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Fixed default arguments

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Reverted usage format:
GA(
    chromosome_length = 20,
    population_size = 25
)
This commit is contained in:
SimpleArt
2020-11-03 14:30:43 -05:00
parent 0c6d8060a6
commit 72a8975d03
2 changed files with 65 additions and 79 deletions
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3
src/EasyGA.py
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@ -33,9 +33,6 @@ class GA(Attributes):
https://github.com/danielwilczak101/EasyGA/wiki https://github.com/danielwilczak101/EasyGA/wiki
""" """
def __init__(self, attributes = None):
super(GA, self).__init__({} if attributes is None else attributes)
def evolve_generation(self, number_of_generations = 1, consider_termination = True): def evolve_generation(self, number_of_generations = 1, consider_termination = True):
"""Evolves the ga the specified number of generations.""" """Evolves the ga the specified number of generations."""

141
src/attributes.py
View File

@ -1,5 +1,6 @@
import random import random
import sqlite3 import sqlite3
from copy import deepcopy
# Import all the data structure prebuilt modules # Import all the data structure prebuilt modules
from structure import Population as create_population from structure import Population as create_population
@ -28,103 +29,91 @@ class Attributes:
been set then they will fall back onto the default attribute. All been set then they will fall back onto the default attribute. All
attributes have been catigorized to explain sections in the ga process.""" attributes have been catigorized to explain sections in the ga process."""
def __init__(self, attributes): def __init__(self,
chromosome_length = 10,
# Default settings for the GA population_size = 10,
default_attributes = { chromosome_impl = None,
'chromosome_length' : 10, gene_impl = lambda: random.randint(1, 10),
'population_size' : 10, population = None,
'chromosome_impl' : None, target_fitness_type = 'max',
'gene_impl' : lambda: random.randint(1, 10), update_fitness = True,
'population' : None, parent_ratio = 0.10,
'target_fitness_type' : 'max', selection_probability = 0.50,
'update_fitness' : True, tournament_size_ratio = 0.10,
'parent_ratio' : 0.10, current_generation = 0,
'selection_probability' : 0.50, current_fitness = 0,
'tournament_size_ratio' : 0.10, generation_goal = 15,
'current_generation' : 0, fitness_goal = None,
'current_fitness' : 0, chromosome_mutation_rate = 0.15,
'generation_goal' : 15, gene_mutation_rate = 0.03,
'fitness_goal' : None, initialization_impl = Initialization_Methods.random_initialization,
'chromosome_mutation_rate' : 0.15, fitness_function_impl = Fitness_Examples.is_it_5,
'gene_mutation_rate' : 0.03, make_population = create_population,
'initialization_impl' : Initialization_Methods.random_initialization, make_chromosome = create_chromosome,
'fitness_function_impl' : Fitness_Examples.is_it_5, make_gene = create_gene,
'make_population' : create_population, parent_selection_impl = Parent_Selection.Rank.tournament,
'make_chromosome' : create_chromosome, crossover_individual_impl = Crossover_Methods.Individual.single_point,
'make_gene' : create_gene, crossover_population_impl = Crossover_Methods.Population.sequential_selection,
'parent_selection_impl' : Parent_Selection.Rank.tournament, survivor_selection_impl = Survivor_Selection.fill_in_best,
'crossover_individual_impl' : Crossover_Methods.Individual.single_point, mutation_individual_impl = Mutation_Methods.Individual.single_gene,
'crossover_population_impl' : Crossover_Methods.Population.sequential_selection, mutation_population_impl = Mutation_Methods.Population.random_selection,
'survivor_selection_impl' : Survivor_Selection.fill_in_best, termination_impl = Termination_Methods.fitness_and_generation_based,
'mutation_individual_impl' : Mutation_Methods.Individual.single_gene, database = None,
'mutation_population_impl' : Mutation_Methods.Population.random_selection, database_name = 'database.db',
'termination_impl' : Termination_Methods.fitness_and_generation_based, 'sql_create_data_structure' = """ CREATE TABLE IF NOT EXISTS data (
'database' : None,
'database_name' : r"database.db",
'sql_create_data_structure' : """ CREATE TABLE IF NOT EXISTS data (
id integer PRIMARY KEY, id integer PRIMARY KEY,
generation integer NOT NULL, generation integer NOT NULL,
