Alot of name changes and file name changes

src/EasyGA.py

@@ -1,15 +1,17 @@
 import random
 # Import all the data structure prebuilt modules
-from initialization import population as create_population
-from initialization import chromosome as create_chromosome
-from initialization import gene as create_gene
-# Import example classes
-from fitness_function import fitness_examples
-from initialization import initialization_examples
-from termination_point import termination_examples
-from selection import selection_examples
-from crossover import crossover_examples
-from mutation import mutation_examples
+from initialization import Population as create_population
+from initialization import Chromosome as create_chromosome
+from initialization import Gene as create_gene
+# Structure Methods
+from fitness_function import Fitness_methods
+from initialization import Initialization_methods
+from termination_point import Termination_methods
+# Population Methods
+from survivor_selection import Survivor_methods
+# Manipulation Methods
+from parent_selection import Parent_methods
+from mutation import Mutation_methods
 
 class GA:
     def __init__(self):
@@ -22,8 +24,9 @@ class GA:
         self.population = None
         # Termination varibles
         self.current_generation = 0
-        self.current_fitness = 0
         self.generation_goal = 3
+
+        self.current_fitness = 0
         self.fitness_goal = 3
         # Mutation variables
         self.mutation_rate = 0.03
@@ -32,21 +35,56 @@ class GA:
         self.update_fitness = False
 
         # Defualt EastGA implimentation structure
-        self.initialization_impl = initialization_examples.random_initialization
-        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 = termination_examples.generation_based
+        self.initialization_impl = Initialization_methods.random_initialization
+        self.fitness_funciton_impl = Fitness_methods.is_it_5
+        # Selects which chromosomes should be automaticly moved to the next population
+        #self.survivor_selection_impl = Survivor_methods.
+        # Methods for accomplishing parent-selection -> Crossover -> Mutation
+        #self.parent_selection_impl = Parent_methods.
+        #self.crossover_impl = Crossover_methods.
+        #self.mutation_impl = Mutation_methods.
+        # The type of termination to impliment
+        self.termination_impl = Termination_methods.generation_based
+
+    def evolve_generation(self, number_of_generations = 1):
+        """Evolves the ga the specified number of generations."""
+        while(number_of_generations > 0):
+            # If its the first generation then initialize the population
+            if(self.current_generation == 0):
+                # Initialize the population
+                self.initialize_population()
+            # First get the fitness of the population
+            self.get_population_fitness(self.population.chromosome_list)
+            # Selection - Triggers flags in the chromosome if its been selected
+            # self.selection_impl(self)
+            # Crossover - Takes the flagged chromosome_list and crosses there genetic
+            # makup to make new offsprings.
+            # self.crossover_impl(self)
+            # Repopulate - Manipulates the population to some desired way
+            # self.repopulate_impl(self)
+            # Mutation - Manipulates the population very slightly
+            # self.mutation_impl(self)
+
+            # Counter for the local number of generations in evolve_generation
+            number_of_generations -= 1
+            # Add one to the current overall generation
+            self.current_generation += 1
+
+    def evolve(self):
+        """Runs the ga until the termination point has been satisfied."""
+        # While the termination point hasnt been reached keep running
+        while(self.active()):
+            self.evolve_generation()
+
+    def active(self):
+        """Returns if the ga should terminate base on the termination implimented"""
+        # Send termination_impl the whole ga class
+        return self.termination_impl(self)
 
     def initialize_population(self):
         """Initialize the population using the initialization
          implimentation that is currently set"""
-        self.population = self.initialization_impl(
-        self.population_size,
-        self.chromosome_length,
-        self.chromosome_impl,
-        self.gene_impl)
+        self.population = self.initialization_impl(self)
 
     def get_population_fitness(self,population):
         """Will get and set the fitness of each chromosome in the population.
@@ -61,43 +99,6 @@ class GA:
                 # Set the chromosomes fitness using the fitness function
                 chromosome.fitness = self.fitness_funciton_impl(chromosome)
 
