parameters: (self,
                 periodicities,
                 num_features,
                 cycle_num_latent_values=11,
                 moving_average_order=4,
                 autoregressive_order=0,
                 exogenous_feature_columns=None,
                 exogenous_update_condition=None,
                 dtype=dtypes.float64,
                 anomaly_prior_probability=None,
                 optimizer=None,
                 model_dir=None,
                 config=None,
                 head_type=ts_head_lib.TimeSeriesRegressionHead)
docstring: Initialize the Estimator.
  
      Args:
        periodicities: The expected periodicity of the data (for example 24 if
          feeding hourly data with a daily periodicity, or 60 * 24 if feeding
          minute-level data with daily periodicity). Either a scalar or a list.
          This parameter can be any real value, and does not control the size of
          the model. However, increasing this without increasing
          `num_values_per_cycle` will lead to smoother periodic behavior, as the
          same number of distinct values will be cycled through over a longer
          period of time.
        num_features: The dimensionality of the time series (one for univariate,
          more than one for multivariate).
        cycle_num_latent_values: Along with `moving_average_order` and
          `num_features`, controls the latent state size of the model. Square
          matrices of size `num_features * (moving_average_order +
          cycle_num_latent_values + 3)` are created and multiplied, so larger
            values may be slow. The trade-off is with resolution: cycling between
              a smaller number of latent values means that only smoother functions
              can be modeled.
        moving_average_order: Controls model size (along with
          `cycle_num_latent_values` and `autoregressive_order`) and the number of
          steps before transient deviations revert to the mean defined by the
          period and level/trend components.
        autoregressive_order: Each contribution from this component is a linear
          combination of this many previous contributions. Also helps to determine
          the model size. Learning autoregressive coefficients typically requires
          more steps and a smaller step size than other components.
        exogenous_feature_columns: A list of `tf.feature_column`s (for example
          `tf.feature_column.embedding_column`) corresponding to exogenous
          features which provide extra information to the model but are not part
          of the series to be predicted. Passed to
          `tf.compat.v1.feature_column.input_layer`.
        exogenous_update_condition: A function taking two Tensor arguments,
          `times` (shape [batch size]) and `features` (a dictionary mapping
          exogenous feature keys to Tensors with shapes [batch size, ...]), and
          returning a boolean Tensor with shape [batch size] indicating whether
          state should be updated using exogenous features for each part of the
          batch. Where it is False, no exogenous update is performed. If None
          (default), exogenous updates are always performed. Useful for avoiding
          "leaky" frequent exogenous updates when sparse updates are desired.
          Called only during graph construction. See the "known anomaly" example
          for example usage.
        dtype: The floating point data type to compute with. float32 may be
          faster, but can be problematic for larger models and longer time series.
        anomaly_prior_probability: If not None, the model attempts to
          automatically detect and ignore anomalies during training. This
          parameter then controls the prior probability of an anomaly. Values
          closer to 0 mean that points will be discarded less frequently. The
          default value (None) means that anomalies are not discarded, which may
          be slightly faster.
        optimizer: The optimization algorithm to use when training, inheriting
          from tf.train.Optimizer. Defaults to Adam with step size 0.02.
        model_dir: See `Estimator`.
        config: See `Estimator`.
        head_type: The kind of head to use for the model (inheriting from
          `TimeSeriesRegressionHead`).
docstring_summary: Initialize the Estimator.
docstring_tokens: Initialize the Estimator .
function: def __init__(self,
                 periodicities,
                 num_features,
                 cycle_num_latent_values=11,
                 moving_average_order=4,
                 autoregressive_order=0,
                 exogenous_feature_columns=None,
                 exogenous_update_condition=None,
                 dtype=dtypes.float64,
                 anomaly_prior_probability=None,
                 optimizer=None,
                 model_dir=None,
                 config=None,
                 head_type=ts_head_lib.TimeSeriesRegressionHead):
      """Initialize the Estimator.
  
      Args:
        periodicities: The expected periodicity of the data (for example 24 if
          feeding hourly data with a daily periodicity, or 60 * 24 if feeding
          minute-level data with daily periodicity). Either a scalar or a list.
