Traktor/main.py
2024-05-23 01:57:24 +02:00

117 lines
3.3 KiB
Python

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