Merge branch 'master' into ryley_beta

CHANGELOG.rst

@@ -0,0 +1 @@
+

MANIFEST.in

@@ -1,19 +0,0 @@
-graft docs
-graft src
-graft ci
-graft tests
-
-include .bumpversion.cfg
-include .coveragerc
-include .cookiecutterrc
-include .editorconfig
-
-include AUTHORS.rst
-include CHANGELOG.rst
-include CONTRIBUTING.rst
-include LICENSE
-include README.rst
-
-include tox.ini .travis.yml .appveyor.yml .readthedocs.yml .pre-commit-config.yaml
-
-global-exclude *.py[cod] __pycache__/* *.so *.dylib

README.md

@@ -25,37 +25,7 @@ ga.evolve()
 Put the out here
 ```
 
-## Customized:
-```python
-import random
-import EasyGA
 
-# Create the Genetic algorithm
-ga = EasyGA.GA()
-
-# Makes a new gene
-new_gene = ga.make_gene("HelloWorld")
-# Makes a chromosome to store genes in
-new_chromosome = ga.make_chromosome()
-# Makes a Population to store chromosomes in
-new_population = ga.make_population()
-
-ga.initialize()
-
-print(ga.population)
-
-for chromosome in ga.population.chromosomes:
-    print(chromosome.genes[0].__dict__)
-```
-
-### Output:
-```python
-<initialization.population_structure.population.population object at 0x7f993002fdf0>
-{'fitness': None, 'value': 47}
-{'fitness': None, 'value': 4}
-{'fitness': None, 'value': 68}
-{'fitness': None, 'value': 57}
-```
 
 # How Testing works
 

setup.py

@@ -1,17 +1,21 @@
-from setuptools import setup
+from setuptools import setup, find_packages
 
 with open("README.md", "r") as fh:
     long_description = fh.read()
 
+
 setup(
     name='EasyGA',
-    version='0.0.8',
+    version='0.0.25',
     description='A ubiquitous or general purpuse GA',
     py_modules=["EasyGA"],
-    package_dir={'':'src'},
+    packages=find_packages(where='EasyGA'),
+    package_dir={
+        '': 'EasyGA',
+    },
     python_requires='>=3.6',
     url="https://github.com/danielwilczak101/EasyGA",
-    author="Daniel Wilczak",
+    author="Daniel Wilczak, Jack RyanNguyen, Ryley Griffith, Jared Curtis, Matthew Chase Oxamendi",
     author_email="danielwilczak101@gmail.com",
     long_description = long_description,
     long_description_content_type = "text/markdown",

src/EasyGA.py

@@ -72,6 +72,7 @@ class GA:
                 self.population.set_all_chromosomes(self.sort_by_best_fitness())
 
             number_of_generations -= 1
+
             self.current_generation += 1
 
     def evolve(self):
@@ -124,10 +125,13 @@ class GA:
         return chromosome_set
 
     def make_gene(self,value):
+        """Let's the user create a gene."""
         return create_gene(value)
 
     def make_chromosome(self):
+        """Let's the user create a chromosome."""
         return create_chromosome()
 
     def make_population(self):
+        """Let's the user create a population."""
         return create_population()

src/__init__.py

@@ -0,0 +1,3 @@
+import EasyGA
+import run_testing
+import test_EasyGA

src/crossover/README.md

@@ -1 +1 @@
-# Crossover function
+# Mutation functions

src/crossover/methods.py

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

src/fitness_function/methods.py

@@ -0,0 +1,16 @@
+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.gene_list:
+            # 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/test_fitness_function.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 +1,3 @@
 # FROM (. means local) file_name IMPORT function_name
 from .initialization_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

@@ -1,13 +1,16 @@
 class Chromosome:
-
-    def __init__(self, genes = None):
-        if genes is None:
+    def __init__(self, gene_list = None):
+        if gene_list is None:
             self.gene_list = []
         else:
             self.gene_list = genes
+
         self.fitness = None
+        # If the chromosome has been selected then the flag would switch to true
+        self.selected = False
 
     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.gene_list)
         self.gene_list.insert(index, gene)
@@ -19,6 +22,7 @@ class Chromosome:
         return self.gene_list
 
     def get_fitness(self):
+        """Return the fitness of the chromosome"""
         return self.fitness
 
     def set_gene(self, gene, index):
@@ -28,9 +32,11 @@ class Chromosome:
         self.gene_list = genes
 
     def set_fitness(self, fitness):
+        """Set the fitness value of the chromosome"""
         self.fitness = fitness
 
     def __repr__(self):
+        """Format the repr() output for the chromosome"""
         output_str = ''
         for gene in self.gene_list:
