def get_move_values(field, my_sign):
move_values = []
possible_moves = Game.free_spots(field)
for current_move in possible_moves:
new_array = np.copy(field)
new_array[current_move[0]][current_move[1]] = my_sign
# find the value of the move
move_values.append(
min_max_iteration(new_array, my_sign, Game.get_other_sign(my_sign), 1, len(possible_moves)))
flattened_field = field.reshape(1,9).astype(int).tolist()
if str(flattened_field) not in allmoves:
allmoves[str(flattened_field)] = flattened_field,move_values
return possible_moves, move_values
def min_max_iteration(field, my_sign, current_sign, depth, maximum_depth):
free_spots = Game.free_spots(field)
if len(free_spots) == 0:
return 0
total = []
for spot in free_spots:
new_array = np.copy(field)
new_array[spot[0]][spot[1]] = current_sign
if Game.fast_is_won3x3(new_array, spot[0], spot[1]):
if my_sign is current_sign:
total.append(100000 / depth)
else:
total.append(-100000 / (maximum_depth - depth))
else:
total.append(min_max_iteration(new_array, my_sign, Game.get_other_sign(current_sign), depth + 1, maximum_depth))
flattened_field = field.reshape(1,9).astype(int).tolist()
minimum = np.amin(total)
maximum = np.amax(total)
normalize_range = maximum-minimum
weights = np.array(-np.ones((3,3)))
for index in range(0,len(free_spots)):
if normalize_range < 0.0001:
weights[free_spots[index][0]][free_spots[index][1]] = 1
else:
weights[free_spots[index][0]][free_spots[index][1]] = ((2 * (total[index] - minimum) / normalize_range) - 1)
if str(flattened_field) not in allmoves and my_sign is current_sign:
allmoves[str(flattened_field)] = flattened_field, weights.reshape(1,9).tolist()
if my_sign is current_sign:
return np.amax(total)
else:
return np.amin(total)