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Removed unnecessary deepcopies

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SimpleArt
2020-12-13 10:28:16 -05:00
parent f3460617f6
commit f64b5f6a6a
1 changed files with 136 additions and 85 deletions
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221
src/attributes.py
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@ -37,19 +37,6 @@ 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."""
target_fitness_type_dict = {
'min' : 'min',
'minimize' : 'min',
'minimise' : 'min',
'minimization' : 'min',
'minimisation' : 'min',
'max' : 'max',
'maximize' : 'max',
'maximise' : 'max',
'maximization' : 'max',
'maximisation' : 'max'
}
def __init__(self,
chromosome_length = 10,
population_size = 10,
@ -76,8 +63,8 @@ class Attributes:
min_selection_probability = 0.25,
max_chromosome_mutation_rate = None,
min_chromosome_mutation_rate = None,
max_gene_mutation_rate = None,
min_gene_mutation_rate = None,
max_gene_mutation_rate = 0.99,
min_gene_mutation_rate = 0.01,
dist = None,
initialization_impl = Initialization_Methods.random_initialization,
fitness_function_impl = Fitness_Examples.is_it_5,
@ -104,71 +91,69 @@ class Attributes:
):
# Initilization variables
self.chromosome_length = deepcopy(chromosome_length)
self.population_size = deepcopy(population_size)
self.chromosome_impl = deepcopy(chromosome_impl)
self.gene_impl = deepcopy(gene_impl)
self.population = deepcopy(population)
self.target_fitness_type = deepcopy(target_fitness_type)
self.update_fitness = deepcopy(update_fitness)
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
# Selection variables
self.parent_ratio = deepcopy(parent_ratio)
self.selection_probability = deepcopy(selection_probability)
self.tournament_size_ratio = deepcopy(tournament_size_ratio)
self.parent_ratio = parent_ratio
self.selection_probability = selection_probability
self.tournament_size_ratio = tournament_size_ratio
# Termination variables
self.current_generation = deepcopy(current_generation)
self.current_fitness = deepcopy(current_fitness)
self.generation_goal = deepcopy(generation_goal)
self.fitness_goal = deepcopy(fitness_goal)
self.tolerance_goal = deepcopy(tolerance_goal)
self.percent_converged = deepcopy(percent_converged)
self.adapt_rate = deepcopy(adapt_rate)
self.adapt_probability_rate = deepcopy(adapt_probability_rate)
self.adapt_population_flag = deepcopy(adapt_population_flag)
self.current_generation = current_generation
self.current_fitness = current_fitness
self.generation_goal = generation_goal
self.fitness_goal = fitness_goal
self.tolerance_goal = tolerance_goal
self.percent_converged = percent_converged
# Mutation variables
self.chromosome_mutation_rate = chromosome_mutation_rate
self.gene_mutation_rate = gene_mutation_rate
# Adapt variables
self.adapt_rate = adapt_rate
self.adapt_probability_rate = adapt_probability_rate
self.adapt_population_flag = adapt_population_flag
# Bounds on probabilities when adapting
self.max_selection_probability = max_selection_probability
self.min_selection_probability = min_selection_probability
self.max_chromosome_mutation_rate = chromosome_mutation_rate if (max_chromosome_mutation_rate is None) else max_chromosome_mutation_rate
self.min_chromosome_mutation_rate = chromosome_mutation_rate if (min_chromosome_mutation_rate is None) else min_chromosome_mutation_rate
self.max_gene_mutation_rate = gene_mutation_rate if (max_gene_mutation_rate is None) else max_gene_mutation_rate
self.min_gene_mutation_rate = gene_mutation_rate if (min_gene_mutation_rate is None) else min_gene_mutation_rate
self.max_chromosome_mutation_rate = max_chromosome_mutation_rate
self.min_chromosome_mutation_rate = min_chromosome_mutation_rate
self.max_gene_mutation_rate = max_gene_mutation_rate
self.min_gene_mutation_rate = min_gene_mutation_rate
# Distance between two chromosomes
if dist is None:
self.dist = lambda chromosome_1, chromosome_2:\
sqrt(abs(chromosome_1.fitness - chromosome_2.fitness))
else:
self.dist = dist
# Mutation variables
self.chromosome_mutation_rate = deepcopy(chromosome_mutation_rate)
self.gene_mutation_rate = deepcopy(gene_mutation_rate)
self.dist = dist
# Default EasyGA implimentation structure
self.initialization_impl = deepcopy(initialization_impl)
self.fitness_function_impl = deepcopy(fitness_function_impl)
self.make_population = deepcopy(make_population)
self.make_chromosome = deepcopy(make_chromosome)