fitness DOUBLE, fitness DOUBLE,
chromosome text chromosome text
); """ ); """
):
}
# Filling in the default settings
for attribute in default_attributes.keys():
if attribute not in attributes.keys():
attributes[attribute] = default_attributes[attribute]
# Initilization variables # Initilization variables
self.chromosome_length = attributes['chromosome_length'] self.chromosome_length = deepcopy(chromosome_length)
self.population_size = attributes['population_size'] self.population_size = deepcopy(population_size)
self.chromosome_impl = attributes['chromosome_impl'] self.chromosome_impl = deepcopy(chromosome_impl)
self.gene_impl = attributes['gene_impl'] self.gene_impl = deepcopy(gene_impl)
self.population = attributes['population'] self.population = deepcopy(population)
self.target_fitness_type = attributes['target_fitness_type'] self.target_fitness_type = deepcopy(target_fitness_type)
self.update_fitness = attributes['update_fitness'] self.update_fitness = deepcopy(update_fitness)
# Selection variables # Selection variables
self.parent_ratio = attributes['parent_ratio'] self.parent_ratio = deepcopy(parent_ratio)
self.selection_probability = attributes['selection_probability'] self.selection_probability = deepcopy(selection_probability)
self.tournament_size_ratio = attributes['tournament_size_ratio'] self.tournament_size_ratio = deepcopy(tournament_size_ratio)
# Termination variables # Termination variables
self.current_generation = attributes['current_generation'] self.current_generation = deepcopy(current_generation)
self.current_fitness = attributes['current_fitness'] self.current_fitness = deepcopy(current_fitness)
self.generation_goal = attributes['generation_goal'] self.generation_goal = deepcopy(generation_goal)
self.fitness_goal = attributes['fitness_goal'] self.fitness_goal = deepcopy(fitness_goal)
# Mutation variables # Mutation variables
self.chromosome_mutation_rate = attributes['chromosome_mutation_rate'] self.chromosome_mutation_rate = deepcopy(chromosome_mutation_rate)
self.gene_mutation_rate = attributes['gene_mutation_rate'] self.gene_mutation_rate = deepcopy(gene_mutation_rate)
# Default EasyGA implimentation structure # Default EasyGA implimentation structure
self.initialization_impl = attributes['initialization_impl'] self.initialization_impl = deepcopy(initialization_impl)
self.fitness_function_impl = attributes['fitness_function_impl'] self.fitness_function_impl = deepcopy(fitness_function_impl)
self.make_population = attributes['make_population'] self.make_population = deepcopy(make_population)
self.make_chromosome = attributes['make_chromosome'] self.make_chromosome = deepcopy(make_chromosome)
self.make_gene = attributes['make_gene'] self.make_gene = deepcopy(make_gene)
# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation # Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
self.parent_selection_impl = attributes['parent_selection_impl'] self.parent_selection_impl = deepcopy(parent_selection_impl)
self.crossover_individual_impl = attributes['crossover_individual_impl'] self.crossover_individual_impl = deepcopy(crossover_individual_impl)
self.crossover_population_impl = attributes['crossover_population_impl'] self.crossover_population_impl = deepcopy(crossover_population_impl)
self.survivor_selection_impl = attributes['survivor_selection_impl'] self.survivor_selection_impl = deepcopy(survivor_selection_impl)
self.mutation_individual_impl = attributes['mutation_individual_impl'] self.mutation_individual_impl = deepcopy(mutation_individual_impl)
self.mutation_population_impl = attributes['mutation_population_impl'] self.mutation_population_impl = deepcopy(mutation_population_impl)
# The type of termination to impliment # The type of termination to impliment
self.termination_impl = attributes['termination_impl'] self.termination_impl = deepcopy(termination_impl)
# Database varibles # Database varibles
self.database_name = attributes['database_name'] self.database = deepcopy(database)
self.database_name = deepcopy(database_name)
# Database varibles self.sql_create_data_structure = deepcopy(sql_create_data_structure)
self.database = attributes['database']
self.database_name = attributes['database_name']
self.sql_create_data_structure = attributes['sql_create_data_structure']
# Getter and setters for all required varibles # Getter and setters for all required varibles