def minimax(board, player_name, depth, start_time):
if depth == MAX_SEARCH_DEPTH or is_out_of_time_for_move(
start_time) or game_over(board):
score = evaluate(board, player_name)
return Move(-1, -1, score)
best_move = Move(
-1, -1, -infinity if player_name == PLAYER_NAMES['P1'] else +infinity)
board_copy = board.get_cloned_board()
for correct_move in get_all_correct_moves(board, player_name,
board.fields):
x, y, piece_x, piece_y = correct_move.x, correct_move.y, correct_move.piece_x, correct_move.piece_y
move_single_piece(board, player_name, correct_move)
player_to_move = get_opponent_player(board, player_name)
potential_move = minimax(board, player_to_move, depth + 1, start_time)
potential_move.x = x
potential_move.y = y
potential_move.piece_x = piece_x
potential_move.piece_y = piece_y
if is_better_score(potential_move, best_move, player_name):
best_move.x = potential_move.x
best_move.y = potential_move.y
best_move.piece_x = potential_move.piece_x
best_move.piece_y = potential_move.piece_y
best_move.score = potential_move.score
board = board_copy.get_cloned_board()
return best_move
def minimax(self, board, player, depth):
depth += 1
# print(depth)
avail_moves = self.available_moves(board)
if self.win(board, self.human):
return -10
elif self.win(board, self.ai):
return 10
elif len(avail_moves) == 0 or depth == self.max_depth:
return 0
moves = []
for index in avail_moves:
move = Move()
move.index = index
board[index[0]][index[1]] = player
new_board = copy(board)
new_depth = copy(depth)
if player == self.ai:
result = self.minimax(new_board, self.human, depth)
move.score = result.score if isinstance(result,
Move) else result
elif player == self.human:
result = self.minimax(new_board, self.ai, depth)
move.score = result.score if isinstance(result,
Move) else result
board[index[0]][index[1]] = 0
moves.append(move)
scores = np.array([move.score for move in moves])
#best_score_ind = scores.argmax() if player == self.ai else scores.argmin()
best_score_ind = np.random.choice(
np.flatnonzero(scores == scores.max() if player ==
self.ai else scores == scores.min()))
return moves[best_score_ind]
