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98 changed files with 288 additions and 1659 deletions

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.gitignore vendored
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*.pyc

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<component name="Black">
<option name="sdkName" value="Python 3.9" />
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<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>

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<?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>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="" vcs="Git" />
</component>
</project>

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{
"python.analysis.extraPaths": [
"./Animals"
]
}

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import random
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,):
self.x = x - 1
self.y = y - 1
self.name = name
self.image_path = image_path
self.image = pygame.image.load(image_path)
self.adult = adult
self.food = food
self.food_image = food_image
self._feed = 0
self.environment = environment # hot/cold/medium
self.activity = activity # diurnal/nocturnal
self.ill = ill
def draw(self, screen, grid_size):
if self.adult:
# Jeśli zwierzę jest dorosłe, skaluj obrazek na większy rozmiar
new_width = grid_size * 2
new_height = grid_size * 2
scaled_image = pygame.transform.scale(self.image, (new_width, new_height))
screen.blit(scaled_image, (self.x * grid_size, self.y * grid_size))
else:
# Jeśli zwierzę nie jest dorosłe, skaluj obrazek na rozmiar jednej kratki
scaled_image = pygame.transform.scale(self.image, (grid_size, grid_size))
screen.blit(scaled_image, (self.x * grid_size, self.y * grid_size))
def draw_exclamation(self, screen, grid_size, x, y):
exclamation_image = pygame.image.load('images/exclamation.png')
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))
def draw_food(self, screen, grid_size, x, y,food_image):
scale = 0.45
food_image = pygame.image.load(food_image)
if(self.adult):
y = y + 1
scale = 0.7
food_image = pygame.transform.scale(food_image, (int(grid_size * scale), int(grid_size * scale)))
screen.blit(food_image, (x * grid_size, (y + 1) * grid_size - int(grid_size * scale)))
def is_ill(self):
chance = random.randint(1, 100)
if chance >= 90:
return True
else: return False
def draw_illness(self, screen, grid_size, x, y):
scale = 0.45
illness_image = pygame.image.load('images/ill.png')
y = y
if self.adult:
x = x + 1
y = y
scale = 0.7
x_blit = x * grid_size + (grid_size - int(grid_size * scale))
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

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from elephant import Elephant
from giraffe import Giraffe
from penguin import Penguin
from parrot import Parrot
from bear import Bear
from owl import Owl
from bat import Bat
def create_animals():
giraffe1 = Giraffe(0, 0, adult=True)
giraffe2 = Giraffe(0, 0, adult=True)
giraffe3 = Giraffe(0, 0, adult=True)
giraffe4 = Giraffe(0, 0)
giraffe5 = Giraffe(0, 0)
bear1 = Bear(0, 0, adult=True)
bear2 = Bear(0, 0, adult=True)
bear3 = Bear(0, 0)
bear4 = Bear(0, 0)
bear5 = Bear(0, 0)
penguin1 = Penguin(0, 0)
penguin2 = Penguin(0, 0)
penguin3 = Penguin(0, 0)
penguin4 = Penguin(0, 0)
elephant1 = Elephant(0, 0, adult=True)
elephant2 = Elephant(0, 0, adult=True)
elephant3 = Elephant(0, 0)
elephant4 = Elephant(0, 0)
elephant5 = Elephant(0, 0)
parrot1 = Parrot(0, 0)
parrot2 = Parrot(0, 0)
owl1 = Owl(0, 0)
owl2 = Owl(0, 0)
bat1 = Bat(0, 0)
bat2 = Bat(0, 0)
Animals = [giraffe1, giraffe2, giraffe3, giraffe4, giraffe5,
bear1, bear2, bear3, bear4, bear5,
elephant1, elephant2, elephant3, elephant4, elephant5,
penguin1, penguin2, penguin3, penguin4,
parrot1, parrot2, owl1, owl2, bat1, bat2]
return Animals
def draw_Animals(Animals, const):
for Animal in Animals:
Animal.draw(const.screen, const.GRID_SIZE)
hunger_level = Animal.getting_hungry(const)
if hunger_level >= 9:
food_image = 'images/empty_bowl.png'
elif hunger_level >= 8:
food_image = 'images/almost_empty.png'
elif hunger_level >= 5:
food_image = 'images/half_bowl.png'
else:
food_image = 'images/full_bowl.png'
Animal.draw_food(const.screen, const.GRID_SIZE, Animal.x, Animal.y, food_image)
if Animal.ill:
Animal.draw_illness(const.screen, const.GRID_SIZE, Animal.x, Animal.y)

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from animal import Animal
import pygame
from datetime import datetime
class Bat(Animal):
def __init__(self, x, y, adult=False):
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)
self._starttime = datetime.now()
def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / (25)
self._starttime = checktime
if const.IS_NIGHT and self._feed < 10:
self._feed += minutes_passed
self._feed = min(self._feed, 10)
return self._feed

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from animal import Animal
import pygame
from datetime import datetime
class Bear(Animal):
def __init__(self, x, y, adult=False):
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)
self._starttime = datetime.now()
def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / (45)
self._starttime = checktime
if const.IS_NIGHT and self._feed < 10 and const.season != "winter":
self._feed += minutes_passed
self._feed = min(self._feed, 10)
return self._feed

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from animal import Animal
import pygame
from datetime import datetime
class Elephant(Animal):
def __init__(self, x, y, adult=False):
name = 'elephant'
image_path = self.choose_picture(name)
environment = "hot"
activity = 'diurnal'
ill = self.is_ill()
if adult:
elephant_food = 'leavs'
food_image = 'images/leaves.png'
else:
elephant_food = 'milk'
food_image = 'images/milk.png'
super().__init__(x, y,name, image_path, food_image,elephant_food, environment, activity, ill, adult)
self._starttime = datetime.now()
def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / (90)
self._starttime = checktime
if not const.IS_NIGHT and self._feed < 10:
self._feed += minutes_passed
self._feed = min(self._feed, 10)
return self._feed

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from animal import Animal
import pygame
from datetime import datetime
class Giraffe(Animal):
def __init__(self, x, y, adult=False):
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)
self._starttime = datetime.now()
def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / (60)
self._starttime = checktime
if not const.IS_NIGHT and self._feed < 10:
self._feed += minutes_passed
self._feed = min(self._feed, 10)
return self._feed

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from animal import Animal
import pygame
from datetime import datetime
class Owl(Animal):
def __init__(self, x, y, adult=False):
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)
self._starttime = datetime.now()
def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / (50)
self._starttime = checktime
if const.IS_NIGHT and self._feed < 10:
self._feed += minutes_passed
self._feed = min(self._feed, 10)
return self._feed

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from animal import Animal
import pygame
from datetime import datetime
class Parrot(Animal):
def __init__(self, x, y, adult=False):
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)
self._starttime = datetime.now()
def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / (30)
self._starttime = checktime
if not const.IS_NIGHT and self._feed < 10:
self._feed += minutes_passed
self._feed = min(self._feed, 10)
return self._feed

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from animal import Animal
import pygame
from datetime import datetime
class Penguin(Animal):
def __init__(self, x, y, adult=False):
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)
self._starttime = datetime.now()
def getting_hungry(self, const):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / (25)
self._starttime = checktime
if not const.IS_NIGHT and self._feed < 10:
self._feed += minutes_passed
self._feed = min(self._feed, 10)
return self._feed

