Fixed default arguments
Reverted usage format:
GA(
chromosome_length = 20,
population_size = 25
)
This commit is contained in:
SimpleArt
@ -1,5 +1,6 @@
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import random
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import sqlite3
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from copy import deepcopy
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# Import all the data structure prebuilt modules
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from structure import Population as create_population
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@ -28,103 +29,91 @@ class Attributes:
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been set then they will fall back onto the default attribute. All
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attributes have been catigorized to explain sections in the ga process."""
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def __init__(self, attributes):
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# Default settings for the GA
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default_attributes = {
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'chromosome_length' : 10,
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'population_size' : 10,
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'chromosome_impl' : None,
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'gene_impl' : lambda: random.randint(1, 10),
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'population' : None,
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'target_fitness_type' : 'max',
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'update_fitness' : True,
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'parent_ratio' : 0.10,
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'selection_probability' : 0.50,
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'tournament_size_ratio' : 0.10,
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'current_generation' : 0,
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'current_fitness' : 0,
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'generation_goal' : 15,
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'fitness_goal' : None,
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'chromosome_mutation_rate' : 0.15,
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'gene_mutation_rate' : 0.03,
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'initialization_impl' : Initialization_Methods.random_initialization,
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'fitness_function_impl' : Fitness_Examples.is_it_5,
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'make_population' : create_population,
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'make_chromosome' : create_chromosome,
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'make_gene' : create_gene,
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'parent_selection_impl' : Parent_Selection.Rank.tournament,
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'crossover_individual_impl' : Crossover_Methods.Individual.single_point,
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'crossover_population_impl' : Crossover_Methods.Population.sequential_selection,
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'survivor_selection_impl' : Survivor_Selection.fill_in_best,
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'mutation_individual_impl' : Mutation_Methods.Individual.single_gene,
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'mutation_population_impl' : Mutation_Methods.Population.random_selection,
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'termination_impl' : Termination_Methods.fitness_and_generation_based,
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'database' : None,
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'database_name' : r"database.db",
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'sql_create_data_structure' : """ CREATE TABLE IF NOT EXISTS data (
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def __init__(self,
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chromosome_length = 10,
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population_size = 10,
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chromosome_impl = None,
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gene_impl = lambda: random.randint(1, 10),
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population = None,
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target_fitness_type = 'max',
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update_fitness = True,
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parent_ratio = 0.10,
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selection_probability = 0.50,
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tournament_size_ratio = 0.10,
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current_generation = 0,
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current_fitness = 0,
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generation_goal = 15,
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fitness_goal = None,
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chromosome_mutation_rate = 0.15,
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gene_mutation_rate = 0.03,
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initialization_impl = Initialization_Methods.random_initialization,
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fitness_function_impl = Fitness_Examples.is_it_5,
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make_population = create_population,
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make_chromosome = create_chromosome,
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make_gene = create_gene,
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parent_selection_impl = Parent_Selection.Rank.tournament,
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crossover_individual_impl = Crossover_Methods.Individual.single_point,
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crossover_population_impl = Crossover_Methods.Population.sequential_selection,
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survivor_selection_impl = Survivor_Selection.fill_in_best,
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mutation_individual_impl = Mutation_Methods.Individual.single_gene,
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mutation_population_impl = Mutation_Methods.Population.random_selection,
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termination_impl = Termination_Methods.fitness_and_generation_based,
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database = None,
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database_name = 'database.db',
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'sql_create_data_structure' = """ CREATE TABLE IF NOT EXISTS data (
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id integer PRIMARY KEY,
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generation integer NOT NULL,
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fitness DOUBLE,
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chromosome text
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); """
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}
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# Filling in the default settings
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for attribute in default_attributes.keys():
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if attribute not in attributes.keys():
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attributes[attribute] = default_attributes[attribute]
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):
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# Initilization variables
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self.chromosome_length = attributes['chromosome_length']
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self.population_size = attributes['population_size']
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self.chromosome_impl = attributes['chromosome_impl']
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self.gene_impl = attributes['gene_impl']
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self.population = attributes['population']
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self.target_fitness_type = attributes['target_fitness_type']
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self.update_fitness = attributes['update_fitness']
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self.chromosome_length = deepcopy(chromosome_length)
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self.population_size = deepcopy(population_size)
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self.chromosome_impl = deepcopy(chromosome_impl)
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self.gene_impl = deepcopy(gene_impl)
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self.population = deepcopy(population)
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self.target_fitness_type = deepcopy(target_fitness_type)
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self.update_fitness = deepcopy(update_fitness)
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# Selection variables
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self.parent_ratio = attributes['parent_ratio']
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self.selection_probability = attributes['selection_probability']
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self.tournament_size_ratio = attributes['tournament_size_ratio']
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self.parent_ratio = deepcopy(parent_ratio)
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self.selection_probability = deepcopy(selection_probability)
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self.tournament_size_ratio = deepcopy(tournament_size_ratio)
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# Termination variables
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self.current_generation = attributes['current_generation']
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self.current_fitness = attributes['current_fitness']
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self.generation_goal = attributes['generation_goal']
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self.fitness_goal = attributes['fitness_goal']
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self.current_generation = deepcopy(current_generation)
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self.current_fitness = deepcopy(current_fitness)
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self.generation_goal = deepcopy(generation_goal)
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self.fitness_goal = deepcopy(fitness_goal)
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# Mutation variables
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self.chromosome_mutation_rate = attributes['chromosome_mutation_rate']
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self.gene_mutation_rate = attributes['gene_mutation_rate']
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self.chromosome_mutation_rate = deepcopy(chromosome_mutation_rate)
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self.gene_mutation_rate = deepcopy(gene_mutation_rate)
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# Default EasyGA implimentation structure
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self.initialization_impl = attributes['initialization_impl']
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self.fitness_function_impl = attributes['fitness_function_impl']
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self.make_population = attributes['make_population']
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self.make_chromosome = attributes['make_chromosome']
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self.make_gene = attributes['make_gene']
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self.initialization_impl = deepcopy(initialization_impl)
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self.fitness_function_impl = deepcopy(fitness_function_impl)
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self.make_population = deepcopy(make_population)
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self.make_chromosome = deepcopy(make_chromosome)
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self.make_gene = deepcopy(make_gene)
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# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
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self.parent_selection_impl = attributes['parent_selection_impl']
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self.crossover_individual_impl = attributes['crossover_individual_impl']
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self.crossover_population_impl = attributes['crossover_population_impl']
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self.survivor_selection_impl = attributes['survivor_selection_impl']
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self.mutation_individual_impl = attributes['mutation_individual_impl']
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self.mutation_population_impl = attributes['mutation_population_impl']
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self.parent_selection_impl = deepcopy(parent_selection_impl)
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self.crossover_individual_impl = deepcopy(crossover_individual_impl)
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self.crossover_population_impl = deepcopy(crossover_population_impl)
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self.survivor_selection_impl = deepcopy(survivor_selection_impl)
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self.mutation_individual_impl = deepcopy(mutation_individual_impl)
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self.mutation_population_impl = deepcopy(mutation_population_impl)
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# The type of termination to impliment
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self.termination_impl = attributes['termination_impl']
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self.termination_impl = deepcopy(termination_impl)
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# Database varibles
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self.database_name = attributes['database_name']
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# Database varibles
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self.database = attributes['database']
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self.database_name = attributes['database_name']
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self.sql_create_data_structure = attributes['sql_create_data_structure']
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self.database = deepcopy(database)
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self.database_name = deepcopy(database_name)
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self.sql_create_data_structure = deepcopy(sql_create_data_structure)
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# Getter and setters for all required varibles
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