.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.9" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (pythonProject)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
 </project>

.idea/sztuczna.iml

@@ -1,10 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
-      <excludeFolder url="file://$MODULE_DIR$/.venv" />
-    </content>
-    <orderEntry type="jdk" jdkName="Python 3.10 (pythonProject)" jdkType="Python SDK" />
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

Animals/animal.py

@@ -3,22 +3,11 @@ import pygame
 from abc import abstractmethod
 
 class Animal:
-
-    def choose_picture(self, name):
-        ran = random.randint(0, 1)
-        if ran == 0:
-            path = f'images/{name}.png'
-            return path
-        else:
-            path = f'images/{name}2.png'
-            return path
-
-    def __init__(self, x, y,name, image_path, food_image, food, environment, activity, ill=False, adult=False,):
+    def __init__(self, x, y,name, image, food_image, food, environment, activity, ill=False, adult=False,):
         self.x = x - 1
         self.y = y - 1
         self.name = name
-        self.image_path = image_path
-        self.image = pygame.image.load(image_path)
+        self.image = image
         self.adult = adult
         self.food = food
         self.food_image = food_image
@@ -74,13 +63,6 @@ class Animal:
         illness_image = pygame.transform.scale(illness_image, (int(grid_size * scale), int(grid_size * scale)))
         screen.blit(illness_image, (x_blit, y * grid_size))
 
-    def draw_snack(self, screen, grid_size, x, y):
-        exclamation_image = pygame.image.load(self.food_image)
-        exclamation_image = pygame.transform.scale(exclamation_image, (int(grid_size * 0.45), int(grid_size * 0.45)))
-        screen.blit(exclamation_image, (x * grid_size, y * grid_size))
-        pygame.display.update()
-        pygame.time.wait(700)
-
     @abstractmethod
     def getting_hungry(self):
         pass

Animals/bat.py

@@ -4,13 +4,13 @@ from datetime import datetime
 
 class Bat(Animal):
     def __init__(self, x, y, adult=False):
+        Bat_image = pygame.image.load('images/bat.png')
         name = 'bat'
-        image_path = self.choose_picture(name)
         environment = "medium"
         food_image = 'images/grains.png'
         parrot_food = 'grains'
         activity = 'nocturnal'
-        super().__init__(x, y,name, image_path, food_image,parrot_food, environment, adult)
+        super().__init__(x, y,name, Bat_image, food_image,parrot_food, environment, adult)
         self._starttime = datetime.now()
 
     def getting_hungry(self, const):

Animals/bear.py

@@ -4,14 +4,14 @@ from datetime import datetime
 
 class Bear(Animal):
     def __init__(self, x, y, adult=False):
+        Bear_image = pygame.image.load('images/bear.png')
         name = 'bear'
-        image_path = self.choose_picture(name)
         environment = "cold"
         activity = 'nocturnal'
         ill = self.is_ill()
         bear_food = 'meat'
         food_image = 'images/meat.png'
-        super().__init__(x, y,name, image_path, food_image,bear_food,environment, activity, ill, adult)
+        super().__init__(x, y,name, Bear_image, food_image,bear_food,environment, activity, ill, adult)
         self._starttime = datetime.now()
 
     def getting_hungry(self, const):

Animals/elephant.py

@@ -4,8 +4,8 @@ from datetime import datetime
 
 class Elephant(Animal):
     def __init__(self, x, y, adult=False):
+        Elephant_image = pygame.image.load('images/elephant.png')
         name = 'elephant'
-        image_path = self.choose_picture(name)
         environment = "hot"
         activity = 'diurnal'
         ill = self.is_ill()
@@ -16,7 +16,7 @@ class Elephant(Animal):
             elephant_food = 'milk'
             food_image = 'images/milk.png'
         
-        super().__init__(x, y,name, image_path, food_image,elephant_food, environment, activity, ill, adult)
+        super().__init__(x, y,name, Elephant_image, food_image,elephant_food, environment, activity, ill, adult)
         self._starttime = datetime.now()
  
     def getting_hungry(self, const):