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140
agent.py
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import pygame
import random
from constants import Constants
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
self.x, self.y, self.direction = istate
def __init__(self, x, y, image_path, grid_size):
self.x = x
self.y = y
self.grid_size = grid_size
self.image= pygame.image.load(image_path)
self.image = pygame.image.load(image_path)
self.image = pygame.transform.scale(self.image, (grid_size, grid_size))
self._dryfood = 0
self._wetfood = 0
def draw(self, const):
# Obróć obrazek zgodnie z kierunkiem
if self.direction == 'E':
self.image= pygame.image.load('images/agent4.png')
elif self.direction == 'S':
self.image= pygame.image.load('images/agent1.png')
elif self.direction == 'W':
self.image= pygame.image.load('images/agent3.png')
else: # direction == 'N'
self.image= pygame.image.load('images/agent2.png')
self.image = pygame.transform.scale(self.image, (const.GRID_SIZE, const.GRID_SIZE))
const.screen.blit(self.image, (self.x * self.grid_size, self.y * self.grid_size))
if const.IS_NIGHT: draw_night(const)
def draw(self, screen):
screen.blit(self.image, (self.x * self.grid_size, self.y * self.grid_size))
def handle_event(self, event, max_x, max_y, animals, obstacles,const):
def move(self, dx, dy):
self.x += dx
self.y += dy
def handle_event(self, event, grid_height,grid_width, animals):
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_UP:
self.move('Go Forward', max_x, max_y, obstacles, animals,const)
elif event.key == pygame.K_LEFT:
self.move('Turn Left', max_x, max_y, obstacles, animals,const)
elif event.key == pygame.K_RIGHT:
self.move('Turn Right', max_x, max_y, obstacles, animals,const)
if event.key == pygame.K_UP and self.y > 0:
self.move(0, -1)
elif event.key == pygame.K_DOWN and self.y < grid_height - 1:
self.move(0, 1)
elif event.key == pygame.K_LEFT and self.x > 0:
self.move(-1, 0)
elif event.key == pygame.K_RIGHT and self.x < grid_width - 1:
self.move(1, 0)
def move(self, action, max_x, max_y, obstacles, animals, goal,const):
if action == 'Go Forward':
new_x, new_y = self.x, self.y
if self.direction == 'N':
new_y -= 1
elif self.direction == 'E':
new_x += 1
elif self.direction == 'S':
new_y += 1
elif self.direction == 'W':
new_x -= 1
# Sprawdź, czy nowe położenie mieści się w granicach kraty i nie jest przeszkodą
if is_border(new_x, new_y, max_x, max_y) and not(is_obstacle(new_x, new_y, obstacles)):
self.x, self.y = new_x, new_y
elif action == 'Turn Left':
self.direction = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'}[self.direction]
elif action == 'Turn Right':
self.direction = {'N': 'E', 'E': 'S', 'S': 'W', 'W': 'N'}[self.direction]
self.istate = (self.x, self.y, self.direction)
feed_animal(self, animals, goal,const)
take_food(self)
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:
if (animal.activity == 'nocturnal' and const.IS_NIGHT) or (animal.activity == 'diurnal' and not(const.IS_NIGHT)):
activity_time = True
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]:
if animal.getting_hungry(const=Constants()) < self._wetfood :
self._wetfood -= animal._feed
animal._feed = 0
else:
animal._feed -= self._wetfood
self._wetfood = 0
print(animal.name, "fed with wet food")
else:
if animal.getting_hungry(const=Constants()) < self._dryfood :
self._dryfood -= animal._feed
animal._feed = 0
else:
animal._feed -= self._dryfood
self._dryfood = 0
print(animal.name, "fed with dry food")
print("Current wet food level: ", self._wetfood)
print("Current dry food level: ", self._dryfood)
else: print(animal.name, " not fed")
def take_food(self):
house_x = 3
house_y = 1
if self.x == house_x and self.y == house_y:
if self._dryfood < 1 or self._wetfood < 1:
self._dryfood = 50
self._wetfood = 50
print("Agent took food and current food level is", self._dryfood, self._wetfood)
if self.x == animal.x and self.y == animal.y:
if animal.feed()== 'True':
animal._feed = 0
print(animal.name,"fed with",animal.food)

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animal.py Normal file
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import pygame
from abc import ABC, abstractmethod
class Animal:
def __init__(self, x, y,name, image, food_image, food, adult=False):
self.x = x - 1
self.y = y - 1
self.name = name
self.image = image
self.adult = adult
self.food = food
self.food_image = food_image
self._feed = 0 #nowe zwierze jest głodne
def draw(self, screen, grid_size):
self.image = pygame.transform.scale(self.image, (grid_size, grid_size))
if self.adult:
# If adult, draw like AdultAnimal
new_width = grid_size * 2
new_height = grid_size * 2
scaled_image = pygame.transform.scale(self.image, (new_width, new_height))
screen.blit(scaled_image, (self.x * grid_size, self.y * grid_size))
else:
# If not adult, draw like normal Animal
screen.blit(self.image, (self.x * grid_size, self.y * grid_size))
def draw_exclamation(self, screen, grid_size, x, y):
exclamation_image = pygame.image.load('images/exclamation.png')
exclamation_image = pygame.transform.scale(exclamation_image, (grid_size,grid_size))
screen.blit(exclamation_image, (x*grid_size, y*grid_size - grid_size))
def draw_food(self, screen, grid_size, x, y):
food_image = pygame.image.load(self.food_image)
food_image = pygame.transform.scale(food_image, (grid_size,grid_size))
screen.blit(food_image, (x*grid_size, y*grid_size + grid_size))
@abstractmethod
def feed(self):
pass
@abstractmethod
def getting_hungry(self):
pass

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bear.py Normal file
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from animal import Animal
import pygame
from datetime import datetime
class Bear(Animal):
def __init__(self, x, y, adult=False):
Bear_image = pygame.image.load('images/bear.png')
name = 'bear'
bear_food = 'meat'
food_image = 'images/meat.png'
super().__init__(x, y,name, Bear_image, food_image,bear_food, adult)
self._starttime = datetime.now()
def feed(self):
self.getting_hungry()
if self._feed < 2:
return 'False'
else:
return 'True'
def getting_hungry(self):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / 60
self._feed += minutes_passed
self._starttime = checktime

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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"
]

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import random
import pygame
import time
class Constants:
def __init__(self):
self.BLACK = (0, 0, 0)
self.RED = (255, 0, 0)
self.GRID_SIZE = 65
self.GRID_WIDTH = 30
self.GRID_HEIGHT = 15
self.WINDOW_SIZE = (self.GRID_WIDTH * self.GRID_SIZE, self.GRID_HEIGHT * self.GRID_SIZE)
self.background_image = pygame.transform.scale(pygame.image.load('images/tło.jpg'), self.WINDOW_SIZE)
self.IS_NIGHT = False
self.TIME_CHANGE = time.time() + 60
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)

