tensorflow_probability/python/optimizer/differential_evolution.py
def _get_initial_args(objective_function,
                      initial_population,
                      initial_position,
                      population_size,
                      population_stddev,
                      max_iterations,
                      func_tolerance,
                      position_tolerance,
                      differential_weight,
                      crossover_prob,
                      seed):
  """Processes initial args."""
  was_iterable = False
  if initial_position is not None:
    initial_position, was_iterable = _ensure_list(initial_position)

  if initial_population is not None:
    initial_population, was_iterable = _ensure_list(initial_population)

  population = _get_starting_population(initial_population,
                                        initial_position,
                                        population_size,
                                        population_stddev,
                                        seed=seed)

  differential_weight = tf.convert_to_tensor(
      value=differential_weight, dtype=population[0].dtype.base_dtype)

  crossover_prob = tf.convert_to_tensor(value=crossover_prob)
  population_values = objective_function(*population)
  if max_iterations is not None:
    max_iterations = tf.convert_to_tensor(value=max_iterations)
  func_tolerance = tf.convert_to_tensor(
      value=func_tolerance, dtype=population_values.dtype.base_dtype)
  position_tolerance = tf.convert_to_tensor(
      value=position_tolerance, dtype=population[0].dtype.base_dtype)
  return (was_iterable,
          population,
          population_values,
          max_iterations,
          func_tolerance,
          position_tolerance,
          differential_weight,
          crossover_prob)
def _get_initial_args(objective_function,
                      initial_population,
                      initial_position,
                      population_size,
                      population_stddev,
                      max_iterations,
                      func_tolerance,
                      position_tolerance,
                      differential_weight,
                      crossover_prob,
                      seed):
  was_iterable = False
  if initial_position is not None:
    initial_position, was_iterable = _ensure_list(initial_position)

  if initial_population is not None:
    initial_population, was_iterable = _ensure_list(initial_population)

  population = _get_starting_population(initial_population,
                                        initial_position,
                                        population_size,
                                        population_stddev,
                                        seed=seed)

  differential_weight = tf.convert_to_tensor(
      value=differential_weight, dtype=population[0].dtype.base_dtype)

  crossover_prob = tf.convert_to_tensor(value=crossover_prob)
  population_values = objective_function(*population)
  if max_iterations is not None:
    max_iterations = tf.convert_to_tensor(value=max_iterations)
  func_tolerance = tf.convert_to_tensor(
      value=func_tolerance, dtype=population_values.dtype.base_dtype)
  position_tolerance = tf.convert_to_tensor(
      value=position_tolerance, dtype=population[0].dtype.base_dtype)
  return (was_iterable,
          population,
          population_values,
          max_iterations,
          func_tolerance,
          position_tolerance,
          differential_weight,
          crossover_prob)
def _get_initial_args ( objective_function , initial_population , initial_position , population_size , population_stddev , max_iterations , func_tolerance , position_tolerance , differential_weight , crossover_prob , seed ) : was_iterable = False if initial_position is not None : initial_position , was_iterable = _ensure_list ( initial_position ) if initial_population is not None : initial_population , was_iterable = _ensure_list ( initial_population ) population = _get_starting_population ( initial_population , initial_position , population_size , population_stddev , seed = seed ) differential_weight = tf . convert_to_tensor ( value = differential_weight , dtype = population [ 0 ] . dtype . base_dtype ) crossover_prob = tf . convert_to_tensor ( value = crossover_prob ) population_values = objective_function ( * population ) if max_iterations is not None : max_iterations = tf . convert_to_tensor ( value = max_iterations ) func_tolerance = tf . convert_to_tensor ( value = func_tolerance , dtype = population_values . dtype . base_dtype ) position_tolerance = tf . convert_to_tensor ( value = position_tolerance , dtype = population [ 0 ] . dtype . base_dtype ) return ( was_iterable , population , population_values , max_iterations , func_tolerance , position_tolerance , differential_weight , crossover_prob )
Processes initial args.
Processes initial args .