def test_dist_poisson_multivariate_batched(self):
dist_sample_shape_correct = [2, 3]
dist_means_correct = [[1, 2, 15], [100, 200, 300]]
dist_stddevs_correct = [[math.sqrt(1), math.sqrt(2), math.sqrt(15)], [math.sqrt(100), math.sqrt(200), math.sqrt(300)]]
dist_rates_correct = [[1, 2, 15], [100, 200, 300]]
dist_log_probs_correct = [[sum([-1, -1.30685, -2.27852])], [sum([-3.22236, -3.56851, -3.77110])]]
dist = Poisson(dist_rates_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_rates = util.to_numpy(dist.rate)
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_rates', 'dist_rates_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.25))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.25))
self.assertTrue(np.allclose(dist_rates, dist_rates_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_poisson_multivariate_from_flat_params(self):
dist_sample_shape_correct = [1, 3]
dist_means_correct = [[1, 2, 15]]
dist_stddevs_correct = [[math.sqrt(1), math.sqrt(2), math.sqrt(15)]]
dist_rates_correct = [[1, 2, 15]]
dist_log_probs_correct = [sum([-1, -1.30685, -2.27852])]
dist = Poisson(dist_rates_correct[0])
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_rates = util.to_numpy(dist.rate)
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_rates', 'dist_rates_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_rates, dist_rates_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def test_dist_poisson_batched(self):
dist_sample_shape_correct = [2, 1]
dist_means_correct = [[4], [100]]
dist_stddevs_correct = [[math.sqrt(4)], [math.sqrt(100)]]
dist_rates_correct = [[4], [100]]
dist_log_probs_correct = [[-1.63288], [-3.22236]]
dist = Poisson(dist_rates_correct)
dist_sample_shape = list(dist.sample().size())
dist_empirical = Empirical([dist.sample() for i in range(empirical_samples)])
dist_rates = util.to_numpy(dist.rate)
dist_means = util.to_numpy(dist.mean)
dist_means_empirical = util.to_numpy(dist_empirical.mean)
dist_stddevs = util.to_numpy(dist.stddev)
dist_stddevs_empirical = util.to_numpy(dist_empirical.stddev)
dist_log_probs = util.to_numpy(dist.log_prob(dist_means_correct))
util.debug('dist_sample_shape', 'dist_sample_shape_correct', 'dist_rates', 'dist_rates_correct', 'dist_means', 'dist_means_empirical', 'dist_means_correct', 'dist_stddevs', 'dist_stddevs_empirical', 'dist_stddevs_correct', 'dist_log_probs', 'dist_log_probs_correct')
self.assertEqual(dist_sample_shape, dist_sample_shape_correct)
self.assertTrue(np.allclose(dist_means, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_means_empirical, dist_means_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_stddevs_empirical, dist_stddevs_correct, atol=0.1))
self.assertTrue(np.allclose(dist_rates, dist_rates_correct, atol=0.1))
self.assertTrue(np.allclose(dist_log_probs, dist_log_probs_correct, atol=0.1))
def forward(self):
count_prior = Poisson(4)
r = pyprob.sample(count_prior)
if 4 < float(r):
l = 6
else:
l = 1 + self.fibonacci(3 * int(r)) + pyprob.sample(count_prior)
pyprob.observe(Poisson(l), name='obs')
return r
def true_posterior(self, observe=6):
count_prior = Poisson(4)
vals = []
log_weights = []
for r in range(40):
for s in range(40):
if 4 < float(r):
l = 6
else:
f = self.fibonacci(3 * r)
l = 1 + f + count_prior.sample()
vals.append(r)
log_weights.append(Poisson(l).log_prob(observe) + count_prior.log_prob(r) + count_prior.log_prob(s))
return Empirical(vals, log_weights)
def test_distributions_remote(self):
num_samples = 4000
prior_normal_mean_correct = Normal(1.75, 0.5).mean
prior_uniform_mean_correct = Uniform(1.2, 2.5).mean
