Added genetic algorithm

ai-wozek/.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.11 (ai-wozek) (2)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (ai-wozek) (2)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (ai-wozek)" project-jdk-type="Python SDK" />
 </project>

ai-wozek/.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+  </component>
+</project>

ai-wozek/.idea/wozek.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.11 (ai-wozek) (2)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.12 (ai-wozek)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

ai-wozek/wozek.py

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