Structure change to make it easier for users to clone and use the repository.

test_EasyGA.py

@@ -0,0 +1,225 @@
+import random
+import EasyGA
+
+# USE THIS COMMAND WHEN TESTING -
+    # python3 -m pytest
+
+# Tests can be broken down into three parts.
+#   - Testing correct size
+#       - Testing size while integrated with our function
+#   - Testing correct value
+#       - Testing integration with other functions
+
+def test_population_size():
+    """Test the population size is create correctly"""
+
+    for i in range(1,100):
+        # Create the ga to test
+        ga = EasyGA.GA()
+        # Set the upper limit of testing
+        ga.population_size = i
+        # Evolve the ga
+        ga.evolve()
+
+        # If they are not equal throw an error
+        assert ga.population.size() == ga.population_size
+
+def test_chromosome_length():
+    """ Test to see if the actual chromosome length is the same as defined."""
+
+    # Test from 0 to 100 chromosome length
+    for i in range(1,100):
+        # Create the ga to test
+        ga = EasyGA.GA()
+        # Set the upper limit of testing
+        ga.chromosome_length = i
+        # Evolve the ga
+        ga.evolve()
+
+        # If they are not equal throw an error
+        assert ga.population.chromosome_list[0].size() == ga.chromosome_length
+
+def test_gene_value():
+    """ """
+    pass
+
+def test_initilization():
+    """ """
+    pass
+
+def test_default():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+    # Print your default genetic algorithm
+    ga.print_generation()
+    ga.print_population()
+
+
+def test_attributes_gene_impl():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set necessary attributes
+    ga.population_size = 3
+    ga.chromosome_length = 5
+    ga.generation_goal =  1
+    # Set gene_impl
+    ga.gene_impl = lambda: random.randint(1, 10)
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+
+def test_attributes_chromosome_impl_lambdas():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set necessary attributes
+    ga.chromosome_length = 3
+    ga.generation_goal = 1
+    # Set gene_impl to None so it won't interfere
+    ga.gene_impl = None
+    # Set chromosome_impl
+    ga.chromosome_impl = lambda: [
+        random.randrange(1,100),
+        random.uniform(10,5),
+        random.choice(["up","down"])
+        ]
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+def test_attributes_chromosome_impl_functions():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set necessary attributes
+    ga.chromosome_length = 3
+    ga.generation_goal = 1
+
+    # Create chromosome_impl user function
+    def user_chromosome_function():
+        chromosome_data = [
+            random.randrange(1,100),
+            random.uniform(10,5),
+            random.choice(["up","down"])
+            ]
+        return chromosome_data
+
+    # Set the chromosome_impl
+    ga.chromosome_impl = user_chromosome_function
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+def test_while_ga_active():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set necessary attributes
+    ga.generation_goal = 1
+
+    # Evolve using ga.active
+    while ga.active():
+        ga.evolve_generation(5)
+
+
+def test_initilization_impl():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set the initialization_impl
+    ga.initialization_impl = EasyGA.Initialization_Methods.random_initialization
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+    assert (ga.initialization_impl == EasyGA.Initialization_Methods.random_initialization) and (ga != None)
+
+def test_parent_selection_impl():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set the parent_selection_impl
+    ga.parent_selection_impl = EasyGA.Parent_Selection.Fitness.roulette
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+    assert (ga.parent_selection_impl == EasyGA.Parent_Selection.Fitness.roulette) and (ga != None)
+
+def test_crossover_population_impl():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set the crossover_population_impl
+    ga.crossover_population_impl = EasyGA.Crossover_Methods.Population.sequential_selection
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+    assert (ga.crossover_population_impl == EasyGA.Crossover_Methods.Population.sequential_selection) and (ga != None)
+
+def test_crossover_individual_impl():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set the crossover_individual_impl
+    ga.crossover_individual_impl = EasyGA.Crossover_Methods.Individual.single_point
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+    assert (ga.crossover_individual_impl == EasyGA.Crossover_Methods.Individual.single_point) and (ga != None)
+
+def test_mutation_population_impl():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set the mutation_population_impl
+    ga.mutation_population_impl = EasyGA.Mutation_Methods.Population.random_selection
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+    assert (ga.mutation_population_impl == EasyGA.Mutation_Methods.Population.random_selection) and (ga != None)
+
+def test_mutation_individual_impl():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set the mutation_population_impl
+    ga.mutation_individual_impl = EasyGA.Mutation_Methods.Individual.single_gene
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+    assert (ga.mutation_individual_impl == EasyGA.Mutation_Methods.Individual.single_gene) and (ga != None)
+
+def test_survivor_selection_impl():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set the survivor_selection_impl
+    ga.survivor_selection_impl = EasyGA.Survivor_Selection.fill_in_random
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+    assert (ga.survivor_selection_impl == EasyGA.Survivor_Selection.fill_in_random) and (ga != None)
+
+def test_termination_impl():
+    # Create the Genetic algorithm
+    ga = EasyGA.GA()
+
+    # Set the termination_impl
+    ga.termination_impl = EasyGA.Termination_Methods.fitness_and_generation_based
+
+    # Evolve the genetic algorithm
+    ga.evolve()
+
+    assert (ga.termination_impl == EasyGA.Termination_Methods.fitness_and_generation_based) and (ga != None)