GA implementation

- ADD pretty_printer method
- crossover draft

with Michał Malinowski

AI/GeneticAlgorithm.py

@@ -76,6 +76,9 @@ def genetic_algorithm_setup(field):
             print(parents)
 
             # Generating next generation using crossover.
+            offspring_x = random.randint(1, D.GSIZE - 2)
+            offspring_y = random.randint(1, D.GSIZE - 2)
+
             offspring_crossover = crossover(parents, offspring_size=(pop_size[0] - parents.shape[0], num_weights))
             print("Crossover")
             print(offspring_crossover)
@@ -98,12 +101,7 @@ def genetic_algorithm_setup(field):
     print("Best solution : ", new_population[best_match_idx, :])
     print("Best solution fitness : ", fitness[best_match_idx])
 
-    import matplotlib.pyplot
-
-    matplotlib.pyplot.plot(best_outputs)
-    matplotlib.pyplot.xlabel("Iteration")
-    matplotlib.pyplot.ylabel("Fitness")
-    matplotlib.pyplot.show()
+    pretty_printer(best_outputs)
 
     # return best iteration of field
     return 0

AI/ga_methods.py

@@ -1,3 +1,4 @@
+import matplotlib
 import numpy
 
 import src.dimensions as D
@@ -64,6 +65,9 @@ def population_fitness(population_text, field, population_size):
 
 
 def crossover(parents, offspring_size):
+    current_parrent = parents[0]
+    new_part = []
+
     offspring = numpy.empty(offspring_size)
     # The point at which crossover takes place between two parents. Usually, it is at the center.
     crossover_point = numpy.uint8(offspring_size[1] / 2)
@@ -93,3 +97,8 @@ def mutation(offspring_crossover, num_mutations=1):
     return offspring_crossover
 
 
+def pretty_printer(best_outputs):
+    matplotlib.pyplot.plot(best_outputs)
+    matplotlib.pyplot.xlabel("Iteration")
+    matplotlib.pyplot.ylabel("Fitness")
+    matplotlib.pyplot.show()