Requested file name changes

src/EasyGA.py

@@ -4,13 +4,14 @@ import random
 from initialization import population as create_population
 from initialization import chromosome as create_chromosome
 from initialization import gene as create_gene
-# Import the default fitness function
+#
-from fitness_function import example_is_it_5
+from fitness_function import fitness_examples
-# Import default termination points
+# Import example classes
-from termination_point import generation_based
+from initialization import initialization_examples
-from termination_point import fitness_based
+from termination_point import termination_examples
-# Import functionality defaults
+from selection import selection_examples
-from initialization import random_initialization
+from crossover import crossover_examples
+from mutation import mutation_examples
 
 class GA:
     def __init__(self):
@@ -31,12 +32,12 @@ class GA:
 
 
         # Defualt EastGA implimentation structure
-        self.initialization_impl = random_initialization
+        self.initialization_impl = initialization_examples.random_initialization
-        self.fitness_funciton_impl = example_is_it_5
+        self.fitness_funciton_impl = fitness_examples.is_it_5
         #self.mutation_impl = PerGeneMutation(Mutation_rate)
         #self.selection_impl = TournamentSelection()
         #self.crossover_impl = FastSinglePointCrossover()
-        self.termination_impl = generation_based
+        self.termination_impl = termination_examples.generation_based
 
         # If we want the fitnesses to be updated by the computer
         self.update_fitness = True

src/crossover/__init__.py

@@ -1 +1,2 @@
-# From file name import function name
+# FROM (. means local) file_name IMPORT function_name
+from .examples import crossover_examples

src/crossover/examples.py

@@ -0,0 +1,3 @@
+class crossover_examples:
+    """Crossover examples will go here """
+    pass

src/fitness_function/__init__.py

@@ -1,2 +1,2 @@
-# FROM (. means local) file_name IMPORT function_name
+# FROM (. means local) file_name IMPORT class name
-from .example_is_it_5 import example_is_it_5
+from .examples import fitness_examples

src/fitness_function/example_is_it_5.py

@@ -1,13 +0,0 @@
-def example_is_it_5(chromosome):
-    """A very simple case test function - If the chromosomes gene value is a 5 add one
-     to the chromosomes overall fitness value."""
-    # Overall fitness value
-    fitness = 0
-    # For each gene in the chromosome
-    for gene in chromosome.genes:
-        # Check if its value = 5
-        if(gene.value == 5):
-            # If its value is 5 then add one to
-            # the overal fitness of the chromosome.
-            fitness += 1
-    return fitness

src/fitness_function/examples.py

@@ -0,0 +1,16 @@
+class fitness_examples:
+    """Fitness function examples used"""
+    
+    def is_it_5(chromosome):
+        """A very simple case test function - If the chromosomes gene value is a 5 add one
+         to the chromosomes overall fitness value."""
+        # Overall fitness value
+        fitness = 0
+        # For each gene in the chromosome
+        for gene in chromosome.genes:
+            # Check if its value = 5
+            if(gene.value == 5):
+                # If its value is 5 then add one to
+                # the overal fitness of the chromosome.
+                fitness += 1
+        return fitness

src/initialization/__init__.py

@@ -1,5 +1,5 @@
 # FROM (. means local) file_name IMPORT function_name
-from .random_initialization import random_initialization
+from .examples import initialization_examples
 from .population_structure.population import population
 from .chromosome_structure.chromosome import chromosome
 from .gene_structure.gene import gene

