def quiesce(fen, alpha, beta, flag, options):
# flag check
if not flag.is_set():
return 0, 0
# heuristic
if options.heuristic == 'NeuralNetwork':
stand_pat = heuristic.nn_heuristic(fen, options, options.model)
elif options.heuristic == 'Random':
stand_pat = heuristic.random_heuristic()
else:
stand_pat = heuristic.heuristic(fen, options)
nodes = 1
if (stand_pat >= beta):
return beta, nodes
board = Board(fen)
if len(board.piece_map()) > 8:
delta = True
else:
delta = False
if delta:
# full delta pruning
if (stand_pat < alpha - 1000):
return alpha, nodes
if (stand_pat > alpha):
alpha = stand_pat
# expansion and search
legal = board.legal_moves
for move in legal:
board.push(move)
new = board.copy()
board.pop()
if board.is_capture(move) or new.is_check():
# delta pruning
if delta and board.is_capture(move):
value = value_captured_piece(
board.piece_type_at(move.to_square)) + 200
if (stand_pat + value < alpha):
continue
score, count = quiesce(new.fen(), -beta, -alpha, flag, options)
score = -score
nodes += count
if (score >= beta):
return beta, nodes
if (score > alpha):
alpha = score
return alpha, nodes
def random_with_first_level_search(self, board: chess.Board):
moves = list(board.legal_moves)
best_move = random.sample(moves, 1)[0]
best_move_value = 0
for move in moves:
board.push(move)
if board.is_checkmate():
move_value = 100
if move_value > best_move_value:
best_move = move
board.pop()
if board.is_into_check(move):
move_value = 90
if move_value > best_move_value:
best_move = move
if board.is_capture(move):
move_value = 80
if move_value > best_move_value:
best_move = move
if board.is_castling(move):
move_value = 70
if move_value > best_move_value:
best_move = move
return best_move
def move_value(board: chess.Board, move: chess.Move, endgame: bool) -> float:
"""
How good is a move?
A promotion is great.
A weaker piece taking a stronger piece is good.
A stronger piece taking a weaker piece is bad.
Also consider the position change via piece-square table.
"""
if move.promotion is not None:
return -float("inf") if board.turn == chess.BLACK else float("inf")
_piece = board.piece_at(move.from_square)
if _piece:
_from_value = evaluate_piece(_piece, move.from_square, endgame)
_to_value = evaluate_piece(_piece, move.to_square, endgame)
position_change = _to_value - _from_value
else:
raise Exception(f"A piece was expected at {move.from_square}")
capture_value = 0.0
if board.is_capture(move):
capture_value = evaluate_capture(board, move)
current_move_value = capture_value + position_change
if board.turn == chess.BLACK:
current_move_value = -current_move_value
return current_move_value
def get_board_piece_take_total_value(board: chess.Board) -> float:
total_value = 0
for move in board.legal_moves:
if board.is_capture(move):
total_value += Macros.BOARD_PIECE_TAKE_VALUE
return total_value
def capture_square_of_move(board: chess.Board, move: Optional[chess.Move]) -> Optional[Square]:
capture_square = None
if move is not None and board.is_capture(move):
if board.is_en_passant(move):
# taken from :func:`chess.Board.push()`
down = -8 if board.turn == chess.WHITE else 8
capture_square = board.ep_square + down
else:
capture_square = move.to_square
return capture_square
def evaluate(self, board: chess.Board) -> None:
""" Assigns highest value to capture moves and no value to others """
self.reset_move_variables()
legal_move_list = list(board.legal_moves)
for m in legal_move_list:
if get_piece_at(board, str(m)[:2].upper()) == self.piece.upper():
piece_at_position = get_piece_at(board, str(m)[2:4]).upper()
if (not board.is_capture(m)) or (not piece_at_position):
self.legal_moves[m] = 1
else:
self.legal_moves[m] = self.value_mapping[
piece_at_position].value
def evaluate(self, board: chess.Board) -> None:
""" Assigns highest value to capture moves and no value to others """
self.reset_move_variables()
legal_move_list = list(board.legal_moves)
for m in legal_move_list:
if board.is_capture(m):
self.legal_moves[m] = 1
else:
self.legal_moves[m] = 0
self.material_difference.append(
self.evaluation_function.evaluate(board))
def evaluate(self, board: chess.Board) -> None:
""" Assigns value to no capture moves and no value to captures """
self.reset_move_variables()
legal_move_list = list(board.legal_moves)
