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Rewrote default GA methods format

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SimpleArt
2021-01-06 10:22:26 -05:00
parent 411e26ab7c
commit 8d90056c92
1 changed files with 51 additions and 107 deletions
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158
src/attributes.py
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@ -39,6 +39,50 @@ class Attributes:
been set then they will fall back onto the default attribute. All
attributes have been catigorized to explain sections in the ga process."""
#=====================#
# Default GA methods: #
#=====================#
# Default EasyGA implimentation structure
fitness_function_impl = Fitness_Examples.is_it_5
make_population = make_population
make_chromosome = make_chromosome
make_gene = make_gene
# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation -> Termination
parent_selection_impl = Parent.Rank.tournament
crossover_individual_impl = Crossover.Individual.single_point
crossover_population_impl = Crossover.Population.sequential
survivor_selection_impl = Survivor.fill_in_best
mutation_individual_impl = Mutation.Individual.individual_genes
mutation_population_impl = Mutation.Population.random_avoid_best
termination_impl = Termination.fitness_generation_tolerance
def dist(self, chromosome_1, chromosome_2):
"""Default distance lambda. Returns the square root of the difference in fitnesses."""
return math.sqrt(abs(chromosome_1.fitness - chromosome_2.fitness))
def weighted_random(self, weight):
"""Returns a random value between 0 and 1. Returns values between the weight and the
nearest of 0 and 1 less frequently than between weight and the farthest of 0 and 1."""
rand_num = random.random()
if rand_num < weight:
return (1-weight) * rand_num / weight
else:
return 1 - weight * (1-rand_num) / (1-weight)
def gene_impl(self, *args, **kwargs):
"""Default gene implementation. Returns a random integer from 1 to 10."""
return random.randint(1, 10)
chromosome_impl = None
#=====================================#
# Special built-in class __methods__: #
#=====================================#
@ -52,8 +96,6 @@ class Attributes:
chromosome_length = 10,
population_size = 10,
chromosome_impl = None,
gene_impl = None,
population = None,
target_fitness_type = 'max',
update_fitness = False,
@ -84,22 +126,6 @@ class Attributes:
max_gene_mutation_rate = 0.15,
min_gene_mutation_rate = 0.01,
dist = None,
fitness_function_impl = None,
make_population = make_population,
make_chromosome = make_chromosome,
make_gene = make_gene,
parent_selection_impl = None,
crossover_individual_impl = None,
crossover_population_impl = None,
survivor_selection_impl = None,
mutation_individual_impl = None,
mutation_population_impl = None,
termination_impl = None,
Database = sql_database.SQL_Database,
database_name = 'database.db',
sql_create_data_structure = """CREATE TABLE IF NOT EXISTS data (
@ -110,7 +136,9 @@ class Attributes:
chromosome TEXT
); """,
Graph = matplotlib_graph.Matplotlib_Graph
Graph = matplotlib_graph.Matplotlib_Graph,
**kwargs
):
# Keep track of the current run
@ -119,8 +147,6 @@ class Attributes:
# Initilization variables
self.chromosome_length = chromosome_length
self.population_size = population_size
self.chromosome_impl = chromosome_impl
self.gene_impl = gene_impl
self.population = population
self.target_fitness_type = target_fitness_type
self.update_fitness = update_fitness
@ -155,26 +181,6 @@ class Attributes:
self.max_gene_mutation_rate = max_gene_mutation_rate
self.min_gene_mutation_rate = min_gene_mutation_rate
# Distance between two chromosomes
self.dist = dist
# Default EasyGA implimentation structure
self.fitness_function_impl = fitness_function_impl
self.make_population = make_population
self.make_chromosome = make_chromosome
self.make_gene = make_gene
# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
self.parent_selection_impl = parent_selection_impl
self.crossover_individual_impl = crossover_individual_impl
self.crossover_population_impl = crossover_population_impl
self.survivor_selection_impl = survivor_selection_impl
self.mutation_individual_impl = mutation_individual_impl
self.mutation_population_impl = mutation_population_impl
# The type of termination to impliment
self.termination_impl = termination_impl
# Database varibles
self.database = Database()
self.database_name = database_name
@ -183,6 +189,10 @@ class Attributes:
# Graphing variables
self.graph = Graph(self.database)
# Any other custom kwargs?
for name, value in kwargs.items():
self.__setattr__(name, value)
def __setattr__(self, name, value):
"""Custom setter for using
@ -214,72 +224,6 @@ class Attributes:
self.__dict__[name] = value
#===========================#
# Default built-in methods: #
#===========================#
def weighted_random(self, weight):
"""Returns a random value between 0 and 1. Returns values between the weight and the
nearest of 0 and 1 less frequently than between weight and the farthest of 0 and 1.
"""
rand_num = random.random()
if rand_num < weight:
return (1-weight) * rand_num / weight
else:
return 1 - weight * (1-rand_num) / (1-weight)
def dist(self, chromosome_1, chromosome_2):
"""Default distance lambda. Returns the square root of the difference in fitnesses."""
return math.sqrt(abs(chromosome_1.fitness - chromosome_2.fitness))
def gene_impl(self, *args, **kwargs):
"""Default gene implementation. Returns a random integer from 1 to 10."""
return random.randint(1, 10)
def fitness_function_impl(self, *args, **kwargs):
"""Default fitness function. Returns the number of genes that are 5."""
return Fitness_Examples.is_it_5(*args, **kwargs)
def parent_selection_impl(self, *args, **kwargs):
"""Default parent selection method using tournament selection."""
return Parent.Rank.tournament(self, *args, **kwargs)
def crossover_individual_impl(self, *args, **kwargs):
"""Default individual crossover method using single point crossover."""
return Crossover.Individual.single_point(self, *args, **kwargs)
def crossover_population_impl(self, *args, **kwargs):
"""Default population crossover method using sequential selection."""
return Crossover.Population.sequential(self, *args, **kwargs)
def survivor_selection_impl(self, *args, **kwargs):
"""Default survivor selection method using the fill in best method."""
return Survivor.fill_in_best(self, *args, **kwargs)
def mutation_individual_impl(self, *args, **kwargs):
"""Default individual mutation method by randomizing individual genes."""
return Mutation.Individual.individual_genes(self, *args, **kwargs)
def mutation_population_impl(self, *args, **kwargs):
"""Default population mutation method selects chromosomes randomly while avoiding the best."""
return Mutation.Population.random_avoid_best(self, *args, **kwargs)
def termination_impl(self, *args, **kwargs):
"""Default termination method by testing the fitness, generation, and tolerance goals."""
return Termination.fitness_generation_tolerance(self, *args, **kwargs)
#============================#
# Built-in database methods: #
#============================#