forked from s444420/AL-2020
88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
import pygame
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import functions
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import sys
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import time
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import decision_tree
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import data
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from agent import Agent
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from settings import Settings
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from board import create_board, draw_board
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from random import randint, choice
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from mcda import choseProducts
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# Inicjalizacja programu i utworzenie obiektu ekrany
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def run():
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pygame.init()
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settings = Settings()
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screen = pygame.display.set_mode((settings.screen_width, settings.screen_height))
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pygame.display.set_caption("Inteligentny wózek widłowy")
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agent = Agent(screen, 50, 50, "Down")
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board = create_board(screen)
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my_tree = decision_tree.build_tree(data.learning_data)
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produsctsFromSupply = choseProducts(5)
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# for row in board:
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# for field in row:
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# print(field.cost_of_travel)
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path = []
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next_step = None
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# Rozpoczęcie głównej pętli programu
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while True:
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# functions.check_events(agent, board)
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# functions.update_screen(board, screen, agent)
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#
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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sys.exit()
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elif event.type == pygame.KEYDOWN:
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if event.key == pygame.K_RIGHT:
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agent.turn_right()
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elif event.key == pygame.K_LEFT:
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agent.turn_left()
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elif event.key == pygame.K_UP:
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agent.move_forward(board)
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print(agent.x, agent.y)
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elif event.key == pygame.K_SPACE:
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board[9][0].item = choice(data.learning_data)
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print("Wybrano: " + board[9][0].item[-1])
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board[9][0].item[-1] = 'Something'
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field = board[9][0]
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if not field.is_shelf:
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path = functions.a_star(board[agent.y][agent.x], field, board)
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path.pop(len(path) - 1)
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next_step = path.pop(len(path) - 1)
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if next_step is not None:
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time.sleep(0.5)
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if functions.check_turn(agent, next_step):
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agent.move_forward(board)
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if len(path) != 0:
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next_step = path.pop()
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else:
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next_step = None
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# print(next_step, path)
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for row in board:
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for field in row:
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if not field.is_shelf:
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field.image = pygame.image.load('img/Field.png')
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else:
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functions.change_turn(agent, next_step)
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if board[agent.y][agent.x].item:
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prediction = decision_tree.print_leaf(decision_tree.classify(board[agent.y][agent.x].item, my_tree))
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print("Agent uważa, że przedmiot to: " + prediction[0])
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board[agent.y][agent.x].item = []
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draw_board(board)
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agent.blitme()
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pygame.display.flip()
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run()
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