Compare commits
No commits in common. "master" and "introduction_astar" have entirely different histories.
master
...
introducti
5
.gitignore
vendored
5
.gitignore
vendored
@ -1,5 +1,2 @@
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__pycache__/
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.idea/
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tree.png
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dataset/
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dataset.zip
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.idea/
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278
AStar.py
278
AStar.py
@ -3,282 +3,4 @@ f(n) = g(n) + h(n)
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g(n) = dotychczasowy koszt -> dodać currentCost w Node lub brać koszt na nowo przy oddtawrzaniu ścieżki
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h(n) = abs(state['x'] - goalTreassure[0]) + abs(state['y'] - goalTreassure[1]) -> odległość Manhatan -> można zrobić jeszcze drugą wersje gdzie mnoży się razy 5.5 ze wzgledu na średni koszt przejścia
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Należy zaimplementować kolejkę priorytetową oraz zaimplementować algorytm przeszukiwania grafu stanów z uwzględnieniem kosztu za pomocą przerobienia algorytmu przeszukiwania grafu stanów
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"""
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import random
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import pygame
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import Node
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import BFS
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from displayControler import NUM_X, NUM_Y
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from Pole import stoneList
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from queue import PriorityQueue
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def getRandomGoalTreasure():
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while True:
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goalTreasure = (random.randint(0, NUM_X - 1), random.randint(0, NUM_Y - 1)) # Współrzędne celu
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if goalTreasure not in stoneList:
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break
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return goalTreasure
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def heuristic(state, goal):
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# Oblicz odległość Manhattanowską między aktualnym stanem a celem
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manhattan_distance = abs(state['x'] - goal[0]) + abs(state['y'] - goal[1])
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return manhattan_distance
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'''def get_cost_for_plant(plant_name):
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plant_costs = {
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"pszenica": 7,
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"kukurydza": 9,
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"ziemniak": 2,
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"slonecznik": 5,
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"borowka": 3,
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"winogrono": 4,
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"mud": 15,
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"dirt": 0,
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}
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if plant_name in plant_costs:
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return plant_costs[plant_name]
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else:
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# Jeśli nazwa rośliny nie istnieje w słowniku, zwróć domyślną wartość
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return 0
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'''
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def A_star(istate, pole, goalTreasure):
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# goalTreasure = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
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# #jeśli chcemy używać random musimy wykreslić sloty z kamieniami, ponieważ tez mogą się wylosować i wtedy traktor w ogóle nie rusza
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#lub zrobić to jakoś inaczej, np. funkcja szukająca najmniej nawodnionej rośliny
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# przeniesione wyżej do funkcji getRandomGoalTreasure, wykorzystywana jest w App.py
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# while True:
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# goalTreasure = (random.randint(0, NUM_X - 1), random.randint(0, NUM_Y - 1)) # Współrzędne celu
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# if goalTreasure not in stoneList:
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# break
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fringe = PriorityQueue() # Kolejka priorytetowa dla wierzchołków do rozpatrzenia
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explored = [] # Lista odwiedzonych stanów
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obrot = 1
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# Tworzenie węzła początkowego
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x = Node.Node(istate)
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x.g = 0
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x.h = heuristic(x.state, goalTreasure)
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fringe.put((x.g + x.h, x)) # Dodanie węzła do kolejki
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total_cost = 0
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while not fringe.empty():
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_, elem = fringe.get() # Pobranie węzła z najniższym priorytetem
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if BFS.goalTest3(elem.state, goalTreasure): # Sprawdzenie, czy osiągnięto cel
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path = []
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cost_list=[]
