def test_pairwise():
list = [1, 2, 3, 4]
pairs = general.pairwise(list)
for p in pairs:
pass
assert type(pairs).__name__ == 'callable_iterator' or 'generator'
def _update(self, agents, function):
"""Method that wraps selection, crossover and mutation over all agents and variables.
Args:
agents (list): List of agents.
function (Function): A Function object that will be used as the objective function.
"""
# Creating a list to hold the new population
new_agents = []
# Retrieving the number of agents
n_agents = len(agents)
# Calculates a list of fitness from every agent
fitness = [agent.fit + c.EPSILON for agent in agents]
# Selects the parents
selected = self._roulette_selection(n_agents, fitness)
# For every pair of selected parents
for s in g.pairwise(selected):
# Performs the crossover
alpha, beta = self._crossover(agents[s[0]], agents[s[1]])
# Performs the mutation
alpha, beta = self._mutation(alpha, beta)
# Checking `alpha` limits
alpha.clip_limits()
# Checking `beta` limits
beta.clip_limits()
# Calculates new fitness for `alpha`
alpha.fit = function(alpha.position)
# Calculates new fitness for `beta`
beta.fit = function(beta.position)
# Appends the mutated agents to the children
new_agents.extend([alpha, beta])
# Joins both populations
agents += new_agents
# Sorting agents
agents.sort(key=lambda x: x.fit)
return agents[:n_agents]
def _crossover(self, space):
"""Crossover a number of individuals pre-selected through a tournament procedure.
Args:
space (TreeSpace): A TreeSpace object.
agents (list): Current iteration agents.
trees (list): Current iteration trees.
"""
# Calculates a list of current trees' fitness
fitness = [agent.fit for agent in space.agents]
# Number of individuals to be crossovered
n_individuals = int(space.n_trees * self.p_crossover)
# Checks if `n_individuals` is an odd number
if n_individuals % 2 != 0:
# If it is, increase it by one
n_individuals += 1
# Gathers a list of selected individuals to be replaced
selected = g.tournament_selection(fitness, n_individuals)
# For every pair in selected individuals
for s in g.pairwise(selected):
# Calculates the amount of father nodes
father_nodes = space.trees[s[0]].n_nodes
# Calculate the amount of mother nodes
mother_nodes = space.trees[s[1]].n_nodes
# Checks if both trees have more than one node
if (father_nodes > 1) and (mother_nodes > 1):
# Prunning father nodes
max_f_nodes = self._prune_nodes(father_nodes)
# Prunning mother nodes
max_m_nodes = self._prune_nodes(mother_nodes)
# Apply the crossover operation
space.trees[s[0]], space.trees[s[1]] = self._cross(
space.trees[s[0]], space.trees[s[1]], max_f_nodes,
max_m_nodes)
import opytimizer.math.general as g
# Creating a list for pairwising
individuals = [1, 2, 3, 4]
# Creating pairwise from list
for pair in g.pairwise(individuals):
# Outputting pairs
print(f'Pair: {pair}')
# Performing a tournmanet selection over list
selected = g.tournament_selection(individuals, 2)
# Outputting selected individuals
print(f'Selected: {selected}')