Fixed alot of the functions
This commit is contained in:
danielwilczak101
@ -1,8 +1,8 @@
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import random
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import EasyGA
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from EasyGA import GA, Parent, Crossover, Mutation, Survivor, Termination
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# USE THIS COMMAND WHEN TESTING -
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# python3 -m pytest
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# python3 -m pytest
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# Tests can be broken down into three parts.
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# - Testing correct size
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@ -13,16 +13,18 @@ import EasyGA
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def test_population_size():
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"""Test the population size is create correctly"""
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for i in range(1,100):
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for i in range(4,100):
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# Create the ga to test
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ga = EasyGA.GA()
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ga = GA()
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ga.generation_goal = 10
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# Set the upper limit of testing
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ga.population_size = i
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# Evolve the ga
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ga.evolve()
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# If they are not equal throw an error
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assert ga.population.size() == ga.population_size
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assert int(len(ga.population)) == ga.population_size
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def test_chromosome_length():
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""" Test to see if the actual chromosome length is the same as defined."""
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@ -30,14 +32,16 @@ def test_chromosome_length():
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# Test from 0 to 100 chromosome length
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for i in range(1,100):
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# Create the ga to test
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ga = EasyGA.GA()
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ga = GA()
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ga.generation_goal = 10
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# Set the upper limit of testing
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ga.chromosome_length = i
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# Evolve the ga
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ga.evolve()
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# If they are not equal throw an error
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assert ga.population.chromosome_list[0].size() == ga.chromosome_length
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assert len(ga.population.chromosome_list[0]) == ga.chromosome_length
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def test_gene_value():
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""" """
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@ -49,7 +53,7 @@ def test_initilization():
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def test_default():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Evolve the genetic algorithm
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ga.evolve()
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@ -61,7 +65,7 @@ def test_default():
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def test_attributes_gene_impl():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set necessary attributes
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ga.population_size = 3
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@ -76,7 +80,7 @@ def test_attributes_gene_impl():
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def test_attributes_chromosome_impl_lambdas():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set necessary attributes
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ga.chromosome_length = 3
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@ -95,7 +99,7 @@ def test_attributes_chromosome_impl_lambdas():
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def test_attributes_chromosome_impl_functions():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set necessary attributes
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ga.chromosome_length = 3
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@ -118,108 +122,96 @@ def test_attributes_chromosome_impl_functions():
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def test_while_ga_active():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set necessary attributes
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ga.generation_goal = 1
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# Evolve using ga.active
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while ga.active():
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ga.evolve_generation(5)
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ga.evolve(5)
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def test_initilization_impl():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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# Set the initialization_impl
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ga.initialization_impl = EasyGA.Initialization_Methods.random_initialization
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# Evolve the genetic algorithm
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ga.evolve()
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assert (ga.initialization_impl == EasyGA.Initialization_Methods.random_initialization) and (ga != None)
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def test_parent_selection_impl():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set the parent_selection_impl
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ga.parent_selection_impl = EasyGA.Parent_Selection.Fitness.roulette
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ga.parent_selection_impl = Parent.Fitness.roulette
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# Evolve the genetic algorithm
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ga.evolve()
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assert (ga.parent_selection_impl == EasyGA.Parent_Selection.Fitness.roulette) and (ga != None)
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assert (ga.parent_selection_impl == Parent.Fitness.roulette) and (ga != None)
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def test_crossover_population_impl():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set the crossover_population_impl
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ga.crossover_population_impl = EasyGA.Crossover_Methods.Population.sequential_selection
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ga.crossover_population_impl = Cossover.Population.sequential_selection
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# Evolve the genetic algorithm
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ga.evolve()
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assert (ga.crossover_population_impl == EasyGA.Crossover_Methods.Population.sequential_selection) and (ga != None)
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assert (ga.crossover_population_impl == Crossover.Population.sequential_selection) and (ga != None)
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def test_crossover_individual_impl():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set the crossover_individual_impl
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ga.crossover_individual_impl = EasyGA.Crossover_Methods.Individual.single_point
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ga.crossover_individual_impl = Crossover.Individual.single_point
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# Evolve the genetic algorithm
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ga.evolve()
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assert (ga.crossover_individual_impl == EasyGA.Crossover_Methods.Individual.single_point) and (ga != None)
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assert (ga.crossover_individual_impl == Crossover.Individual.single_point) and (ga != None)
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def test_mutation_population_impl():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set the mutation_population_impl
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ga.mutation_population_impl = EasyGA.Mutation_Methods.Population.random_selection
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ga.mutation_population_impl = Mutation.Population.random_selection
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# Evolve the genetic algorithm
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ga.evolve()
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assert (ga.mutation_population_impl == EasyGA.Mutation_Methods.Population.random_selection) and (ga != None)
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assert (ga.mutation_population_impl == Mutation.Population.random_selection) and (ga != None)
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def test_mutation_individual_impl():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set the mutation_population_impl
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ga.mutation_individual_impl = EasyGA.Mutation_Methods.Individual.single_gene
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ga.mutation_individual_impl = Mutation.Individual.single_gene
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# Evolve the genetic algorithm
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ga.evolve()
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assert (ga.mutation_individual_impl == EasyGA.Mutation_Methods.Individual.single_gene) and (ga != None)
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assert (ga.mutation_individual_impl == Mutation.Individual.single_gene) and (ga != None)
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def test_survivor_selection_impl():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set the survivor_selection_impl
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ga.survivor_selection_impl = EasyGA.Survivor_Selection.fill_in_random
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ga.survivor_selection_impl = Survivor.fill_in_random
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# Evolve the genetic algorithm
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ga.evolve()
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assert (ga.survivor_selection_impl == EasyGA.Survivor_Selection.fill_in_random) and (ga != None)
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assert (ga.survivor_selection_impl == Survivor.fill_in_random) and (ga != None)
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def test_termination_impl():
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# Create the Genetic algorithm
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ga = EasyGA.GA()
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ga = GA()
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# Set the termination_impl
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ga.termination_impl = EasyGA.Termination_Methods.fitness_and_generation_based
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ga.termination_impl = Termination.fitness_and_generation_based
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# Evolve the genetic algorithm
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ga.evolve()
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assert (ga.termination_impl == EasyGA.Termination_Methods.fitness_and_generation_based) and (ga != None)
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assert (ga.termination_impl == Termination.fitness_and_generation_based) and (ga != None)
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