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while elem.parent is not None: # Odtworzenie ścieżki
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path.append([elem.parent, elem.action])
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elem = elem.parent
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for node, action in path:
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# Obliczanie kosztu ścieżki dla każdego pola i wyświetlanie
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plant_cost = get_plant_name_and_cost_from_coordinates(node.state['x'],node.state['y'], pole)
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if action == "left" or action == "right": # Liczenie kosztu tylko dla pól nie będących obrotami
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total_cost += obrot
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cost_list.append(obrot)
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else:
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total_cost += plant_cost
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cost_list.append(plant_cost)
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return path,cost_list,total_cost
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explored.append(elem.state)
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for resp in succ3A(elem.state):
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child_state = resp[1]
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if child_state not in explored:
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child = Node.Node(child_state)
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child.parent = elem
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child.action = resp[0]
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# Pobranie nazwy rośliny z danego slotu na podstawie współrzędnych
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plant_cost = get_plant_name_and_cost_from_coordinates(child_state['x'], child_state['y'], pole)
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# Pobranie kosztu dla danej rośliny
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#plant_cost = get_cost_for_plant(plant_name)
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if child.action == "left" or child.action == "right":
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child.g = elem.g + obrot
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else:
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child.g = elem.g + plant_cost
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# Obliczenie heurystyki dla dziecka
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child.h = heuristic(child.state, goalTreasure)
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in_fringe = False
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for priority, item in fringe.queue:
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if item.state == child.state:
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in_fringe = True
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if priority > child.g + child.h:
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# Jeśli znaleziono węzeł w kolejce o gorszym priorytecie, zastąp go nowym
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fringe.queue.remove((priority, item))
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fringe.put((child.g + child.h, child))
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break
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if not in_fringe:
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# Jeśli stan dziecka nie jest w kolejce, dodaj go do kolejki
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fringe.put((child.g + child.h, child))
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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quit()
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return False
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def get_plant_name_and_cost_from_coordinates(x, y, pole):
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if (x, y) in pole.slot_dict: # Sprawdzenie, czy podane współrzędne znajdują się na polu
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slot = pole.slot_dict[(x, y)] # Pobranie slotu na podstawie współrzędnych
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if slot.plant: # Sprawdzenie, czy na slocie znajduje się roślina
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return slot.plant.stan.koszt # Zwrócenie nazwy rośliny na slocie
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else:
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return 0 # jeśli na slocie nie ma rośliny
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else:
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return 0 # jeśli podane współrzędne są poza polem
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#to ogólnie identyczna funkcja jak w BFS ale nie chciałam tam ruszać, żeby przypadkiem nie zapsuć do BFS,
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#tylko musiałam dodac sprawdzenie kolizji, bo traktor brał sloty z Y których nie ma na planszy
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def succ3A(state):
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resp = []
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if state["direction"] == "N":
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if state["y"] > 0 and (state['x'], state["y"] - 1) not in stoneList:
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resp.append(["forward", {'x': state["x"], 'y': state["y"]-1, 'direction': state["direction"]}])
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resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "E"}])
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resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "W"}])
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elif state["direction"] == "S":
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if state["y"] < NUM_Y - 1 and (state['x'], state["y"] + 1) not in stoneList:
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resp.append(["forward", {'x': state["x"], 'y': state["y"]+1, 'direction': state["direction"]}])
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resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "W"}])
