Merge pull request 'Created genetic algorithm. Added returned matrix of genetic algorithm into settings file. Replaced fields with map generated by algorithm.' (#14) from genetic_algorithm into main

Reviewed-on: s473634/TurboTraktor#14

main.py

@@ -1,7 +1,7 @@
 import pygame
 import sys
 import random
-from settings import SIZE, directions, draw_lines_on_window
+from settings import SIZE, directions, draw_lines_on_window, matrix_plants_type
 from src.map import drawRoads, seedForFirstTime, return_fields_list, WORLD_MATRIX, get_type_by_position
 from src.Tractor import Tractor
 from src.bfs import Astar
@@ -45,6 +45,7 @@ def recognize_plants(fields, destination):
     print(pred)
     return pred
 
+
 # pygame initialization
 pygame.init()
 clock = pygame.time.Clock()

settings.py

@@ -1,6 +1,11 @@
 from cmath import sqrt
 import pygame
+import pickle
 
+def load_matrix(filename):
+    with open(filename, 'rb') as file:
+        matrix = pickle.load(file)
+    return matrix
 
 screen_width = 1368
 screen_height = 936
@@ -18,6 +23,7 @@ field_width = 4
 field_height = 4
 field_size = field_width*field_height
 fields_amount = 25
+matrix_plants_type = load_matrix("genetic_algorithm_matrix.pkl")
 
 directions = {0: 'UP', 90: 'RIGHT', 180: 'DOWN', 270: 'LEFT'}
 
@@ -30,3 +36,5 @@ def draw_lines_on_window(background):
     pygame.draw.line(background, (0, 0, 0), (968, 649), (1336 , 649))
     pygame.draw.line(background, (0, 0, 0), (968, 285), (968, 649))
     pygame.draw.line(background, (0, 0, 0), (1336, 285), (1336, 649))
+
+

