experimental/fun_mcmc/fun_mcmc_lib.py
def sign_adaptation(control: FloatNest,
                    output: FloatTensor,
                    set_point: FloatTensor,
                    adaptation_rate: FloatTensor = 0.01) -> FloatNest:
  """A function to do simple sign-based control of a variable.

  ```
  control = control * (1. + adaptation_rate) ** sign(output - set_point)
  ```

  Args:
    control: The control variable.
    output: The output variable.
    set_point: The set point for `output`. This function will adjust `control`
      so that `output` matches `set_point`.
    adaptation_rate: Adaptation rate.

  Returns:
    control: New control.
  """

  def _get_new_control(control, output, set_point):
    new_control = mcmc_util.choose(output > set_point,
                                   control * (1. + adaptation_rate),
                                   control / (1. + adaptation_rate))
    return new_control

  output = maybe_broadcast_structure(output, control)
  set_point = maybe_broadcast_structure(set_point, control)

  return tf.nest.map_structure(_get_new_control, control, output, set_point)
def sign_adaptation(control: FloatNest,
                    output: FloatTensor,
                    set_point: FloatTensor,
                    adaptation_rate: FloatTensor = 0.01) -> FloatNest:
  """A function to do simple sign-based control of a variable.

  ```
  control = control * (1. + adaptation_rate) ** sign(output - set_point)
  ```

  Args:
    control: The control variable.
    output: The output variable.
    set_point: The set point for `output`. This function will adjust `control`
      so that `output` matches `set_point`.
    adaptation_rate: Adaptation rate.

  Returns:
    control: New control.
  """

  def _get_new_control(control, output, set_point):
    new_control = mcmc_util.choose(output > set_point,
                                   control * (1. + adaptation_rate),
                                   control / (1. + adaptation_rate))
    return new_control

  output = maybe_broadcast_structure(output, control)
  set_point = maybe_broadcast_structure(set_point, control)

  return tf.nest.map_structure(_get_new_control, control, output, set_point)
def sign_adaptation ( control : FloatNest , output : FloatTensor , set_point : FloatTensor , adaptation_rate : FloatTensor = 0.01 ) -> FloatNest : def _get_new_control ( control , output , set_point ) : new_control = mcmc_util . choose ( output > set_point , control * ( 1. + adaptation_rate ) , control / ( 1. + adaptation_rate ) ) return new_control output = maybe_broadcast_structure ( output , control ) set_point = maybe_broadcast_structure ( set_point , control ) return tf . nest . map_structure ( _get_new_control , control , output , set_point )
A function to do simple sign-based control of a variable.

  ```
  control = control * (1. + adaptation_rate) ** sign(output - set_point)
  ```

  Args:
    control: The control variable.
    output: The output variable.
    set_point: The set point for `output`. This function will adjust `control`
      so that `output` matches `set_point`.
    adaptation_rate: Adaptation rate.

  Returns:
    control: New control.
A function to do simple sign - based control of a variable .