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resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "E"}])
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elif state["direction"] == "E":
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if state["x"] < NUM_X - 1 and (state['x'] + 1, state["y"]) not in stoneList:
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resp.append(["forward", {'x': state["x"]+1, 'y': state["y"], 'direction': state["direction"]}])
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resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "S"}])
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resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "N"}])
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else: #state["direction"] == "W"
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if state["x"] > 0 and (state['x'] - 1, state["y"]) not in stoneList:
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resp.append(["forward", {'x': state["x"]-1, 'y': state["y"], 'direction': state["direction"]}])
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resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "N"}])
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resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "S"}])
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return resp
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def heuristic2(state, goal):
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# Oblicz odległość Manhattanowską między aktualnym stanem a celem
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manhattan_distance = (abs(state['x'] - goal[0]) + abs(state['y'] - goal[1])) * 2.5
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return manhattan_distance
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def A_star2(istate, pole, goalTreasure):
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# goalTreasure = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
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# #jeśli chcemy używać random musimy wykreslić sloty z kamieniami, ponieważ tez mogą się wylosować i wtedy traktor w ogóle nie rusza
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#lub zrobić to jakoś inaczej, np. funkcja szukająca najmniej nawodnionej rośliny
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# przeniesione wyżej do funkcji getRandomGoalTreasure, wykorzystywana jest w App.py
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# while True:
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# goalTreasure = (random.randint(0, NUM_X - 1), random.randint(0, NUM_Y - 1)) # Współrzędne celu
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# if goalTreasure not in stoneList:
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# break
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fringe = PriorityQueue() # Kolejka priorytetowa dla wierzchołków do rozpatrzenia
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explored = [] # Lista odwiedzonych stanów
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obrot = 1
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# Tworzenie węzła początkowego
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x = Node.Node(istate)
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x.g = 0
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x.h = heuristic2(x.state, goalTreasure)
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fringe.put((x.g + x.h, x)) # Dodanie węzła do kolejki
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total_cost=0
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while not fringe.empty():
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_, elem = fringe.get() # Pobranie węzła z najniższym priorytetem
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if BFS.goalTest3(elem.state, goalTreasure): # Sprawdzenie, czy osiągnięto cel
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path = []
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cost_list=[]
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while elem.parent is not None: # Odtworzenie ścieżki
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path.append([elem.parent, elem.action])
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elem = elem.parent
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for node, action in path:
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# Obliczanie kosztu ścieżki dla każdego pola i wyświetlanie
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plant_cost = get_plant_name_and_cost_from_coordinates(node.state['x'],node.state['y'], pole)
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if action == "left" or action == "right": # Liczenie kosztu tylko dla pól nie będących obrotami
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total_cost += obrot
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cost_list.append(obrot)
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else:
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total_cost += plant_cost
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cost_list.append(plant_cost)
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return path,cost_list,total_cost
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explored.append(elem.state)
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for resp in succ3A(elem.state):
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child_state = resp[1]
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if child_state not in explored:
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child = Node.Node(child_state)
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child.parent = elem
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child.action = resp[0]
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# Pobranie nazwy rośliny z danego slotu na podstawie współrzędnych
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plant_cost = get_plant_name_and_cost_from_coordinates(child_state['x'], child_state['y'], pole)
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if child.action == "left" or child.action == "right":
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child.g = elem.g + obrot
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else:
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child.g = elem.g + plant_cost
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# Obliczenie heurystyki dla dziecka
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child.h = heuristic2(child.state, goalTreasure)
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in_fringe = False
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for priority, item in fringe.queue:
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if item.state == child.state:
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in_fringe = True
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if priority > child.g + child.h:
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# Jeśli znaleziono węzeł w kolejce o gorszym priorytecie, zastąp go nowym
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fringe.queue.remove((priority, item))
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fringe.put((child.g + child.h, child))
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break
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if not in_fringe:
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# Jeśli stan dziecka nie jest w kolejce, dodaj go do kolejki
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fringe.put((child.g + child.h, child))
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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quit()
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return False
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"""
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TO TEST SPEED OF ASTAR
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test_speed = False
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if test_speed:
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time1 = 0
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time2 = 0
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cost1 = 0
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cost2 = 0
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for i in range(500):
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print(i)
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start = time.time()
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aStarRoot, cost_list, total_cost = AStar.A_star({'x': 0, 'y': 0, 'direction': "E"}, pole, goalTreasure)
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end = time.time()
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time1 += end - start
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cost1 += total_cost
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start = time.time()
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aStarRoot2, cost_list, total_cost = AStar.A_star2({'x': 0, 'y': 0, 'direction': "E"}, pole, goalTreasure)
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end = time.time()
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time2 += end - start
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cost2 += total_cost
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print(time1, time2)
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print(float(cost1 / 1000), float(cost2 / 1000))
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"""
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12
BFS.py
12
BFS.py
@ -108,12 +108,12 @@ def succ3(state):
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resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "E"}])
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resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "W"}])
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elif state["direction"] == "S":
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if state["y"] < NUM_Y - 1 and (state['x'], state["y"] + 1) not in stoneList:
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if state["y"] < NUM_Y and (state['x'], state["y"] + 1) not in stoneList:
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resp.append(["forward", {'x': state["x"], 'y': state["y"]+1, 'direction': state["direction"]}])
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resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "W"}])
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resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "E"}])
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elif state["direction"] == "E":
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if state["x"] < NUM_X - 1 and (state['x'] + 1, state["y"]) not in stoneList:
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if state["x"] < NUM_X and (state['x'] + 1, state["y"]) not in stoneList:
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resp.append(["forward", {'x': state["x"]+1, 'y': state["y"], 'direction': state["direction"]}])
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resp.append(["right", {'x': state["x"], 'y': state["y"], 'direction': "S"}])
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resp.append(["left", {'x': state["x"], 'y': state["y"], 'direction': "N"}])
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@ -133,12 +133,8 @@ def check3(tab, state):
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return True
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def BFS3(istate,GT):
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randomGT=False
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if(randomGT==True):
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goalTreassuere = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
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else:
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goalTreassuere=GT
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def BFS3(istate):
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goalTreassuere = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
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print(goalTreassuere)
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fringe = []
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explored = []
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49
Climate.py
49
Climate.py
@ -1,49 +0,0 @@
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#THESE DICTIONARIES ARE USED FOR DISPLAY AND FOR DOCUMENTATION PURPOSES