src/genetic_algorithm.py

@@ -0,0 +1,161 @@
+import random
+import copy
+import pickle
+import numpy
+
+# Constants
+GRID_SIZE = 5
+PLANT_TYPES = [1, 2, 3]
+POPULATION_SIZE = 1000
+MAX_GENERATIONS = 100
+MUTATION_RATE = 0.1
+
+def generate_random_chromosome():
+    # Generate a random chromosome (random matrix)
+    chromosome = [[0] * GRID_SIZE for _ in range(GRID_SIZE)]
+
+    for row in range(GRID_SIZE):
+        for col in range(GRID_SIZE):
+            available_types = PLANT_TYPES.copy()
+            chromosome[row][col] = random.choice(available_types)
+
+    return chromosome
+
+def calculate_fitness(chromosome):
+    # Calculate the fitness by counting the number of adjacent fields with the same plant type
+    fitness = 0
+    appearances = [0,0,0]
+
+    for row in range(GRID_SIZE):
+        for col in range(GRID_SIZE):
+            plant_type = chromosome[row][col]
+
+            # Check left neighbor
+            if col > 0 and chromosome[row][col - 1] == plant_type:
+                fitness += 1
+            # Check top neighbor
+            if row > 0 and chromosome[row - 1][col] == plant_type:
+                fitness += 1
+
+    
+    for row in chromosome:
+        appearances[0] = appearances[0] + row.count(1)
+        appearances[1] = appearances[1] + row.count(2)
+        appearances[2] = appearances[2] + row.count(3)
+
+    for i in range(len(appearances)):
+        if appearances[i] < 7 and appearances[i] > 9:
+            fitness = fitness + abs(appearances[i]-8)
+    return fitness
+
+def selection(population):
+    # Perform tournament selection to choose parents for reproduction
+    tournament_size = 5
+    parents = []
+
+    for _ in range(len(population)):
+        tournament = random.sample(population, tournament_size)
+        tournament.sort(key=lambda chromosome: calculate_fitness(chromosome))
+        parents.append(tournament[0])
+
+    return parents
+
+def crossover(parent1, parent2):
+    # Perform single-point crossover to create two offspring
+    crossover_point = random.randint(0, (GRID_SIZE*GRID_SIZE)-1)
+    
+    row = crossover_point // GRID_SIZE
+    column = crossover_point % GRID_SIZE
+    offspring1 = []
+    offspring2 = []
+    counter = 0
+    while counter<row:
+        offspring1.append(parent1[counter])
+        offspring2.append(parent2[counter])
+        counter += 1
+    # The place, where the parents are 'cutten' is here
+    offspring1.append(parent1[counter][:column] + parent2[counter][column:])
+    offspring2.append(parent2[counter][:column] + parent1[counter][column:])
+
+    while counter<GRID_SIZE-1:
+        offspring1.append(parent2[counter])
+        offspring2.append(parent1[counter])
+        counter += 1
+
+    return offspring1, offspring2
+
+
+def genetic_algorithm():
+
+    results = []
+    population = [generate_random_chromosome() for _ in range(POPULATION_SIZE)]
+
+    # Main loop
+    for gen in range(MAX_GENERATIONS):
+        parents = selection(population)
+        offspring = []
+
+        # Crossover
+        for i in range(0, len(parents), 2):
+            parent1 = parents[i]
+            parent2 = parents[i + 1]
+            child1, child2 = crossover(parent1, parent2)
+            offspring.append(child1)
+            offspring.append(child2)
+
+        # Replacement
+        population = offspring
+
+        # Count best fitness of each population
+        best_chromosome = min(population, key=lambda chromosome: calculate_fitness(chromosome))
+        best_fitness = calculate_fitness(best_chromosome)
+        results.append((copy.deepcopy(best_chromosome), best_fitness))
+        if best_fitness == 0:
+            break
+
+    results.sort(key = lambda x: x[1])
+    best_tuple = results[0]
+    best_chromosome, best_fitness = best_tuple
+    return best_chromosome
+
+# Replacing numbers into plants:
+def replace_numbers(number_matrix):
+    return_matrix = number_matrix.copy()
+    for row in range(GRID_SIZE):
+        for col in range(GRID_SIZE):
+            if number_matrix[row][col] == 1:
+                return_matrix[row][col] = "carrot"
+            elif number_matrix[row][col] == 2:
+                return_matrix[row][col] = "potato"
+            elif number_matrix[row][col] == 3:
+                return_matrix[row][col] = "wheat"
+            else:
+                number_matrix[row][col] = "error"
+    return return_matrix
+
+# Make the matrix as a list:
+def list_of_matrix(plant_matrix):
+    return_list = []
+    for i in range(len(plant_matrix)):
+        return_list += plant_matrix[i]
+    return return_list
+
+# Run the genetic algorithm
+best_solution = genetic_algorithm()
+
+# Print the best solution found
+print("Best Solution:")
+for row in best_solution:
+    print(row)
+print("Best fitness: ", calculate_fitness(best_solution))
+print("")
+
+replaced_matrix = replace_numbers(best_solution)
+print("Replaced Matrix:")
+for row in replaced_matrix:
+    print(row)
+return_list = list_of_matrix(replaced_matrix)
+
+
+with open("genetic_algorithm_matrix.pkl", "wb") as file:
+    pickle.dump(return_list, file)

src/map.py

@@ -1,6 +1,6 @@
 from cmath import sqrt
 import pygame
-from settings import screen_height, screen_width, SIZE, SPECIES, block_size, tile, road_coords, fields_amount, field_size, field_height, field_width
+from settings import screen_height, screen_width, SIZE, SPECIES, block_size, tile, road_coords, fields_amount, field_size, field_height, field_width, matrix_plants_type
 from src.Plant import Plant
 import random
 from src.Field import Field
@@ -60,9 +60,11 @@ def drawRoads(screen):
     return screen
 
 def seedForFirstTime():
+    plants_type = matrix_plants_type
+    
     plant_group = pygame.sprite.Group()
     for field in range(fields_amount):
-        plant_name = random.choice(SPECIES)
+        plant_name = matrix_plants_type[field]
         blocks_seeded_in_field = 0
         while (blocks_seeded_in_field < field_size):