-
-    def evolve(self):
-        """Runs the ga until the termination point has been satisfied."""
-        # While the termination point hasnt been reached keep running
-        while(self.active()):
-            self.evolve_generation()
-
-    def active(self):
-        """Returns if the ga should terminate base on the termination implimented"""
-        # Send termination_impl the whole ga class
-        return self.termination_impl(self)
-
-
-    def evolve_generation(self, number_of_generations = 1):
-        """Evolves the ga the specified number of generations."""
-        while(number_of_generations > 0):
-            # If its the first generation then initialize the population
-            if(self.current_generation == 0):
-                # Initialize the population
-                self.initialize_population()
-            # First get the fitness of the population
-            self.get_population_fitness(self.population.chromosomes)
-            # Selection - Triggers flags in the chromosome if its been selected
-            # self.selection_impl(self)
-            # Crossover - Takes the flagged chromosomes and crosses there genetic
-            # makup to make new offsprings.
-            # self.crossover_impl(self)
-            # Repopulate - Manipulates the population to some desired way
-            # self.repopulate_impl(self)
-            # Mutation - Manipulates the population very slightly
-            # self.mutation_impl(self)
-
-            # Counter for the local number of generations in evolve_generation
-            number_of_generations -= 1
-            # Add one to the current overall generation
-            self.current_generation += 1
-
     def make_gene(self,value):
         """Let's the user create a gene."""
         return create_gene(value)

src/crossover/README.md

@@ -1 +0,0 @@
-# Crossover function

src/crossover/examples.py

@@ -1,15 +0,0 @@
-class crossover_examples:
-    """ Crossover explination goes here.
-
-    Points - Defined as sections between the chromosomes genetic makeup
-    """
-
-    def single_point_crossover(ga):
-        """Single  point crossover is when a "point" is selected and the genetic
-        make up of the two parent chromosomes are "Crossed" or better known as swapped"""
-        pass
-
-    def multi_point_crossover(ga,number_of_points = 2):
-        """Multi point crossover is when a specific number (More then one) of
-        "points" are created to merge the genetic makup of the chromosomes."""
-        pass

src/fitness_function/__init__.py

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

src/fitness_function/methods.py

@@ -1,13 +1,13 @@
-class fitness_examples:
+class Fitness_methods:
     """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:
+        for gene in chromosome.gene_list:
             # Check if its value = 5
             if(gene.value == 5):
                 # If its value is 5 then add one to

src/fitness_function/test_examples.py

@@ -1,12 +0,0 @@
-class test_fitness_funciton:
-    def get_fitness(self, chromosome):
-        # For every gene in chromosome
-        for i in range(len(chromosome.genes)):
-            # If the gene has a five then add one to the fitness
-            # Example -> Chromosome = [5],[2],[2],[5],[5] then fitness = 3
-            if (chromosome.genes[i].get_value == 5):
-                # Add to the genes fitness
-                chromosome.genes[i].fitness += 1
-                # Add to the chromosomes fitness
-                chromosome.fitness += 1
-        return chromosome.fitness

src/fitness_function/test_methods.py

@@ -0,0 +1 @@
+

src/initialization/__init__.py

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

src/initialization/chromosome_structure/chromosome.py

@@ -1,11 +1,11 @@
-class chromosome:
+class Chromosome:
 
-    def __init__(self, genes = None):
+    def __init__(self, gene_list = None):
         """Initialize the chromosome based on input gene list, defaulted to an empty list"""
-        if genes is None:
-            self.genes = []
+        if gene_list is None:
+            self.gene_list = []
         else:
-            self.genes = genes
+            self.gene_list = gene_list
         # The fitness of the overal chromosome
         self.fitness = None
         # If the chromosome has been selected then the flag would switch to true
@@ -14,16 +14,16 @@ class chromosome:
     def add_gene(self, gene, index = -1):
         """Add a gene to the chromosome at the specified index, defaulted to end of the chromosome"""
         if index == -1:
-            index = len(self.genes)
-        self.genes.insert(index, gene)
+            index = len(self.gene_list)
+        self.gene_list.insert(index, gene)
 
     def remove_gene(self, index):
         """Remove a gene from the chromosome at the specified index"""
-        del self.genes[index]
+        del self.gene_list[index]
 
     def get_genes(self):
         """Return all genes in the chromosome"""
-        return self.genes
+        return self.gene_list
 
     def get_fitness(self):
         """Return the fitness of the chromosome"""
@@ -31,11 +31,11 @@ class chromosome:
 
     def set_gene(self, gene, index):
         """Set a gene at a specific index"""
-        self.genes[index] = gene
+        self.gene_list[index] = gene
 