313
dane.csv
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adult,active_time,ill,season,guests,hunger,wet_food,dry_food,decision
True,True,True,spring,12,4.0462,37.63803,5.60819,2
True,False,False,spring,9,2.89229,34.37597,27.75948,1
False,True,True,summer,15,2.4002,45.06447,41.50999,3
True,False,False,summer,5,0.73248,46.1058,33.44281,1
True,False,False,summer,12,0.62973,49.99647,41.07699,1
True,False,False,winter,9,7.0889,46.16796,45.904,2
False,True,False,summer,2,9.07977,22.08011,20.53507,3
False,False,True,winter,11,3.5635,14.75823,43.46342,2
False,False,True,winter,9,8.03113,20.6384,30.81177,2
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True,True,False,spring,6,4.12955,47.91516,12.84722,1
True,True,True,spring,10,2.70845,1.0623,42.6941,3
True,True,True,spring,15,4.34025,6.89381,3.03923,2
True,False,False,autumn,5,7.24464,31.34759,6.44719,1
True,True,True,summer,6,3.8924,18.08964,18.77435,1
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False,True,False,summer,3,6.21725,3.60564,23.51978,1
True,True,True,winter,4,4.18565,25.24209,11.02056,1
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True,True,False,autumn,9,0.91339,36.35922,16.09576,1
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True,True,True,winter,4,2.27279,20.58575,32.17293,1
True,False,True,spring,7,6.14608,34.46015,17.22245,1
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True,True,True,autumn,3,6.42039,4.90383,43.29857,3
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True,False,False,winter,11,5.89082,28.91495,15.58095,1
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True,False,False,summer,12,5.68671,49.18777,22.53807,3
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True,True,True,summer,12,7.65812,4.16785,34.57922,3
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True,False,False,summer,9,6.9442,46.79146,13.92798,2
True,False,False,autumn,1,3.09242,18.02023,11.03004,1
True,True,True,spring,3,0.19271,1.00203,1.16671,1
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True,False,False,autumn,11,0.48844,39.36689,0.03464,1
False,True,True,summer,6,1.72816,26.85829,16.53262,1
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True,True,True,winter,11,8.50892,23.89966,46.14267,3
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True,True,False,summer,11,4.65313,3.44791,3.96313,3
True,False,True,summer,2,2.56716,10.85536,49.88738,1
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True,True,True,winter,6,8.2623,37.47298,33.76759,2
True,True,True,spring,10,2.20409,13.6178,5.80078,2
True,False,True,autumn,7,9.06057,37.8724,23.62209,2
False,False,True,autumn,3,6.69861,37.07336,16.87187,2
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True,False,True,autumn,9,2.65212,19.06994,37.33364,1
True,True,False,summer,15,3.47148,35.84529,0.00000,1
False,True,True,summer,8,1.51025,9.44246,19.05913,1
False,False,True,autumn,6,6.48485,45.61986,15.91179,2
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False,True,True,winter,5,8.37177,12.99299,42.31566,3
True,True,True,autumn,6,3.38746,48.86975,49.62605,1
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False,False,True,winter,1,6.08476,37.67744,4.49812,2
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True,True,True,summer,8,6.11265,30.32015,46.47287,3
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True,False,True,summer,4,7.23924,39.09963,42.82872,3
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False,True,False,spring,3,4.50329,22.95269,0.41795,1
True,True,True,summer,1,0.47824,14.79432,24.64273,1
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True,True,False,winter,11,7.31457,26.08142,16.5835,2
False,True,True,summer,2,4.90627,19.73976,49.56272,3
True,True,False,winter,8,3.02707,35.9547,29.52088,1
True,True,True,summer,7,5.02577,5.37674,16.61368,3
False,False,True,spring,8,9.58805,8.12549,0.00000,2
False,True,True,summer,6,2.08786,37.11126,36.15777,3
True,True,False,spring,7,8.11839,12.2032,8.26737,2
True,False,True,summer,7,4.08923,20.77025,11.25944,1
False,False,True,winter,4,4.85557,0.00000,39.4493,2
False,False,False,summer,14,9.12718,41.84025,49.51895,2
True,True,True,summer,15,9.4014,34.10345,26.84361,3
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True,False,True,winter,1,9.50102,43.46168,28.81571,2
True,True,False,spring,5,1.35366,6.95688,33.37058,1
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True,True,True,winter,10,4.18282,27.82423,30.42216,1
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True,True,False,spring,13,3.32803,7.59913,1.89442,3
True,False,True,spring,1,5.15927,44.02139,4.03454,1
True,True,True,autumn,13,5.52565,7.75133,38.62709,3
True,True,False,spring,3,8.44216,30.01593,12.45777,3
False,False,True,summer,13,2.88557,5.18905,5.87065,3
True,True,True,spring,11,2.45261,7.22671,49.68806,3
True,True,True,winter,4,8.55814,3.29899,32.82852,2
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True,True,False,summer,1,7.71442,43.2301,27.42849,3
False,True,True,summer,6,1.21255,3.7357,4.31858,1
True,True,True,winter,8,9.53076,12.54774,17.63524,2
True,True,True,autumn,3,8.47955,19.04656,3.62988,2
True,False,True,winter,3,2.58264,28.29242,0.00000,1
True,False,False,autumn,5,6.83145,0.00000,15.88102,1
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True,False,True,winter,7,1.40722,4.06585,2.57929,1
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True,True,True,spring,9,5.1789,0.97673,8.31413,2
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True,True,True,autumn,10,9.23444,43.51842,19.90954,2
False,False,False,autumn,9,3.84582,40.34953,9.01663,1
True,True,False,winter,5,3.90587,32.97826,0.67046,1
True,True,True,autumn,2,9.19994,0.00000,34.36662,3
False,True,True,summer,6,5.614,29.08038,0.00000,2
True,True,True,autumn,2,2.15339,6.36751,6.45082,1
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True,False,True,autumn,0,8.12282,48.68677,8.38304,2
False,True,True,spring,12,3.26729,29.61584,1.69993,2
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True,False,True,summer,0,7.24464,8.85289,34.29828,3
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True,False,False,winter,5,6.74504,47.09367,1.97357,2
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True,False,False,spring,11,7.91742,34.52557,30.96412,3
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True,False,False,spring,14,9.29029,46.83676,12.58912,2
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True,False,True,autumn,2,3.52708,37.61372,32.83573,1
True,True,True,winter,1,4.37134,43.19138,22.04785,1
True,False,True,summer,9,1.1614,10.9739,42.3009,1
False,False,False,winter,9,7.27324,29.74731,47.17759,2
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True,True,True,winter,1,8.23562,36.01552,25.03969,2
True,True,False,winter,3,6.65062,6.75622,24.91086,3
False,False,False,spring,10,2.30179,19.62758,25.57147,1
True,True,False,autumn,10,6.60812,6.61336,12.39931,3
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True,False,True,autumn,5,6.69302,42.62701,13.01677,2
False,False,False,spring,14,8.53868,33.42545,2.43572,2
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True,True,False,spring,14,5.34262,15.45545,21.48404,3
True,False,False,winter,9,7.02885,4.88308,27.56619,3
False,False,False,autumn,13,3.55279,0.17091,5.43831,1
True,True,True,autumn,1,9.98637,27.57982,15.82173,2
False,True,True,summer,9,8.25408,13.10493,27.07596,3
False,False,False,spring,15,1.9089,33.25115,44.57492,1
True,False,False,autumn,12,6.65534,38.00972,20.31047,2
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False,True,True,winter,13,1.24017,36.88006,16.50894,1
False,True,True,winter,13,6.1878,15.18876,9.02381,2
True,True,True,winter,5,5.45157,13.27868,39.39805,2
True,False,True,spring,5,2.82881,28.62319,24.03077,1
False,False,True,spring,2,4.8246,41.45269,48.89539,2
True,False,False,spring,8,9.3343,39.02018,45.01066,3
True,True,True,autumn,4,1.8456,2.94366,37.44996,1
False,True,True,summer,14,0.95333,4.57964,26.37633,1
False,False,True,autumn,13,9.84087,24.03819,41.72097,2
True,False,False,autumn,9,3.70617,32.70115,1.69105,1
True,True,True,spring,12,6.77783,6.67976,20.46179,3
False,False,True,summer,15,7.15829,31.24546,10.37666,3
False,True,True,summer,1,2.28393,18.13299,34.38756,3
True,False,True,autumn,7,3.96302,39.84093,47.0172,1
True,False,False,summer,1,0.65085,20.20581,14.96995,1
True,False,False,winter,2,9.24331,26.34543,30.5147,2
True,False,False,summer,3,2.86309,15.56342,1.04324,1
True,True,True,autumn,8,5.71809,24.41045,48.78273,2
True,True,True,autumn,8,5.71809,24.41045,48.78273,2
True,True,True,winter,8,5.71809,0.99999,48.78273,3
True,True,True,winter,8,5.71809,1.98675,48.79993,3
True,True,False,winter,8,7.71809,1.02345,38.78273,3
True,False,False,summer,1,4.03947,33.84073,6.48891,1
True,False,False,winter,4,2.46471,24.07929,17.77792,1
False,False,False,winter,9,3.66415,23.68306,24.43865,1
True,True,True,autumn,8,5.71809,24.41045,48.78273,2
False,True,True,spring,3,5.3009,34.83862,6.75862,2
True,False,True,winter,8,6.23275,8.1183,19.6922,3
False,True,True,autumn,2,4.21016,11.24334,34.98395,3
True,True,False,autumn,0,2.79424,13.25106,5.69617,1
True,True,False,winter,15,2.43843,14.61703,49.57393,2
True,True,True,summer,8,2.28654,20.9895,5.64007,1
False,False,False,autumn,1,2.5607,26.85209,47.10784,1
True,True,True,winter,14,1.56638,18.02703,7.05011,1
False,False,False,winter,13,3.86632,28.9884,20.1928,1
True,False,False,summer,13,6.37654,34.3833,34.53892,3
True,False,False,summer,13,6.37654,34.3833,34.53892,3
True,False,False,spring,11,8.35634,0.00000,34.53892,3
True,False,True,summer,13,9.37654,34.3833,0.00000,2
True,False,False,winter,13,7.77754,34.3833,0.00000,2
True,True,True,summer,5,8.10422,7.6617,23.41017,3
1 adult active_time ill season guests hunger wet_food dry_food decision
2 True True True spring 12 4.0462 37.63803 5.60819 2
3 True False False spring 9 2.89229 34.37597 27.75948 1
4 False True True summer 15 2.4002 45.06447 41.50999 3
5 True False False summer 5 0.73248 46.1058 33.44281 1
6 True False False summer 12 0.62973 49.99647 41.07699 1
7 True False False winter 9 7.0889 46.16796 45.904 2
8 False True False summer 2 9.07977 22.08011 20.53507 3
9 False False True winter 11 3.5635 14.75823 43.46342 2
10 False False True winter 9 8.03113 20.6384 30.81177 2
11 True True True summer 0 0.01966 28.27203 3.37575 1
12 False False False autumn 12 8.27518 5.91931 1.10505 2
13 False False True summer 1 5.058 11.01892 48.04589 3
14 False True False winter 9 5.64777 17.19678 12.20864 1
15 True True True summer 0 6.86046 19.03315 47.13198 3
16 True False True winter 13 0.42516 12.62312 19.15853 1
17 True False True summer 14 4.46951 33.59537 47.343 1
18 True True False autumn 11 6.49386 0.64398 1.0515 1
19 False False False winter 10 1.9354 22.80015 26.01165 1
20 True False False summer 7 3.55716 1.91094 41.82462 1
21 False True True winter 8 9.01072 15.89341 9.10422 2
22 False True True spring 4 4.64513 10.39766 7.57955 2
23 True False False autumn 2 4.11019 45.85039 30.34183 1
24 True True True spring 8 7.09003 6.92577 24.77301 3
25 True True False spring 6 4.12955 47.91516 12.84722 1
26 True True True spring 10 2.70845 1.0623 42.6941 3
27 True True True spring 15 4.34025 6.89381 3.03923 2
28 True False False autumn 5 7.24464 31.34759 6.44719 1
29 True True True summer 6 3.8924 18.08964 18.77435 1
30 False False True spring 13 4.86962 48.62578 6.05301 2
31 False True False spring 11 1.60186 31.34926 14.41007 2
32 False True False autumn 2 4.19839 19.58116 28.63919 1
33 False True True autumn 6 1.50346 25.88282 24.97486 1
34 False True False spring 7 2.67431 17.05587 24.7939 1
35 True True True spring 7 1.72828 38.17304 24.3685 1
36 False True True summer 12 7.55322 26.52797 42.24754 3
37 False True False summer 5 8.74993 30.39317 4.5676 2
38 False True False summer 5 7.42862 48.73014 5.94891 2
39 True False False spring 4 9.3662 12.90556 41.31266 3
40 False True False autumn 8 4.72785 0.1623 19.48377 1
41 False False False winter 14 6.49349 29.52121 21.87192 1
42 True False False summer 14 1.57899 26.90783 33.03528 1
43 False True False summer 3 6.21725 3.60564 23.51978 1
44 True True True winter 4 4.18565 25.24209 11.02056 1
45 False False True autumn 8 6.5479 39.16107 4.75438 2
46 False False False summer 13 2.14609 39.97177 13.98651 1
47 True True False winter 9 7.43651 10.09353 15.70939 3
48 False False True autumn 5 7.85569 40.47073 49.75818 2
49 True False True summer 1 9.03492 23.44692 20.0026 3
50 False True True autumn 8 2.36724 42.81768 21.34668 2
51 False False True summer 15 6.8222 15.2733 15.14799 3
52 True False True summer 4 8.63882 41.36166 7.98981 2
53 False True False autumn 12 0.48943 8.67832 40.4952 1
54 False False False autumn 3 3.0489 14.81219 8.32707 1
55 False True True winter 6 0.41014 49.94757 12.61713 1
56 True False True winter 12 5.55017 0.98544 10.25287 1
57 True False False summer 11 9.92135 6.80759 48.0665 3
58 False True True winter 2 7.14394 11.41862 18.29288 2
59 False True True winter 10 1.39924 36.41807 42.95548 2
60 False True False summer 1 0.95193 46.53851 25.70391 1
61 False True False winter 4 1.44658 6.61497 31.58116 1
62 False True False autumn 10 7.9404 26.82316 49.5898 2
63 True False False spring 5 5.14951 32.36345 11.32114 1
64 True True False summer 5 7.55665 49.12578 29.32983 3
65 True False True autumn 3 3.80646 11.86722 35.43034 1
66 False False False winter 12 3.65822 0.14026 19.18031 1
67 True False False spring 8 8.58384 46.33595 11.52974 2
68 True False False summer 3 6.34127 22.66891 19.15813 1
69 False True False spring 13 7.86448 8.85557 40.03913 3
70 True True False spring 13 8.89316 49.89548 21.04525 2
71 True True False autumn 9 0.91339 36.35922 16.09576 1
