Added atrributes class

src/EasyGA.py

@@ -1,69 +1,11 @@
 import random
 
-# Import all the data structure prebuilt modules
+from attributes import attributes
-from structure import Population as create_population
-from structure import Chromosome as create_chromosome
-from structure import Gene as create_gene
 
-# Structure Methods
-from fitness_function  import Fitness_Examples
-from initialization    import Initialization_Methods
-from termination_point import Termination_Methods
-
-# Parent/Survivor Selection Methods
-from parent_selection   import Parent_Selection
-from survivor_selection import Survivor_Selection
-
-# Genetic Operator Methods
-from mutation  import Mutation_Methods
-from crossover import Crossover_Methods
-
-class GA:
-
-    def __init__(self):
-        """Initialize the GA."""
-
-        # Initilization variables
-        self.chromosome_length   = 10
-        self.population_size     = 10
-        self.chromosome_impl     = None
-        self.gene_impl           = lambda: random.randint(1, 10)
-        self.population          = None
-        self.target_fitness_type = 'maximum'
-        self.update_fitness      = True
-
-        # Selection variables
-        self.parent_ratio          = 0.1
-        self.selection_probability = 0.75
-        self.tournament_size_ratio = 0.1
-
-        # Termination variables
-        self.current_generation = 0
-        self.current_fitness    = 0
-        self.generation_goal    = 15
-        self.fitness_goal       = 9
-
-        # Mutation variables
-        self.mutation_rate = 0.10
-
-        # Default EasyGA implimentation structure
-        self.initialization_impl   = Initialization_Methods.random_initialization
-        self.fitness_function_impl = Fitness_Examples.is_it_5
-        self.make_population       = create_population
-        self.make_chromosome       = create_chromosome
-        self.make_gene             = create_gene
-
-        # Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
-        self.parent_selection_impl     = Parent_Selection.Tournament.with_replacement
-        self.crossover_individual_impl = Crossover_Methods.Individual.single_point
-        self.crossover_population_impl = Crossover_Methods.Population.random_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
-
-        # The type of termination to impliment
-        self.termination_impl = Termination_Methods.generation_based
 
+class GA(attributes):
+    def __init__(self):             # Inhert all the ga attributes 
+       super(GA, self).__init__()   # from the attributes class
 
     def evolve_generation(self, number_of_generations = 1, consider_termination = True):
         """Evolves the ga the specified number of generations."""
@@ -136,14 +78,3 @@ class GA:
         return sorted(chromosome_set,                                    # list to be sorted
                      key = lambda chromosome: chromosome.get_fitness(),  # by fitness
                      reverse = True)                                     # from highest to lowest fitness
-
-    # Example of how the setter error checking will look like
-    @property
-    def chromosome_length(self):
-        return self._chromosome_length
-
-    @chromosome_length.setter
-    def chromosome_length(self, value_input):
-        if(value_input == 0):
-            raise ValueError("Sorry your chromosome length must be greater then 0")
-        self._chromosome_length = value_input

src/attributes.py

@@ -0,0 +1,75 @@
+import random
+
+# Import all the data structure prebuilt modules
+from structure import Population as create_population
+from structure import Chromosome as create_chromosome
+from structure import Gene as create_gene
+
+# Structure Methods
+from fitness_function  import Fitness_Examples
+from initialization    import Initialization_Methods
+from termination_point import Termination_Methods
+
+# Parent/Survivor Selection Methods
+from parent_selection   import Parent_Selection
+from survivor_selection import Survivor_Selection
+
+# Genetic Operator Methods
+from mutation  import Mutation_Methods
+from crossover import Crossover_Methods
+
+class attributes:
+    def __init__(self):
+        """Initialize the GA."""
+
+        # Initilization variables
+        self.chromosome_length   = 10
+        self.population_size     = 10
+        self.chromosome_impl     = None
+        self.gene_impl           = lambda: random.randint(1, 10)
+        self.population          = None
+        self.target_fitness_type = 'maximum'
+        self.update_fitness      = True
+
+        # Selection variables
+        self.parent_ratio          = 0.1
+        self.selection_probability = 0.75
+        self.tournament_size_ratio = 0.1
+
+        # Termination variables
+        self.current_generation = 0
+        self.current_fitness    = 0
+        self.generation_goal    = 15
+        self.fitness_goal       = 9
+
+        # Mutation variables
+        self.mutation_rate = 0.10
+
+        # Default EasyGA implimentation structure
+        self.initialization_impl   = Initialization_Methods.random_initialization
+        self.fitness_function_impl = Fitness_Examples.is_it_5
+        self.make_population       = create_population
+        self.make_chromosome       = create_chromosome
+        self.make_gene             = create_gene
+
+        # Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
+        self.parent_selection_impl     = Parent_Selection.Tournament.with_replacement
+        self.crossover_individual_impl = Crossover_Methods.Individual.single_point
+        self.crossover_population_impl = Crossover_Methods.Population.random_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
+
+        # The type of termination to impliment
+        self.termination_impl = Termination_Methods.generation_based
+
+    # Example of how the setter error checking will look like
+    @property
+    def chromosome_length(self):
+        return self._chromosome_length
+
+    @chromosome_length.setter
+    def chromosome_length(self, value_input):
+        if(value_input == 0):
+            raise ValueError("Sorry your chromosome length must be greater then 0")
+        self._chromosome_length = value_input

src/run_testing.py

@@ -4,7 +4,7 @@ import EasyGA
 # Create the Genetic algorithm
 ga = EasyGA.GA()
 
-ga.chromosome_length = 0
+ga.chromosome_length = 100
 
 ga.evolve()