GA implementation in env

- use ga in traktor environment on field variables
- adjusted fitness function

AI/GeneticAlgorithm.py

@@ -1,85 +1,91 @@
-import numpy
+import random
+
+import keyboard as keyboard
+
+import field as F
+from ga_methods import *
+from src import mapschema as maps
 
-import ga_methods
 
 # Genetic Algorithm
-if __name__ == "__main__":
+def genetic_algorithm_setup(field):
+    population_units = ["", "w", "p", "s"]
 
-    """
+    # new_population to be
-    The y=target is to maximize this equation ASAP:
+    population_text = []
-        y = w1x1+w2x2+w3x3+w4x4+w5x5+6wx6
-        where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7)
-        What are the best values for the 6 weights w1 to w6?
-        We are going to use the genetic algorithm for the best possible values after a number of generations.
-    """
 
-    # Inputs of the equation.
+    # Populate the population_text array
-    equation_inputs = [4, -2, 3.5, 5, -11, -4.7]
+    for row in range(D.GSIZE):
+        population_text.append([])
+        for column in range(D.GSIZE):
+            population_text[row].append(random.choice(population_units))
 
-    # Number of the weights we are looking to optimize.
+    # printer
-    num_weights = len(equation_inputs)
+    for _ in population_text:
+        print(population_text)
 
     """
     Genetic algorithm parameters:
         Mating pool size
         Population size
     """
+
+    # units per population in generation
     sol_per_pop = 8
     num_parents_mating = 4
 
-    # Defining the population size.
+    population_values = []
-    pop_size = (sol_per_pop,
+    fitness_row = []
-                num_weights)  # The population will have sol_per_pop chromosome where each chromosome has num_weights genes.
-    # Creating the initial population.
-    new_population = numpy.random.uniform(low=-4.0, high=4.0, size=pop_size)
-    print(new_population)
 
-    """
+    # population Fitness
-    new_population[0, :] = [2.4,  0.7, 8, -2,   5,   1.1]
+    for i in range(0, D.GSIZE):
-    new_population[1, :] = [-0.4, 2.7, 5, -1,   7,   0.1]
+        for j in range(D.GSIZE):
-    new_population[2, :] = [-1,   2,   2, -3,   2,   0.9]
+            fitness_row.append(local_fitness(field, i, j, population_text))
-    new_population[3, :] = [4,    7,   12, 6.1, 1.4, -4]
+        population_values.append(fitness_row)
-    new_population[4, :] = [3.1,  4,   0,  2.4, 4.8,  0]
-    new_population[5, :] = [-2,   3,   -7, 6,   3,    3]
-    """
 
     best_outputs = []
-    num_generations = 1000
+    num_generations = 10
-    for generation in range(num_generations):
-        print("Generation : ", generation)
-        # Measuring the fitness of each chromosome in the population.
-        fitness = ga_methods.cal_pop_fitness(equation_inputs, new_population)
-        print("Fitness")
-        print(fitness)
 
-        best_outputs.append(numpy.max(numpy.sum(new_population * equation_inputs, axis=1)))
+    generation = 0
-        # The best result in the current iteration.
-        print("Best result : ", numpy.max(numpy.sum(new_population * equation_inputs, axis=1)))
 
-        # Selecting the best parents in the population for mating.
+    while generation < num_generations:
-        parents = ga_methods.select_mating_pool(new_population, fitness,
+        if keyboard.is_pressed('space'):
-                                                num_parents_mating)
+            generation += 1
-        print("Parents")
-        print(parents)
 
-        # Generating next generation using crossover.
+            print("Generation : ", generation)
-        offspring_crossover = ga_methods.crossover(parents,
+            # Measuring the fitness of each chromosome in the population.
-                                                   offspring_size=(pop_size[0] - parents.shape[0], num_weights))
-        print("Crossover")
-        print(offspring_crossover)
 
-        # Adding some variations to the offspring using mutation.
+            fitness = cal_pop_fitness(population_values)
-        offspring_mutation = ga_methods.mutation(offspring_crossover, num_mutations=2)
+            print("Fitness")
-        print("Mutation")
+            print(fitness)
-        print(offspring_mutation)
 