72 False False False summer 3 7.36268 28.50462 29.52973 1
73 True True True spring 3 6.1319 37.71758 33.50616 1
74 False True True spring 0 0.77228 42.89976 19.19004 1
75 True False True autumn 3 7.73055 20.87865 37.18248 1
76 False False False summer 1 8.30392 34.47046 8.77926 2
77 True True False summer 8 2.96562 17.50839 23.22476 1
78 True True True winter 4 2.27279 20.58575 32.17293 1
79 True False True spring 7 6.14608 34.46015 17.22245 1
80 False False True winter 1 8.32044 12.09058 37.28732 3
81 False True False spring 14 9.56618 48.49473 46.37651 2
82 True False True spring 10 9.33146 47.99213 35.92519 2
83 False True False spring 6 0.47396 37.45415 36.87019 1
84 True False False summer 12 7.61463 4.36339 36.07375 3
85 True True True autumn 3 6.42039 4.90383 43.29857 3
86 False True False winter 13 6.89007 43.09184 3.04284 2
87 True False True winter 1 6.56604 19.04681 27.58314 2
88 True False True spring 3 1.96784 2.18597 34.02966 1
89 False False False spring 0 3.76673 1.64674 34.50649 1
90 False False True summer 2 0.05382 10.48896 24.03557 1
91 True False True spring 14 5.12387 44.44585 8.35502 3
92 True True False autumn 10 4.90335 43.27857 27.22901 1
93 True False False winter 11 5.89082 28.91495 15.58095 1
94 True False True autumn 3 7.39589 21.53402 44.50694 3
95 True False True autumn 3 7.39589 21.53402 0.00000 2
96 True False False summer 12 5.68671 49.18777 22.53807 3
97 False False False winter 0 0.86765 41.28704 33.77284 1
98 False False True summer 10 5.97643 36.23669 48.32615 2
99 False True True autumn 7 1.76947 38.34692 13.28679 1
100 True True True summer 13 8.63622 16.14861 44.91355 3
101 True False False winter 11 8.48481 37.52722 47.76888 2
102 True True True summer 12 7.65812 4.16785 34.57922 3
103 False False True winter 7 8.44897 42.99815 44.66558 2
104 False False False winter 7 0.53067 13.47003 18.45329 1
105 False False False winter 14 3.24747 9.51144 6.62824 1
106 True True False spring 12 5.19071 47.53107 34.68942 2
107 True False False summer 9 6.9442 46.79146 13.92798 2
108 True False False autumn 1 3.09242 18.02023 11.03004 1
109 True True True spring 3 0.19271 1.00203 1.16671 1
110 False True True winter 5 9.00758 37.95091 11.54697 2
111 True False True spring 9 6.11904 37.42698 4.82627 2
112 False False True autumn 9 6.29507 22.99044 15.46992 2
113 False False False winter 13 5.4099 11.75134 6.91861 1
114 False False False winter 5 9.92035 21.82547 10.2415 2
115 False True False winter 10 6.06913 1.46795 12.76663 1
116 True False True spring 5 8.1218 37.03643 32.04156 3
117 True False False autumn 11 0.48844 39.36689 0.03464 1
118 False True True summer 6 1.72816 26.85829 16.53262 1
119 False False False spring 5 6.04025 29.55673 18.85232 2
120 False True True winter 2 1.75157 0.6601 49.91163 1
121 False True True winter 9 6.75125 15.22221 6.72688 2
122 False True True autumn 9 6.72535 24.07403 33.94074 2
123 False False False winter 14 0.02843 48.49973 15.81701 1
124 False False True autumn 15 1.07944 45.94025 4.05257 1
125 False False True winter 12 7.26288 15.82501 22.56163 2
126 True True True winter 11 8.50892 23.89966 46.14267 3
127 True False True autumn 11 8.10923 24.31448 6.70919 2
128 True True False summer 11 4.65313 3.44791 3.96313 3
129 True False True summer 2 2.56716 10.85536 49.88738 1
130 False False True autumn 4 8.19265 5.43942 48.74041 3
131 False True False autumn 5 5.6574 9.75738 25.96888 3
132 False True False autumn 5 5.6574 9.75738 0.00000 2
133 False True False winter 14 4.87066 33.40134 18.98246 2
134 True True True winter 6 8.2623 37.47298 33.76759 2
135 True True True spring 10 2.20409 13.6178 5.80078 2
136 True False True autumn 7 9.06057 37.8724 23.62209 2
137 False False True autumn 3 6.69861 37.07336 16.87187 2
138 False False True autumn 3 6.69861 0.00000 16.87187 3
139 True False True autumn 13 4.96475 46.87852 3.1412 1
140 True False True autumn 9 2.65212 19.06994 37.33364 1
141 True True False summer 15 3.47148 35.84529 0.00000 1
142 False True True summer 8 1.51025 9.44246 19.05913 1
143 False False True autumn 6 6.48485 45.61986 15.91179 2
144 False False False spring 2 8.98075 39.32941 42.47669 2
145 False True True winter 5 8.37177 12.99299 42.31566 3
146 True True True autumn 6 3.38746 48.86975 49.62605 1
147 False True True summer 7 7.09358 22.83074 38.5172 2
148 False False True spring 9 1.00148 11.16064 0.52706 1
149 False False True winter 1 6.08476 37.67744 4.49812 2
150 False True False spring 5 4.5182 32.48803 33.44274 1
151 True True True summer 8 6.11265 30.32015 46.47287 3
152 False True True winter 4 8.50937 22.72015 0.00000 2
153 True False True summer 4 7.23924 39.09963 42.82872 3
154 False False True summer 5 1.28353 7.18667 38.93923 1
155 False True False spring 3 4.50329 22.95269 0.41795 1
156 True True True summer 1 0.47824 14.79432 24.64273 1
157 False True False spring 10 8.43205 19.1333 20.95803 2
158 False True False spring 6 9.94659 18.83814 39.26147 2
159 False True False spring 3 3.68802 1.58951 26.4255 1
160 False False True autumn 8 4.79336 22.56564 4.95207 2
161 False True True spring 10 1.63541 0.00000 31.82704 1
162 False True False spring 2 1.2274 47.87731 32.98744 1
163 True True False winter 11 7.31457 26.08142 16.5835 2
164 False True True summer 2 4.90627 19.73976 49.56272 3
165 True True False winter 8 3.02707 35.9547 29.52088 1
166 True True True summer 7 5.02577 5.37674 16.61368 3
167 False False True spring 8 9.58805 8.12549 0.00000 2
168 False True True summer 6 2.08786 37.11126 36.15777 3
169 True True False spring 7 8.11839 12.2032 8.26737 2
170 True False True summer 7 4.08923 20.77025 11.25944 1
171 False False True winter 4 4.85557 0.00000 39.4493 2
172 False False False summer 14 9.12718 41.84025 49.51895 2
173 True True True summer 15 9.4014 34.10345 26.84361 3
174 False True False spring 14 8.61728 28.58017 39.3705 2
175 False True False winter 8 7.12808 12.04193 43.86622 2
176 True False True winter 1 9.50102 43.46168 28.81571 2
177 True True False spring 5 1.35366 6.95688 33.37058 1
178 False False True autumn 11 7.61014 11.10761 41.58039 2
179 False True True summer 2 6.86814 37.72905 14.64706 3
180 True False False winter 1 8.12812 22.55081 9.43532 2
181 True True True winter 10 4.18282 27.82423 30.42216 1
182 False False False summer 7 2.52646 3.74242 10.61286 1
183 False False True spring 13 3.15065 19.01632 34.56097 2
184 True True False summer 3 0.18108 46.67684 46.76693 1
185 True True True spring 0 1.50217 5.27541 16.18378 1
186 False True True summer 5 3.71758 11.15496 12.57224 3
187 False True False summer 6 9.92613 8.59078 21.32207 3
188 True False False winter 11 0.1261 2.42716 17.23296 1
189 True False True summer 14 6.90049 10.76539 3.92394 3
190 False True True autumn 15 1.76164 35.60051 2.5168 2
191 False False True spring 11 2.39225 36.14198 9.13906 3
192 True True False summer 2 4.04026 0.00000 12.47216 1
193 True True False spring 13 3.32803 7.59913 1.89442 3
194 True False True spring 1 5.15927 44.02139 4.03454 1
195 True True True autumn 13 5.52565 7.75133 38.62709 3
196 True True False spring 3 8.44216 30.01593 12.45777 3
197 False False True summer 13 2.88557 5.18905 5.87065 3
198 True True True spring 11 2.45261 7.22671 49.68806 3
199 True True True winter 4 8.55814 3.29899 32.82852 2
200 False False True summer 4 9.85169 47.62867 17.3155 3
201 False False True winter 2 5.30151 26.50068 48.79306 2
202 False False False autumn 9 7.59806 0.00000 36.92142 2
203 False True True autumn 1 9.28424 17.58014 28.42461 2
204 True False True summer 14 7.82306 35.29264 46.36975 3
205 False False True winter 2 1.10909 46.37088 40.88245 1
206 True True False summer 1 7.71442 43.2301 27.42849 3
207 False True True summer 6 1.21255 3.7357 4.31858 1
208 True True True winter 8 9.53076 12.54774 17.63524 2
209 True True True autumn 3 8.47955 19.04656 3.62988 2
210 True False True winter 3 2.58264 28.29242 0.00000 1
211 True False False autumn 5 6.83145 0.00000 15.88102 1
212 True False False summer 10 3.24742 16.50963 26.24036 1
213 False False False summer 8 8.66174 49.55046 33.2433 2
214 True False True winter 7 1.40722 4.06585 2.57929 1
215 False True False winter 12 4.483 2.42211 20.55941 2
216 False False True spring 12 2.98512 30.55243 5.53733 3
217 False False True autumn 6 0.84086 33.57311 32.42908 1
218 False True True winter 12 1.07916 8.27438 7.9284 1
219 False False True spring 12 3.17402 46.59657 14.21739 3
220 False True True winter 11 7.09559 14.18261 43.41709 2
221 False False True autumn 3 0.53006 21.37664 17.14295 1
222 False False False autumn 14 4.67143 4.11788 0.04226 1
223 True False True winter 12 3.48493 35.24303 0.00000 1
224 True True True spring 9 5.1789 0.97673 8.31413 2
225 False False False spring 5 1.50319 28.57762 27.80054 1
226 True True True autumn 10 9.23444 43.51842 19.90954 2
227 False False False autumn 9 3.84582 40.34953 9.01663 1
228 True True False winter 5 3.90587 32.97826 0.67046 1
229 True True True autumn 2 9.19994 0.00000 34.36662 3
230 False True True summer 6 5.614 29.08038 0.00000 2
231 True True True autumn 2 2.15339 6.36751 6.45082 1
232 True False True spring 1 9.19416 32.05433 8.27667 2
233 True False True autumn 0 8.12282 48.68677 8.38304 2
234 False True True spring 12 3.26729 29.61584 1.69993 2
235 True False True summer 8 1.99886 31.26437 3.51834 1
236 False False True summer 7 7.41314 44.88982 34.46453 2
237 True False True summer 0 7.24464 8.85289 34.29828 3
238 False True False summer 8 2.38628 21.76861 47.20283 3
239 True True True autumn 3 7.12112 12.08359 41.06062 3
240 True False False winter 5 6.74504 47.09367 1.97357 2
241 False False False summer 8 1.23539 35.47945 7.67276 1
242 True False False spring 11 7.91742 34.52557 30.96412 3
243 False True True winter 14 5.91181 7.53226 16.37669 3
244 False True True spring 13 6.07261 47.43572 15.83885 2
245 True True True autumn 2 9.22518 31.25996 28.06488 3
246 False True True summer 3 8.47609 0.23934 31.25786 3
247 False True False autumn 6 0.97126 13.65648 25.59887 1
248 True False False spring 14 9.29029 46.83676 12.58912 2
249 False False False winter 14 0.14092 4.6673 20.3859 1
250 True False True autumn 2 3.52708 37.61372 32.83573 1
251 True True True winter 1 4.37134 43.19138 22.04785 1
252 True False True summer 9 1.1614 10.9739 42.3009 1
253 False False False winter 9 7.27324 29.74731 47.17759 2
254 False True True winter 9 3.17153 35.14715 21.37868 2
255 False True False autumn 0 2.37863 20.35733 46.96943 1
256 False False True autumn 4 0.70656 8.70201 5.26527 1
257 True True True winter 1 8.23562 36.01552 25.03969 2
258 True True False winter 3 6.65062 6.75622 24.91086 3
259 False False False spring 10 2.30179 19.62758 25.57147 1
260 True True False autumn 10 6.60812 6.61336 12.39931 3
261 False False True summer 3 8.9948 47.39225 18.11157 2
262 True False True autumn 5 6.69302 42.62701 13.01677 2
263 False False False spring 14 8.53868 33.42545 2.43572 2
264 False True True autumn 10 4.46205 10.37542 39.58137 3
265 True True False spring 14 5.34262 15.45545 21.48404 3
266 True False False winter 9 7.02885 4.88308 27.56619 3
267 False False False autumn 13 3.55279 0.17091 5.43831 1
268 True True True autumn 1 9.98637 27.57982 15.82173 2
269 False True True summer 9 8.25408 13.10493 27.07596 3
270 False False False spring 15 1.9089 33.25115 44.57492 1
271 True False False autumn 12 6.65534 38.00972 20.31047 2
272 False True True autumn 1 0.01592 6.24929 15.51308 1
273 False True True winter 13 1.24017 36.88006 16.50894 1
274 False True True winter 13 6.1878 15.18876 9.02381 2
275 True True True winter 5 5.45157 13.27868 39.39805 2
276 True False True spring 5 2.82881 28.62319 24.03077 1
277 False False True spring 2 4.8246 41.45269 48.89539 2
278 True False False spring 8 9.3343 39.02018 45.01066 3
279 True True True autumn 4 1.8456 2.94366 37.44996 1
280 False True True summer 14 0.95333 4.57964 26.37633 1
281 False False True autumn 13 9.84087 24.03819 41.72097 2
282 True False False autumn 9 3.70617 32.70115 1.69105 1
283 True True True spring 12 6.77783 6.67976 20.46179 3
284 False False True summer 15 7.15829 31.24546 10.37666 3
285 False True True summer 1 2.28393 18.13299 34.38756 3
286 True False True autumn 7 3.96302 39.84093 47.0172 1
287 True False False summer 1 0.65085 20.20581 14.96995 1
288 True False False winter 2 9.24331 26.34543 30.5147 2
289 True False False summer 3 2.86309 15.56342 1.04324 1
290 True True True autumn 8 5.71809 24.41045 48.78273 2
291 True True True autumn 8 5.71809 24.41045 48.78273 2
292 True True True winter 8 5.71809 0.99999 48.78273 3
293 True True True winter 8 5.71809 1.98675 48.79993 3
294 True True False winter 8 7.71809 1.02345 38.78273 3
295 True False False summer 1 4.03947 33.84073 6.48891 1
296 True False False winter 4 2.46471 24.07929 17.77792 1
297 False False False winter 9 3.66415 23.68306 24.43865 1
298 True True True autumn 8 5.71809 24.41045 48.78273 2
299 False True True spring 3 5.3009 34.83862 6.75862 2
300 True False True winter 8 6.23275 8.1183 19.6922 3
301 False True True autumn 2 4.21016 11.24334 34.98395 3
302 True True False autumn 0 2.79424 13.25106 5.69617 1
303 True True False winter 15 2.43843 14.61703 49.57393 2
304 True True True summer 8 2.28654 20.9895 5.64007 1
305 False False False autumn 1 2.5607 26.85209 47.10784 1
306 True True True winter 14 1.56638 18.02703 7.05011 1
307 False False False winter 13 3.86632 28.9884 20.1928 1
308 True False False summer 13 6.37654 34.3833 34.53892 3
309 True False False summer 13 6.37654 34.3833 34.53892 3
310 True False False spring 11 8.35634 0.00000 34.53892 3
311 True False True summer 13 9.37654 34.3833 0.00000 2
312 True False False winter 13 7.77754 34.3833 0.00000 2
313 True True True summer 5 8.10422 7.6617 23.41017 3

