Added genetic algorithm
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@ -3,5 +3,5 @@
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<component name="Black">
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<option name="sdkName" value="Python 3.11 (ai-wozek) (2)" />
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</project>
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</project>
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@ -4,7 +4,7 @@
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<content url="file://$MODULE_DIR$">
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<excludeFolder url="file://$MODULE_DIR$/venv" />
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</content>
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</module>
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@ -1,8 +1,5 @@
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import pygame
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import sys
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import random
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import os
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import time
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from collections import deque
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import heapq
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import torch
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@ -11,7 +8,6 @@ from classes import *
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import numpy as np
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import pandas as pd
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import torchvision.transforms as transforms
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import math
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from PIL import Image
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class Node():
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@ -385,7 +381,7 @@ tree_data_base = pd.read_csv('paczki.csv')
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def entropy(data):
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labels = data.iloc[:, -1] # Ostatnia kolumna zawiera etykiety klas i pomija 1 wiersz bo jest tytulowy
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counts = labels.value_counts() #tu zlicza wszystkie opcje
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probabilities = counts / len(labels)
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probabilities = counts / len(labels)
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entropy = -sum(probabilities * np.log2(probabilities))
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return entropy
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@ -400,11 +396,143 @@ def information_gain(data, attribute):
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return (total_entropy - weighted_entropy)
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# Define constants
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POPULATION_SIZE = 100
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GENERATIONS = 50
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MUTATION_RATE = 0.1
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# Define movements
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MOVES = ['UP', 'DOWN', 'LEFT', 'RIGHT']
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# Fitness function
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def fitness(path, start, goal, obstacles, center):
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position = start[:]
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for move in path:
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if move == 'UP':
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position[1] -= 1
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elif move == 'DOWN':
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position[1] += 1
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elif move == 'LEFT':
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position[0] -= 1
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elif move == 'RIGHT':
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position[0] += 1
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# If we reach an obstacle
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if position in obstacles:
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return 0
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# If we reach the goal
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if position == goal:
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break
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# Calculate fitness based on distance from center
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distance_from_center = abs(center[0] - goal[0]) + abs(center[1] - goal[1])
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# We want to maximize distance from center
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return distance_from_center
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# Generate initial population
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def generate_initial_population():
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return [[random.choice(MOVES) for _ in range(50)] for _ in range(POPULATION_SIZE)]
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# Selection
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def selection(population, fitnesses):
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selected = random.choices(population, weights=fitnesses, k=POPULATION_SIZE // 2)
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return selected
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# Crossover
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def crossover(parent1, parent2):
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crossover_point = random.randint(0, len(parent1))
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return parent1[:crossover_point] + parent2[crossover_point:]
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# Mutation
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def mutate(path):
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if random.random() < MUTATION_RATE:
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index = random.randint(0, len(path) - 1)
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path[index] = random.choice(MOVES)
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return path
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# Genetic Algorithm
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def genetic_algorithm(start, goal, obstacles, center):
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population = generate_initial_population()
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for generation in range(GENERATIONS):
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fitnesses = [fitness(path, start, goal, obstacles, center) for path in population]
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population = selection(population, fitnesses)
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new_population = []
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for i in range(0, len(population), 2):
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if i + 1 < len(population):
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child1 = crossover(population[i], population[i + 1])
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child2 = crossover(population[i + 1], population[i])
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new_population.append(mutate(child1))
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new_population.append(mutate(child2))
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population = new_population
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best_path = max(population, key=lambda p: fitness(p, start, goal, obstacles, center))
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return best_path
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def find_best_destination(freight, current_position, obstacles, center):
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if freight.content != 'car_parts':
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img = freight.image
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image = Image.open(img).convert("RGB")
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tensor = transform(image).unsqueeze(0)
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with torch.no_grad():
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tensor = tensor.to(device)
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outputs = model(tensor)
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probabilities = torch.nn.functional.softmax(outputs, dim=1)
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score = probabilities.cpu().numpy().flatten()
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best_goal = None
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if score[0] > score[1] and score[0] > score[2]:
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print("flammable")
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best_goal, best_path = find_best_position_with_ga(current_position, sorted(free_lime), obstacles, center)
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free_lime.remove(best_goal)
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elif score[1] > score[0] and score[1] > score[2]:
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print("fragile")
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best_goal, best_path = find_best_position_with_ga(current_position, sorted(free_pink), obstacles, center)
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free_pink.remove(best_goal)
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elif score[2] > score[0] and score[2] > score[1]:
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print("toxic")
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best_goal, best_path = find_best_position_with_ga(current_position, sorted(free_red), obstacles, center)
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free_red.remove(best_goal)
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else:
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best_goal, best_path = find_best_position_with_ga(current_position, sorted(free_cyan), obstacles, center)
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free_cyan.remove(best_goal)
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return best_goal
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def find_best_position_with_ga(current_position, goal_positions, obstacles, center):
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best_path = None
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best_score = float('-inf') # Мы ищем максимальное значение
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best_goal = None
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for goal in goal_positions:
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path = genetic_algorithm(current_position, goal, obstacles, center)
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score = fitness(path, current_position, goal, obstacles, center)
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if score > best_score:
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best_score = score
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best_path = path
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best_goal = goal
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return best_goal, best_path
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# Main game loop
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def game_loop():
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init_game()
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obstacles = []
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center = [GRID_WIDTH // 2, GRID_HEIGHT // 2]
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current=Node(forklift_pos,rotation,'start',None,0)
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dest=current_freight.pop()
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final=Node([dest[0],dest[1]],'N','final',None,0)
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@ -442,32 +570,11 @@ def game_loop():
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reset_truck_bed_freight()
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if(forklift_pos==final.position):
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handle_freight()
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destination=[]
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obstacles.append(final.position)
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if carrying_freight:
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if(carried_freight.content!='car_parts'):
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img=carried_freight.image
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image=Image.open(img).convert("RGB")
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tensor=transform(image).unsqueeze(0)
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with torch.no_grad():
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tensor = tensor.to(device)
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outputs = model(tensor)
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probabilities = torch.nn.functional.softmax(outputs, dim=1)
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score=probabilities.cpu().numpy().flatten()
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if(score[0]>score[1] and score[0]>score[2]):
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print("flammable")
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destination=free_lime.pop()
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elif(score[1]>score[0] and score[1]>score[2]):
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print("fragile")
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destination=free_pink.pop()
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elif(score[2]>score[0] and score[2]>score[1]):
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print("toxic")
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destination=free_red.pop()
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else:
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destination=free_cyan.pop()
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#return probabilities.cpu().numpy().flatten()
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destination = find_best_destination(carried_freight, forklift_pos, obstacles, center)
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print(destination)
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final=Node([destination[0],destination[1]],'N','final',None,0)
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final = Node([destination[0],destination[1]],'N','final',None,0)
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else:
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destination=current_freight.pop()
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print(destination)
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@ -489,7 +596,7 @@ def game_loop():
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rotate_forklift('R')
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load_images()
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i=i+1
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pygame.time.wait(500)
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pygame.time.wait(100)
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pygame.quit()
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sys.exit()
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