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seasons={
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0:"zima",
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1:"wiosna",
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2:"lato",
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3:"jesien"}
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time={
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0:"rano",
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1:"poludnie",
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2:"wieczor",
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3:"noc"}
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rain={
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0:"brak",
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1:"lekki deszcz",
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2:"normalny deszcz",
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3:"ulewa"
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}
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temperature={
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0:"bardzo zimno",
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1:"zimno",
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2:"przecietnie",
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3:"cieplo",
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4:"upal",}
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|
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def getNextSeason(season):
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if(season==3):
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return 0
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else:
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return season+1
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def getNextTime(currentTime):
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if(currentTime==3):
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return 0
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else:
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return currentTime+1
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def getAmount(type):
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if(type=="seasons"):
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return len(seasons)
|
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if(type=="rain"):
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return len(rain)
|
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if(type=="time"):
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return len(time)
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if(type=="temperature"):
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return len(temperature)
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47
Condition.py
47
Condition.py
@ -1,47 +0,0 @@
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import random
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import Climate
|
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import Ui
|
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|
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class Condition:
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def __init__(self):
|
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self.season=self.setRandomSeason()
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self.currentTime=self.setRandomTime()
|
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self.rain=self.setRandomRain()
|
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self.temperature=self.setRandomRain()
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self.clock=0
|
||||
|
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def setRandomSeason(self):
|
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return self.randomizer(Climate.getAmount("seasons"))
|
||||
|
||||
def setRandomTime(self):
|
||||
return self.randomizer(Climate.getAmount("time"))
|
||||
|
||||
def setRandomRain(self):
|
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return self.randomizer(Climate.getAmount("rain"))
|
||||
|
||||
def setRandomTemperature(self):
|
||||
return self.randomizer(Climate.getAmount("temperature"))
|
||||
|
||||
def randomizer(self,max):
|
||||
return random.randint(0,max-1)
|
||||
|
||||
def cycle(self):
|
||||
if(self.clock==11):
|
||||
self.currentTime=0
|
||||
self.rain=self.setRandomRain()
|
||||
self.temperature=self.setRandomTemperature()
|
||||
self.season=Climate.getNextSeason(self.season)
|
||||
self.clock=0
|
||||
return
|
||||
else:
|
||||
self.currentTime=Climate.getNextTime(self.currentTime)
|
||||
self.rain=self.setRandomRain()
|
||||
self.temperature=self.setRandomTemperature()
|
||||
self.clock=self.clock+1
|
||||
|
||||
def return_condition(self):
|
||||
return [self.temperature,self.rain,self.season,self.currentTime]
|
||||
|
||||
|
||||
def getCondition(self):
|
||||
return ([Climate.temperature[self.temperature],Climate.rain[self.rain],Climate.seasons[self.season],Climate.time[self.currentTime]])
|
||||
9219
Data/dataTree.csv
9219
Data/dataTree.csv
File diff suppressed because it is too large
Load Diff
@ -1,248 +0,0 @@
|
||||
plant_water_level,growth,disease,fertility,tractor_water_level,temperature,rain,season,current_time,action
|
||||
1,20,0,40,60,2,0,2,1,1
|
||||
20,40,0,40,60,2,0,2,1,1
|
||||
87,20,0,40,60,2,0,2,1,0
|
||||
27,43,1,40,60,2,0,2,1,0
|
||||
89,56,1,40,60,2,1,1,1,0
|
||||
67,100,1,37,55,1,3,3,3,0
|
||||
67,40,1,87,90,4,0,1,0,0
|
||||
1,20,0,40,60,2,0,0,1,0
|
||||
20,40,0,40,60,2,0,0,1,0
|
||||
87,20,0,56,45,2,0,0,2,0
|
||||
27,43,1,40,60,2,0,0,3,0
|
||||
89,56,1,40,89,2,1,0,1,0
|
||||
67,100,1,37,55,1,3,0,3,0
|
||||
67,40,1,87,90,4,0,0,0,0
|
||||
1,100,0,45,20,2,0,2,1,0
|
||||
20,100,0,40,34,0,1,2,0,0
|
||||
87,100,0,56,60,2,0,1,1,0
|
||||
27,100,0,89,67,1,2,2,2,0
|
||||
89,100,0,40,60,2,1,1,1,0
|
||||
76,100,0,37,55,1,3,3,3,0
|
||||
67,100,0,87,90,4,0,1,0,0
|
||||
1,20,0,40,0,2,0,2,1,0
|
||||
20,40,0,40,0,2,0,2,1,0
|
||||
87,20,0,40,0,2,0,2,1,0
|
||||
27,43,1,40,0,2,0,2,1,0
|
||||
89,56,1,40,0,2,1,1,1,0
|
||||
67,100,1,37,0,1,3,3,3,0
|
||||
67,40,1,87,0,4,0,1,0,0
|
||||
1,20,0,40,0,2,0,0,1,0
|
||||
20,40,0,40,0,2,0,0,1,0
|
||||
87,20,0,56,0,2,0,0,2,0
|