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@ -1,48 +0,0 @@
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
headers = ['adult','active_time','ill','season','guests','hunger','wet_food','dry_food']
# Wczytanie danych
data = pd.read_csv('dane.csv', header=0)
X = data[headers]
Y = data['decision']
X = pd.get_dummies(data=X, columns=['season'])
clf = DecisionTreeClassifier(max_depth=6)
X1, X2, Y1, Y2 = train_test_split(X, Y, train_size=0.8)
clf = clf.fit(X1, Y1)
pred = clf.predict(X2)
accuracy = accuracy_score(Y2, pred)
print("Dokładność:", accuracy)
#zapisanie drzewa do pliku
plt.figure(figsize=(50,30))
plot_tree(clf, filled=True, feature_names=X.columns.tolist(), class_names=['nie karmi', 'karmi mokrą karmą', 'karmi suchą karmą'])
plt.savefig('tree.png')
# dane do decyzji
def feed_decision(adult,active_time,ill,season,guests,hunger,dry_food,wet_food):
X_new = pd.DataFrame({
'adult': [adult],
'active_time': [active_time],
'ill': [ill],
'season': [season],
'guests':[guests],
'hunger': [hunger],
'wet_food': [wet_food],
'dry_food': [dry_food]
})
X_new = pd.get_dummies(X_new)
missing_columns = set(X.columns) - set(X_new)
for col in missing_columns:
X_new[col] = False
X_new = X_new.reindex(columns=X.columns, fill_value=0)
print("Atrybuty zwierzęcia:", adult,active_time,ill,season,guests,hunger,wet_food,dry_food)
return (clf.predict(X_new))

