def test_logprob(self):
num_timesteps = 5
y = self._build_placeholder([1.0, 2.5, 4.3, 6.1, 7.8])
ssm = SemiLocalLinearTrendStateSpaceModel(
num_timesteps=num_timesteps,
level_scale=self._build_placeholder(0.5),
slope_scale=self._build_placeholder(0.5),
slope_mean=self._build_placeholder(0.2),
autoregressive_coef=self._build_placeholder(0.3),
initial_state_prior=tfd.MultivariateNormalDiag(
scale_diag=self._build_placeholder([1., 1.])))
lp = ssm.log_prob(y[:, np.newaxis])
expected_lp = -9.846248626708984
self.assertAllClose(self.evaluate(lp), expected_lp)
def test_matches_locallineartrend(self):
"""SemiLocalLinearTrend with trivial AR process is a LocalLinearTrend."""
level_scale = self._build_placeholder(0.5)
slope_scale = self._build_placeholder(0.5)
initial_level = self._build_placeholder(3.)
initial_slope = self._build_placeholder(-2.)
num_timesteps = 5
y = self._build_placeholder([1.0, 2.5, 4.3, 6.1, 7.8])
semilocal_ssm = SemiLocalLinearTrendStateSpaceModel(
num_timesteps=num_timesteps,
level_scale=level_scale,
slope_scale=slope_scale,
slope_mean=self._build_placeholder(0.),
autoregressive_coef=self._build_placeholder(1.),
initial_state_prior=tfd.MultivariateNormalDiag(
loc=[initial_level, initial_slope],
scale_diag=self._build_placeholder([1., 1.])))
local_ssm = LocalLinearTrendStateSpaceModel(
num_timesteps=num_timesteps,
level_scale=level_scale,
slope_scale=slope_scale,
initial_state_prior=tfd.MultivariateNormalDiag(
loc=[initial_level, initial_slope],
scale_diag=self._build_placeholder([1., 1.])))
semilocal_lp = semilocal_ssm.log_prob(y[:, tf.newaxis])
local_lp = local_ssm.log_prob(y[:, tf.newaxis])
self.assertAllClose(self.evaluate(semilocal_lp),
self.evaluate(local_lp))
semilocal_mean = semilocal_ssm.mean()
local_mean = local_ssm.mean()
self.assertAllClose(self.evaluate(semilocal_mean),
self.evaluate(local_mean))
semilocal_variance = semilocal_ssm.variance()
local_variance = local_ssm.variance()
self.assertAllClose(self.evaluate(semilocal_variance),
self.evaluate(local_variance))