parameters: (self, configuration)
docstring: Initialize a state space model.
  
      Args:
        configuration: A StateSpaceModelConfiguration object.
docstring_summary: Initialize a state space model.
docstring_tokens: Initialize a state space model .
function: def __init__(self, configuration):
      """Initialize a state space model.
  
      Args:
        configuration: A StateSpaceModelConfiguration object.
      """
      self._configuration = configuration
      if configuration.filtering_postprocessor is not None:
        filtering_postprocessor_names = (
            configuration.filtering_postprocessor.output_names)
      else:
        filtering_postprocessor_names = []
      super(StateSpaceModel, self).__init__(
          train_output_names=(["mean", "covariance", "log_likelihood"]
                              + filtering_postprocessor_names),
          predict_output_names=["mean", "covariance"],
          num_features=configuration.num_features,
          dtype=configuration.dtype,
          exogenous_feature_columns=configuration.exogenous_feature_columns,
          exogenous_update_condition=configuration.exogenous_update_condition,
          static_unrolling_window_size_threshold=
          configuration.static_unrolling_window_size_threshold)
      self._kalman_filter = None
      self.prior_state_mean = None
      self.prior_state_var = None
      self.state_transition_noise_covariance = None
      self._total_observation_count = None
      self._observation_noise_covariance = None
      # Capture the current variable scope and use it to define all model
      # variables. Especially useful for ensembles, where variables may be defined
      # for every component model in one function call, which would otherwise
      # prevent the user from separating variables from different models into
      # different scopes.
      self._variable_scope = variable_scope.get_variable_scope()
function_tokens: def __init__ ( self , configuration ) : self . _configuration = configuration if configuration . filtering_postprocessor is not None : filtering_postprocessor_names = ( configuration . filtering_postprocessor . output_names ) else : filtering_postprocessor_names = [ ] super ( StateSpaceModel , self ) . __init__ ( train_output_names = ( [ "mean" , "covariance" , "log_likelihood" ] + filtering_postprocessor_names ) , predict_output_names = [ "mean" , "covariance" ] , num_features = configuration . num_features , dtype = configuration . dtype , exogenous_feature_columns = configuration . exogenous_feature_columns , exogenous_update_condition = configuration . exogenous_update_condition , static_unrolling_window_size_threshold = configuration . static_unrolling_window_size_threshold ) self . _kalman_filter = None self . prior_state_mean = None self . prior_state_var = None self . state_transition_noise_covariance = None self . _total_observation_count = None self . _observation_noise_covariance = None # Capture the current variable scope and use it to define all model # variables. Especially useful for ensembles, where variables may be defined # for every component model in one function call, which would otherwise # prevent the user from separating variables from different models into # different scopes. self . _variable_scope = variable_scope . get_variable_scope ( )