22
draw.py
View File

@ -1,22 +0,0 @@
import pygame
def draw_goal(const, goal):
x, y = goal
rect = (x * const.GRID_SIZE, y * const.GRID_SIZE, const.GRID_SIZE, const.GRID_SIZE)
pygame.draw.rect(const.screen, const.RED, rect)
pygame.display.flip()
pygame.time.delay(2000)
def draw_grid(const):
for y in range(0, const.GRID_HEIGHT * const.GRID_SIZE, const.GRID_SIZE):
for x in range(0, const.GRID_WIDTH * const.GRID_SIZE, const.GRID_SIZE):
rect = pygame.Rect(x, y, const.GRID_SIZE, const.GRID_SIZE)
pygame.draw.rect(const.screen, const.BLACK, rect, 1)
def draw_house(const):
X = 2
Y = 0
image_path = 'images/house.png'
image_surface = pygame.image.load(image_path) # Wczytanie obrazka do obiektu Surface
scaled_image = pygame.transform.scale(image_surface, (const.GRID_SIZE * 2, const.GRID_SIZE * 2))
const.screen.blit(scaled_image, (X * const.GRID_SIZE, Y * const.GRID_SIZE))

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from animal import Animal
import pygame
from datetime import datetime
class Elephant(Animal):
def __init__(self, x, y, adult=False):
Elephant_image = pygame.image.load('images/elephant.png')
name = 'elphant'
if adult:
elephant_food = 'leavs'
food_image = 'images/leaves.png'
else:
elephant_food = 'milk'
food_image = 'images/milk.png'
super().__init__(x, y,name, Elephant_image, food_image,elephant_food, adult)
self._starttime = datetime.now()
def feed(self):
self.getting_hungry()
if self._feed < 0.3:
return 'False'
else:
return 'True'
def getting_hungry(self):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / 60
self._feed += minutes_passed
self._starttime = checktime

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import pygame
class Enclosure:
def __init__(self, x1, y1, x2, y2, gate1, gate2, type, imageH, imageV, imageGate):
self.x1 = x1 - 1
self.y1 = y1 - 1
# (x1,y1) - wierzchołek przekątnej
self.x2 = x2 - 1
self.y2 = y2 - 1
# (x2,y2) - 2 wierzchołek przekątnej
self.gate1 = gate1
self.gate2 = gate2
self.type = type
self.imageH = imageH
self.imageV = imageV
self.imageGate = imageGate
self.animals = set()
def gatebuild(self, screen, grid_size):
self.imageGate = pygame.transform.scale(self.imageGate, (grid_size, grid_size))
gate_x1, gate_y1 = self.gate1
gate_x2, gate_y2 = self.gate2
gate_x1 -= 1
gate_y1 -= 1
gate_x2 -= 1
gate_y2 -= 1
rect1 = pygame.Rect(gate_x1 * grid_size, gate_y1 * grid_size, grid_size, grid_size)
rect2 = pygame.Rect(gate_x2 * grid_size, gate_y2 * grid_size, grid_size, grid_size)
screen.blit(self.imageGate, (gate_x1 * grid_size, gate_y1 * grid_size))
screen.blit(self.imageGate, (gate_x2 * grid_size, gate_y2 * grid_size))
def draw(self, screen, grid_size):
self.imageH = pygame.transform.scale(self.imageH, (grid_size, grid_size))
self.imageV = pygame.transform.scale(self.imageV, (grid_size, grid_size))
gate_x1, gate_y1 = self.gate1
gate_x2, gate_y2 = self.gate2
gate_x1 -= 1
gate_y1 -= 1
gate_x2 -= 1
gate_y2 -= 1
if self.x1 < self.x2:
for i in range(self.x1, self.x2 + 1):
if (i, self.y1) != (gate_x1, gate_y1) and (i, self.y1) != (gate_x2, gate_y2):
screen.blit(self.imageH, (i * grid_size, self.y1 * grid_size))
if (i, self.y2) != (gate_x1, gate_y1) and (i, self.y2) != (gate_x2, gate_y2):
screen.blit(self.imageH, (i * grid_size, self.y2 * grid_size))
for j in range(self.y1, self.y2 + 1):
if (self.x1, j) != (gate_x1, gate_y1) and (self.x1, j) != (gate_x2, gate_y2):
screen.blit(self.imageV, (self.x1 * grid_size, j * grid_size))
if (self.x2, j) != (gate_x1, gate_y1) and (self.x2, j) != (gate_x2, gate_y2):
screen.blit(self.imageV, (self.x2 * grid_size, j * grid_size))
def create_enclosures():
fenceH = pygame.image.load('images/fenceHor.png')
fenceV = pygame.image.load('images/fenceVer.png')
gate = pygame.image.load('images/gate.png')
en1 = Enclosure(0, 5, 9, 11, (9, 6), (4, 11), "hot", fenceH, fenceV, gate) # Lewa klatka
en2 = Enclosure(4, 13, 28, 16, (12, 13), (20, 13), 'cold', fenceH, fenceV, gate) # Dolna klatka
en3 = Enclosure(19, 5, 31, 11, (23, 5), (25, 11), 'hot', fenceH, fenceV, gate) # Prawa klatka
en4 = Enclosure(11, 5, 16, 11, (12, 5), (16, 8), 'cold', fenceH, fenceV, gate) # Środkowa klatka
en5 = Enclosure(13, 0, 29, 3, (16, 3), (27, 3), 'medium', fenceH, fenceV, gate) # Górna klatka
Enclosures = [en1, en2, en3, en4, en5]
return Enclosures
def draw_enclosures(Enclosures, const):
for enclosure in Enclosures:
enclosure.draw(const.screen, const.GRID_SIZE)
def draw_gates(Enclosures, const):
for enclosure in Enclosures:
enclosure.gatebuild(const.screen, const.GRID_SIZE)

