117 lines
3.3 KiB
Python
117 lines
3.3 KiB
Python
import pygame
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from board import Board
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from constant import width, height, rows, cols
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from tractor import Tractor
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from kolejka import Stan, Odwiedzone
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from queue import Queue
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import pandas as pd
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from neuralnetwork import load_model
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data = pd.read_csv('dane.csv')
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from decisiontree import train_decision_tree
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model_path = 'model.pth'
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neuralnetwork_model = load_model(model_path)
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model, feature_columns = train_decision_tree(data)
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fps = 5
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WIN = pygame.display.set_mode((width, height))
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pygame.display.set_caption('Inteligentny Traktor')
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def goal_test(elem, board):
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return board.is_dirt(elem.row, elem.col)
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def actions(elem, istate):
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akcje = []
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while elem.row != istate.row or elem.col != istate.col or elem.direction != istate.direction:
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akcje.append(elem.a)
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elem = elem.p[0]
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return akcje[::-1]
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def graphsearch(istate, board):
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explored = Odwiedzone()
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fringe = Queue()
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fringe.put(istate)
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moves = ["up", "left", "right"]
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while not fringe.empty():
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elem = fringe.get()
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if goal_test(elem, board):
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return actions(elem, istate), elem
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explored.dodaj_stan(elem)
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for action in moves:
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stan = elem.succ(action, board)
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if stan is not None:
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if not fringe_check(fringe, stan) and not explored.check(stan):
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stan.parrent(elem, action)
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fringe.put(stan)
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return [], None
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def fringe_check(fringe, stan):
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with fringe.mutex:
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for item in fringe.queue:
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if stan.direction == item.direction and stan.col == item.col and stan.row == item.row:
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return True
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return False
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def main():
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initial_state = Stan(4, 4, "down")
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run = True
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clock = pygame.time.Clock()
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board = Board()
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board.load_images()
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tractor = Tractor(4, 4, model, feature_columns, neuralnetwork_model)
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while run:
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clock.tick(fps)
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if all(not board.is_dirt(row, col) for row in range(rows) for col in range(cols)):
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print("Traktor odwiedził wszystkie pola.")
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break
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akcje, nowy_stan = graphsearch(initial_state, board)
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if not akcje:
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print("Nie znaleziono ścieżki do najbliższego pola dirt.")
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board.generate_board()
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initial_state = Stan(4, 4, "down")
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tractor = Tractor(4, 4, model, feature_columns, neuralnetwork_model)
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while board.is_rock(initial_state.row, initial_state.col):
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board.generate_board()
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continue
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print("akcje: >", akcje)
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while akcje:
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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run = False
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akcja = akcje.pop(0)
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if akcja == "left":
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tractor.turn_left()
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elif akcja == "right":
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tractor.turn_right()
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elif akcja == "up":
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tractor.move_forward(board)
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board.draw_cubes(WIN)
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tractor.draw(WIN)
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pygame.display.update()
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pygame.time.delay(500)
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initial_state = nowy_stan
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initial_state.direction = tractor.direction
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while True:
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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pygame.quit()
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return
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pygame.quit()
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main()
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