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