def simulated_annealing(input_file): current_state = parse_file(input_file) state_list = [current_state] current_heuristic_val = eval(current_state[2],current_state[3],current_state[0],current_state[1]) max_state = current_state max_heuristic_val = current_heuristic_val T = 100000 while (T > 0.1): new_state = find_next_state(current_state) new_state_heuristic_val = eval(new_state[2],new_state[3],new_state[0],new_state[1]) d = delta(current_heuristic_val, new_state_heuristic_val) if d > 0: current_state = new_state current_heuristic_val = new_state_heuristic_val state_list.append(current_state) else: if (acceptance_probabilty(T,d)): current_state = new_state current_heuristic_val = new_state_heuristic_val state_list.append(current_state) if (compare_heuristic_greater(current_heuristic_val,max_heuristic_val)): max_heuristic_val = current_heuristic_val max_state = current_state T = update_temperature(T) print_board(max_state) print(max_heuristic_val)
def hillclimb(input_file):
'''
Menggunakan algoritma Hill-Climbing untuk mencari kombinasi optimal
jika diberikan bidaknya.
Parameter: input_file (str)
Mencetak board yang sudah diselesaikan.
'''
# membuat current state berupa list apa saja bidak
current_state = parse_file(input_file)
# membuat list yang menyimpan current_state board dan nilai heuristik keadaan board
max_heuristic_val = eval(current_state[2], current_state[3],
current_state[0], current_state[1])
running = True
while running:
new_state = find_next_state(current_state)
new_state_heuristic_val = eval(new_state[2], new_state[3],
new_state[0], new_state[1])
if compare_heuristic_greater(new_state_heuristic_val,
max_heuristic_val):
current_state = new_state
max_heuristic_val = new_state_heuristic_val
else:
running = False
print_board(current_state)
print(max_heuristic_val)
def genetic_algorithm(input_file, init_pop=4096, epoc_length=1000):
state = parse_file(input_file)
genetic_result = genetic(state[0], state[2], init_pop, epoc_length)
new_state = (state[0], genetic_result[0], state[2], genetic_result[1])
print_board(new_state)
print(eval(new_state[2], new_state[3], new_state[0], new_state[1]))
def print(self):
print_board(self.tiles)
print(total_value)
"""
"""
randomboard = mctsnode.generate_random_board(30)
print("RANDOM BOARD")
print_board(part2_to_part1(randomboard))
randomuppers = randomboard.thrown_uppers
randomlowers = randomboard.thrown_lowers
randomturn = randomboard.turn
print(randomuppers)
print(randomlowers)
"""
"""
u = {'s': [(-4, -2), (2, 4)], 'p': [], 'r': []}
l = {'s': [], 'p': [], 'r': [(0, 3)]}
testboard = Board(u, l, 0, 0, 37, None)
print_board(part2_to_part1(testboard))
print(testboard.generate_turns()[0])
"""
thrown_uppers = {'s': [], 'p': [(4,-1), (3,-3), (0,-3)], 'r': [(4,-3)]}
thrown_lowers = {'s': [(0,-1), (-3,4), (-4,3)], 'p': [(-2,2)], 'r': [(-4,0), (-1,4)]}
testboard = Board(thrown_uppers, thrown_lowers, 1, 1, 0, None)
print_board(part2_to_part1(testboard))
mctsnode = MCTSNode(testboard, "UPPER")
print(len(mctsnode.simultaneous_moves))
""" Quick game I designed to allow you to play tic tac toe against your computer. The project goal was to use a minimax function and alpha-beta pruning to teach the computer how to play both optimally and quickly."""
import util as util
import time
X = "X"
O = "O"
EMPTY = " "
# Welcome human
print("Welcome to Tic Tac Toe: Human Vs Computer.")
print("Let's see if you can beat the machine.")
time.sleep(1)
# Initialize board and give directions
board = util.initial_state()
util.print_board(board)
print("Directions:")
print(
"Use the grid to give the X (on top) and Y (on side) coordinate of the position you would like to play in."
)
print(
"For example the space in the upper left-hand corner would be X: 0, Y: 0")
time.sleep(3)
# Make sure human puts in a valid choice
while True:
human = input("Will you be player X or O?: ").upper()
if human == "X" or human == "O":
break
print("Please type X or O.")
# Asign which player is who based off above