Animals/giraffe.py

@@ -4,14 +4,14 @@ from datetime import datetime
 
 class Giraffe(Animal):
     def __init__(self, x, y, adult=False):
+        Giraffe_image = pygame.image.load('images/giraffe.png')
         name = 'giraffe'
-        image_path = self.choose_picture(name)
         environment = "hot"
         activity = 'diurnal'
         ill = self.is_ill()
         food_image = 'images/leaves.png'
         giraffe_food = 'leaves'
-        super().__init__(x, y, name, image_path, food_image,giraffe_food, environment, activity, ill, adult)
+        super().__init__(x, y, name, Giraffe_image, food_image,giraffe_food, environment, activity, ill, adult)
         self._starttime = datetime.now()
 
     def getting_hungry(self, const):

Animals/owl.py

@@ -4,13 +4,13 @@ from datetime import datetime
 
 class Owl(Animal):
     def __init__(self, x, y, adult=False):
+        Owl_image = pygame.image.load('images/owl.png')
         name = 'owl'
-        image_path = self.choose_picture(name)
         environment = "medium"
         food_image = 'images/grains.png'
         parrot_food = 'grains'
         activity = 'nocturnal'
-        super().__init__(x, y,name, image_path, food_image,parrot_food, environment, adult)
+        super().__init__(x, y,name, Owl_image, food_image,parrot_food, environment, adult)
         self._starttime = datetime.now()
 
     def getting_hungry(self, const):

Animals/parrot.py

@@ -4,14 +4,14 @@ from datetime import datetime
 
 class Parrot(Animal):
     def __init__(self, x, y, adult=False):
+        Parrot_image = pygame.image.load('images/parrot.png')
         name = 'parrot'
-        image_path = self.choose_picture(name)
         environment = "medium"
         activity = 'diurnal'
         ill = self.is_ill()
         food_image = 'images/grains.png'
         parrot_food = 'grains'
-        super().__init__(x, y, name, image_path, food_image, parrot_food, environment, activity, ill, adult)
+        super().__init__(x, y, name, Parrot_image, food_image, parrot_food, environment, activity, ill, adult)
         self._starttime = datetime.now()
 
     def getting_hungry(self, const):

Animals/penguin.py

@@ -4,14 +4,14 @@ from datetime import datetime
 
 class Penguin(Animal):
     def __init__(self, x, y, adult=False):
+        Penguin_image = pygame.image.load('images/penguin.png')
         name = 'penguin'
-        image_path = self.choose_picture(name)
         environment = "cold"
         activity = 'diurnal'
         ill = self.is_ill()
         food_image = 'images/fish.png'
         penguin_food = 'fish'
-        super().__init__(x, y, name, image_path, food_image, penguin_food, environment, activity, ill, adult)
+        super().__init__(x, y, name, Penguin_image, food_image, penguin_food, environment, activity, ill, adult)
         self._starttime = datetime.now()
 
     def getting_hungry(self, const):

agent.py

@@ -5,19 +5,7 @@ from state_space_search import is_border, is_obstacle
 from night import draw_night
 from decision_tree import feed_decision
 from constants import Constants
-from classification import AnimalClassifier
 
-const = Constants()
-
-classes = [
-    "bat",
-    "bear",
-    "elephant",
-    "giraffe",
-    "owl",
-    "parrot",
-    "penguin"
-]
 class Agent:
     def __init__(self, istate, image_path, grid_size):
         self.istate = istate
@@ -80,7 +68,6 @@ class Agent:
 
 def feed_animal(self, animals, goal,const): 
     goal_x, goal_y = goal
-    neuron = AnimalClassifier('./model/best_model.pth', classes)
     if self.x == goal_x and self.y == goal_y:
         for animal in animals:
             if animal.x == goal_x and animal.y == goal_y:
@@ -89,12 +76,6 @@ def feed_animal(self, animals, goal,const):
                 else:
                     activity_time = False
                 guests = random.randint(1, 15)
-                guess = neuron.classify(animal.image_path)
-                if guess == animal.name:
-                    print(f"I'm sure this is {guess} and i give it {animal.food} as a snack")
-                    animal.draw_snack(const.screen, const.GRID_SIZE, animal.x, animal.y)
-                else:
-                    print(f"I was wrong, this is not a {guess} but a {animal.name}")
                 decision = feed_decision(animal.adult, activity_time, animal.ill, const.season, guests, animal._feed, self._dryfood, self._wetfood)
                 if decision != [1]:
                     if decision == [2]:

classification.py

@@ -1,47 +0,0 @@
-import torch
-import torchvision.transforms as transforms
-import PIL.Image as Image
-
-class AnimalClassifier:
-    def __init__(self, model_path, classes, image_size=224, mean=None, std=None):
-        self.classes = classes
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        self.model = torch.load(model_path, map_location=torch.device('cpu'))
-        self.model = self.model.to(self.device)
-        self.model = self.model.eval()
-        self.image_size = image_size
-        self.mean = mean if mean is not None else [0.5164, 0.5147, 0.4746]
-        self.std = std if std is not None else [0.2180, 0.2126, 0.2172]
-        self.image_transforms = transforms.Compose([
-            transforms.Resize((self.image_size, self.image_size)),
-            transforms.ToTensor(),
-            transforms.Normalize(torch.Tensor(self.mean), torch.Tensor(self.std))
-        ])
-
-    def classify(self, image_path):
-        image = Image.open(image_path)
-
-        if image.mode == 'RGBA':
-            image = image.convert('RGB')
-
-        image = self.image_transforms(image).float()
-        image = image.unsqueeze(0).to(self.device)
-
-        with torch.no_grad():
-            output = self.model(image)
-
-        _, predicted = torch.max(output.data, 1)
-
-        return self.classes[predicted.item()]
-
-classes = [
-    "bat",
-    "bear",
-    "elephant",
-    "giraffe",
-    "owl",
-    "parrot",
-    "penguin"
-]
-
-

constants.py

@@ -6,7 +6,7 @@ class Constants:
     def __init__(self):
         self.BLACK = (0, 0, 0)
         self.RED = (255, 0, 0)
-        self.GRID_SIZE = 65
+        self.GRID_SIZE = 50
         self.GRID_WIDTH = 30
         self.GRID_HEIGHT = 15
         self.WINDOW_SIZE = (self.GRID_WIDTH * self.GRID_SIZE, self.GRID_HEIGHT * self.GRID_SIZE)
@@ -17,10 +17,6 @@ class Constants:
 
         self.season = random.choice(["spring", "summer", "autumn", "winter"])
 
-        self.SIZE = 224
-        self.mean = [0.5164, 0.5147, 0.4746]
-        self.std = [0.2180, 0.2126, 0.2172]
-
 def init_pygame(const):
     pygame.init()
     const.screen = pygame.display.set_mode(const.WINDOW_SIZE)

