dodanie algorytmu genetycznego

src/dna.py

@@ -0,0 +1,49 @@
+import random
+import string
+from src.a_star import a_star_search
+from random import shuffle
+import numpy as np
+
+class DNA:
+
+    def __init__(self, target):
+        shuffle(target)
+        self.genes = target.copy()
+        self.fitness = 1.0
+
+    def calcFitness(self, AGENT_MAP):
+        start = (0, 0)
+        sum = 1
+
+        agent_direction = "right"
+
+        for goal in self.genes:
+            path = a_star_search(start, goal, agent_direction, AGENT_MAP)
+            sum = sum + len(path) - 1
+            start = goal
+
+        self.fitness = sum
+
+    def crossover(self, partner):
+
+        child = DNA(self.genes)
+        start = abs(random.randint(0, len(self.genes)) - 1)
+        end = abs(random.randint(start - 1, len(self.genes)))
+
+        child.genes = child.genes[start:end]
+
+        for i in range(len(self.genes)):
+            node = partner.genes[i]
+
+            if node not in child.genes:
+                child.genes.append(node)
+
+        return child
+
+    def mutate(self, mutationRate):
+
+        for i in range(len(self.genes)):
+            if random.random() < mutationRate:
+                indexA = abs(random.randint(0, len(self.genes)) - 1)
+                indexB = (indexA + 1) % len(self.genes)
+                self.genes[indexA], self.genes[indexB] = self.genes[indexB], self.genes[indexA]

src/genetic.py

@@ -0,0 +1,51 @@
+import numpy as np
+from src.dna import DNA
+import random
+import copy
+
+def genetic(goals, AGENT_MAP):
+    recordDistance = 1000000000
+
+    #count = 0
+    generation = 0
+    totalPopulaton = 10
+    population = []
+    matingPool = []
+
+    mutationRate = 0.01
+
+    for i in range(totalPopulaton):
+        population.append(DNA(goals))
+
+    for j in range(totalPopulaton):
+        matingPool.clear()
+        generation = generation + 1
+        for i in range(totalPopulaton):
+            population[i].calcFitness(AGENT_MAP)
+
+            if population[i].fitness < recordDistance:
+                recordDistance = population[i].fitness
+                best = copy.deepcopy(population[i])
+                bestGeneration = generation
+
+        for i in range(totalPopulaton):
+            n = population[i].fitness * 100
+
+            for j in range(int(n)):
+                matingPool.append(population[i])
+
+        for i in range(totalPopulaton):
+            if matingPool:
+                a = random.randint(0, len(matingPool) - 1)
+                b = random.randint(0, len(matingPool) - 1)
+
+                partnerA = matingPool[a]
+                partnerB = matingPool[b]
+
+                child = partnerA.crossover(partnerB)
+
+                child.mutate(mutationRate)
+
+                population[i] = child
+
+    return best

src/main.py

@@ -11,6 +11,7 @@ from mlxtend.data import loadlocal_mnist
 from src.a_star import a_star_search
 from src.training_data.scripts.gen_data_id3 import generate_size, generate_weight, generate_price, generate_flammability, generate_explosiveness
 from src.tree import predict_result, load_tree_from_structure
+from src.genetic import genetic
 import torch
 from torch import nn
 from torch import optim
@@ -30,6 +31,7 @@ IMAGE = pygame.transform.scale(pygame.image.load('img/wozek2.png'), (75, 75))
 BACKGROUND = pygame.transform.scale(pygame.image.load('img/background.png'), (WIDTH, HEIGHT))
 DOCK = pygame.transform.scale(pygame.image.load('img/dock_left.png'), (75, 75))
 TRASH = pygame.transform.scale(pygame.image.load('img/trash.png'), (75, 75))
+GATE = pygame.transform.scale(pygame.image.load('img/shelf.png'), (75, 75))
 get_pix_from_position = {}
 
 
@@ -307,6 +309,7 @@ class Agent:
 
     def lift_package_bfs(self):
         for package in Package_list:
+            print(package.pos)
             if package.is_package_up:
                 package.is_package_up = False
             elif package.pos == agent.pos:
@@ -343,6 +346,9 @@ Shelf_list = [
     Shelf((5, 8), 'Electronic', 5), Shelf((6, 8), 'Electronic', 5), Shelf((7, 8), 'Electronic', 5),
     Shelf((8, 8), 'Electronic', 5)]
 
+gate_list = [(2, 1), (1, 5), (2, 6), (3, 4)]
+goals_from_genetic = []
+
 coord_goals = [(0, 0)]
 free_shelf = [(5, 8), (6, 8), (7, 8), (8, 8), (5, 6), (6, 6), (7, 6), (8, 6)]
 
@@ -354,7 +360,6 @@ Package_list = [
 
 agent.path = a_star_search((agent.pos_coord[1], agent.pos_coord[0]), coord_goals.pop(0), agent.agent_direction, AGENT_MAP)
 
-
 # Pętla służąca do tworzenia plam oleju na podstawie mapy magazynu
 Stain_list = []
 
@@ -364,6 +369,8 @@ for index_x in range(9):
             Stain_list.append(Stain((index_y, index_x)))
 running = True
 
+newRandom = None #tymczasowe
+
 while running:
 
     for event in pygame.event.get():
@@ -398,18 +405,31 @@ while running:
         agent.move_bfs()
 
     else:
+        if agent.pos_coord == (0, 0) or agent.pos_coord == newRandom or agent.pos_coord == (8, 0): #powstrzymuje agenta przed zostawianiem paczek w punktach kontrolnych
             agent.lift_package_bfs()
 
         if agent.pos_coord == (0, 0):
 
             if Package_list[-1].is_accepted == 1:
-                coord_goals.append(free_shelf[random.randint(0, 7)])
+                newRandom = free_shelf[random.randint(0, 7)]
+                coord_goals.append(newRandom)
+                # Obliczanie optymalnej ścieżki
+                genetic_goals = gate_list.copy()
+                genetic_goals.append(newRandom)
+                goals_from_genetic = genetic(genetic_goals, AGENT_MAP).genes
+                goals_from_genetic.append((0, 0))
+                print("GOALS", goals_from_genetic)
+
                 coord_goals.append((0, 0))
             else:
                 coord_goals.append((8, 0))
                 coord_goals.append((0, 0))
+                goals_from_genetic = []
+                goals_from_genetic.append((8, 0))
+                goals_from_genetic.append((0, 0))
 
-        agent.goal = coord_goals.pop(0)
+        #agent.goal = coord_goals.pop(0)
+        agent.goal = goals_from_genetic.pop(0)
         agent.goal_achieved = False
 
         agent.path = a_star_search((agent.pos_coord[1], agent.pos_coord[0]), (agent.goal[1], agent.goal[0]),
@@ -432,6 +452,8 @@ while running:
     screen.blit(agent.image, agent.rect)
     for package in Package_list:
         screen.blit(package.image, package.rect)
+    for gate in gate_list:
+        screen.blit(GATE, (gate[0] * size, gate[1] * size))
 
     time.sleep(0.05)