tensorflow_probability/python/optimizer/differential_evolution.py
def _binary_crossover(population,
                      population_size,
                      mutants,
                      crossover_prob,
                      seed):
  """Performs recombination by binary crossover for the current population.

  Let v_i denote the i'th component of the member v and m_i the corresponding
  component of the mutant vector corresponding to v. Then the crossed over
  vector w_i is determined by setting w_i =
  (m_i with probability=crossover_prob else v_i). In addition, DE requires that
  at least one of the components is crossed over (otherwise we end
  up with no change). This is done by choosing on index say k randomly where
  a force crossover is performed (i.e. w_k = m_k). This is the scheme
  implemented in this function.

  Args:
    population: A Python list of `Tensor`s where each `Tensor` in the list
      must be of rank at least 1 and all the elements must have a common
      first dimension. The base population to cross over.
    population_size: A scalar integer `Tensor`. The number of elements in the
      population (i.e. size of the first dimension of any member of
      `population`).
    mutants: A Python list of `Tensor`s with the same structure as `population`.
      The mutated population.
    crossover_prob: A postive real scalar `Tensor` bounded above by 1.0. The
      probability of a crossover being performed for each axis.
    seed: `int` or None. The random seed for this `Op`. If `None`, no seed is
      applied.

  Returns:
    A list of `Tensor`s of the same structure, dtype and shape as `population`.
    The recombined population.
  """
  sizes = [tf.cast(tf.size(input=x), dtype=tf.float64) for x in population]
  seed_stream = distributions.SeedStream(seed, salt='binary_crossover')
  force_crossover_group = distributions.Categorical(sizes).sample(
      [population_size, 1], seed=seed_stream())
  recombinants = []
  for i, population_part in enumerate(population):
    pop_part_flat = tf.reshape(population_part, [population_size, -1])
    mutant_part_flat = tf.reshape(mutants[i], [population_size, -1])
    part_size = tf.size(input=population_part) // population_size
    force_crossovers = tf.one_hot(
        tf.random.uniform([population_size],
                          minval=0,
                          maxval=part_size,
                          dtype=tf.int32,
                          seed=seed_stream()),
        part_size,
        on_value=True,
        off_value=False,
        dtype=tf.bool)  # Tensor of shape [population_size, size]
    group_mask = tf.math.equal(force_crossover_group, i)
    force_crossovers &= group_mask
    do_binary_crossover = tf.random.uniform(
        [population_size, part_size],
        dtype=crossover_prob.dtype.base_dtype,
        seed=seed_stream()) < crossover_prob
    do_binary_crossover |= force_crossovers
    recombinant_flat = tf.where(
        do_binary_crossover,
        x=mutant_part_flat,
        y=pop_part_flat)
    recombinant = tf.reshape(recombinant_flat, tf.shape(input=population_part))
    recombinants.append(recombinant)
  return recombinants
def _binary_crossover(population,
                      population_size,
                      mutants,
                      crossover_prob,
                      seed):
  sizes = [tf.cast(tf.size(input=x), dtype=tf.float64) for x in population]
  seed_stream = distributions.SeedStream(seed, salt='binary_crossover')
  force_crossover_group = distributions.Categorical(sizes).sample(
      [population_size, 1], seed=seed_stream())
  recombinants = []
  for i, population_part in enumerate(population):
    pop_part_flat = tf.reshape(population_part, [population_size, -1])
    mutant_part_flat = tf.reshape(mutants[i], [population_size, -1])
    part_size = tf.size(input=population_part) // population_size
    force_crossovers = tf.one_hot(
        tf.random.uniform([population_size],
                          minval=0,
                          maxval=part_size,
                          dtype=tf.int32,
                          seed=seed_stream()),
        part_size,
        on_value=True,
        off_value=False,
        dtype=tf.bool)  # Tensor of shape [population_size, size]
    group_mask = tf.math.equal(force_crossover_group, i)
    force_crossovers &= group_mask
    do_binary_crossover = tf.random.uniform(
        [population_size, part_size],
        dtype=crossover_prob.dtype.base_dtype,
        seed=seed_stream()) < crossover_prob
    do_binary_crossover |= force_crossovers
    recombinant_flat = tf.where(
        do_binary_crossover,
        x=mutant_part_flat,
        y=pop_part_flat)
    recombinant = tf.reshape(recombinant_flat, tf.shape(input=population_part))
    recombinants.append(recombinant)
  return recombinants
def _binary_crossover ( population , population_size , mutants , crossover_prob , seed ) : sizes = [ tf . cast ( tf . size ( input = x ) , dtype = tf . float64 ) for x in population ] seed_stream = distributions . SeedStream ( seed , salt = 'binary_crossover' ) force_crossover_group = distributions . Categorical ( sizes ) . sample ( [ population_size , 1 ] , seed = seed_stream ( ) ) recombinants = [ ] for i , population_part in enumerate ( population ) : pop_part_flat = tf . reshape ( population_part , [ population_size , - 1 ] ) mutant_part_flat = tf . reshape ( mutants [ i ] , [ population_size , - 1 ] ) part_size = tf . size ( input = population_part ) // population_size force_crossovers = tf . one_hot ( tf . random . uniform ( [ population_size ] , minval = 0 , maxval = part_size , dtype = tf . int32 , seed = seed_stream ( ) ) , part_size , on_value = True , off_value = False , dtype = tf . bool ) # Tensor of shape [population_size, size] group_mask = tf . math . equal ( force_crossover_group , i ) force_crossovers &= group_mask do_binary_crossover = tf . random . uniform ( [ population_size , part_size ] , dtype = crossover_prob . dtype . base_dtype , seed = seed_stream ( ) ) < crossover_prob do_binary_crossover |= force_crossovers recombinant_flat = tf . where ( do_binary_crossover , x = mutant_part_flat , y = pop_part_flat ) recombinant = tf . reshape ( recombinant_flat , tf . shape ( input = population_part ) ) recombinants . append ( recombinant ) return recombinants
Performs recombination by binary crossover for the current population.

  Let v_i denote the i'th component of the member v and m_i the corresponding
  component of the mutant vector corresponding to v. Then the crossed over
  vector w_i is determined by setting w_i =
  (m_i with probability=crossover_prob else v_i). In addition, DE requires that
  at least one of the components is crossed over (otherwise we end
  up with no change). This is done by choosing on index say k randomly where
  a force crossover is performed (i.e. w_k = m_k). This is the scheme
  implemented in this function.

  Args:
    population: A Python list of `Tensor`s where each `Tensor` in the list
      must be of rank at least 1 and all the elements must have a common
      first dimension. The base population to cross over.
    population_size: A scalar integer `Tensor`. The number of elements in the
      population (i.e. size of the first dimension of any member of
      `population`).
    mutants: A Python list of `Tensor`s with the same structure as `population`.
      The mutated population.
    crossover_prob: A postive real scalar `Tensor` bounded above by 1.0. The
      probability of a crossover being performed for each axis.
    seed: `int` or None. The random seed for this `Op`. If `None`, no seed is
      applied.

  Returns:
    A list of `Tensor`s of the same structure, dtype and shape as `population`.
    The recombined population.
Performs recombination by binary crossover for the current population .