             output_str += gene.__repr__()

src/initialization/gene_structure/gene.py

@@ -6,16 +6,20 @@ def check_gene(value):
 class Gene:
 
     def __init__(self, value):
+        """Initialize a gene with fitness of value None and the input value"""
         self.fitness = None
         self.value = check_gene(value)
 
     def get_fitness(self):
+        """Return fitness of the gene"""
         return self.fitness
 
     def get_value(self):
+        """Return value of the gene"""
         return self.value
 
     def set_fitness(self, fitness):
+        """Set fitness of the gene"""
         self.fitness = fitness
 
     def set_value(self, value):
@@ -23,4 +27,5 @@ class Gene:
         self.value = value
 
     def __repr__(self):
+        """Format the repr() output value"""
         return f'[{self.value}]'

src/initialization/methods.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_methods:
+    """Initialization examples that are used as defaults and examples"""
+
+    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(ga.population_size):
+            chromosome = create_chromosome()
+            #Fill the Chromosome with genes
+            for j in range(ga.chromosome_length):
+                # Using the chromosome_impl to set every index inside of the chromosome
+                if ga.chromosome_impl != None:
+                    # Each chromosome location is specified with its own function
+                    chromosome.add_gene(create_gene(ga.chromosome_impl(j)))
+                    # Will break if chromosome_length != len(lists) in domain
+                elif ga.gene_impl != None:
+                    # gene_impl = [range function,lowerbound,upperbound]
+                    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")
+            population.add_chromosome(chromosome)
+        return population

src/initialization/population_structure/population.py

@@ -1,24 +1,25 @@
 class Population:
-
-    # fitness = Empty; population = [chromosome, chromosome, etc.]
-    def __init__(self, chromosomes = None):
-        if chromosomes is 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 chromosome_list is None:
           self.chromosome_list = []
         else:
-          self.chromosome_list = chromosomes
+          self.chromosome_list = chromosome_list
         self.fitness = None
         self.mating_pool = []
 
     def get_closet_fitness(self,value):
-        # Get the chomosome that has the closets fitness to the value defined
+        """Get the chomosome that has the closets fitness to the value defined"""
         pass
 
     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.chromosome_list)
         self.chromosome_list.insert(index, chromosome)
 
     def remove_chromosome(self, index):
+        """removes a chromosome from the indicated index"""
         del self.chromosome_list[index]
 
     def get_all_chromosomes(self):
@@ -26,6 +27,7 @@ class Population:
         return self.chromosome_list
 
     def get_fitness(self):
+        """returns the population's fitness"""
         return self.fitness
 
     def set_all_chromosomes(self, chromosomes):
@@ -37,6 +39,7 @@ class Population:
         self.chromosome_list[index] = chromosome
 
     def set_fitness(self, fitness):
+        """Sets the fitness value of the population"""
         self.fitness = fitness
 
     def __repr__(self):
@@ -44,8 +47,9 @@ class Population:
             return f'{self.chromosome_list[index]}'
 
     def print_all(self):
-        # Ex .Current population
-        #     Chromosome 1 - [gene][gene][gene][.etc] /  Chromosome fitness - #
+        """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.chromosome_list)):
             print(f'Chromosome - {index} {self.chromosome_list[index]}', end = "")

src/mutation/methods.py

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

src/parent_selection/__init__.py

@@ -0,0 +1,2 @@
+# FROM (. means local) file_name IMPORT function_name
+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/survivor_selection/README.md

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

src/survivor_selection/__init__.py

@@ -0,0 +1,2 @@
+# FROM (. means local) file_name IMPORT function_name
+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/methods.py

@@ -0,0 +1,16 @@
+class Termination_methods:
+    """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.fitness_goal):
+            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.generation_goal):
+            status = False
+        return status