tensorflow_probability/python/mcmc/transformed_kernel.py
def one_step(self, current_state, previous_kernel_results):
    """Runs one iteration of the Transformed Kernel.

    Args:
      current_state: `Tensor` or Python `list` of `Tensor`s
        representing the current state(s) of the Markov chain(s),
        _after_ application of `bijector.forward`. The first `r`
        dimensions index independent chains,
        `r = tf.rank(target_log_prob_fn(*current_state))`. The
        `inner_kernel.one_step` does not actually use `current_state`,
        rather it takes as input
        `previous_kernel_results.transformed_state` (because
        `TransformedTransitionKernel` creates a copy of the input
        inner_kernel with a modified `target_log_prob_fn` which
        internally applies the `bijector.forward`).
      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.
    """
    with tf.compat.v1.name_scope(
        name=mcmc_util.make_name(self.name, 'transformed_kernel', 'one_step'),
        values=[previous_kernel_results]):
      transformed_next_state, kernel_results = self._inner_kernel.one_step(
          previous_kernel_results.transformed_state,
          previous_kernel_results.inner_results)
      transformed_next_state_parts = (
          transformed_next_state
          if mcmc_util.is_list_like(transformed_next_state) else
          [transformed_next_state])
      next_state_parts = self._forward_transform(transformed_next_state_parts)
      next_state = (
          next_state_parts if mcmc_util.is_list_like(transformed_next_state)
          else next_state_parts[0])
      kernel_results = TransformedTransitionKernelResults(
          transformed_state=transformed_next_state,
          inner_results=kernel_results)
      return next_state, kernel_results
def one_step(self, current_state, previous_kernel_results):
    """Runs one iteration of the Transformed Kernel.

    Args:
      current_state: `Tensor` or Python `list` of `Tensor`s
        representing the current state(s) of the Markov chain(s),
        _after_ application of `bijector.forward`. The first `r`
        dimensions index independent chains,
        `r = tf.rank(target_log_prob_fn(*current_state))`. The
        `inner_kernel.one_step` does not actually use `current_state`,
        rather it takes as input
        `previous_kernel_results.transformed_state` (because
        `TransformedTransitionKernel` creates a copy of the input
        inner_kernel with a modified `target_log_prob_fn` which
        internally applies the `bijector.forward`).
      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.
    """
    with tf.compat.v1.name_scope(
        name=mcmc_util.make_name(self.name, 'transformed_kernel', 'one_step'),
        values=[previous_kernel_results]):
      transformed_next_state, kernel_results = self._inner_kernel.one_step(
          previous_kernel_results.transformed_state,
          previous_kernel_results.inner_results)
      transformed_next_state_parts = (
          transformed_next_state
          if mcmc_util.is_list_like(transformed_next_state) else
          [transformed_next_state])
      next_state_parts = self._forward_transform(transformed_next_state_parts)
      next_state = (
          next_state_parts if mcmc_util.is_list_like(transformed_next_state)
          else next_state_parts[0])
      kernel_results = TransformedTransitionKernelResults(
          transformed_state=transformed_next_state,
          inner_results=kernel_results)
      return next_state, kernel_results
Runs one iteration of the Transformed Kernel.

    Args:
      current_state: `Tensor` or Python `list` of `Tensor`s
        representing the current state(s) of the Markov chain(s),
        _after_ application of `bijector.forward`. The first `r`
        dimensions index independent chains,
        `r = tf.rank(target_log_prob_fn(*current_state))`. The
        `inner_kernel.one_step` does not actually use `current_state`,
        rather it takes as input
        `previous_kernel_results.transformed_state` (because
        `TransformedTransitionKernel` creates a copy of the input
        inner_kernel with a modified `target_log_prob_fn` which
        internally applies the `bijector.forward`).
      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.