self.make_gene = deepcopy(make_gene)
self.initialization_impl = initialization_impl
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 = deepcopy(parent_selection_impl)
self.crossover_individual_impl = deepcopy(crossover_individual_impl)
self.crossover_population_impl = deepcopy(crossover_population_impl)
self.survivor_selection_impl = deepcopy(survivor_selection_impl)
self.mutation_individual_impl = deepcopy(mutation_individual_impl)
self.mutation_population_impl = deepcopy(mutation_population_impl)
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 = deepcopy(termination_impl)
self.termination_impl = termination_impl
# Database varibles
self.database = Database()
self.database_name = deepcopy(database_name)
self.sql_create_data_structure = deepcopy(sql_create_data_structure)
self.database_name = database_name
self.sql_create_data_structure = sql_create_data_structure
# Graphing variables
self.graph = Graph(self.database)
@ -193,6 +178,9 @@ class Attributes:
# Use averaging for crossover
self.crossover_individual_impl = Crossover_Methods.Individual.Arithmetic.average
# Use averaging for mutation
self.mutation_individual_impl = Mutation_Methods.Individual.Arithmetic.average
# Euclidean norm
self.dist = lambda chromosome_1, chromosome_2:\
sqrt(sum(
@ -229,24 +217,6 @@ class Attributes:
# Getter and setters for all required varibles
@property
def database_name(self):
"""Getter function for the database name"""
return self._database_name
@database_name.setter
def database_name(self, value_input):
"""Setter function with error checking for the database name"""
# Update the database class of the name change
self.database._database_name = value_input
# Set the name in the ga attribute
self._database_name = value_input
@property
def chromosome_length(self):
"""Getter function for chromosome length"""
@ -292,12 +262,93 @@ class Attributes:
@target_fitness_type.setter
def target_fitness_type(self, value_input):
"""Setter function for target fitness type for
converting input to min/max."""
"""Setter function for target fitness type."""
if value_input in self.target_fitness_type_dict.keys():
self._target_fitness_type = self.target_fitness_type_dict[value_input]
self._target_fitness_type = value_input
# Custom input
@property
def max_chromosome_mutation_rate(self):
"""Getter function for max chromosome mutation rate"""
return self._max_chromosome_mutation_rate
@max_chromosome_mutation_rate.setter
def max_chromosome_mutation_rate(self, value_input):
"""Setter function with error checking and default value for max chromosome mutation rate"""
# Default value
if value_input is None:
self._max_chromosome_mutation_rate = min(self.chromosome_mutation_rate*2, (1+self.chromosome_mutation_rate)/2)
# Otherwise check value
elif 0 < value_input < 1:
self._max_chromosome_mutation_rate = value_input
# Throw error
else:
self._target_fitness_type = value_input
raise ValueError("Max chromosome mutation rate must be between 0 and 1")
@property
def min_chromosome_mutation_rate(self):
"""Getter function for min chromosome mutation rate"""
return self._min_chromosome_mutation_rate
@min_chromosome_mutation_rate.setter
def min_chromosome_mutation_rate(self, value_input):
"""Setter function with error checking and default value for min chromosome mutation rate"""
# Default value
if value_input is None:
self._min_chromosome_mutation_rate = self.chromosome_mutation_rate/2
# Otherwise check value
elif 0 < value_input < 1:
self._min_chromosome_mutation_rate = value_input
# Throw error
else:
raise ValueError("Min chromosome mutation rate must be between 0 and 1")
@property
def dist(self):
"""Getter function for the distance between chromosomes."""
return self._dist
@dist.setter
def dist(self, value_input):
"""Setter function for the distance between chromosomes."""
# Default value by comparing fitnesses of chromosomes
if value_input is None:
self._dist = lambda chromosome_1, chromosome_2:\
sqrt(abs(chromosome_1.fitness - chromosome_2.fitness))
# Given input
else:
self._dist = value_input
@property
def database_name(self):
"""Getter function for the database name"""
return self._database_name
@database_name.setter
def database_name(self, value_input):
"""Setter function with error checking for the database name"""
# Update the database class of the name change
self.database._database_name = value_input
# Set the name in the ga attribute
self._database_name = value_input