tensorflow_probability/python/mcmc/hmc.py
def one_step(self, current_state, previous_kernel_results):
    """Runs one iteration of Hamiltonian Monte Carlo.

    Args:
      current_state: `Tensor` or Python `list` of `Tensor`s representing the
        current state(s) of the Markov chain(s). The first `r` dimensions index
        independent chains, `r = tf.rank(target_log_prob_fn(*current_state))`.
      previous_kernel_results: `collections.namedtuple` containing `Tensor`s
        representing values from previous calls to this function (or from the
        `bootstrap_results` function.)

    Returns:
      next_state: Tensor or Python list of `Tensor`s representing the state(s)
        of the Markov chain(s) after taking exactly one step. Has same type and
        shape as `current_state`.
      kernel_results: `collections.namedtuple` of internal calculations used to
        advance the chain.

    Raises:
      ValueError: if there isn't one `step_size` or a list with same length as
        `current_state`.
    """
    previous_step_size_assign = (
        [] if self.step_size_update_fn is None
        else (previous_kernel_results.extra.step_size_assign
              if mcmc_util.is_list_like(
                  previous_kernel_results.extra.step_size_assign)
              else [previous_kernel_results.extra.step_size_assign]))

    with tf.control_dependencies(previous_step_size_assign):
      next_state, kernel_results = self._impl.one_step(
          current_state, previous_kernel_results)
      if self.step_size_update_fn is not None:
        step_size_assign = self.step_size_update_fn(  # pylint: disable=not-callable
            self.step_size, kernel_results)
        kernel_results = kernel_results._replace(
            extra=HamiltonianMonteCarloExtraKernelResults(
                step_size_assign=step_size_assign))
      return next_state, kernel_results
def one_step(self, current_state, previous_kernel_results):
    previous_step_size_assign = (
        [] if self.step_size_update_fn is None
        else (previous_kernel_results.extra.step_size_assign
              if mcmc_util.is_list_like(
                  previous_kernel_results.extra.step_size_assign)
              else [previous_kernel_results.extra.step_size_assign]))

    with tf.control_dependencies(previous_step_size_assign):
      next_state, kernel_results = self._impl.one_step(
          current_state, previous_kernel_results)
      if self.step_size_update_fn is not None:
        step_size_assign = self.step_size_update_fn(  # pylint: disable=not-callable
            self.step_size, kernel_results)
        kernel_results = kernel_results._replace(
            extra=HamiltonianMonteCarloExtraKernelResults(
                step_size_assign=step_size_assign))
      return next_state, kernel_results
def one_step ( self , current_state , previous_kernel_results ) : previous_step_size_assign = ( [ ] if self . step_size_update_fn is None else ( previous_kernel_results . extra . step_size_assign if mcmc_util . is_list_like ( previous_kernel_results . extra . step_size_assign ) else [ previous_kernel_results . extra . step_size_assign ] ) ) with tf . control_dependencies ( previous_step_size_assign ) : next_state , kernel_results = self . _impl . one_step ( current_state , previous_kernel_results ) if self . step_size_update_fn is not None : step_size_assign = self . step_size_update_fn ( # pylint: disable=not-callable self . step_size , kernel_results ) kernel_results = kernel_results . _replace ( extra = HamiltonianMonteCarloExtraKernelResults ( step_size_assign = step_size_assign ) ) return next_state , kernel_results
Runs one iteration of Hamiltonian Monte Carlo.

    Args:
      current_state: `Tensor` or Python `list` of `Tensor`s representing the
        current state(s) of the Markov chain(s). The first `r` dimensions index
        independent chains, `r = tf.rank(target_log_prob_fn(*current_state))`.
      previous_kernel_results: `collections.namedtuple` containing `Tensor`s
        representing values from previous calls to this function (or from the
        `bootstrap_results` function.)

    Returns:
      next_state: Tensor or Python list of `Tensor`s representing the state(s)
        of the Markov chain(s) after taking exactly one step. Has same type and
        shape as `current_state`.
      kernel_results: `collections.namedtuple` of internal calculations used to
        advance the chain.

    Raises:
      ValueError: if there isn't one `step_size` or a list with same length as
        `current_state`.
Runs one iteration of Hamiltonian Monte Carlo .