def test_logprob(self): y = self._build_placeholder([1.0, 1.3, 1.9, 2.9, 2.1]) ssm = LocalLevelStateSpaceModel( num_timesteps=5, level_scale=0.5, initial_state_prior=tfd.MultivariateNormalDiag( scale_diag=self._build_placeholder([1.]))) lp = ssm.log_prob(y[..., np.newaxis]) expected_lp = -6.5021 self.assertAllClose(self.evaluate(lp), expected_lp)
def testEqualsLocalLevel(self): # An AR1 process with coef 1 is just a random walk, equivalent to a local # level model. Test that both models define the same distribution # (log-prob). num_timesteps = 10 observed_time_series = self._build_placeholder( np.random.randn(num_timesteps, 1)) level_scale = self._build_placeholder(0.1) # We'll test an AR1 process, and also (just for kicks) that the trivial # embedding as an AR2 process gives the same model. coefficients_order1 = np.array([1.]).astype(self.dtype) coefficients_order2 = np.array([1., 0.]).astype(self.dtype) ar1_ssm = AutoregressiveStateSpaceModel( num_timesteps=num_timesteps, coefficients=coefficients_order1, level_scale=level_scale, initial_state_prior=tfd.MultivariateNormalDiag( scale_diag=[level_scale])) ar2_ssm = AutoregressiveStateSpaceModel( num_timesteps=num_timesteps, coefficients=coefficients_order2, level_scale=level_scale, initial_state_prior=tfd.MultivariateNormalDiag( scale_diag=[level_scale, 1.])) local_level_ssm = LocalLevelStateSpaceModel( num_timesteps=num_timesteps, level_scale=level_scale, initial_state_prior=tfd.MultivariateNormalDiag( scale_diag=[level_scale])) ar1_lp, ar2_lp, ll_lp = self.evaluate( (ar1_ssm.log_prob(observed_time_series), ar2_ssm.log_prob(observed_time_series), local_level_ssm.log_prob(observed_time_series))) self.assertAllClose(ar1_lp, ll_lp) self.assertAllClose(ar2_lp, ll_lp)