def one_step(self, current_state, previous_kernel_results):
    """Takes one step of the TransitionKernel.

    Args:
      current_state: `Tensor` or Python `list` of `Tensor`s representing the
        current state(s) of the Markov chain(s).
      previous_kernel_results: A (possibly nested) `tuple`, `namedtuple` or
        `list` of `Tensor`s representing internal calculations made within the
        previous call to this function (or as returned by `bootstrap_results`).

    Returns:
      next_state: `Tensor` or Python `list` of `Tensor`s representing the
        next state(s) of the Markov chain(s).
      kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
        `Tensor`s representing internal calculations made within this function.

    Raises:
      ValueError: if `inner_kernel` results doesn't contain the member
        "target_log_prob".
    """
    with tf.compat.v1.name_scope(
        name=mcmc_util.make_name(self.name, 'mh', 'one_step'),
        values=[current_state, previous_kernel_results]):
      # Take one inner step.
      [
          proposed_state,
          proposed_results,
      ] = self.inner_kernel.one_step(
          current_state,
          previous_kernel_results.accepted_results)

      if (not has_target_log_prob(proposed_results) or
          not has_target_log_prob(previous_kernel_results.accepted_results)):
        raise ValueError('"target_log_prob" must be a member of '
                         '`inner_kernel` results.')

      # Compute log(acceptance_ratio).
      to_sum = [proposed_results.target_log_prob,
                -previous_kernel_results.accepted_results.target_log_prob]
      try:
        if (not mcmc_util.is_list_like(
            proposed_results.log_acceptance_correction)
            or proposed_results.log_acceptance_correction):
          to_sum.append(proposed_results.log_acceptance_correction)
      except AttributeError:
        warnings.warn('Supplied inner `TransitionKernel` does not have a '
                      '`log_acceptance_correction`. Assuming its value is `0.`')
      log_accept_ratio = mcmc_util.safe_sum(
          to_sum, name='compute_log_accept_ratio')

      # If proposed state reduces likelihood: randomly accept.
      # If proposed state increases likelihood: always accept.
      # I.e., u < min(1, accept_ratio),  where u ~ Uniform[0,1)
      #       ==> log(u) < log_accept_ratio
      log_uniform = tf.math.log(
          tf.random.uniform(
              shape=tf.shape(input=proposed_results.target_log_prob),
              dtype=proposed_results.target_log_prob.dtype.base_dtype,
              seed=self._seed_stream()))
      is_accepted = log_uniform < log_accept_ratio

      next_state = mcmc_util.choose(
          is_accepted,
          proposed_state,
          current_state,
          name='choose_next_state')

      kernel_results = MetropolisHastingsKernelResults(
          accepted_results=mcmc_util.choose(
              is_accepted,
              proposed_results,
              previous_kernel_results.accepted_results,
              name='choose_inner_results'),
          is_accepted=is_accepted,
          log_accept_ratio=log_accept_ratio,
          proposed_state=proposed_state,
          proposed_results=proposed_results,
          extra=[],
      )

      return next_state, kernel_results
def one_step(self, current_state, previous_kernel_results):
    """Takes one step of the TransitionKernel.

    Args:
      current_state: `Tensor` or Python `list` of `Tensor`s representing the
        current state(s) of the Markov chain(s).
      previous_kernel_results: A (possibly nested) `tuple`, `namedtuple` or
        `list` of `Tensor`s representing internal calculations made within the
        previous call to this function (or as returned by `bootstrap_results`).

    Returns:
      next_state: `Tensor` or Python `list` of `Tensor`s representing the
        next state(s) of the Markov chain(s).
      kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
        `Tensor`s representing internal calculations made within this function.

    Raises:
      ValueError: if `inner_kernel` results doesn't contain the member
        "target_log_prob".
    """
    with tf.compat.v1.name_scope(
        name=mcmc_util.make_name(self.name, 'mh', 'one_step'),
        values=[current_state, previous_kernel_results]):
      # Take one inner step.
      [
          proposed_state,
          proposed_results,
      ] = self.inner_kernel.one_step(
          current_state,
          previous_kernel_results.accepted_results)

      if (not has_target_log_prob(proposed_results) or
          not has_target_log_prob(previous_kernel_results.accepted_results)):
        raise ValueError('"target_log_prob" must be a member of '
                         '`inner_kernel` results.')

