Fixed scope of function decorators

src/crossover/crossover_methods.py

@@ -1,20 +1,28 @@
 import random
 
+def append_children_from_mating_pool(crossover_method):
+    def helper(ga):
+        mating_pool = ga.population.mating_pool
+        ga.population.append_children(
+            [chromosome for chromosome in crossover_method(ga, mating_pool)]
+        )
+    return helper
+
+def values_to_chromosome(crossover_method):
+    def helper(ga, parent_1, parent_2):
+        return ga.make_chromosome([
+                   ga.make_gene(value)
+               for value in crossover_method(ga, parent_1, parent_2)])
+    return helper
+
+
 class Crossover_Methods:
 
     class Population:
         """Methods for selecting chromosomes to crossover."""
 
-        def __append_children_from_mating_pool(crossover_method):
-            def helper(ga):
-                mating_pool = ga.population.mating_pool
-                ga.population.append_children([
-                    [chromosome for cromosome in crossover_method(ga, mating_pool)]
-                )
-            return helper
 
-
-        @__append_children_from_mating_pool
+        @append_children_from_mating_pool
         def sequential_selection(ga, mating_pool):
             """Select sequential pairs from the mating pool.
             Every parent is paired with the previous parent.
@@ -29,7 +37,7 @@ class Crossover_Methods:
                       )
 
 
-        @__append_children_from_mating_pool
+        @append_children_from_mating_pool
         def random_selection(ga, mating_pool):
             """Select random pairs from the mating pool.
             Every parent is paired with a random parent.
@@ -46,13 +54,6 @@ class Crossover_Methods:
     class Individual:
         """Methods for crossing parents."""
 
-        def __values_to_chromosome(crossover_method):
-            def helper(ga, parent_1, parent_2):
-                return ga.make_chromosome([
-                           ga.make_gene(value)
-                       for value in crossover_method(ga, parent_1, parent_2)])
-            return helper
-
 
         def single_point(ga, parent_1, parent_2):
             """Cross two parents by swapping genes at one random point."""
@@ -77,7 +78,7 @@ class Crossover_Methods:
         class Arithmetic:
             """Crossover methods for numerical genes."""
 
-            @Individual._Individual__values_to_chromosome
+            @values_to_chromosome
             def int_random(ga, parent_1, parent_2):
                 """Cross two parents by taking a random integer value between each of the genes."""
 
@@ -88,7 +89,7 @@ class Crossover_Methods:
                     yield random.randint(*sorted([value_1, value_2]))
 
 
-            @Individual._Individual__values_to_chromosome
+            @values_to_chromosome
             def int_weighted(ga, parent_1, parent_2):
                 """Cross two parents by taking a a weighted average of the genes."""
 
@@ -102,7 +103,7 @@ class Crossover_Methods:
                     yield int(weight*value_1+(1-weight)*value_2)
 
 
-            @Individual._Individual__values_to_chromosome
+            @values_to_chromosome
             def float_random(ga, parent_one, parent_two):
                 """Cross two parents by taking a random numeric value between each of the genes."""
 
@@ -113,7 +114,7 @@ class Crossover_Methods:
                     yield random.uniform([value_1, value_2])
 
 
-            @Individual._Individual__values_to_chromosome
+            @values_to_chromosome
             def float_weighted(ga, parent_one, parent_two):
                 """Cross two parents by taking a a weighted average of the genes."""
 

src/mutation/mutation_methods.py

@@ -1,20 +1,33 @@
 import random
 from math import ceil
 
+def loop_selections(selection_method):
+    def helper(ga):
+         # Loop until enough mutations occur
+        for n in range(ceil(len(ga.population)*ga.chromosome_mutation_rate)):
+              selection_method(ga)
+    return helper
+
+
+def loop_mutations(mutation_method):
+    def helper(ga, old_chromosome):
+        chromosome = ga.make_chromosome(list(old_chromosome))
+
+        # Loops until enough mutations occur
+        for n in range(ceil(len(chromosome)*ga.gene_mutation_rate)):
+            mutation_method(ga, chromosome)
+
+        return chromosome
+    return helper
+
+
 class Mutation_Methods:
 
     class Population:
         """Methods for selecting chromosomes to mutate"""
 
-        def __loop_selections(selection_method):
-            def helper(ga):
-                # Loop until enough mutations occur
-                for n in range(ceil(len(ga.population)*ga.chromosome_mutation_rate)):
-                    selection_method(ga)
-            return helper
 
-
-        @__loop_selections
+        @loop_selections
         def random_selection(ga):
             """Selects random chromosomes"""
 
@@ -22,7 +35,7 @@ class Mutation_Methods:
             ga.population[index] = ga.mutation_individual_impl(ga, ga.population[index])
 
 
-        @__loop_selections
+        @loop_selections
         def random_selection_then_cross(ga):
             """Selects random chromosomes and self-crosses with parent"""
 
@@ -34,19 +47,8 @@ class Mutation_Methods:
     class Individual:
         """Methods for mutating a single chromosome"""
 