genetics.py

@@ -1,148 +0,0 @@
-from state_space_search import graphsearch, generate_cost_map
-import random
-
-# Parametry algorytmu genetycznego
-POPULATION_SIZE = 700
-MUTATION_RATE = 0.01
-NUM_GENERATIONS = 600
-
-# Generowanie początkowej populacji
-def generate_individual(animals):
-    return random.sample(animals, len(animals))
-
-def generate_population(animals, size):
-    return [generate_individual(animals) for _ in range(size)]
-
-# Obliczanie odległości między zwierzetami
-def calculate_distance(animal1, animal2):
-    x1, y1 = animal1
-    x2, y2 = animal2
-    return abs(x1 - x2) + abs(y1 - y2) # Odległość Manhattana
-
-def calculate_total_distance(animals):
-    total_distance = 0
-    for i in range(len(animals) - 1):
-        total_distance += calculate_distance(animals[i], animals[i+1])
-    total_distance += calculate_distance(animals[-1], animals[0])  # Zamknięcie cyklu
-    return total_distance
-
-# Selekcja rodziców za pomocą metody ruletki
-def select_parents(population, num_parents):
-    fitness_scores = [1 / calculate_total_distance(individual) for individual in population]
-    total_fitness = sum(fitness_scores)
-    selection_probs = [fitness / total_fitness for fitness in fitness_scores]
-
-    parents = random.choices(population, weights=selection_probs, k=num_parents)
-    return parents
-
-# Krzyżowanie rodziców (OX,Davis)
-def crossover(parent1, parent2):
-    child1 = [None] * len(parent1)
-    child2 = [None] * len(parent1)
-    start_index = random.randint(0, len(parent1) - 1)
-    end_index = random.randint(start_index, len(parent1) - 1)
-    child1[start_index:end_index+1] = parent1[start_index:end_index+1]
-    child2[start_index:end_index+1] = parent2[start_index:end_index+1]
-
-    # Uzupełnienie brakujących zwierząt z drugiego rodzica
-    for i in range(len(parent1)):
-        if parent2[i] not in child1:
-            for j in range(len(parent2)):
-                if child1[j] is None:
-                    child1[j] = parent2[i]
-                    break
-
-    for i in range(len(parent1)):
-        if parent1[i] not in child2:
-            for j in range(len(parent1)):
-                if child2[j] is None:
-                    child2[j] = parent1[i]
-                    break
-
-    return child1, child2
-
-# Mutacja: zamiana dwóch losowych zwierząt z prawdopodobieństwem MUTATION_RATE
-def mutate(individual):
-    if random.random() < MUTATION_RATE:
-        index1, index2 = random.sample(range(len(individual)), 2)
-        individual[index1], individual[index2] = individual[index2], individual[index1]
-
-# Algorytm genetyczny
-def genetic_algorithm(animals):
-    population = generate_population(animals, POPULATION_SIZE)
-
-    for generation in range(NUM_GENERATIONS):
-        # Selekcja rodziców
-        parents = select_parents(population, POPULATION_SIZE // 2)
-
-        # Krzyżowanie i tworzenie nowej populacji
-        next_generation = []
-        for i in range(0, len(parents), 2):
-            parent1 = parents[i]
-            if i + 1 < len(parents):
-                parent2 = parents[i + 1]
-            else:
-                parent2 = parents[0]
-            child1, child2 = crossover(parent1, parent2)
-            next_generation.extend([child1, child2])
-
-        # Mutacja nowej populacji
-        for individual in next_generation:
-            mutate(individual)
-
-        # Zastąpienie starej populacji nową
-        population = next_generation
-
-    # Znalezienie najlepszego osobnika
-    best_individual = min(population, key=calculate_total_distance)
-
-    return best_individual
-
-# def calculate_distance(start, goal, max_x, max_y, obstacles, cost_map):
-#     istate = (start[0], start[1], 'N')  # Zakładamy, że zaczynamy od kierunku północnego
-#     actions, cost = graphsearch(istate, goal, max_x, max_y, obstacles, cost_map)
-#     return cost
-
-# def calculate_total_distance(animals, max_x, max_y, obstacles, cost_map):
-#     total_distance = 0
-#     for i in range(len(animals) - 1):
-#         total_distance += calculate_distance(animals[i], animals[i+1], max_x, max_y, obstacles, cost_map)
-#     total_distance += calculate_distance(animals[-1], animals[0], max_x, max_y, obstacles, cost_map)  # Zamknięcie cyklu
-#     return total_distance
-
-# # Selekcja rodziców za pomocą metody ruletki
-# def select_parents(population, num_parents, max_x, max_y, obstacles, cost_map):
-#     fitness_scores = [1 / calculate_total_distance(individual, max_x, max_y, obstacles, cost_map) for individual in population]
-#     total_fitness = sum(fitness_scores)
-#     selection_probs = [fitness / total_fitness for fitness in fitness_scores]
-
-#     parents = random.choices(population, weights=selection_probs, k=num_parents)
-#     return parents
-
-
-# def genetic_algorithm(animals, max_x, max_y, obstacles, cost_map):
-#     population = generate_population(animals, POPULATION_SIZE)
-
-#     for generation in range(NUM_GENERATIONS):
-#         # Selekcja rodziców
-#         parents = select_parents(population, POPULATION_SIZE // 2, max_x, max_y, obstacles, cost_map)
-
-#         # Krzyżowanie i tworzenie nowej populacji
-#         next_generation = []
-#         for i in range(0, len(parents), 2):
-#             parent1 = parents[i]
-#             parent2 = parents[i + 1]
-#             child1, child2 = crossover(parent1, parent2)
-#             next_generation.extend([child1, child2])
-
-#         # Mutacja nowej populacji
-#         for individual in next_generation:
-#             mutate(individual)
-
-#         # Zastąpienie starej populacji nową
-#         population = next_generation
-
-#     # Znalezienie najlepszego osobnika
-#     best_individual = min(population, key=lambda individual: calculate_total_distance(individual, max_x, max_y, obstacles, cost_map))
-
-#     return best_individual 

main.py

@@ -13,7 +13,6 @@ from constants import Constants, init_pygame
 from draw import draw_goal, draw_grid, draw_house
 from season import draw_background
 from night import change_time
-from genetics import genetic_algorithm
 
 const = Constants()
 init_pygame(const)
@@ -78,13 +77,12 @@ def main():
     actions = []
     clock = pygame.time.Clock()
     spawned = False
-    route = False
 
