import chess import chess.engine from stockfish import Stockfish import numpy as np import random def pick_move(board: chess.Board) -> chess.Move | None: """ Pick a random move :param board: chess board :return: a valid move or None if no valid move available """ if len(list(board.legal_moves)) == 0: return None return random.choice(list(board.legal_moves)) def simulate_game(board: chess.Board, move: chess.Move, depth: int): """ Simulate a game starting with the given move :param board: chess board :param move: chosen move :param depth: number of moves that should be simulated after playing the chosen move :return: the score for the simulated game """ engine = chess.engine.SimpleEngine.popen_uci("./stockfish/stockfish-ubuntu-x86-64-avx2") board.push(move) for i in range(depth): if board.is_game_over(): engine.quit() return r = engine.play(board, chess.engine.Limit(depth=2)) board.push(r.move) engine.quit() def simulate_stockfish_prob(board: chess.Board, move: chess.Move, games: int = 10, depth: int = 10) -> (float, float): """ Simulate a game using :param board: chess board :param move: chosen move :param games: number of games that should be simulated after playing the move :param depth: simulation depth per game :return: """ board.push(move) copied_board = board.copy() scores = [] stockfish = Stockfish("./stockfish/stockfish-ubuntu-x86-64-avx2", depth=2, parameters={"Threads": 8, "Hash": 2048}) stockfish.set_elo_rating(1200) stockfish.set_fen_position(board.fen()) def reset_game(): nonlocal scores, copied_board, board score = eval.score_stockfish(copied_board).white().score(mate_score=100_000) scores.append(score) copied_board = board.copy() stockfish.set_fen_position(board.fen()) for _ in range(games): for d in range(depth): if copied_board.is_game_over() or d == depth - 1: reset_game() break if d == depth - 1: reset_game() top_moves = stockfish.get_top_moves(3) chosen_move = random.choice(top_moves)['Move'] stockfish.make_moves_from_current_position([chosen_move]) copied_board.push(chess.Move.from_uci(chosen_move)) print(scores) # TODO: return distribution here? return np.array(scores).mean(), np.array(scores).std()