||||
27,43,1,40,0,2,0,0,3,0
|
||||
89,56,1,40,0,2,1,0,1,0
|
||||
67,100,1,37,0,1,3,0,3,0
|
||||
67,40,1,87,0,4,0,0,0,0
|
||||
1,100,0,45,0,2,0,2,1,0
|
||||
20,100,0,40,0,0,1,2,0,0
|
||||
87,100,0,56,0,2,0,1,1,0
|
||||
27,100,0,89,0,1,2,2,2,0
|
||||
89,100,0,40,0,2,1,1,1,0
|
||||
76,100,0,37,0,1,3,3,3,0
|
||||
67,100,0,87,0,4,0,1,0,0
|
||||
1,45,0,56,44,2,1,1,1,1
|
||||
20,55,0,43,34,2,0,2,2,1
|
||||
15,23,0,23,26,2,1,3,3,1
|
||||
45,67,0,12,67,3,0,1,0,1
|
||||
59,88,0,34,87,3,0,2,1,1
|
||||
32,32,0,32,90,3,0,3,2,1
|
||||
44,43,0,19,27,2,0,1,3,1
|
||||
33,11,0,28,76,2,0,2,0,1
|
||||
54,90,0,44,5,3,0,3,1,1
|
||||
21,76,0,50,25,3,1,1,2,1
|
||||
29,64,0,38,36,2,0,2,3,1
|
||||
11,54,0,65,44,3,1,1,2,1
|
||||
23,55,0,34,43,3,0,2,1,1
|
||||
51,32,0,32,62,3,1,3,3,1
|
||||
54,76,0,21,76,2,0,1,2,1
|
||||
95,88,0,43,78,2,0,2,1,0
|
||||
23,23,0,23,9,2,0,3,3,1
|
||||
44,34,0,91,72,3,0,1,0,1
|
||||
33,11,0,82,67,3,0,2,2,1
|
||||
45,9,0,44,50,2,0,3,3,1
|
||||
21,67,0,50,52,2,1,1,0,1
|
||||
92,46,0,83,63,3,0,2,1,0
|
||||
20,55,1,43,34,0,0,2,2,0
|
||||
15,23,1,23,26,0,1,3,3,0
|
||||
45,67,1,12,67,0,0,1,0,0
|
||||
59,88,1,34,87,0,0,2,1,0
|
||||
32,32,0,32,90,0,0,3,2,0
|
||||
44,43,0,19,27,4,0,1,3,0
|
||||
33,11,0,28,76,4,0,2,0,0
|
||||
54,90,0,44,5,4,0,3,1,0
|
||||
21,76,0,50,25,4,1,1,2,0
|
||||
29,64,0,38,36,4,0,2,3,0
|
||||
11,54,0,65,44,0,1,1,2,0
|
||||
23,55,0,34,43,0,0,2,1,0
|
||||
51,32,0,32,62,0,1,3,3,0
|
||||
80,76,1,39,7,3,0,1,0,0
|
||||
98,77,0,15,91,1,3,2,3,0
|
||||
3,48,1,73,41,2,2,0,3,0
|
||||
20,15,1,97,87,4,1,2,1,0
|
||||
93,6,0,37,0,0,1,0,1,0
|
||||
4,31,0,1,5,2,3,1,2,0
|
||||
42,52,0,33,19,3,2,3,0,0
|
||||
76,43,0,77,18,4,0,0,3,0
|
||||
31,13,1,21,42,0,1,2,3,0
|
||||
96,65,1,63,35,1,3,3,2,0
|
||||
29,39,0,40,37,3,3,0,0,0
|
||||
82,53,0,55,9,0,1,3,2,0
|
||||
21,35,0,58,1,1,2,2,0,0
|
||||
92,98,0,69,16,3,0,0,1,0
|
||||
34,23,0,95,2,2,3,0,3,0
|
||||
36,28,0,62,22,0,1,1,1,0
|
||||
66,88,1,10,85,3,1,2,3,0
|
||||
53,51,0,79,90,2,2,3,2,0
|
||||
9,74,0,60,4,4,1,2,3,1
|
||||
17,0,0,38,58,1,2,3,0,0
|
||||
12,76,0,50,25,3,1,1,2,1
|
||||
92,64,0,38,36,2,0,2,3,0
|
||||
11,54,0,65,44,3,1,1,2,1
|
||||
32,55,0,34,43,3,0,2,1,1
|
||||
15,32,0,32,62,3,1,3,3,1
|
||||
45,76,0,21,76,2,0,1,2,1
|
||||
59,88,0,43,78,2,0,2,1,1
|
||||
32,23,0,23,9,2,0,3,3,1
|
||||
14,34,0,91,72,3,0,1,0,1
|
||||
13,11,0,82,67,3,0,2,2,1
|
||||
45,9,0,44,50,2,0,3,3,1
|
||||
21,67,0,50,52,2,1,1,0,1
|
||||
92,46,0,83,63,3,0,2,1,0
|
||||
2,40,1,34,43,1,3,2,2,0
|
||||
51,32,1,32,62,2,1,3,3,0
|
||||
54,76,1,21,76,3,0,1,0,0
|
||||
98,38,0,50,44,4,0,1,0,0
|
||||
63,7,0,93,79,2,0,2,1,1
|
||||
91,59,0,94,24,4,0,3,2,0
|
||||
11,49,0,54,76,2,0,1,3,1
|
||||
33,31,0,59,39,3,0,1,3,1
|
||||
28,50,0,26,0,4,0,2,2,0
|
||||
54,83,0,36,0,3,0,2,1,0
|
||||
49,78,0,68,0,2,0,3,2,0
|
||||
59,21,0,43,100,1,0,3,2,1
|
||||
1,30,0,52,100,2,0,0,3,0
|
||||
60,9,0,40,40,3,0,0,3,0
|
||||
85,94,0,87,85,4,0,1,3,0
|
||||
79,68,0,56,90,1,0,2,2,1
|
||||
75,22,0,25,95,1,0,3,2,1
|
||||
100,51,0,33,12,0,0,2,2,0
|
||||
90,70,0,71,81,0,0,2,1,0
|
||||
47,26,0,6,78,4,0,1,1,1
|
||||
14,89,0,70,18,4,0,1,0,1
|
||||
99,19,0,74,91,2,0,3,0,0
|
||||
18,48,0,15,32,2,0,3,0,1
|
||||
5,57,0,14,34,0,1,1,3,1
|
||||
22,67,0,9,5,0,1,2,2,0
|
||||
95,81,0,46,86,1,1,3,1,0
|
||||
39,65,0,84,0,1,1,0,0,0
|
||||
84,75,0,30,0,2,1,1,1,0
|
||||
86,41,0,2,67,2,1,2,2,0
|
||||
64,53,0,53,47,1,1,3,3,1
|
||||
69,61,0,0,73,2,1,0,0,0
|
||||
94,40,1,0,18,3,1,1,2,0
|
||||
62,82,1,20,50,4,1,2,3,0
|
||||
57,1,1,17,92,0,1,3,2,0
|
||||
80,35,1,58,45,0,0,3,1,0
|
||||
30,47,1,8,47,1,0,2,1,0
|
||||
82,32,0,99,39,1,3,1,3,0
|
||||
20,84,0,0,51,2,3,2,3,0
|
||||
42,88,0,0,54,2,2,2,0,0
|
||||
66,45,0,91,10,3,2,1,0,0
|
||||
81,14,0,19,55,3,0,1,2,1
|
||||
74,37,0,88,78,4,0,3,2,1
|
||||
89,99,0,100,60,4,0,3,3,0
|
||||
15,20,0,45,11,0,0,1,3,1
|
||||
92,28,0,85,90,2,0,1,1,0
|
||||
55,4,0,13,95,2,0,2,1,1
|
||||
2,6,0,35,0,2,0,2,0,0
|
||||
61,56,0,90,0,2,0,3,0,0
|
||||
76,11,0,61,10,3,0,3,1,1
|
||||
26,80,0,57,9,3,0,1,2,1
|
||||
40,44,0,81,8,3,0,2,3,1
|
||||
50,66,0,23,7,3,0,3,0,1
|
||||
48,15,0,77,6,2,0,0,1,0
|
||||
11,54,0,65,44,3,3,1,2,0
|
||||
23,55,0,34,43,3,3,2,1,0
|
||||
51,32,0,32,62,3,3,3,3,0
|
||||
54,76,0,21,76,2,3,1,2,0
|
||||
95,88,0,43,78,2,3,2,1,0
|
||||
23,23,0,23,9,2,3,3,3,0
|
||||
44,34,0,91,72,3,3,1,0,0
|
||||
33,11,0,82,67,3,3,2,2,0
|
||||
45,9,0,44,50,2,3,3,3,0
|
||||
21,67,0,50,52,2,3,1,0,0
|
||||
92,46,0,83,63,3,3,2,1,0
|
||||
20,55,1,43,34,0,3,2,2,0
|
||||
15,23,1,23,26,0,3,3,3,0
|
||||
45,67,1,12,67,0,3,1,0,0
|
||||
59,88,1,34,87,0,3,2,1,0
|
||||
32,32,0,32,90,0,3,3,2,0
|
||||
1,60,0,55,11,0,1,0,0,1
|
||||
2,70,0,44,12,1,1,0,1,1
|
||||
3,44,0,11,13,2,1,0,2,1
|
||||
4,55,0,34,66,3,0,0,3,1
|
||||
5,66,0,90,77,0,0,1,2,1
|
||||
6,22,0,89,88,0,0,2,2,1
|
||||
7,1,0,45,9,0,1,2,3,1
|
||||
8,2,0,34,22,3,1,2,3,1
|
||||
9,3,0,56,34,3,1,0,1,1
|
||||
10,6,0,78,5,3,0,3,1,1
|
||||
11,8,0,36,67,2,0,0,0,1
|
||||
12,59,0,57,23,2,1,1,0,1
|
||||
13,67,0,29,34,1,1,0,1,1
|
||||
14,20,0,30,90,1,1,2,2,1
|
||||
15,21,0,66,89,0,1,3,3,1
|
||||
44,100,0,91,72,3,3,1,0,0
|
||||
33,100,0,82,67,3,3,2,2,0
|
||||
45,100,0,44,50,2,3,3,3,0
|
||||
21,100,0,50,52,2,3,1,0,0
|
||||
92,100,0,83,63,3,3,2,1,0
|
||||
20,100,1,43,34,0,3,2,2,0
|
||||
15,100,1,23,26,0,3,3,3,0
|
||||
45,100,1,12,67,0,3,1,0,0
|
||||
59,100,1,34,87,0,3,2,1,0
|
||||
32,100,0,32,90,0,3,3,2,0
|
||||
1,100,0,55,11,0,1,0,0,0
|
||||
2,100,0,44,12,1,1,0,1,0
|
||||
3,100,0,11,13,2,1,0,2,0
|
||||
4,100,0,34,66,3,0,0,3,0
|
||||
5,100,0,90,77,0,0,1,2,0
|
||||
6,100,0,89,88,0,0,2,2,0
|
||||
7,100,0,45,9,0,1,2,3,0
|
||||
8,100,0,34,22,3,1,2,3,0
|
||||
9,100,0,56,34,3,1,0,1,0
|
||||
10,100,0,78,5,3,0,3,1,0
|
||||
11,100,0,36,67,2,0,0,0,0
|
||||
12,100,0,57,23,2,1,1,0,0
|
||||
13,100,0,29,34,1,1,0,1,0
|
||||
14,100,0,30,90,1,1,2,2,0
|
||||
15,100,0,66,89,0,1,3,3,0
|
||||
1,6,0,5,10,4,1,1,3,1
|
||||
2,7,0,4,20,4,1,2,2,1
|
||||
3,4,0,11,30,4,1,3,1,1
|
||||
4,5,0,43,5,2,0,1,2,1
|
||||
5,6,0,9,17,2,0,2,1,1
|
||||
6,2,0,98,18,4,0,3,1,1
|
||||
7,11,0,54,19,4,1,0,2,1
|
||||
8,20,0,43,22,4,1,1,1,1
|
||||
9,30,0,65,43,4,1,2,3,1
|
||||
10,60,0,87,50,1,0,3,3,1
|
||||
11,80,0,63,76,1,0,0,2,1
|
||||
12,95,0,75,32,1,1,1,1,1
|
||||
13,76,0,30,43,2,1,2,0,1
|
||||
14,2,0,92,9,2,1,3,0,1
|
||||
1,6,0,5,10,4,3,1,3,0
|
||||
2,7,0,4,20,4,3,2,2,0
|
||||
3,4,0,11,30,4,3,3,1,0
|
||||
4,5,0,43,5,2,3,1,2,0
|
||||
5,6,0,9,17,2,3,2,1,0
|
||||
6,2,0,98,18,4,3,3,1,0