-    # # Lista zawierająca klatki do odwiedzenia
-    # enclosures_to_visit = Enclosures.copy()
-    # current_enclosure_index = -1  # Indeks bieżącej klatki
-    # actions_to_compare_list = []  # Lista zawierająca ścieżki do porównania
-    # goals_to_compare_list = list()  # Lista zawierająca cele do porównania
+    # Lista zawierająca klatki do odwiedzenia
+    enclosures_to_visit = Enclosures.copy()
+    current_enclosure_index = -1  # Indeks bieżącej klatki
+    actions_to_compare_list = []  # Lista zawierająca ścieżki do porównania
+    goals_to_compare_list = list()  # Lista zawierająca cele do porównania
 
     while True:
         for event in pygame.event.get():
@@ -95,6 +93,7 @@ def main():
 
         change_time(const)
         draw_background(const)
+        draw_grid(const)
         draw_enclosures(Enclosures, const)
         draw_gates(Enclosures, const)
         draw_house(const)
@@ -108,11 +107,6 @@ def main():
                 animal._feed = random.randint(0, 10)
             spawned = True
             
-        if not route:
-            animals = [(animal.x, animal.y) for animal in Animals]
-            best_route = genetic_algorithm(animals)
-            route = True
-            
         draw_Animals(Animals, const)
         draw_Terrain_Obstacles(Terrain_Obstacles, const)
         agent.draw(const)
@@ -125,34 +119,31 @@ def main():
             pygame.time.wait(200)
         else:
             if agent._dryfood > 1 and agent._wetfood > 1 : 
-                # if not goals_to_compare_list:
-                #     current_enclosure_index = (current_enclosure_index + 1) % len(enclosures_to_visit)
-                #     current_enclosure = enclosures_to_visit[current_enclosure_index] 
+                if not goals_to_compare_list:
+                    current_enclosure_index = (current_enclosure_index + 1) % len(enclosures_to_visit)
+                    current_enclosure = enclosures_to_visit[current_enclosure_index] 
 
-                #     for animal in current_enclosure.animals:
-                #         goal = (animal.x, animal.y)
-                #         goals_to_compare_list.append(goal)
+                    for animal in current_enclosure.animals:
+                        goal = (animal.x, animal.y)
+                        goals_to_compare_list.append(goal)
 
-                #         actions_to_compare = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)    
-                #         actions_to_compare_list.append((actions_to_compare, goal))
+                        actions_to_compare = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)    
+                        actions_to_compare_list.append((actions_to_compare, goal))
 
-                # chosen_path_and_goal = min(actions_to_compare_list, key=lambda x: len(x[0]))
-                # goal = chosen_path_and_goal[1]  
-                # draw_goal(const, goal)
-
-                # # Usuń wybrany element z listy
-                # actions_to_compare_list.remove(chosen_path_and_goal)
-                # goals_to_compare_list.remove(goal)
-                goal = best_route.pop(0)
-                best_route.append(goal)
+                chosen_path_and_goal = min(actions_to_compare_list, key=lambda x: len(x[0]))
+                goal = chosen_path_and_goal[1]  
                 draw_goal(const, goal)
 
-                actions, cost = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
+                # Usuń wybrany element z listy
+                actions_to_compare_list.remove(chosen_path_and_goal)
+                goals_to_compare_list.remove(goal)
+
+                actions = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
 
             else:
                 goal = (3,1)
                 draw_goal(const, goal)
-                actions, cost = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
+                actions = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
 
 if __name__ == "__main__":
     main()

