Support passing classes

Author: ahmedfgad

examples/example_lifecycle_classes.py

@@ -0,0 +1,78 @@
+import pygad
+import numpy
+
+"""
+Use a method to build the lifecycle.
+"""
+
+class Fitness:
+    def __call__(self, ga_instance, solution, solution_idx):
+        fitness = numpy.sum(solution)
+        return fitness
+
+class Crossover:
+    def __call__(self, parents, offspring_size, ga_instance):
+        return numpy.random.rand(offspring_size[0], offspring_size[1])
+
+class Mutation:
+    def __call__(self, offspring, ga_instance):
+        return offspring
+
+class OnStart:
+    def __call__(self, ga_instance):
+        print("on_start")
+
+class OnFitness:
+    def __call__(self, ga_instance, fitness):
+        print("on_fitness")
+
+class OnCrossover:
+    def __call__(self, ga_instance, offspring):
+        print("on_crossover")
+
+class OnMutation:
+    def __call__(self, ga_instance, offspring):
+        print("on_mutation")
+
+class OnParents:
+    def __call__(self, ga_instance, parents):
+        print("on_parents")
+
+class OnGeneration:
+    def __call__(self, ga_instance):
+        print("on_generation")
+
+class OnStop:
+    def __call__(self, ga_instance, fitness):
+        print("on_stop")
+
+num_generations = 10 # Number of generations.
+num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
+
+sol_per_pop = 10 # Number of solutions in the population.
+num_genes = 5
+
+ga_instance = pygad.GA(num_generations=num_generations,
+                       num_parents_mating=num_parents_mating,
+                       sol_per_pop=sol_per_pop,
+                       num_genes=num_genes,
+
+                       fitness_func=Fitness(),
+
+                       crossover_type=Crossover(),
+                       mutation_type=Mutation(),
+
+                       on_start=OnStart(),
+                       on_fitness=OnFitness(),
+                       on_crossover=OnCrossover(),
+                       on_mutation=OnMutation(),
+                       on_parents=OnParents(),
+                       on_generation=OnGeneration(),
+                       on_stop=OnStop(),
+                       
+                       suppress_warnings=True)
+
+# Running the GA to optimize the parameters of the function.
+ga_instance.run()
+
+ga_instance.plot_fitness()

examples/example_lifecycle_methods.py

@@ -0,0 +1,73 @@
+import pygad
+import numpy
+
+"""
+Use a method to build the lifecycle.
+"""
+
+class GAOperations:
+    def fitness_func(self, ga_instance, solution, solution_idx):
+        fitness = numpy.sum(solution)
+        return fitness
+
+    def crossover(self, parents, offspring_size, ga_instance):
+        return numpy.random.rand(offspring_size[0], offspring_size[1])
+
+    def mutation(self, offspring, ga_instance):
+        return offspring
+
+class Lifecycle:
+    def on_start(self, ga_instance):
+        print("on_start")
+
+    def on_fitness(self, ga_instance, fitness):
+        print("on_fitness")
+
+    def on_crossover(self, ga_instance, offspring):
+        print("on_crossover")
+
+    def on_mutation(self, ga_instance, offspring):
+        print("on_mutation")
+
+    def on_parents(self, ga_instance, parents):
+        print("on_parents")
+
+    def on_generation(self, ga_instance):
+        print("on_generation")
+
+    def on_stop(self, ga_instance, fitness):
+        print("on_stop")
+
+ga_obj = GAOperations()
+lifecycle_obj = Lifecycle()
+
+num_generations = 10 # Number of generations.
+num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
+
+sol_per_pop = 10 # Number of solutions in the population.
+num_genes = 5
+
+ga_instance = pygad.GA(num_generations=num_generations,
+                       num_parents_mating=num_parents_mating,
+                       sol_per_pop=sol_per_pop,
+                       num_genes=num_genes,
+
+                       fitness_func=ga_obj.fitness_func,
+
+                       crossover_type=ga_obj.crossover,
+                       mutation_type=ga_obj.mutation,
+
+                       on_start=lifecycle_obj.on_start,
+                       on_fitness=lifecycle_obj.on_fitness,
+                       on_crossover=lifecycle_obj.on_crossover,
+                       on_mutation=lifecycle_obj.on_mutation,
+                       on_parents=lifecycle_obj.on_parents,
+                       on_generation=lifecycle_obj.on_generation,
+                       on_stop=lifecycle_obj.on_stop,
+                       
+                       suppress_warnings=True)
+
+# Running the GA to optimize the parameters of the function.
+ga_instance.run()
+
+ga_instance.plot_fitness()

examples/pygad_lifecycle.py

@@ -37,12 +37,15 @@ def on_stop(ga_instance, last_population_fitness):
                        fitness_func=fitness_function,
                        sol_per_pop=10,
                        num_genes=len(function_inputs),
+
                        on_start=on_start,
                        on_fitness=on_fitness,
                        on_parents=on_parents,
                        on_crossover=on_crossover,
                        on_mutation=on_mutation,
                        on_generation=on_generation,
-                       on_stop=on_stop)
+                       on_stop=on_stop,
+                       
+                       suppress_warnings=True)
 
 ga_instance.run()