|
||||
7,11,0,54,19,4,3,0,2,0
|
||||
8,20,0,43,22,4,3,1,1,0
|
||||
9,30,0,65,43,4,3,2,3,0
|
||||
10,60,0,87,50,1,3,3,3,0
|
||||
11,80,0,63,76,1,3,0,2,0
|
||||
12,95,0,75,32,1,3,1,1,0
|
||||
13,76,0,30,43,2,3,2,0,0
|
||||
14,2,0,92,9,2,3,3,0,0
|
||||
|
29
Drzewo.py
29
Drzewo.py
@ -1,29 +0,0 @@
|
||||
from sklearn import tree as skltree
|
||||
import pandas,os
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
atributes=['plant_water_level','growth','disease','fertility','tractor_water_level','temperature','rain','season','current_time'] #Columns in CSV file has to be in the same order
|
||||
class Drzewo:
|
||||
def __init__(self):
|
||||
self.tree=self.treeLearn()
|
||||
|
||||
def treeLearn(self):
|
||||
csvdata=pandas.read_csv('Data/dataTree2.csv')
|
||||
#csvdata = pandas.read_csv('Data/dataTree2.csv')
|
||||
x=csvdata[atributes]
|
||||
decision=csvdata['action']
|
||||
self.tree=skltree.DecisionTreeClassifier()
|
||||
self.tree=self.tree.fit(x.values,decision)
|
||||
|
||||
def plotTree(self):
|
||||
plt.figure(figsize=(20,30))
|
||||
skltree.plot_tree(self.tree,filled=True,feature_names=atributes)
|
||||
plt.title("Drzewo decyzyjne wytrenowane na przygotowanych danych: ")
|
||||
plt.savefig('tree.png')
|
||||
#plt.show()
|
||||
def makeDecision(self,values):
|
||||
action=self.tree.predict([values]) #0- nie podlewac, 1-podlewac
|
||||
if(action==[0]):
|
||||
return "Nie"
|
||||
if(action==[1]):
|
||||
return "Tak"
|
||||
@ -1,139 +0,0 @@
|
||||
import json
|
||||
import random
|
||||
|
||||
from displayControler import NUM_Y, NUM_X
|
||||
|
||||
iterat = 2500
|
||||
population = 120
|
||||
roulette = True
|
||||
|
||||
plants = ['corn', 'potato', 'tomato', 'carrot']
|
||||
initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
|
||||
yield_reduction = {
|
||||
'corn': {'corn': -4.5, 'potato': -3, 'tomato': -7, 'carrot': -7},
|
||||
'potato': {'corn': -7, 'potato': -5, 'tomato': -10, 'carrot': -6},
|
||||
'tomato': {'corn': -4, 'potato': -5, 'tomato': -7, 'carrot': -7},
|
||||
'carrot': {'corn': -11, 'potato': -5, 'tomato': -4, 'carrot': -7}
|
||||
}
|
||||
yield_reduction2 = {
|
||||
'corn': {'corn': None, 'potato': -4, 'tomato': -2, 'carrot': -4},
|
||||
'potato': {'corn': None, 'potato': -5, 'tomato': -5, 'carrot': -2},
|
||||
'tomato': {'corn': -5, 'potato': -3, 'tomato': -7, 'carrot': None},
|
||||
'carrot': {'corn': -3, 'potato': -6, 'tomato': -4, 'carrot': -9}
|
||||
}
|
||||
yield_multiplier = {'corn': 1.25, 'potato': 1.17, 'tomato': 1.22, 'carrot': 1.13}
|
||||
yield_multiplier2 = {'corn': 1.25, 'potato': 1.19, 'tomato': 1.22, 'carrot': 1.15}
|
||||
|
||||
|
||||
def calculate_yields(garden):
|
||||
rows = len(garden)
|
||||
cols = len(garden[0])
|
||||
|
||||
total_yields = 0
|
||||
|
||||
for i in range(rows):
|
||||
for j in range(cols):
|
||||
plant = garden[i][j]
|
||||
yield_count = initial_yields[plant]
|
||||
|
||||
# Sprawdzanie sąsiadów
|
||||
neighbors = [
|
||||
(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
|
||||
]
|
||||
|
||||
for ni, nj in neighbors:
|
||||
if 0 <= ni < rows and 0 <= nj < cols:
|
||||
neighbor_plant = garden[ni][nj]
|
||||
yield_count += yield_reduction[plant][neighbor_plant]
|
||||
|
||||
yield_count *= yield_multiplier[plant]
|
||||
total_yields += yield_count
|
||||
|
||||
return total_yields
|
||||
|
||||
|
||||
def calculate_yields2(garden):
|
||||
rows = len(garden)
|
||||
cols = len(garden[0])
|
||||
|
||||
total_yields = 0
|
||||
|
||||
for i in range(rows):
|
||||
for j in range(cols):
|
||||
plant = garden[i][j]
|
||||
yield_count = initial_yields[plant]
|
||||
|
||||
# Sprawdzanie sąsiadów
|
||||
neighbors = [
|
||||
(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
|
||||
]
|
||||
neighbor_flag = False
|
||||
for ni, nj in neighbors:
|
||||
if 0 <= ni < rows and 0 <= nj < cols:
|
||||
neighbor_plant = garden[ni][nj]
|
||||
|
||||
if yield_reduction2[plant][neighbor_plant] is not None: # jeśli jest wartość None to plony dla tej rośliny będą wyzerowane
|
||||
yield_count += yield_reduction2[plant][neighbor_plant]
|
||||
else:
|
||||
neighbor_flag = True
|
||||
|
||||
if not neighbor_flag:
|
||||
yield_count *= yield_multiplier2[plant]
|
||||
total_yields += yield_count
|
||||
|
||||
return total_yields
|
||||
|
||||
|
||||
def generate_garden(rows=20, cols=12):
|
||||
return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
|
||||
|
||||
|
||||
def generate_garden_with_yields(t, rows=NUM_Y, cols=NUM_X):
|
||||
garden = generate_garden(rows, cols)
|
||||
if t == 1:
|
||||
total_yields = calculate_yields(garden)
|
||||
else:
|
||||
total_yields = calculate_yields2(garden)
|
||||
return [garden, total_yields]
|
||||
|
||||
|
||||
def generate():
|
||||
s1 = 0
|
||||
s2 = 0
|
||||
n = 150
|
||||
for i in range(n):
|
||||
x = generate_garden_with_yields(1)
|
||||
s1 += x[1]
|
||||
y = generate_garden_with_yields(2)
|
||||
s2 += y[1]
|
||||
return [s1/n, s2/n]
|
||||
|
||||
|
||||
data = generate()
|
||||
# print(data)
|
||||
|
||||
# Odczyt z pliku
|
||||
with open(f'pole_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
|
||||
garden_data = json.load(file)
|
||||
|
||||
# print("Odczytane dane ogrodu:")
|
||||
# for row in garden_data:
|
||||
# print(row)
|
||||
|
||||
print("Wygenerowane przy pomocy GA: ", calculate_yields(garden_data))
|
||||
print(f"Przeciętny ogród wygenerowany randomowo ma {data[0]} plonów")
|
||||
print("Uśredniony przyrost plonów (ile razy więcej plonów): ", calculate_yields(garden_data)/data[0])
|
||||
|
||||
|
||||
|
||||
# Odczyt z pliku
|
||||
with open(f'pole2_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
|
||||
garden_data2 = json.load(file)
|
||||
|
||||
# print("Odczytane dane ogrodu:")
|
||||
# for row in garden_data2:
|
||||
# print(row)
|
||||
|
||||
print("Wygenerowane: przy pomocy GA2", calculate_yields2(garden_data2))
|
||||
print(f"Przeciętny ogród wygenerowany randomowo ma {data[1]} plonów")
|
||||
print("Uśredniony przyrost plonów (ile razy więcej plonów): ", calculate_yields2(garden_data2)/data[1])
|
||||
@ -1,208 +0,0 @@
|
||||
import copy
|
||||
import json
|
||||
import random
|
||||
from displayControler import NUM_X, NUM_Y
|
||||
|
||||
# Definiowanie stałych dla roślin i plonów
|
||||
plants = ['corn', 'potato', 'tomato', 'carrot']
|
||||
initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
|