model/model.py

@@ -1,129 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import torchvision.datasets
-from torchvision import datasets, transforms, models
-from torch.utils.data import DataLoader
-
-
-def set_device():
-    if torch.cuda.is_available():
-        device = 'cuda'
-    else:
-        device = 'cpu'
-    return torch.device(device)
-
-
-train_dataset_path = './data/train'
-test_dataset_path = './data/val'
-number_of_classes = 7
-
-SIZE = 224
-mean = [0.5164, 0.5147, 0.4746]
-std = [0.2180, 0.2126, 0.2172]
-
-train_transforms = transforms.Compose([
-    transforms.Resize((SIZE, SIZE)),
-    transforms.RandomHorizontalFlip(),
-    transforms.RandomRotation(10),
-    transforms.ToTensor(),
-    transforms.Normalize(torch.Tensor(mean), torch.Tensor(std))
-])
-
-test_transforms = transforms.Compose([
-    transforms.Resize((SIZE, SIZE)),
-    transforms.ToTensor(),
-    transforms.Normalize(torch.Tensor(mean), torch.Tensor(std))
-])
-
-train_dataset = torchvision.datasets.ImageFolder(root=train_dataset_path, transform=train_transforms)
-test_dataset = torchvision.datasets.ImageFolder(root=test_dataset_path, transform=test_transforms)
-
-train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
-test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)
-
-resnet18_model = models.resnet18(weights=None)
-num_ftrs = resnet18_model.fc.in_features
-resnet18_model.fc = nn.Linear(num_ftrs, number_of_classes)
-device = set_device()
-resnet18_model = resnet18_model.to(device)
-loss_fn = nn.CrossEntropyLoss()
-
-optimizer = optim.SGD(resnet18_model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.003)
-
-
-def save_checkpoint(model, epoch, optimizer, best_acc):
-    state = {
-        'epoch': epoch + 1,
-        'model': model.state_dict(),
-        'best accuracy': best_acc,
-        'optimizer': optimizer.state_dict()
-    }
-    torch.save(state, 'model_best_checkpoint.pth.tar')
-def train_nn(model, train_loader, test_loader, criterion, optimizer, n_epochs):
-    device = set_device()
-    best_acc = 0
-
-    for epoch in range(n_epochs):
-        print("Epoch number %d " % (epoch + 1))
-        model.train()
-        running_loss = 0.0
-        running_correct = 0.0
-        total = 0
-
-        for data in train_loader:
-            images, labels = data
-            images = images.to(device)
-            labels = labels.to(device)
-            total += labels.size(0)
-
-            optimizer.zero_grad()
-            outputs = model(images)
-            _, predicted = torch.max(outputs.data, 1)
-            loss = criterion(outputs, labels)
-            loss.backward()
-            optimizer.step()
-
-            running_loss += loss.item()
-            running_correct += (labels == predicted).sum().item()
-
-        epoch_loss = running_loss/len(train_loader)
-        epoch_acc = 100 * running_correct / total
-        print(f"Training dataset. Got {running_correct} out of {total} images correctly ({epoch_acc}). Epoch loss: {epoch_loss}")
-
-        test_data_acc = evaluate_model_on_test_set(model, test_loader)
-
-        if test_data_acc > best_acc:
-            best_acc = test_data_acc
-            save_checkpoint(model, epoch, optimizer, best_acc)
-
-    print("Finished")
-    return model
-def evaluate_model_on_test_set(model, test_loader):
-    model.eval()
-    predicted_correctly_on_epoch = 0
-    total = 0
-    device = set_device()
-
-    with torch.no_grad():
-        for data in test_loader:
-            images, labels = data
-            images = images.to(device)
-            labels = labels.to(device)
-            total += labels.size(0)
-
-            outputs = model(images)
-            _, predicted = torch.max(outputs.data, 1)
-
-            predicted_correctly_on_epoch += (predicted == labels).sum().item()
-
-    epoch_acc = 100 * predicted_correctly_on_epoch / total
-    print(f"Testing dataset. Got {predicted_correctly_on_epoch} out of {total} images correctly ({epoch_acc})")
-    return epoch_acc
-
-
-train_nn(resnet18_model, train_loader, test_loader, loss_fn, optimizer, n_epochs=30)
-
-checkpoint = torch.load('model_best_checkpoint.pth.tar')
-resnet18_model.load_state_dict(checkpoint['model'])
-torch.save(resnet18_model, 'best_model.pth')

state_space_search.py

@@ -40,7 +40,7 @@ def graphsearch(istate, goal, max_x, max_y, obstacles, cost_map):
         state, _, _ = node
 
         if goaltest(state, goal):
-            return build_action_sequence(node), current_cost(node, cost_map)
+            return build_action_sequence(node)
 
         explored.add(state)
 
@@ -61,7 +61,7 @@ def graphsearch(istate, goal, max_x, max_y, obstacles, cost_map):
                     else:
                         break
 
-    return False, float('inf')
+    return False
 
 def is_state_in_queue(state, queue):
     for _, (s, _, _) in queue.queue:
@@ -125,4 +125,3 @@ def generate_cost_map(Animals, Terrain_Obstacles, cost_map={}):
             cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = bush_cost
 
     return cost_map
-