-        # Creating the new population based on the parents and offspring.
+            # best_outputs.append(best_Output(new_population))
-        new_population[0:parents.shape[0], :] = parents
+            # The best result in the current iteration.
-        new_population[parents.shape[0]:, :] = offspring_mutation
+            # print("Best result : ", best_Output(new_population))
+
+            # Selecting the best parents in the population for mating.
+            parents = select_mating_pool(new_population, fitness,
+                                         num_parents_mating)
+            print("Parents")
+            print(parents)
+
+            # Generating next generation using crossover.
+            offspring_crossover = crossover(parents, offspring_size=(pop_size[0] - parents.shape[0], num_weights))
+            print("Crossover")
+            print(offspring_crossover)
+
+            # Adding some variations to the offspring using mutation.
+            offspring_mutation = mutation(offspring_crossover, num_mutations=2)
+            print("Mutation")
+            print(offspring_mutation)
+
+            # Creating the new population based on the parents and offspring.
+            new_population[0:parents.shape[0], :] = parents
+            new_population[parents.shape[0]:, :] = offspring_mutation
 
     # Getting the best solution after iterating finishing all generations.
     # At first, the fitness is calculated for each solution in the final generation.
-    fitness = ga_methods.cal_pop_fitness(equation_inputs, new_population)
+    fitness = cal_pop_fitness(new_population)
     # Then return the index of that solution corresponding to the best fitness.
     best_match_idx = numpy.where(fitness == numpy.max(fitness))
 
@@ -92,3 +98,24 @@ if __name__ == "__main__":
     matplotlib.pyplot.xlabel("Iteration")
     matplotlib.pyplot.ylabel("Fitness")
     matplotlib.pyplot.show()
+
+    # return best iteration of field
+    return 0
+
+
+if __name__ == "__main__":
+
+    # Define the map of the field
+    mapschema = maps.createField()
+
+    # Create field array
+    field = []
+
+    # Populate the field array
+    for row in range(D.GSIZE):
+        field.append([])
+        for column in range(D.GSIZE):
+            fieldbit = F.Field(row, column, mapschema[column][row])
+            field[row].append(fieldbit)
+
+    genetic_algorithm_setup(field)

AI/ga_methods.py

@@ -1,13 +1,49 @@
 import numpy
 
+import src.dimensions as D
+
+
 # Genetic Algorithm methods
-x = "Hello world"
+
+def local_fitness(field, x, y, plants):
+    soil_value = 0
+    if field[x][y].field_type == "soil":
+        soil_value = 1
+    else:
+        soil_value = 0.5
+
+    if plants[x][y] == "":
+        plant_value = 0
+    elif plants[x][y] == "w":
+        plant_value = 1
+    elif plants[x][y] == "p":
+        plant_value = 2
+    elif plants[x][y] == "s":
+        plant_value = 3
+
+    neighbour_bonus = 1
+    if x - 1 >= 0:
+        if plants[x][y] == plants[x - 1][y]:
+            neighbour_bonus += 1
+    if x + 1 < D.GSIZE:
+        if plants[x][y] == plants[x + 1][y]:
+            neighbour_bonus += 1
+    if y - 1 >= 0:
+        if plants[x][y] == plants[x][y - 1]:
+            neighbour_bonus += 1
+    if y + 1 < D.GSIZE:
+        if plants[x][y] == plants[x][y + 1]:
+            neighbour_bonus += 1
+
+    # TODO * multiculture_bonus
+    local_fitness_value = (soil_value + plant_value) * (0.5 * neighbour_bonus + 1)
+    return local_fitness_value
 
 
-def cal_pop_fitness(equation_inputs, pop):
+def cal_pop_fitness(pop):
     # Calculating the fitness value of each solution in the current population.
     # The fitness function calulates the sum of products between each input and its corresponding weight.
-    fitness = numpy.sum(pop * equation_inputs, axis=1)
+    fitness = sum(map(sum, pop))
     return fitness
 
 
@@ -50,3 +86,7 @@ def mutation(offspring_crossover, num_mutations=1):
             offspring_crossover[idx, gene_idx] = offspring_crossover[idx, gene_idx] + random_value
             gene_idx = gene_idx + mutations_counter
     return offspring_crossover
+
+
+def best_Output(new_population):
+    return numpy.max(numpy.sum(new_population * equation_inputs, axis=1))

main.py

@@ -43,13 +43,15 @@ if __name__ == "__main__":
             fieldbit = F.Field(row, column, mapschema[column][row])
             field[row].append(fieldbit)
 
+    # genetic_algorithm_setup(field)
+
     # Create Tractor object
     tractor = T.Tractor(field, [0, 0])
 
     # Define the map of plants
     mapschema = maps.createPlants()
 
-    # Createt plants array
+    # Create plants array
     plants = []
 
     # Populate the plants array