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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

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from animal import Animal
import pygame
from datetime import datetime
class Giraffe(Animal):
def __init__(self, x, y, adult=False):
Giraffe_image = pygame.image.load('images/giraffe.png')
name = 'giraffe'
food_image = 'images/leaves.png'
giraffe_food = 'leaves'
super().__init__(x, y,name, Giraffe_image, food_image,giraffe_food, adult)
self._starttime = datetime.now()
def feed(self):
self.getting_hungry()
if self._feed < 0.8:
return 'False'
else:
return 'True'
def getting_hungry(self):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / 60
self._feed += minutes_passed
self._starttime = checktime

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import random
import pygame
import sys
sys.path.append('./Animals')
from animals import create_animals, draw_Animals
from elephant import Elephant
from giraffe import Giraffe
from penguin import Penguin
from parrot import Parrot
from bear import Bear
from agent import Agent
from enclosure import create_enclosures, draw_enclosures, draw_gates
from spawner import Spawner
from state_space_search import graphsearch, generate_cost_map
from terrain_obstacle import create_obstacles, draw_Terrain_Obstacles
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)
BLACK = (0, 0, 0)
GRID_SIZE = 50
GRID_WIDTH = 20
GRID_HEIGHT = 10
pygame.init()
WINDOW_SIZE = (GRID_WIDTH * GRID_SIZE, GRID_HEIGHT * GRID_SIZE)
screen = pygame.display.set_mode(WINDOW_SIZE)
pygame.display.set_caption("Mini Zoo")
obstacles = set()
animals_position = set()
terrain_obstacles_position = set()
Animals = create_animals()
Enclosures = create_enclosures()
Terrain_Obstacles = create_obstacles()
def spawn_all_animals():
background_image = pygame.image.load('images/tło.jpg')
background_image = pygame.transform.scale(background_image, WINDOW_SIZE)
an1 = Parrot(16, 2)
an2 = Penguin(8, 6)
an3 = Bear(14, 9)
old_an2 = Giraffe(18,4, adult=True)
old_an1 = Elephant(4, 7, adult=True)
an4 = Elephant(4,3)
Animals = [an1, an2, an3, an4, old_an1, old_an2]
def draw_grid():
for y in range(0, GRID_HEIGHT * GRID_SIZE, GRID_SIZE):
for x in range(0, GRID_WIDTH * GRID_SIZE, GRID_SIZE):
rect = pygame.Rect(x, y, GRID_SIZE, GRID_SIZE)
pygame.draw.rect(screen, BLACK, rect, 1)
def draw_Animals():
for Animal in Animals:
spawner1 = Spawner(Animal)
spawner1.spawn_animal(obstacles, animals_position, Enclosures)
Animal.draw(screen, GRID_SIZE)
if Animal.feed() == 'True':
Animal.draw_exclamation(screen, GRID_SIZE, Animal.x, Animal.y)
else:
Animal.draw_food(screen,GRID_SIZE,Animal.x,Animal.y)
def spawn_obstacles():
for terrain_obstacle in Terrain_Obstacles:
spawner2 = Spawner(terrain_obstacle)
spawner2.spawn_terrain_obstacles(obstacles, animals_position, terrain_obstacles_position, const.GRID_WIDTH, const.GRID_HEIGHT)
def generate_obstacles():
for en in Enclosures:
# Pobierz współrzędne bramy
gate_x, gate_y = en.gate1
gate_x -= 1
gate_y -= 1
gate_x2, gate_y2 = en.gate2
gate_x2 -= 1
gate_y2 -= 1
# Dodaj lewy brzeg prostokąta
for y in range(en.y1, en.y2 + 1):
if (en.x1, y) != (gate_x, gate_y) and (en.x1, y) != (gate_x2, gate_y2):
obstacles.add((en.x1, y))
# Dodaj prawy brzeg prostokąta
for y in range(en.y1, en.y2 + 1):
if (en.x2, y) != (gate_x, gate_y) and (en.x2, y) != (gate_x2, gate_y2):
obstacles.add((en.x2, y))
# Dodaj górny brzeg prostokąta
for x in range(en.x1+1, en.x2):
if (x, en.y1) != (gate_x, gate_y) and (x, en.y1) != (gate_x2, gate_y2):
obstacles.add((x, en.y1))
# Dodaj dolny brzeg prostokąta
for x in range(en.x1+1, en.x2):
if (x, en.y2) != (gate_x, gate_y) and (x, en.y2) != (gate_x2, gate_y2):
obstacles.add((x, en.y2))
return obstacles
def main():
initial_state = (0, 0, 'S')
agent = Agent(initial_state, 'images/agent1.png', const.GRID_SIZE)
obstacles = generate_obstacles()
actions = []
agent = Agent(0, 0, 'images/avatar.png', GRID_SIZE)
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
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
agent.handle_event(event, const.GRID_WIDTH, const.GRID_HEIGHT, Animals, obstacles,const)
agent.handle_event(event,GRID_HEIGHT,GRID_WIDTH,Animals)
change_time(const)
draw_background(const)
draw_enclosures(Enclosures, const)
draw_gates(Enclosures, const)
draw_house(const)
if not spawned:
spawn_all_animals()
spawn_obstacles()
cost_map = generate_cost_map(Animals, Terrain_Obstacles)
for animal in Animals:
# animal._feed = 0
animal._feed = random.randint(0, 10)
spawned = True
screen.blit(background_image,(0,0))
draw_grid()
draw_Animals()
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)
agent.draw(screen)
pygame.display.flip()
clock.tick(10)
if actions:
action = actions.pop(0)
agent.move(action, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, Animals, goal,const)
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]
# 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))
# 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)
draw_goal(const, goal)
actions, cost = 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)
if __name__ == "__main__":
main()
main()

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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')

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import time
import pygame
DAY_LENGTH = 90 # Długość dnia w sekundach
def draw_night(const):
overlay = pygame.Surface(const.WINDOW_SIZE)
overlay.fill((0, 0, 0))
overlay.set_alpha(128) # Ustawienie przezroczystości (0 - całkowicie przeźroczyste, 255 - nieprzeźroczyste)
const.screen.blit(overlay, (0, 0))
def change_time(const):
current_time = time.time()
# Sprawdzamy, czy nadszedł czas zmiany pory dnia
if current_time >= const.TIME_CHANGE:
# Zmieniamy porę dnia
const.IS_NIGHT = not const.IS_NIGHT # Jeśli było dzień, teraz będzie noc, i odwrotnie
const.TIME_CHANGE = current_time + DAY_LENGTH

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from animal import Animal
import pygame
from datetime import datetime
class Parrot(Animal):
def __init__(self, x, y, adult=False):
Parrot_image = pygame.image.load('images/parrot.png')
name = 'parrot'
food_image = 'images/grains.png'
parrot_food = 'grains'
super().__init__(x, y,name, Parrot_image, food_image,parrot_food, adult)
self._starttime = datetime.now()
def feed(self):
self.getting_hungry()
if self._feed < 1.5:
return 'False'
else:
return 'True'
def getting_hungry(self):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / 60
self._feed += minutes_passed
self._starttime = checktime

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from animal import Animal
import pygame
from datetime import datetime
class Penguin(Animal):
def __init__(self, x, y, adult=False):
Penguin_image = pygame.image.load('images/penguin.png')
name = 'penguin'
food_image = 'images/fish.png'
penguin_food = 'fish'
super().__init__(x, y,name, Penguin_image, food_image,penguin_food, adult)
self._starttime = datetime.now()
def feed(self):
self.getting_hungry()
if self._feed < 2:
return 'False'
else:
return 'True'
def getting_hungry(self):
checktime = datetime.now()
delta = checktime - self._starttime
minutes_passed = delta.total_seconds() / 60
self._feed += minutes_passed
self._starttime = checktime