-    def set_genes(self, genes):
+    def set_genes(self, gene_list):
         """Set the entire gene set of the chromosome"""
-        self.genes = genes
+        self.gene_list = gene_list
 
     def set_fitness(self, fitness):
         """Set the fitness value of the chromosome"""
@@ -44,6 +44,6 @@ class chromosome:
     def __repr__(self):
         """Format the repr() output for the chromosome"""
         output_str = ''
-        for gene in self.genes:
+        for gene in self.gene_list:
             output_str += gene.__repr__()
         return output_str

src/initialization/gene_structure/gene.py

@@ -3,7 +3,7 @@ def check_gene(value):
     assert value != "" , "Gene can not be empty"
     return value
 
-class gene:
+class Gene:
 
     def __init__(self, value):
         """Initialize a gene with fitness of value None and the input value"""

src/initialization/methods.py

@@ -1,31 +1,31 @@
 # 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
+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:
+class Initialization_methods:
     """Initialization examples that are used as defaults and examples"""
 
-    def random_initialization(population_size, chromosome_length, chromosome_impl, gene_impl):
+    def random_initialization(ga):
         """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):
+        for i in range(ga.population_size):
             chromosome = create_chromosome()
             #Fill the Chromosome with genes
-            for j in range(chromosome_length):
+            for j in range(ga.chromosome_length):
                 # Using the chromosome_impl to set every index inside of the chromosome
-                if chromosome_impl != None:
+                if ga.chromosome_impl != None:
                     # Each chromosome location is specified with its own function
-                    chromosome.add_gene(create_gene(chromosome_impl(j)))
+                    chromosome.add_gene(create_gene(ga.chromosome_impl(j)))
                     # Will break if chromosome_length != len(lists) in domain
-                elif gene_impl != None:
+                elif ga.gene_impl != None:
                     # gene_impl = [range function,lowerbound,upperbound]
-                    function = gene_impl[0]
-                    chromosome.add_gene(create_gene(function(*gene_impl[1:])))
+                    function = ga.gene_impl[0]
+                    chromosome.add_gene(create_gene(function(*ga.gene_impl[1:])))
                 else:
                     #Exit because either were not specified
                     print("Your domain or range were not specified")

src/initialization/population_structure/population.py

@@ -1,11 +1,11 @@
-class population:
+class Population:
 
-    def __init__(self, chromosomes = None):
+    def __init__(self, chromosome_list = None):
         """Intiialize the population with fitness of value None, and a set of chromosomes dependant on user-passed parameter"""
-        if chromosomes is None:
-          self.chromosomes = []
+        if chromosome_list is None:
+          self.chromosome_list = []
         else:
-          self.chromosomes = chromosomes
+          self.chromosome_list = chromosome_list
         self.fitness = None
 
     def get_closet_fitness(self,value):
@@ -15,42 +15,42 @@ class population:
     def add_chromosome(self, chromosome, index = -1):
         """Adds a chromosome to the population at the input index, defaulted to the end of the chromosome set"""
         if index == -1:
-            index = len(self.chromosomes)
-        self.chromosomes.insert(index, chromosome)
+            index = len(self.chromosome_list)
+        self.chromosome_list.insert(index, chromosome)
 
     def remove_chromosome(self, index):
         """removes a chromosome from the indicated index"""
-        del self.chromosomes[index]
+        del self.chromosome_list[index]
 
     def get_all_chromosomes(self):
         """returns all chromosomes in the population"""
-        return chromosomes
+        return chromosome_list
 
     def get_fitness(self):
         """returns the population's fitness"""
         return self.fitness
 
