def test_expected_value(self):
for i, pdfs in enumerate(zip(self.priors, self.posteriors)):
prior, posterior = pdfs
with self.subTest(i=i):
bayesparam = beer.BayesianParameter(prior, posterior)
self.assertArraysAlmostEqual(
bayesparam.expected_value().numpy(),
posterior.expected_sufficient_statistics.numpy())
def test_create(self):
for i, pdfs in enumerate(zip(self.priors, self.posteriors)):
prior, posterior = pdfs
with self.subTest(i=i):
bayesparam = beer.BayesianParameter(prior, posterior)
self.assertArraysAlmostEqual(
bayesparam.natural_grad.numpy(),
np.zeros(len(bayesparam.natural_grad)))
def test_expected_value(self):
for i in range(self.nparams):
with self.subTest(i=i):
pdfs = zip(self.priors[i], self.posteriors[i])
params = [
beer.BayesianParameter(prior, posterior)
for prior, posterior in pdfs
]
param_set = beer.BayesianParameterSet(params)
for j, param in enumerate(param_set):
posterior = self.posteriors[i][j]
self.assertArraysAlmostEqual(
param.expected_value().numpy(),
posterior.expected_sufficient_statistics.numpy())
def test_create(self):
for i in range(self.nparams):
with self.subTest(i=i):
pdfs = zip(self.priors[i], self.posteriors[i])
params = [
beer.BayesianParameter(prior, posterior)
for prior, posterior in pdfs
]
param_set = beer.BayesianParameterSet(params)
self.assertEqual(len(params), len(param_set))
for param in param_set:
self.assertArraysAlmostEqual(
param.natural_grad.numpy(),
np.zeros(len(param.natural_grad)))