-        def __loop_mutations(mutation_method):
-            def helper(ga, old_chromosome):
-                chromosome = ga.make_chromosome(list(old_chromosome))
 
-                # Loops until enough mutations occur
-                for n in range(ceil(len(chromosome)*ga.gene_mutation_rate)):
-                    mutation_method(ga, chromosome)
-
-                return chromosome
-            return helper
-
-
-        @__loop_mutations
+        @loop_mutations
         def individual_genes(ga, chromosome):
             """Mutates a random gene in the chromosome and resets the fitness."""
             index = random.randint(0, len(chromosome)-1)
@@ -68,7 +70,7 @@ class Mutation_Methods:
                 """Methods for mutating a chromosome
                 by changing the order of the genes."""
 
-                @Individual._Individual__loop_mutations
+                @loop_mutations
                 def swap_genes(ga, chromosome):
                     """Mutates a random gene in the chromosome and resets the fitness."""
 

src/parent_selection/parent_selection_methods.py

@@ -1,36 +1,37 @@
 import random
 
-class Parent_Selection:
-
-    def __check_selection_probability(selection_method):
-        def helper(ga):
-            if 0 < ga.selection_probability < 1:
-                selection_method(ga)
-            else:
-                raise Exception("Selection probability must be greater than 0 to select parents.")
-        return helper
-
-
-    def __check_positive_fitness(selection_method):
-        def helper(ga):
-            if ga.get_chromosome_fitness(0) == 0 or ga.get_chromosome_fitness(-1) < 0:
-                raise Exception("Converted fitness values must be all positive. Consider using rank selection instead.")
-            else:
-                selection_method(ga)
-        return helper
-
-
-    def __ensure_sorted(selection_method):
-        def helper(ga):
-            ga.population.sort_by_best_fitness(ga)
+def check_selection_probability(selection_method):
+    def helper(ga):
+        if 0 < ga.selection_probability < 1:
             selection_method(ga)
-        return helper
+        else:
+            raise Exception("Selection probability must be greater than 0 to select parents.")
+    return helper
+
+
+def check_positive_fitness(selection_method):
+    def helper(ga):
+        if ga.get_chromosome_fitness(0) == 0 or ga.get_chromosome_fitness(-1) < 0:
+            raise Exception("Converted fitness values must be all positive. Consider using rank selection instead.")
+        else:
+            selection_method(ga)
+    return helper
+
+
+def ensure_sorted(selection_method):
+    def helper(ga):
+        ga.population.sort_by_best_fitness(ga)
+        selection_method(ga)
+    return helper
+
+
+class Parent_Selection:
 
 
     class Rank:
 
-        @Parent_Selection._Parent_Selection__check_selection_probability
-        @Parent_Selection._Parent_Selection__ensure_sorted
+        @check_selection_probability
+        @ensure_sorted
         def tournament(ga):
             """
             Will make tournaments of size tournament_size and choose the winner (best fitness) 
@@ -68,9 +69,9 @@ class Parent_Selection:
 
     class Fitness:
 
-        @Parent_Selection._Parent_Selection__check_selection_probability
-        @Parent_Selection._Parent_Selection__check_positive_fitness
-        @Parent_Selection._Parent_Selection__ensure_sorted
+        @check_selection_probability
+        @check_positive_fitness
+        @ensure_sorted
         def roulette(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
@@ -105,9 +106,9 @@ class Parent_Selection:
                         break
 
 
-        @Parent_Selection._Parent_Selection__check_selection_probability
-        @Parent_Selection._Parent_Selection__check_positive_fitness
-        @Parent_Selection._Parent_Selection__ensure_sorted
+        @check_selection_probability
+        @check_positive_fitness
+        @ensure_sorted
         def stochastic(ga):
             """Stochastic 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

src/survivor_selection/survivor_selection_methods.py

@@ -1,13 +1,13 @@
 import random
 
+def append_to_next_population(survivor_method):
+    return lambda ga: ga.population.append_children(survivor_method(ga))
+
 class Survivor_Selection:
     """Survivor selection determines which individuals should be brought to the next generation"""
 
-    def __append_to_next_population(survivor_method):
-        return lambda ga: ga.population.append_children(survivor_method(ga))
 
-
-    @__append_to_next_population
+    @append_to_next_population
     def fill_in_best(ga):
         """Fills in the next population with the best chromosomes from the last population"""
 
@@ -15,7 +15,7 @@ class Survivor_Selection:
         return ga.population[:needed_amount]
 
 
-    @__append_to_next_population
+    @append_to_next_population
     def fill_in_random(ga):
         """Fills in the next population with random chromosomes from the last population"""
 
@@ -23,7 +23,7 @@ class Survivor_Selection:
         return [random.choice(ga.population) for n in range(needed_amount)]
 
 
-    @__append_to_next_population
+    @append_to_next_population
     def fill_in_parents_then_random(ga):
         """Fills in the next population with all parents followed by random chromosomes from the last population"""