def test_adder_transition_to_powers(self):
num_steps = 3
dtype = dtypes.float64
adder = level_trend.AdderStateSpaceModel(
configuration=state_space_model.StateSpaceModelConfiguration(
dtype=dtype))
test_utils.transition_power_test_template(
test_case=self, model=adder, num_steps=num_steps)
def test_adder_noise_accumulator(self):
num_steps = 3
dtype = dtypes.float64
use_level_noise = True
adder = level_trend.AdderStateSpaceModel(
use_level_noise=use_level_noise,
configuration=state_space_model.StateSpaceModelConfiguration(
dtype=dtype))
test_utils.noise_accumulator_test_template(
test_case=self, model=adder, num_steps=num_steps)
def _replicate_level_trend_models(multivariate_configuration,
univariate_configuration):
"""Helper function to construct a multivariate level/trend component."""
with variable_scope.variable_scope("adder"):
# Construct a level and trend model for each feature, with correlated
# transition noise.
adder_features = []
for feature in range(multivariate_configuration.num_features):
with variable_scope.variable_scope("feature{}".format(feature)):
adder_features.append(level_trend.AdderStateSpaceModel(
configuration=univariate_configuration))
adder_part = state_space_model.StateSpaceCorrelatedFeaturesEnsemble(
ensemble_members=adder_features,
configuration=multivariate_configuration)
return adder_part