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Cleaner random functions used.

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SimpleArt
2020-11-20 19:58:25 -05:00
parent 73461f7915
commit 470b33aa31
3 changed files with 9 additions and 9 deletions
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2
src/crossover/crossover_methods.py
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@ -72,7 +72,7 @@ class Crossover_Methods:
def single_point(ga, parent_1, parent_2): def single_point(ga, parent_1, parent_2):
"""Cross two parents by swapping genes at one random point.""" """Cross two parents by swapping genes at one random point."""
swap_index = random.randint(0, len(parent_1)-1) swap_index = random.randrange(len(parent_1))
return parent_1[:swap_index] + parent_2[swap_index:] return parent_1[:swap_index] + parent_2[swap_index:]

10
src/mutation/mutation_methods.py
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@ -37,7 +37,7 @@ class Mutation_Methods:
def random_selection(ga): def random_selection(ga):
"""Selects random chromosomes.""" """Selects random chromosomes."""
index = random.randint(0, len(ga.population)-1) index = random.randrange(len(ga.population))
ga.population[index] = ga.mutation_individual_impl(ga, ga.population[index]) ga.population[index] = ga.mutation_individual_impl(ga, ga.population[index])
@ -45,7 +45,7 @@ class Mutation_Methods:
def random_selection_then_cross(ga): def random_selection_then_cross(ga):
"""Selects random chromosomes and self-crosses with parent.""" """Selects random chromosomes and self-crosses with parent."""
index = random.randint(0, len(ga.population)-1) index = random.randrange(len(ga.population))
chromosome = ga.population[index] chromosome = ga.population[index]
ga.population[index] = ga.crossover_individual_impl(ga, chromosome, ga.mutation_individual_impl(ga, chromosome)) ga.population[index] = ga.crossover_individual_impl(ga, chromosome, ga.mutation_individual_impl(ga, chromosome))
@ -56,7 +56,7 @@ class Mutation_Methods:
@loop_mutations @loop_mutations
def individual_genes(ga, chromosome): def individual_genes(ga, chromosome):
"""Mutates a random gene in the chromosome.""" """Mutates a random gene in the chromosome."""
index = random.randint(0, len(chromosome)-1) index = random.randrange(len(chromosome))
# Using the chromosome_impl # Using the chromosome_impl
if ga.chromosome_impl is not None: if ga.chromosome_impl is not None:
@ -79,7 +79,7 @@ class Mutation_Methods:
def swap_genes(ga, chromosome): def swap_genes(ga, chromosome):
"""Swaps two random genes in the chromosome.""" """Swaps two random genes in the chromosome."""
index_one = random.randint(0, len(chromosome)-1) index_one = random.randrange(len(chromosome))
index_two = random.randint(0, len(chromosome)-1) index_two = random.randrange(len(chromosome))
chromosome[index_one], chromosome[index_two] = chromosome[index_two], chromosome[index_one] chromosome[index_one], chromosome[index_two] = chromosome[index_two], chromosome[index_one]

6
src/parent_selection/parent_selection_methods.py
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@ -68,7 +68,7 @@ class Parent_Selection:
while (len(ga.population.get_mating_pool()) < len(ga.population)*ga.parent_ratio): while (len(ga.population.get_mating_pool()) < len(ga.population)*ga.parent_ratio):
# Generate a random tournament group and sort by fitness. # Generate a random tournament group and sort by fitness.
tournament_group = sorted([random.randint(0, len(ga.population)-1) for n in range(tournament_size)]) tournament_group = sorted([random.randrange(len(ga.population)) for _ in range(tournament_size)])
# For each chromosome, add it to the mating pool based on its rank in the tournament. # For each chromosome, add it to the mating pool based on its rank in the tournament.
for index in range(tournament_size): for index in range(tournament_size):
@ -78,7 +78,7 @@ class Parent_Selection:
# second ranked fitness has probability: selection_probability * (1-selection_probability) # second ranked fitness has probability: selection_probability * (1-selection_probability)
# third ranked fitness has probability: selection_probability * (1-selection_probability)^2 # third ranked fitness has probability: selection_probability * (1-selection_probability)^2
# etc. # etc.
if random.uniform(0, 1) < ga.selection_probability * pow(1-ga.selection_probability, index): if random.random() < ga.selection_probability * pow(1-ga.selection_probability, index):
ga.population.set_parent(tournament_group[index]) ga.population.set_parent(tournament_group[index])
# Stop if parent ratio reached # Stop if parent ratio reached
@ -141,7 +141,7 @@ class Parent_Selection:
while (len(ga.population.get_mating_pool()) < len(ga.population)*ga.parent_ratio): while (len(ga.population.get_mating_pool()) < len(ga.population)*ga.parent_ratio):
# Selected chromosome # Selected chromosome
index = random.randint(0, len(ga.population)-1) index = random.randrange(len(ga.population))
# Probability of becoming a parent is fitness/max_fitness # Probability of becoming a parent is fitness/max_fitness
if random.uniform(ga.selection_probability, 1) < ga.get_chromosome_fitness(index)/max_fitness: if random.uniform(ga.selection_probability, 1) < ga.get_chromosome_fitness(index)/max_fitness: