Merge branch 'master' of https://github.com/danielwilczak101/EasyGA

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
 

src/EasyGA.py

@@ -1,4 +1,5 @@
 import random
+
 # Import all the data prebuilt modules
 from initialization.population_structure.population import population as create_population
 from initialization.chromosome_structure.chromosome import chromosome as create_chromosome
@@ -7,9 +8,10 @@ from initialization.gene_structure.gene import gene as create_gene
 # Import functionality defaults
 from initialization.random_initialization import random_initialization
 
-
 class GA:
     def __init__(self):
+        """Initialize the GA."""
+        
         # Default variables
         self.chromosome_impl = None
         self.gene_impl = None
@@ -19,6 +21,7 @@ class GA:
         self.chromosome_length = 3
         self.population_size = 5
         self.mutation_rate = 0.03
+        
         # Defualt EastGA implimentation structure
         self.initialization_impl = random_initialization
         self.update_fitness = True
@@ -28,7 +31,8 @@ class GA:
         #self.termination_impl = GenerationTermination(Total_generations)
         #self.evaluation_impl = TestEvaluation()
 
-    def initialize(self):
+    def initialize_population(self):
+        """Initialize the population"""
         self.population = self.initialization_impl(
         self.population_size,
         self.chromosome_length,
@@ -40,27 +44,41 @@ class GA:
         pass
     
     def evolve(self):
-        """Updates the ga to the next generation.
+        """Runs the ga until the ga is no longer active."""
-        If update_fitness is set then all fitness values are updated.
+        
-        Otherwise only fitness values set to None (i.e. uninitialized fitness values) are updated."""
+        # run one iteration while the ga is active
-        for chromosome in self.population.get_all_chromosomes():
+        while self.active():
-            if self.update_fitness or chromosome.get_fitness() is None:
+            self.evolve_generation(1)
-                chromosome.set_fitness(self.fitness_impl(chromosome))
     
     def active(self):
         """Returns if the ga should terminate or not"""
         return self.current_generation < self.generations
 
     def evolve_generation(self, number_of_generations):
-        # If you want to evolve through a number of generations
+        """Evolves the ga the specified number of generations.
-        # and be able to pause and output data based on that generation run.
+        If update_fitness is set then all fitness values are updated.
-        pass
+        Otherwise only fitness values set to None (i.e. uninitialized fitness values) are updated."""
+        
+        # run the specified number of times
+        for n in range(number_of_generations):
+        
+            # for each chromosome in the population
+            for chromosome in self.population.get_all_chromosomes():
+                
+                # if the fitness should be updated, update it
+                if self.update_fitness or chromosome.get_fitness() is None:
+                    chromosome.set_fitness(self.fitness_impl(chromosome))
+            
+            # apply selection, crossover, and mutation
 
     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/initialization/random_initialization.py

@@ -4,6 +4,8 @@ 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
@@ -11,10 +13,11 @@ def random_initialization(population_size, chromosome_length, chromosome_impl, g
         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 != lists in domain
+                # Will break if chromosome_length != len(lists) in domain
             elif gene_impl != None:
                 # gene_impl = [range function,lowerbound,upperbound]
                 function = gene_impl[0]

src/run_testing.py

@@ -15,6 +15,6 @@ def user_gene_domain(gene_index):
 # If the user wants to use a domain
 ga.chromosome_impl = user_gene_domain
 
-ga.initialize()
+ga.initialize_population()
 
 ga.population.print_all()