Minor edits

src/attributes.py

@@ -19,6 +19,8 @@ from mutation  import Mutation_Methods
 from crossover import Crossover_Methods
 
 class attributes:
+    """SAMPLE TEXT"""
+
     def __init__(self):
         """Initialize the GA."""
 
@@ -55,7 +57,7 @@ class attributes:
         # Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
         self.parent_selection_impl     = Parent_Selection.Rank.tournament
         self.crossover_individual_impl = Crossover_Methods.Individual.single_point
-        self.crossover_population_impl = Crossover_Methods.Population.random_selection
+        self.crossover_population_impl = Crossover_Methods.Population.sequential_selection
         self.survivor_selection_impl   = Survivor_Selection.fill_in_best
         self.mutation_individual_impl  = Mutation_Methods.Individual.single_gene
         self.mutation_population_impl  = Mutation_Methods.Population.random_selection
@@ -67,6 +69,7 @@ class attributes:
     # Getter and setters for all varibles
     @property
     def chromosome_length(self):
+        """SAMPLE TEXT"""
         return self._chromosome_length
 
     @chromosome_length.setter

src/crossover/crossover_methods.py

@@ -3,7 +3,7 @@ import random
 class Crossover_Methods:
 
     class Population:
-        """Methods for selecting chromosomes to crossover"""
+        """Methods for selecting chromosomes to crossover."""
 
         def sequential_selection(ga):
             """Select sequential pairs from the mating pool.
@@ -41,22 +41,22 @@ class Crossover_Methods:
 
 
     class Individual:
-        """Methods for crossing parents"""
+        """Methods for crossing parents."""
 
         def single_point(ga, parent_one, parent_two):
-            """Cross two parents by swapping genes at one random point"""
+            """Cross two parents by swapping genes at one random point."""
 
             index = random.randint(0, parent_one.size()-1)
             return ga.make_chromosome(parent_one.get_gene_list()[:index] + parent_two.get_gene_list()[index:])
 
 
         def multi_point(ga, parent_one, parent_two):
-            """Cross two parents by swapping genes at multiple points"""
+            """Cross two parents by swapping genes at multiple points."""
             pass
 
 
         def uniform(ga, parent_one, parent_two):
-            """Cross two parents by swapping all genes randomly"""
+            """Cross two parents by swapping all genes randomly."""
 
             return ga.make_chromosome([                 # Make a new chromosome
                        random.choice([                  #     by selecting random genes from
@@ -66,10 +66,10 @@ class Crossover_Methods:
                    for i in range(parent_one.size())])  #     for each gene
 
         class Arithmetic:
-            """Crossover methods for numerical genes"""
+            """Crossover methods for numerical genes."""
 
             def int_random(ga, parent_one, parent_two):
-                """Cross two parents by taking a random integer value between each of the genes"""
+                """Cross two parents by taking a random integer value between each of the genes."""
 
                 return ga.make_chromosome([                             # Make a new chromosome
                            ga.make_gene(                                #     filled with new genes
@@ -81,7 +81,7 @@ class Crossover_Methods:
 
 
             def int_weighted(ga, parent_one, parent_two):
-                """Cross two parents by taking a a weighted average of the genes"""
+                """Cross two parents by taking a a weighted average of the genes."""
 
                 # the percentage of genes taken from the first gene
                 weight = 0.25
@@ -95,7 +95,7 @@ class Crossover_Methods:
 
 
             def float_random(ga, parent_one, parent_two):
-                """Cross two parents by taking a random numeric value between each of the genes"""
+                """Cross two parents by taking a random numeric value between each of the genes."""
 
                 return ga.make_chromosome([                             # Make a new chromosome
                            ga.make_gene(                                #     filled with new genes
@@ -108,7 +108,7 @@ class Crossover_Methods:
 
 
             def float_weighted(ga, parent_one, parent_two):
-                """Cross two parents by taking a a weighted average of the genes"""
+                """Cross two parents by taking a a weighted average of the genes."""
 
                 # the percentage of genes taken from the first gene
                 weight = 0.25

src/parent_selection/parent_selection_methods.py

@@ -55,7 +55,8 @@ class Parent_Selection:
             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."""
+            the ball falls is a randomly generated number between 0 and 1.
+            """
 
             # Make sure the population is sorted by fitness
             ga.population.sort_by_best_fitness(ga)