation = 6
posterior_correct = util.empirical_to_categorical(
self.true_posterior(observation), max_val=40)
posterior = util.empirical_to_categorical(
self._model.posterior_distribution(
samples,
observation=observation,
inference_engine=pyprob.InferenceEngine.
RANDOM_WALK_METROPOLIS_HASTINGS),
max_val=40)
posterior_probs = util.to_numpy(posterior._probs[0])
posterior_probs_correct = util.to_numpy(posterior_correct._probs[0])
kl_divergence = float(
util.kl_divergence_categorical(posterior_correct, posterior))
util.debug('samples', 'posterior_probs', 'posterior_probs_correct',
'kl_divergence')
self.assertLess(kl_divergence, 0.25)
def test_model_remote_hmm_posterior_random_walk_metropolis_hastings(self):
observation = self._observation
posterior_mean_correct = self._posterior_mean_correct
posterior = self._model.posterior_distribution(
samples,
inference_engine=pyprob.InferenceEngine.
RANDOM_WALK_METROPOLIS_HASTINGS,
observation=observation)
posterior_mean_unweighted = posterior.unweighted().mean
posterior_mean = posterior.mean
l2_distance = float(
F.pairwise_distance(posterior_mean, posterior_mean_correct).sum())
kl_divergence = float(
sum([
util.kl_divergence_categorical(Categorical(i), Categorical(j))
for (i, j) in zip(posterior_mean, posterior_mean_correct)
]))
util.debug('samples', 'posterior_mean_unweighted', 'posterior_mean',
'posterior_mean_correct', 'l2_distance', 'kl_divergence')
self.assertLess(l2_distance, 10)