src/initialization/examples.py

@@ -0,0 +1,33 @@
+# Import the data structure
+from .population_structure.population import population as create_population
+from .chromosome_structure.chromosome import chromosome as create_chromosome
+from .gene_structure.gene import gene as create_gene
+
+class initialization_examples:
+    """Initialization examples that are used as defaults and examples"""
+
+    def random_initialization(population_size, chromosome_length, chromosome_impl, gene_impl):
+        """Takes the initialization inputs and choregraphs them to output the type of population
+        with the given parameters."""
+        # Create the population object
+        population = create_population()
+
+        # Fill the population with chromosomes
+        for i in range(population_size):
+            chromosome = create_chromosome()
+            #Fill the Chromosome with genes
+            for j in range(chromosome_length):
+                # Using the chromosome_impl to set every index inside of the chromosome
+                if chromosome_impl != None:
+                    # Each chromosome location is specified with its own function
+                    chromosome.add_gene(create_gene(chromosome_impl(j)))
+                    # Will break if chromosome_length != len(lists) in domain
+                elif gene_impl != None:
+                    # gene_impl = [range function,lowerbound,upperbound]
+                    function = gene_impl[0]
+                    chromosome.add_gene(create_gene(function(*gene_impl[1:])))
+                else:
+                    #Exit because either were not specified
+                    print("Your domain or range were not specified")
+            population.add_chromosome(chromosome)
+        return population

src/initialization/random_initialization.py

@@ -1,30 +0,0 @@
-# Import the data structure
-from .population_structure.population import population as create_population
-from .chromosome_structure.chromosome import chromosome as create_chromosome
-from .gene_structure.gene import gene as create_gene
-
-def random_initialization(population_size, chromosome_length, chromosome_impl, gene_impl):
-    """Takes the initialization inputs and choregraphs them to output the type of population
-    with the given parameters."""
-    # Create the population object
-    population = create_population()
-    
-    # Fill the population with chromosomes
-    for i in range(population_size):
-        chromosome = create_chromosome()
-        #Fill the Chromosome with genes
-        for j in range(chromosome_length):
-            # Using the chromosome_impl to set every index inside of the chromosome
-            if chromosome_impl != None:
-                # Each chromosome location is specified with its own function
-                chromosome.add_gene(create_gene(chromosome_impl(j)))
-                # Will break if chromosome_length != len(lists) in domain
-            elif gene_impl != None:
-                # gene_impl = [range function,lowerbound,upperbound]
-                function = gene_impl[0]
-                chromosome.add_gene(create_gene(function(*gene_impl[1:])))
-            else:
-                #Exit because either were not specified
-                print("Your domain or range were not specified")
-        population.add_chromosome(chromosome)
-    return population

src/mutation/__init__.py

@@ -0,0 +1,2 @@
+# FROM (. means local) file_name IMPORT function_name
+from .examples import mutation_examples

src/mutation/examples.py

@@ -0,0 +1,3 @@
+class mutation_examples:
+    """Selection examples will go here """
+    pass

src/selection/__init__.py

@@ -0,0 +1,2 @@
+# FROM (. means local) file_name IMPORT function_name
+from .examples import selection_examples

src/selection/examples.py

@@ -0,0 +1,3 @@
+class selection_examples:
+    """Selection examples will go here """
+    pass

src/termination_point/__init__.py

@@ -1,3 +1,2 @@
-# FROM (. means local) file_name IMPORT function_name
+# FROM (. means local) file_name IMPORT class name
-from .generation_based import generation_based
+from .examples import termination_examples
-from .fitness_based import fitness_based

src/termination_point/examples.py

@@ -0,0 +1,16 @@
+class termination_examples:
+    """Example functions that can be used to terminate the the algorithms loop"""
+
+    def fitness_based(ga):
+        """Fitness based approach to terminate when the goal fitness has been reached"""
+        status = True
+        if(ga.current_fitness > ga.goal_fitness):
+            status = False
+        return status
+
+    def generation_based(ga):
+        """Generation based approach to terminate when the goal generation has been reached"""
+        status = True
+        if(ga.current_generation > ga.max_generations):
+            status = False
+        return status

src/termination_point/fitness_based.py

@@ -1,5 +0,0 @@
-def fitness_based(ga):
-    status = True
-    if(ga.current_fitness > ga.goal_fitness):
-        status = False
-    return status

src/termination_point/generation_based.py

@@ -1,7 +0,0 @@
-def generation_based(ga):
-    """Generation based approach to termination - If the current generation is
-    less then the """
-    status = True
-    if(ga.current_generation > ga.max_generations):
-        status = False
-    return status