def test_dist_empirical(self):
values = Variable(util.Tensor([1, 2, 3]))
log_weights = Variable(util.Tensor([1, 2, 3]))
dist_mean_correct = 2.5752103328704834
dist_stddev_correct = 0.6514633893966675
dist_expectation_sin_correct = 0.3921678960323334
dist_map_sin_mean_correct = 0.3921678960323334
dist_min_correct = 1
dist_max_correct = 3
# dist_sample_shape_correct = []
dist = Empirical(values, log_weights)
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_mean = float(dist.mean)
dist_mean_empirical = float(dist_empirical.mean)
dist_stddev = float(dist.stddev)
dist_stddev_empirical = float(dist_empirical.stddev)
dist_expectation_sin = float(dist.expectation(torch.sin))
dist_map_sin_mean = float(dist.map(torch.sin).mean)
dist_min = float(dist.min)
dist_max = float(dist.max)
dist_sample_shape = list(dist.sample().size())
util.debug('dist_mean', 'dist_mean_empirical', 'dist_mean_correct', 'dist_stddev', 'dist_stddev_empirical', 'dist_stddev_correct', 'dist_expectation_sin', 'dist_expectation_sin_correct', 'dist_map_sin_mean', 'dist_map_sin_mean_correct', 'dist_min', 'dist_min_correct', 'dist_max', 'dist_max_correct')
# self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertAlmostEqual(dist_mean, dist_mean_correct, places=1)
self.assertAlmostEqual(dist_mean_empirical, dist_mean_correct, places=1)
self.assertAlmostEqual(dist_stddev, dist_stddev_correct, places=1)
self.assertAlmostEqual(dist_stddev_empirical, dist_stddev_correct, places=1)
self.assertAlmostEqual(dist_expectation_sin, dist_expectation_sin_correct, places=1)
self.assertAlmostEqual(dist_map_sin_mean, dist_map_sin_mean_correct, places=1)
self.assertAlmostEqual(dist_min, dist_min_correct, places=1)
self.assertAlmostEqual(dist_max, dist_max_correct, places=1)
def test_model_rmh_posterior_with_stop_and_resume_to_disk(self):
file_name = os.path.join(tempfile.mkdtemp(), str(uuid.uuid4()))
posterior_num_runs = 50
posterior_num_traces_each_run = 50
posterior_num_traces_correct = posterior_num_traces_each_run * posterior_num_runs
true_posterior = Normal(7.25, math.sqrt(1/1.2))
posterior_mean_correct = float(true_posterior.mean)
posterior_stddev_correct = float(true_posterior.stddev)
prior_mean_correct = 1.
prior_stddev_correct = math.sqrt(5)
initial_trace = None
for i in range(posterior_num_runs):
posterior_traces = self._model.posterior_traces(num_traces=posterior_num_traces_each_run, inference_engine=InferenceEngine.RANDOM_WALK_METROPOLIS_HASTINGS, observe={'obs0': 8, 'obs1': 9}, initial_trace=initial_trace, file_name=file_name)
initial_trace = posterior_traces[-1]
posterior_traces.close()
posterior = Empirical(file_name=file_name)
posterior.finalize()
posterior = posterior.map(lambda trace: trace.result)
posterior_num_traces = posterior.length
posterior_mean = float(posterior.mean)
posterior_mean_unweighted = float(posterior.unweighted().mean)
posterior_stddev = float(posterior.stddev)
posterior_stddev_unweighted = float(posterior.unweighted().stddev)
kl_divergence = float(pyprob.distributions.Distribution.kl_divergence(true_posterior, Normal(posterior.mean, posterior.stddev)))
util.eval_print('posterior_num_runs', 'posterior_num_traces_each_run', 'posterior_num_traces', 'posterior_num_traces_correct', 'prior_mean_correct', 'posterior_mean_unweighted', 'posterior_mean', 'posterior_mean_correct', 'prior_stddev_correct', 'posterior_stddev_unweighted', 'posterior_stddev', 'posterior_stddev_correct', 'kl_divergence')
self.assertEqual(posterior_num_traces, posterior_num_traces_correct)
self.assertAlmostEqual(posterior_mean, posterior_mean_correct, places=0)
self.assertAlmostEqual(posterior_stddev, posterior_stddev_correct, places=0)
self.assertLess(kl_divergence, 0.25)
