def test_matrices_from_component(self):
num_timesteps = 4
drift_scale = 1.23
period = 12
frequency_multipliers = [1, 3]
component = SmoothSeasonal(period=period,
frequency_multipliers=frequency_multipliers)
ssm = component.make_state_space_model(num_timesteps, [drift_scale])
frequency_0 = 2 * np.pi * frequency_multipliers[0] / period
frequency_1 = 2 * np.pi * frequency_multipliers[1] / period
first_frequency_transition = tf.linalg.LinearOperatorFullMatrix(
[[tf.cos(frequency_0), tf.sin(frequency_0)],
[-tf.sin(frequency_0), tf.cos(frequency_0)]])
second_frequency_transition = tf.linalg.LinearOperatorFullMatrix(
[[tf.cos(frequency_1), tf.sin(frequency_1)],
[-tf.sin(frequency_1), tf.cos(frequency_1)]])
latents_transition = self.evaluate(
tf.linalg.LinearOperatorBlockDiag(
[first_frequency_transition,
second_frequency_transition]).to_dense())
for t in range(num_timesteps):
observation_matrix = self.evaluate(
ssm.get_observation_matrix_for_timestep(t).to_dense())
self.assertAllClose([[1.0, 0.0, 1.0, 0.0]], observation_matrix)
observation_noise_mean = self.evaluate(
ssm.get_observation_noise_for_timestep(t).mean())
observation_noise_covariance = self.evaluate(
ssm.get_observation_noise_for_timestep(t).covariance())
self.assertAllClose([0.0], observation_noise_mean)
self.assertAllClose([[0.0]], observation_noise_covariance)
transition_matrix = self.evaluate(
ssm.get_transition_matrix_for_timestep(t).to_dense())
self.assertAllClose(latents_transition, transition_matrix)
transition_noise_mean = self.evaluate(
ssm.get_transition_noise_for_timestep(t).mean())
transition_noise_covariance = self.evaluate(
ssm.get_transition_noise_for_timestep(t).covariance())
self.assertAllClose(np.zeros([4]), transition_noise_mean)
self.assertAllClose(
np.square(drift_scale) * np.eye(4),
transition_noise_covariance)
def test_accepts_tensor_valued_period_and_frequency_multipliers(self):
period = tf.constant(100.)
frequency_multipliers = tf.constant([1., 3.])
drift_scale = tf.constant([2.])
component = SmoothSeasonal(
period=period, frequency_multipliers=frequency_multipliers)
ssm = component.make_state_space_model(
num_timesteps=3, param_vals=[drift_scale])
self.assertAllEqual(component.latent_size, 4)
self.assertAllEqual(ssm.latent_size, 4)
def _build_sts(self, observed_time_series=None):
smooth_seasonal = SmoothSeasonal(period=42,
frequency_multipliers=[1, 2, 4],
allow_drift=False,
observed_time_series=observed_time_series)
# The test harness doesn't like models with no parameters, so wrap with Sum.
return tfp.sts.Sum([smooth_seasonal],
observed_time_series=observed_time_series)
def _build_sts(self, observed_time_series=None):
return SmoothSeasonal(period=42,
frequency_multipliers=[1, 2, 4],
observed_time_series=observed_time_series)