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)
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.contrib.layers.FeatureColumn
            objects (for example tf.contrib.layers.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.contrib.layers.input_from_feature_columns.
        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`.
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):
      """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.contrib.layers.FeatureColumn
            objects (for example tf.contrib.layers.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.contrib.layers.input_from_feature_columns.
        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`.
      """
      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)
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 ) : 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 )