||||
yield_reduction = {
|
||||
'corn': {'corn': -4.5, 'potato': -3, 'tomato': -7, 'carrot': -7},
|
||||
'potato': {'corn': -7, 'potato': -5, 'tomato': -10, 'carrot': -6},
|
||||
'tomato': {'corn': -4, 'potato': -5, 'tomato': -7, 'carrot': -7},
|
||||
'carrot': {'corn': -11, 'potato': -5, 'tomato': -4, 'carrot': -7}
|
||||
}
|
||||
yield_multiplier = {'corn': 1.25, 'potato': 1.17, 'tomato': 1.22, 'carrot': 1.13}
|
||||
|
||||
|
||||
# Generowanie listy 20x12 z losowo rozmieszczonymi roślinami
|
||||
def generate_garden(rows=20, cols=12):
|
||||
return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
|
||||
|
||||
|
||||
# Funkcja do obliczania liczby plonów
|
||||
def calculate_yields(garden):
|
||||
rows = len(garden)
|
||||
cols = len(garden[0])
|
||||
|
||||
total_yields = 0
|
||||
|
||||
for i in range(rows):
|
||||
for j in range(cols):
|
||||
plant = garden[i][j]
|
||||
yield_count = initial_yields[plant]
|
||||
|
||||
# Sprawdzanie sąsiadów
|
||||
neighbors = [
|
||||
(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
|
||||
]
|
||||
|
||||
for ni, nj in neighbors:
|
||||
if 0 <= ni < rows and 0 <= nj < cols:
|
||||
neighbor_plant = garden[ni][nj]
|
||||
yield_count += yield_reduction[plant][neighbor_plant]
|
||||
|
||||
yield_count *= yield_multiplier[plant]
|
||||
total_yields += yield_count
|
||||
|
||||
return total_yields
|
||||
|
||||
|
||||
# Funkcja do generowania planszy/ogrodu i zapisywania go jako lista z liczbą plonów
|
||||
def generate_garden_with_yields(rows=NUM_Y, cols=NUM_X):
|
||||
garden = generate_garden(rows, cols)
|
||||
total_yields = calculate_yields(garden)
|
||||
return [garden, total_yields]
|
||||
|
||||
|
||||
# Funkcja do generowania linii cięcia i zapisywania jej jako liczba roślin w kolumnie z pierwszej planszy/ogrodu
|
||||
def line():
|
||||
path = []
|
||||
flag = False
|
||||
x = random.randint(4, 8)
|
||||
position = (0, x)
|
||||
path.append(position)
|
||||
while not flag: # wybór punktu dopóki nie wybierze się skrajnego
|
||||
# prawdopodobieństwo "ruchu" -> 0.6: w prawo, 0.2: w góre, 0.2: w dół
|
||||
p = [(position[0] + 1, position[1]), (position[0], position[1] + 1), (position[0], position[1] - 1)]
|
||||
w = [0.6, 0.2, 0.2]
|
||||
position2 = random.choices(p, w)[0]
|
||||
if position2 not in path: # sprawdzenie czy dany punkt nie był już wybrany aby nie zapętlać się
|
||||
path.append(position2)
|
||||
position = position2
|
||||
if position[0] == NUM_X or position[1] == 0 or position[1] == NUM_Y: # sprawdzenie czy osiągnięto skrajny punkt
|
||||
flag = True
|
||||
info = [] # przeformatowanie sposobu zapisu na liczbę roślin w kolumnie, które będzię się dzidziczyło z pierwszej planszy/ogrodu
|
||||
for i in range(len(path) - 1):
|
||||
if path[i + 1][0] - path[i][0] == 1:
|
||||
info.append(NUM_Y - path[i][1])
|
||||
if len(info) < NUM_X: # uzupełnienie informacji o dziedziczeniu z planszy/ogrodu
|
||||
if path[-1:][0][1] == 0:
|
||||
x = NUM_Y
|
||||
else:
|
||||
x = 0
|
||||
while len(info) < NUM_X:
|
||||
info.append(x)
|
||||
# return path, info
|
||||
return info
|
||||
|
||||
|
||||
# Funkcja do generowania potomstwa
|
||||
def divide_gardens(garden1, garden2):
|
||||
info = line()
|
||||
new_garden1 = [[] for _ in range(NUM_Y)]
|
||||
new_garden2 = [[] for _ in range(NUM_Y)]
|
||||
for i in range(NUM_X):
|
||||
for j in range(NUM_Y):
|
||||
# do utworzonych kolumn w nowych planszach/ogrodach dodajemy dziedziczone rośliny
|
||||
if j < info[i]:
|
||||
new_garden1[j].append(garden1[j][i])
|
||||
new_garden2[j].append(garden2[j][i])
|
||||
else:
|
||||
new_garden1[j].append(garden2[j][i])
|
||||
new_garden2[j].append(garden1[j][i])
|
||||
|
||||
return [new_garden1, calculate_yields(new_garden1)], [new_garden2, calculate_yields(new_garden2)]
|
||||
|
||||
|
||||
# Funkcja do mutacji danej planszy/ogrodu
|
||||
def mutation(garden, not_used):
|
||||
new_garden = copy.deepcopy(garden)
|
||||
for i in range(NUM_X):
|
||||
x = random.randint(0, 11) # wybieramy, w którym wierszu w i-tej kolumnie zmieniamy roślinę na inną
|
||||
other_plants = [plant for plant in plants if plant != new_garden[x][i]]
|
||||
new_garden[x][i] = random.choice(other_plants)
|
||||
return [new_garden, calculate_yields(new_garden)]
|
||||
|
||||
|
||||
# Funkcja do generowania pierwszego pokolenia
|
||||
def generate(n):
|
||||
generation = []
|
||||
for i in range(n * 3):
|
||||
generation.append(generate_garden_with_yields())
|
||||
generation.sort(reverse=True, key=lambda x: x[1])
|
||||
return generation[:n]
|
||||
|
||||
|
||||
# Funkcja do implementacji ruletki (sposobu wyboru) - sumuje wszystkie plony generacji
|
||||
def sum_yields(x):
|
||||
s = 0
|
||||
for i in range(len(x)):
|
||||
s += x[i][1]
|
||||
return s
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
roulette = True
|
||||
attemps = 150
|
||||
iterat = 2500
|
||||
population = 120
|
||||
best = []
|
||||
for a in range(attemps):
|
||||
generation = generate(population)
|
||||
print(generation[0][1])
|
||||
for i in range(iterat): # ile iteracji - nowych pokoleń
|
||||
print(a, i)
|
||||
new_generation = generation[:(population // 7)] # dziedziczenie x najlepszych osobników
|
||||
j = 0
|
||||
while j < (
|
||||
population - (
|
||||
population // 7)): # dobór reszty osobników do pełnej liczby populacji danego pokolenia
|
||||
if roulette: # zasada ruletki -> "2 rzuty kulką"
|
||||
s = sum_yields(generation) # suma wszystkich plnów całego pokolenia
|
||||
z = []
|
||||
if s == 0: # wtedy każdy osobnik ma takie same szanse
|
||||
z.append(random.randint(0, population - 1))
|
||||
z.append(random.randint(0, population - 1))
|
||||
else:
|
||||
weights = [] # wagi prawdopodobieństwa dla każdego osobnika generacji
|
||||
pos = [] # numery od 0 do 49 odpowiadające numerom osobnikom w generacji
|
||||
for i in range(population):
|
||||
weights.append(generation[i][1] / s)
|
||||
pos.append(i)
|
||||
z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
|
||||
z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
|
||||
else: # metoda rankingu
|
||||
z = random.sample(range(0, int(population // 1.7)), 2)
|
||||
|
||||
# krzyzowanie 90% szans, mutacja 10% szans
|
||||
function = [divide_gardens, mutation]
|
||||
weight = [0.9, 0.1]
|
||||
fun = random.choices(function, weight)[0]
|
||||
h = fun(generation[z[0]][0], generation[z[ | ||||