prior_categorical_mean_correct = 1. # Categorical([0.1, 0.5, 0.4])
prior_poisson_mean_correct = Poisson(4.0).mean
prior_bernoulli_mean_correct = Bernoulli(0.2).mean
prior_beta_mean_correct = Beta(1.2, 2.5).mean
prior_exponential_mean_correct = Exponential(2.2).mean
prior_gamma_mean_correct = Gamma(0.5, 1.2).mean
prior_log_normal_mean_correct = LogNormal(0.5, 0.2).mean
prior_binomial_mean_correct = Binomial(10, 0.72).mean
prior_weibull_mean_correct = Weibull(1.1, 0.6).mean
prior = self._model.prior(num_samples)
prior_normal = prior.map(
lambda trace: trace.named_variables['normal'].value)
prior_uniform = prior.map(
lambda trace: trace.named_variables['uniform'].value)
prior_categorical = prior.map(
lambda trace: trace.named_variables['categorical'].value)
prior_poisson = prior.map(
lambda trace: trace.named_variables['poisson'].value)
prior_bernoulli = prior.map(
lambda trace: trace.named_variables['bernoulli'].value)
prior_beta = prior.map(
lambda trace: trace.named_variables['beta'].value)
prior_exponential = prior.map(
lambda trace: trace.named_variables['exponential'].value)
prior_gamma = prior.map(
lambda trace: trace.named_variables['gamma'].value)
prior_log_normal = prior.map(
lambda trace: trace.named_variables['log_normal'].value)
prior_binomial = prior.map(
lambda trace: trace.named_variables['binomial'].value)
prior_weibull = prior.map(
lambda trace: trace.named_variables['weibull'].value)
prior_normal_mean = util.to_numpy(prior_normal.mean)
prior_uniform_mean = util.to_numpy(prior_uniform.mean)
prior_categorical_mean = util.to_numpy(int(prior_categorical.mean))
prior_poisson_mean = util.to_numpy(prior_poisson.mean)
prior_bernoulli_mean = util.to_numpy(prior_bernoulli.mean)
prior_beta_mean = util.to_numpy(prior_beta.mean)
prior_exponential_mean = util.to_numpy(prior_exponential.mean)
prior_gamma_mean = util.to_numpy(prior_gamma.mean)
prior_log_normal_mean = util.to_numpy(prior_log_normal.mean)
prior_binomial_mean = util.to_numpy(prior_binomial.mean)
prior_weibull_mean = util.to_numpy(prior_weibull.mean)
util.eval_print('num_samples', 'prior_normal_mean',
'prior_normal_mean_correct', 'prior_uniform_mean',
'prior_uniform_mean_correct', 'prior_categorical_mean',
'prior_categorical_mean_correct', 'prior_poisson_mean',
'prior_poisson_mean_correct', 'prior_bernoulli_mean',
'prior_bernoulli_mean_correct', 'prior_beta_mean',
'prior_beta_mean_correct', 'prior_exponential_mean',
'prior_exponential_mean_correct', 'prior_gamma_mean',
'prior_gamma_mean_correct', 'prior_log_normal_mean',
'prior_log_normal_mean_correct', 'prior_binomial_mean',
'prior_binomial_mean_correct', 'prior_weibull_mean',
'prior_weibull_mean_correct')
self.assertTrue(
np.allclose(prior_normal_mean, prior_normal_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_uniform_mean,
prior_uniform_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_categorical_mean,
prior_categorical_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_poisson_mean,
prior_poisson_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_bernoulli_mean,
prior_bernoulli_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_beta_mean, prior_beta_mean_correct, atol=0.1))
self.assertTrue(
np.allclose(prior_exponential_mean,
prior_exponential_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_gamma_mean, prior_gamma_mean_correct, atol=0.1))
self.assertTrue(
np.allclose(prior_log_normal_mean,
prior_log_normal_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_binomial_mean,
prior_binomial_mean_correct,
atol=0.1))
self.assertTrue(
np.allclose(prior_weibull_mean,
prior_weibull_mean_correct,
atol=0.1))