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import pygame
def draw_background(const):
season_images = {
"spring": "images/tłowiosna.jpg",
"summer": "images/tło.jpg",
"autumn": "images/tłojesień.jpg",
"winter": "images/tłozima.jpg"
}
background_image = pygame.transform.scale(pygame.image.load(season_images[const.season]), const.WINDOW_SIZE)
const.screen.blit(background_image, (0, 0))

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import random
class Spawner:
def __init__(self, entity):
self.entity = entity
def spawn_animal(self, blocked, taken, enclosures):
self.enclosures = [enclosure for enclosure in enclosures if enclosure.type == self.entity.environment]
# Wyrażenie listowe filtrujące tylko te wybiegi, które pasują do środowiska zwierzęcia
enclosure = random.choice(self.enclosures)
enclosure.animals.add(self.entity) # Przydzielenie zwierzęcia do wybiegu
while True:
if self.entity.adult:
self.entity.x = random.randint(enclosure.x1+1, enclosure.x2-2)
self.entity.y = random.randint(enclosure.y1+1, enclosure.y2-2)
else:
self.entity.x = random.randint(enclosure.x1+1, enclosure.x2)
self.entity.y = random.randint(enclosure.y1+1, enclosure.y2)
if self.check(blocked | {(8,5),(3,10),(15,2),(26,2),(11,4),(15,7),(22,4),(24,10),(11,12),(19,12)}, taken):
break
def spawn_terrain_obstacles(self, blocked1, blocked2, taken, grid_width, grid_height):
blocked1 = blocked1 | {(2,0),(3,0),(2,1),(3,1),(8,5),(3,10),(15,2),(26,2),(11,4),(15,7),(22,4),(24,10),(11,12),(19,12)}
while True:
self.entity.x = random.randint(0, grid_width - 1)
self.entity.y = random.randint(0, grid_height - 1)
y = self.entity.y
x = self.entity.x
if (x, y) not in blocked1 and (x, y) not in blocked2 and (x, y) not in taken:
taken.add((self.entity.x, self.entity.y))
break
def check(self, blocked, taken):
x = self.entity.x
y = self.entity.y
if (x,y) in blocked or (x,y) in taken:
return False
if self.entity.adult:
adult_fields = [(x, y), (x+1,y), (x,y+1), (x+1,y+1)] # Duże zwierze zajmuje 4 pola
if any(field in taken for field in adult_fields): # Jeśli stawiane zwierze jest dorosłe i jakiekolwiek pole jest zajęte, to nie można postawić zwierzęcia
return False
for field in adult_fields: # Dodaj wszystkie pola zajęte przez duże zwierzę
taken.add(field)
else:
taken.add((x,y))
return True

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from queue import PriorityQueue
DEFAULT_COST_VALUE = 1
def is_border(x, y, max_x, max_y):
return 0 <= x < max_x and 0 <= y < max_y
def is_obstacle(x, y, obstacles):
return (x, y) in obstacles
def succ(current_state, max_x, max_y, obstacles):
successors = []
x, y, direction = current_state
# Akcja: Do przodu
direction_x, direction_y = {'N': (0, -1), 'E': (1, 0), 'S': (0, 1), 'W': (-1, 0)}[direction] # Słownik przesunięć w zależności od kierunku
new_x, new_y = x + direction_x, y + direction_y
if is_border(new_x, new_y, max_x, max_y) and not(is_obstacle(new_x, new_y, obstacles)):
successors.append(((new_x, new_y, direction), 'Go Forward'))
# Akcja: Obrót w lewo
left_turns = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'} # Słownik kierunków po obrocie w lewo
successors.append(((x, y, left_turns[direction]), 'Turn Left'))
# Akcja: Obrót w prawo
right_turns = {'N': 'E', 'E': 'S', 'S': 'W', 'W': 'N'} # Słownik kierunków po obrocie w prawo
successors.append(((x, y, right_turns[direction]), 'Turn Right'))
return successors
def graphsearch(istate, goal, max_x, max_y, obstacles, cost_map):
fringe = PriorityQueue()
explored = set()
fringe.put((0, (istate, None , None)))
while not fringe.empty():
_, node = fringe.get()
state, _, _ = node
if goaltest(state, goal):
return build_action_sequence(node), current_cost(node, cost_map)
explored.add(state)
successors = succ(state, max_x, max_y, obstacles)
for new_state, action in successors:
new_node = (new_state, node, action)
p_new_state = current_cost(node, cost_map) + heuristic(state, goal)
if not is_state_in_queue(new_state, fringe) and new_state not in explored:
fringe.put((p_new_state, new_node))
elif is_state_in_queue(new_state, fringe):
for i, (p_existing_state, (existing_state, _, _)) in enumerate(fringe.queue):
if existing_state == new_state and p_existing_state > p_new_state:
fringe.queue[i] = (p_new_state, new_node)
else:
break
return False, float('inf')
def is_state_in_queue(state, queue):
for _, (s, _, _) in queue.queue:
if s == state:
return True
return False
def build_action_sequence(node):
actions = []
while node[1] is not None: # Dopóki nie dojdziemy do korzenia
_, parent, action = node
actions.append(action)
node = parent
actions.reverse()
return actions
def goaltest(state, goal):
x, y, _ = state
goal_x, goal_y = goal
return (x,y) == (goal_x, goal_y)
def current_cost(node, cost_map):
cost = 0
while node[1] is not None: # Dopóki nie dojdziemy do korzenia
_, parent, action = node
# Dodaj koszt pola z mapy kosztów tylko jeśli akcja to "Forward"
if action == 'Go Forward':
state, _, _ = node
cost += cost_map.get(state[:2], DEFAULT_COST_VALUE) # Pobiera koszt przejścia przez dane pole, a jeśli koszt nie jest zdefiniowany to bierze wartość domyślną
if action == 'Turn Right' or action == 'Turn Left':
cost += DEFAULT_COST_VALUE
node = parent # Przejdź do rodzica
return cost
def heuristic(state, goal):
x, y, _ = state
goal_x, goal_y = goal
return abs(x - goal_x) + abs(y - goal_y) # Odległość Manhattana do celu
def generate_cost_map(Animals, Terrain_Obstacles, cost_map={}):
adult_animal_cost = 15 # Default : 15
baby_animal_cost = 10 # Default : 10
puddle_cost = 50 # Default : 50
bush_cost = 20 # Default : 20
for animal in Animals:
if animal.adult:
# cost_map[(animal.x + 1, animal.y + 1)] = adult_animal_cost
# cost_map[(animal.x + 1, animal.y)] = adult_animal_cost
# cost_map[(animal.x, animal.y + 1)] = adult_animal_cost
cost_map[(animal.x, animal.y)] = adult_animal_cost
else:
cost_map[(animal.x, animal.y)] = baby_animal_cost
for terrain_obstacle in Terrain_Obstacles:
if terrain_obstacle.type == 'puddle':
cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = puddle_cost
else:
cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = bush_cost
return cost_map

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import pygame
class Terrain_Obstacle:
def __init__(self, x, y, type , image):
self.x = x - 1
self.y = y - 1
self.type = type
self.image = image
def draw(self, screen, grid_size):
scaled_image = pygame.transform.scale(self.image, (grid_size, grid_size))
screen.blit(scaled_image, (self.x * grid_size, self.y * grid_size))
def create_obstacles():
puddle_image = pygame.image.load('images/puddle.png')
bush_image = pygame.image.load('images/bush.png')
puddle1 = Terrain_Obstacle(0,0,'puddle', puddle_image)
puddle2 = Terrain_Obstacle(0,0,'puddle', puddle_image)
puddle3 = Terrain_Obstacle(0,0,'puddle', puddle_image)
puddle4 = Terrain_Obstacle(0,0,'puddle', puddle_image)
puddle5 = Terrain_Obstacle(0,0,'puddle', puddle_image)
puddle6 = Terrain_Obstacle(0,0,'puddle', puddle_image)
puddle7 = Terrain_Obstacle(0,0,'puddle', puddle_image)
bush1 = Terrain_Obstacle(0,0,'bush', bush_image)
bush2 = Terrain_Obstacle(0,0,'bush', bush_image)
bush3 = Terrain_Obstacle(0,0,'bush', bush_image)
bush4 = Terrain_Obstacle(0,0,'bush', bush_image)
bush5 = Terrain_Obstacle(0,0,'bush', bush_image)
Terrain_Obstacles = [puddle1, puddle2, puddle3, puddle4, puddle5, puddle6, puddle7, bush1, bush2, bush3, bush4, bush5]
return Terrain_Obstacles
def draw_Terrain_Obstacles(Terrain_Obstacles, const):
for terrain_obstacle in Terrain_Obstacles:
terrain_obstacle.draw(const.screen, const.GRID_SIZE)

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