      # Compute log(acceptance_ratio).
      to_sum = [proposed_results.target_log_prob,
                -previous_kernel_results.accepted_results.target_log_prob]
      try:
        if (not mcmc_util.is_list_like(
            proposed_results.log_acceptance_correction)
            or proposed_results.log_acceptance_correction):
          to_sum.append(proposed_results.log_acceptance_correction)
      except AttributeError:
        warnings.warn('Supplied inner `TransitionKernel` does not have a '
                      '`log_acceptance_correction`. Assuming its value is `0.`')
      log_accept_ratio = mcmc_util.safe_sum(
          to_sum, name='compute_log_accept_ratio')

      # If proposed state reduces likelihood: randomly accept.
      # If proposed state increases likelihood: always accept.
      # I.e., u < min(1, accept_ratio),  where u ~ Uniform[0,1)
      #       ==> log(u) < log_accept_ratio
      log_uniform = tf.math.log(
          tf.random.uniform(
              shape=tf.shape(input=proposed_results.target_log_prob),
              dtype=proposed_results.target_log_prob.dtype.base_dtype,
              seed=self._seed_stream()))
      is_accepted = log_uniform < log_accept_ratio

      next_state = mcmc_util.choose(
          is_accepted,
          proposed_state,
          current_state,
          name='choose_next_state')

      kernel_results = MetropolisHastingsKernelResults(
          accepted_results=mcmc_util.choose(
              is_accepted,
              proposed_results,
              previous_kernel_results.accepted_results,
              name='choose_inner_results'),
          is_accepted=is_accepted,
          log_accept_ratio=log_accept_ratio,
          proposed_state=proposed_state,
          proposed_results=proposed_results,
          extra=[],
      )

      return next_state, kernel_results
def one_step ( self , current_state , previous_kernel_results ) : with tf . compat . v1 . name_scope ( name = mcmc_util . make_name ( self . name , 'mh' , 'one_step' ) , values = [ current_state , previous_kernel_results ] ) : # Take one inner step. [ proposed_state , proposed_results , ] = self . inner_kernel . one_step ( current_state , previous_kernel_results . accepted_results ) if ( not has_target_log_prob ( proposed_results ) or not has_target_log_prob ( previous_kernel_results . accepted_results ) ) : raise ValueError ( '"target_log_prob" must be a member of ' '`inner_kernel` results.' ) # Compute log(acceptance_ratio). to_sum = [ proposed_results . target_log_prob , - previous_kernel_results . accepted_results . target_log_prob ] try : if ( not mcmc_util . is_list_like ( proposed_results . log_acceptance_correction ) or proposed_results . log_acceptance_correction ) : to_sum . append ( proposed_results . log_acceptance_correction ) except AttributeError : warnings . warn ( 'Supplied inner `TransitionKernel` does not have a ' '`log_acceptance_correction`. Assuming its value is `0.`' ) log_accept_ratio = mcmc_util . safe_sum ( to_sum , name = 'compute_log_accept_ratio' ) # If proposed state reduces likelihood: randomly accept. # If proposed state increases likelihood: always accept. # I.e., u < min(1, accept_ratio),  where u ~ Uniform[0,1) #       ==> log(u) < log_accept_ratio log_uniform = tf . math . log ( tf . random . uniform ( shape = tf . shape ( input = proposed_results . target_log_prob ) , dtype = proposed_results . target_log_prob . dtype . base_dtype , seed = self . _seed_stream ( ) ) ) is_accepted = log_uniform < log_accept_ratio next_state = mcmc_util . choose ( is_accepted , proposed_state , current_state , name = 'choose_next_state' ) kernel_results = MetropolisHastingsKernelResults ( accepted_results = mcmc_util . choose ( is_accepted , proposed_results , previous_kernel_results . accepted_results , name = 'choose_inner_results' ) , is_accepted = is_accepted , log_accept_ratio = log_accept_ratio , proposed_state = proposed_state , proposed_results = proposed_results , extra = [ ] , ) return next_state , kernel_results
Takes one step of the TransitionKernel.

    Args:
      current_state: `Tensor` or Python `list` of `Tensor`s representing the
        current state(s) of the Markov chain(s).
      previous_kernel_results: A (possibly nested) `tuple`, `namedtuple` or
        `list` of `Tensor`s representing internal calculations made within the
        previous call to this function (or as returned by `bootstrap_results`).

    Returns:
      next_state: `Tensor` or Python `list` of `Tensor`s representing the
        next state(s) of the Markov chain(s).
      kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
        `Tensor`s representing internal calculations made within this function.

    Raises:
      ValueError: if `inner_kernel` results doesn't contain the member
        "target_log_prob".
Takes one step of the TransitionKernel .