tensorflow_probability/python/sts/sum.py
def make_component_state_space_models(self,
                                        num_timesteps,
                                        param_vals,
                                        initial_step=0):
    """Build an ordered list of Distribution instances for component models.

    Args:
      num_timesteps: Python `int` number of timesteps to model.
      param_vals: a list of `Tensor` parameter values in order corresponding to
        `self.parameters`, or a dict mapping from parameter names to values.
      initial_step: optional `int` specifying the initial timestep to model.
        This is relevant when the model contains time-varying components,
        e.g., holidays or seasonality.

    Returns:
      component_ssms: a Python list of `LinearGaussianStateSpaceModel`
        Distribution objects, in order corresponding to `self.components`.
    """

    with tf.compat.v1.name_scope('make_component_state_space_models'):

      # List the model parameters in canonical order
      param_map = self._canonicalize_param_vals_as_map(param_vals)
      param_vals_list = [param_map[p.name] for p in self.parameters]

      # Build SSMs for each component model. We process the components in
      # canonical order, extracting the parameters for each component from the
      # (ordered) list of parameters.
      remaining_param_vals = param_vals_list[1:]
      component_ssms = []
      for component in self.components:
        num_parameters = len(component.parameters)
        component_param_vals = remaining_param_vals[:num_parameters]
        remaining_param_vals = remaining_param_vals[num_parameters:]

        component_ssms.append(
            component.make_state_space_model(
                num_timesteps,
                param_vals=component_param_vals,
                initial_step=initial_step))

    return component_ssms
def make_component_state_space_models(self,
                                        num_timesteps,
                                        param_vals,
                                        initial_step=0):
    with tf.compat.v1.name_scope('make_component_state_space_models'):

      # List the model parameters in canonical order
      param_map = self._canonicalize_param_vals_as_map(param_vals)
      param_vals_list = [param_map[p.name] for p in self.parameters]

      # Build SSMs for each component model. We process the components in
      # canonical order, extracting the parameters for each component from the
      # (ordered) list of parameters.
      remaining_param_vals = param_vals_list[1:]
      component_ssms = []
      for component in self.components:
        num_parameters = len(component.parameters)
        component_param_vals = remaining_param_vals[:num_parameters]
        remaining_param_vals = remaining_param_vals[num_parameters:]

        component_ssms.append(
            component.make_state_space_model(
                num_timesteps,
                param_vals=component_param_vals,
                initial_step=initial_step))

    return component_ssms
def make_component_state_space_models ( self , num_timesteps , param_vals , initial_step = 0 ) : with tf . compat . v1 . name_scope ( 'make_component_state_space_models' ) : # List the model parameters in canonical order param_map = self . _canonicalize_param_vals_as_map ( param_vals ) param_vals_list = [ param_map [ p . name ] for p in self . parameters ] # Build SSMs for each component model. We process the components in # canonical order, extracting the parameters for each component from the # (ordered) list of parameters. remaining_param_vals = param_vals_list [ 1 : ] component_ssms = [ ] for component in self . components : num_parameters = len ( component . parameters ) component_param_vals = remaining_param_vals [ : num_parameters ] remaining_param_vals = remaining_param_vals [ num_parameters : ] component_ssms . append ( component . make_state_space_model ( num_timesteps , param_vals = component_param_vals , initial_step = initial_step ) ) return component_ssms
Build an ordered list of Distribution instances for component models.

    Args:
      num_timesteps: Python `int` number of timesteps to model.
      param_vals: a list of `Tensor` parameter values in order corresponding to
        `self.parameters`, or a dict mapping from parameter names to values.
      initial_step: optional `int` specifying the initial timestep to model.
        This is relevant when the model contains time-varying components,
        e.g., holidays or seasonality.

    Returns:
      component_ssms: a Python list of `LinearGaussianStateSpaceModel`
        Distribution objects, in order corresponding to `self.components`.
Build an ordered list of Distribution instances for component models .