Test Implementation for selection/crossover/mutation

The current test implementation includes random mutation, single point crossover, and tournament selection. The implementation, in short, is a nested approach. The selection method is the only thing actually called by the GA. Both crossover and mutation occur within the selection method. As long as these three systems all follow a standard input/output system, any implementation we build, as well as any user implementations, will work perfectly. The selection function must take GA as a parameter and output a new population. Crossover takes in GA and outputs a population. Mutation takes a chromosome set and outputs a new chromosome set.

Many of the changes in this commit are regarding this test implementation. I have also changed many of the file names from "x_examples" to "x_types" and updated the class names to follow capitalziation standards. I did this because I feel personally like the built-in mutation, crossover, and selection implementations are less "examples" and more just already built implementations to make the code required from the user smaller.

src/initialization/__init__.py

@@ -0,0 +1,5 @@
+# FROM (. means local) file_name IMPORT function_name
+from .initialization_types import Initialization_Types
+from .population_structure.population import Population
+from .chromosome_structure.chromosome import Chromosome
+from .gene_structure.gene import Gene

src/initialization/chromosome_structure/chromosome.py

@@ -1,6 +1,11 @@
+<<<<<<< Updated upstream
 class chromosome:
     
     # fitness = Empty; genes = [gene, gene, gene, etc.]
+=======
+class Chromosome:
+
+>>>>>>> Stashed changes
     def __init__(self, genes = None):
         if genes is None:
             self.genes = []

src/initialization/gene_structure/gene.py

@@ -3,7 +3,12 @@ def check_gene(value):
     assert value != "" , "Gene can not be empty"
     return value
 
+<<<<<<< Updated upstream
 class gene:
+=======
+class Gene:
+
+>>>>>>> Stashed changes
     def __init__(self, value):
         self.fitness = None
         self.value = check_gene(value)
@@ -17,7 +22,12 @@ class gene:
     def set_fitness(self, fitness):
         self.fitness = fitness
 
+<<<<<<< Updated upstream
     def set_value(self):
+=======
+    def set_value(self, value):
+        """Set value of the gene"""
+>>>>>>> Stashed changes
         self.value = value
 
     def __repr__(self):

src/initialization/initialization_types.py

@@ -0,0 +1,32 @@
+# 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_Types:
+    """Initialization examples that are used as defaults and examples"""
+
+    def random_initialization(self, 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:
+                    function = ga.gene_impl[0]
+                    chromosome.add_gene(create_gene(function(*ga.gene_impl[1:])))
+                else:
+                    #Exit because either were not specified
+                    print("You did not specify any initialization constraints.")
+            population.add_chromosome(chromosome)
+        return population

src/initialization/population_structure/population.py

@@ -1,4 +1,4 @@
-class population:
+class Population:
 
     # fitness = Empty; population = [chromosome, chromosome, etc.]
     def __init__(self, chromosomes = None):
@@ -21,7 +21,12 @@ class population:
         del self.chromosomes[index]
 
     def get_all_chromosomes(self):
+<<<<<<< Updated upstream
         return chromosomes
+=======
+        """returns all chromosomes in the population"""
+        return self.chromosomes
+>>>>>>> Stashed changes
 
     def get_fitness(self):
         return self.fitness