-    def set_all_chromosomes(self, chromosomes):
+    def set_all_chromosomes(self, chromosome_list):
         """sets the chromosome set of the population"""
-        self.chromosomes = chromosomes
+        self.chromosome_list = chromosome_list
 
     def set_chromosome(self, chromosome, index):
         """sets a specific chromosome at a specific index"""
-        self.chromosomes[index] = chromosome
+        self.chromosome_list[index] = chromosome
 
     def set_fitness(self, fitness):
-        """Sets the fitness value of the population""" 
+        """Sets the fitness value of the population"""
         self.fitness = fitness
 
     def __repr__(self):
         """Sets the repr() output format"""
-        return ''.join([chromosome.__repr__() for chromosome in self.chromosomes])
+        return ''.join([chromosome.__repr__() for chromosome in self.chromosome_list])
 
     def print_all(self):
         """Prints information about the population in the following format:"""
         """Ex .Current population"""
         """Chromosome 1 - [gene][gene][gene][.etc] /  Chromosome fitness - """
         print("Current population:")
-        for index in range(len(self.chromosomes)):
-            print(f'Chromosome - {index} {self.chromosomes[index]}', end = "")
-            print(f' / Fitness = {self.chromosomes[index].fitness}')
+        for index in range(len(self.chromosome_list)):
+            print(f'Chromosome - {index} {self.chromosome_list[index]}', end = "")
+            print(f' / Fitness = {self.chromosome_list[index].fitness}')

src/mutation/__init__.py

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

src/mutation/examples.py

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

src/mutation/methods.py

@@ -0,0 +1,3 @@
+class Mutation_methods:
+    """Mutation examples will go here """
+    pass

src/parent_selection/__init__.py

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

src/parent_selection/methods.py

@@ -0,0 +1,37 @@
+class Parent_methods:
+    """Selection defintion here"""
+
+    def tournament_selection(ga,matchs):
+         """Tournament selection involves running several "tournaments" among a
+        few individuals (or "chromosomes")chosen at random from the population.
+        The winner of each tournament (the one with the best fitness) is selected
+        for crossover.
+        Ex
+        Chromsome 1----1 wins ------
+        Chromsome 2----            - --1 wins----
+                                   -            -
+        Chromsome 3----3 wins ------            -- 5 Wins --->Chromosome 5 becomes Parent
+        Chromsome 4----                         -
+                                                -
+        Chromsome 5----5 wins ---------5 wins----
+        Chromsome 6----
+       ^--Matchs--^
+        """
+
+    def small_tournament(ga):
+        """ Small tournament is only one round of tournament. Beat the other
+        randomly selected chromosome and your are selected as a parent.
+        Chromosome 1----
+                        -- 1 wins -> Becomes selected for crossover.
+        Chromosome 2----
+        """
+        pass
+
+    def roulette_selection(ga):
+        """Roulette selection works based off of how strong the fitness is of the
+        chromosomes in the population. The stronger the fitness the higher the probability
+        that it will be selected. Using the example of a casino roulette wheel.
+        Where the chromosomes are the numbers to be selected and the board size for
+        those numbers are directly proportional to the chromosome's current fitness. Where
+        the ball falls is a randomly generated number between 0 and 1"""
+        pass

src/selection/examples.py

@@ -1,15 +0,0 @@
-class selection_examples:
-    """Selection defintion here"""
-
-    def tournament_selection():
-        """ """
-        pass
-
-    def roulette_selection():
-        """Roulette selection works based off of how strong the fitness is of the
-        chromosomes in the population. The stronger the fitness the higher the probability
-        that it will be selected. Using the example of a casino roulette wheel.
-        Where the chromosomes are the numbers to be selected and the board size for
-        those numbers are directly proportional to the chromosome's current fitness. Where
-        the ball falls is a randomly generated number between 0 and 1"""
-        pass

src/survivor_selection/README.md

@@ -0,0 +1 @@
+# Selection functions

src/survivor_selection/__init__.py

@@ -1,2 +1,2 @@
 # FROM (. means local) file_name IMPORT function_name
-from .examples import crossover_examples
+from .methods import Survivor_methods

src/survivor_selection/methods.py

@@ -0,0 +1,8 @@
+class Survivor_methods:
+    """Survivor methods defintion here"""
+
+    def elitism():
+        pass
+
+    def remove_two_worst():
+        pass

src/termination_point/__init__.py

@@ -1,2 +1,2 @@
 # FROM (. means local) file_name IMPORT class name
-from .examples import termination_examples
+from .methods import Termination_methods

src/termination_point/methods.py

@@ -1,4 +1,4 @@
-class termination_examples:
+class Termination_methods:
     """Example functions that can be used to terminate the the algorithms loop"""
 
     def fitness_based(ga):