tensorflow_probability/python/mcmc/replica_exchange_mc.py
def bootstrap_results(self, init_state):
    """Returns an object with the same type as returned by `one_step`.

    Args:
      init_state: `Tensor` or Python `list` of `Tensor`s representing the
        initial state(s) of the Markov chain(s).

    Returns:
      kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
        `Tensor`s representing internal calculations made within this function.
        This inculdes replica states.
    """
    with tf.compat.v1.name_scope(
        name=mcmc_util.make_name(self.name, 'remc', 'bootstrap_results'),
        values=[init_state]):
      replica_results = [
          self.replica_kernels[i].bootstrap_results(init_state)
          for i in range(self.num_replica)
      ]

      init_state_parts = (
          list(init_state)
          if mcmc_util.is_list_like(init_state) else [init_state])

      # Convert all states parts to tensor...
      replica_states = [[
          tf.convert_to_tensor(value=s) for s in init_state_parts
      ] for i in range(self.num_replica)]

      if not mcmc_util.is_list_like(init_state):
        replica_states = [s[0] for s in replica_states]

      return ReplicaExchangeMCKernelResults(
          replica_states=replica_states,
          replica_results=replica_results,
          sampled_replica_states=replica_states,
          sampled_replica_results=replica_results,
      )
def bootstrap_results(self, init_state):
    """Returns an object with the same type as returned by `one_step`.

    Args:
      init_state: `Tensor` or Python `list` of `Tensor`s representing the
        initial state(s) of the Markov chain(s).

    Returns:
      kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
        `Tensor`s representing internal calculations made within this function.
        This inculdes replica states.
    """
    with tf.compat.v1.name_scope(
        name=mcmc_util.make_name(self.name, 'remc', 'bootstrap_results'),
        values=[init_state]):
      replica_results = [
          self.replica_kernels[i].bootstrap_results(init_state)
          for i in range(self.num_replica)
      ]

      init_state_parts = (
          list(init_state)
          if mcmc_util.is_list_like(init_state) else [init_state])

      # Convert all states parts to tensor...
      replica_states = [[
          tf.convert_to_tensor(value=s) for s in init_state_parts
      ] for i in range(self.num_replica)]

      if not mcmc_util.is_list_like(init_state):
        replica_states = [s[0] for s in replica_states]

      return ReplicaExchangeMCKernelResults(
          replica_states=replica_states,
          replica_results=replica_results,
          sampled_replica_states=replica_states,
          sampled_replica_results=replica_results,
      )
def bootstrap_results ( self , init_state ) : with tf . compat . v1 . name_scope ( name = mcmc_util . make_name ( self . name , 'remc' , 'bootstrap_results' ) , values = [ init_state ] ) : replica_results = [ self . replica_kernels [ i ] . bootstrap_results ( init_state ) for i in range ( self . num_replica ) ] init_state_parts = ( list ( init_state ) if mcmc_util . is_list_like ( init_state ) else [ init_state ] ) # Convert all states parts to tensor... replica_states = [ [ tf . convert_to_tensor ( value = s ) for s in init_state_parts ] for i in range ( self . num_replica ) ] if not mcmc_util . is_list_like ( init_state ) : replica_states = [ s [ 0 ] for s in replica_states ] return ReplicaExchangeMCKernelResults ( replica_states = replica_states , replica_results = replica_results , sampled_replica_states = replica_states , sampled_replica_results = replica_results , )
Returns an object with the same type as returned by `one_step`.

    Args:
      init_state: `Tensor` or Python `list` of `Tensor`s representing the
        initial state(s) of the Markov chain(s).

    Returns:
      kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
        `Tensor`s representing internal calculations made within this function.
        This inculdes replica states.
Returns an object with the same type as returned by one_step .