          This parameter can be any real value, and does not control the size of
          the model. However, increasing this without increasing
          `num_values_per_cycle` will lead to smoother periodic behavior, as the
          same number of distinct values will be cycled through over a longer
          period of time.
        num_features: The dimensionality of the time series (one for univariate,
          more than one for multivariate).
        cycle_num_latent_values: Along with `moving_average_order` and
          `num_features`, controls the latent state size of the model. Square
          matrices of size `num_features * (moving_average_order +
          cycle_num_latent_values + 3)` are created and multiplied, so larger
            values may be slow. The trade-off is with resolution: cycling between
              a smaller number of latent values means that only smoother functions
              can be modeled.
        moving_average_order: Controls model size (along with
          `cycle_num_latent_values` and `autoregressive_order`) and the number of
          steps before transient deviations revert to the mean defined by the
          period and level/trend components.
        autoregressive_order: Each contribution from this component is a linear
          combination of this many previous contributions. Also helps to determine
          the model size. Learning autoregressive coefficients typically requires
          more steps and a smaller step size than other components.
        exogenous_feature_columns: A list of `tf.feature_column`s (for example
          `tf.feature_column.embedding_column`) corresponding to exogenous
          features which provide extra information to the model but are not part
          of the series to be predicted. Passed to
          `tf.compat.v1.feature_column.input_layer`.
        exogenous_update_condition: A function taking two Tensor arguments,
          `times` (shape [batch size]) and `features` (a dictionary mapping
          exogenous feature keys to Tensors with shapes [batch size, ...]), and
          returning a boolean Tensor with shape [batch size] indicating whether
          state should be updated using exogenous features for each part of the
          batch. Where it is False, no exogenous update is performed. If None
          (default), exogenous updates are always performed. Useful for avoiding
          "leaky" frequent exogenous updates when sparse updates are desired.
          Called only during graph construction. See the "known anomaly" example
          for example usage.
        dtype: The floating point data type to compute with. float32 may be
          faster, but can be problematic for larger models and longer time series.
        anomaly_prior_probability: If not None, the model attempts to
          automatically detect and ignore anomalies during training. This
          parameter then controls the prior probability of an anomaly. Values
          closer to 0 mean that points will be discarded less frequently. The
          default value (None) means that anomalies are not discarded, which may
          be slightly faster.
        optimizer: The optimization algorithm to use when training, inheriting
          from tf.train.Optimizer. Defaults to Adam with step size 0.02.
        model_dir: See `Estimator`.
        config: See `Estimator`.
        head_type: The kind of head to use for the model (inheriting from
          `TimeSeriesRegressionHead`).
      """
      if anomaly_prior_probability is not None:
        filtering_postprocessor = StateInterpolatingAnomalyDetector(
            anomaly_prior_probability=anomaly_prior_probability)
      else:
        filtering_postprocessor = None
      state_space_model_configuration = (
          state_space_model.StateSpaceModelConfiguration(
              num_features=num_features,
              dtype=dtype,
              filtering_postprocessor=filtering_postprocessor,
              exogenous_feature_columns=exogenous_feature_columns,
              exogenous_update_condition=exogenous_update_condition))
      model = structural_ensemble.MultiResolutionStructuralEnsemble(
          cycle_num_latent_values=cycle_num_latent_values,
          moving_average_order=moving_average_order,
          autoregressive_order=autoregressive_order,
          periodicities=periodicities,
          configuration=state_space_model_configuration)
      super(StructuralEnsembleRegressor, self).__init__(
          model=model,
          optimizer=optimizer,
          model_dir=model_dir,
          config=config,
          head_type=head_type)
function_tokens: def __init__ ( self , periodicities , num_features , cycle_num_latent_values = 11 , moving_average_order = 4 , autoregressive_order = 0 , exogenous_feature_columns = None , exogenous_update_condition = None , dtype = dtypes . float64 , anomaly_prior_probability = None , optimizer = None , model_dir = None , config = None , head_type = ts_head_lib . TimeSeriesRegressionHead ) : if anomaly_prior_probability is not None : filtering_postprocessor = StateInterpolatingAnomalyDetector ( anomaly_prior_probability = anomaly_prior_probability ) else : filtering_postprocessor = None state_space_model_configuration = ( state_space_model . StateSpaceModelConfiguration ( num_features = num_features , dtype = dtype , filtering_postprocessor = filtering_postprocessor , exogenous_feature_columns = exogenous_feature_columns , exogenous_update_condition = exogenous_update_condition ) ) model = structural_ensemble . MultiResolutionStructuralEnsemble ( cycle_num_latent_values = cycle_num_latent_values , moving_average_order = moving_average_order , autoregressive_order = autoregressive_order , periodicities = periodicities , configuration = state_space_model_configuration ) super ( StructuralEnsembleRegressor , self ) . __init__ ( model = model , optimizer = optimizer , model_dir = model_dir , config = config , head_type = head_type )