# The only difference between this engine and RandomCapture is the
# not condition.
for m in legal_move_list:
if not board.is_capture(m):
self.legal_moves[m] = 1
else:
self.legal_moves[m] = 0
self.material_difference.append(
self.evaluation_function.evaluate(board))
def get_board_attack_total_value(board: chess.Board) -> float:
total_value = 0
for move in board.legal_moves:
captured_piece = board.is_capture(move)
score_before = KEEP_PIECES_STRATEGY(board)[2] if captured_piece else 0
board.push_uci(str(move))
if captured_piece:
total_value += score_before - KEEP_PIECES_STRATEGY(board)[1]
total_value += evaluate_game_state(board, True)
board.pop()
return total_value
def evaluate(self, board: chess.Board) -> None:
""" Assigns highest value to capture moves based off value system """
self.reset_move_variables()
legal_move_list = list(board.legal_moves)
for m in legal_move_list:
piece_at_position = get_piece_at(board, str(m)[2:4]).upper()
if (not board.is_capture(m)) or (not piece_at_position):
self.legal_moves[m] = 0.0
else:
self.legal_moves[m] = self.value_mapping[
piece_at_position].value
self.material_difference.append(
self.evaluation_function.evaluate(board))
def quiescence_search(board: chess.Board, alpha: int, beta: int) -> int:
standard_eval = evaluation(board)
if standard_eval >= beta:
return beta
if alpha < standard_eval:
alpha = standard_eval
for move in board.legal_moves:
if board.is_capture(move):
board.push(move)
score = -quiescence_search(board, -beta, -alpha)
board.pop()
if score >= beta:
return beta
if score > alpha:
alpha = score
return alpha
def MVVLVA(self, board: chess.Board):
dic = {}
for move in board.legal_moves:
if board.is_capture(move):
# get victim type
v = board.piece_at(move.to_square)
# en passant
if v == None:
v = chess.Piece(chess.PAWN, True)
v = v.piece_type
# get attacker type
a = board.piece_at(move.from_square).piece_type
# 105 =< value <= 605
dic[move] = (v * 100) + 6 - (a / 100)
else:
dic[move] = 0
return sorted(dic, key=dic.get, reverse=True)
def MVV_LVA(
board: chess.Board, piece_values: Iterable = ConventionalPieceValues
) -> List[chess.Move]:
"""
Most Valuable Victim - Least Valuable Aggressor implementation for
move sorting
Args:
board (chess.Board): current board state to evaluate
Returns:
(List[chess.Move]): sorted list of moves according to MVV_LVA capture
heuristic
"""
available_captures: Dict[int, List[chess.Move]] = {}
move_list = list(board.legal_moves)
# For each move, evaluate if any captures. If so, rank captures based
# off value gained
for move in move_list:
if board.is_capture(move):
# Get difference in value between aggressor and victim pieces
aggressor_piece = get_piece_at(board, str(move)[:2]).upper()
victim_piece = get_piece_at(board, str(move)[2:]).upper()
if aggressor_piece and victim_piece:
value_diff = (
piece_values[victim_piece].value
- piece_values[aggressor_piece].value
)
if value_diff not in available_captures:
available_captures[value_diff] = [move]
else:
available_captures[value_diff].append(move)
# If any available captures, sort captures by value_diff of captures
# and return list of sorted captures
if available_captures:
move_list_sorted = []
for val_diff in sorted(available_captures, reverse=True):
move_list_sorted.extend(available_captures[val_diff])
return move_list_sorted
# If no captures, return shuffled list of all legal moves
random.shuffle(move_list)
return move_list
def quiesce(self, board: chess.Board, alpha, beta, depthleft):
stand_pat = evaluation.evaluation(board)
if depthleft == 0:
return stand_pat
if (stand_pat >= beta):
return beta
if (alpha < stand_pat):
alpha = stand_pat
for move in self.order(board):
if board.is_capture(move):
board.push(move)
score = -self.quiesce(board, -beta, -alpha, depthleft - 1)
board.pop()
if score >= beta:
return beta
if score > alpha:
alpha = score
return alpha
def quiescence_search(board : chess.Board, alpha : int, beta : int, max_depth : int) -> int:
standard_eval = evaluation(board)
if max_depth == 0:
return standard_eval
if standard_eval >= beta:
return beta
if alpha < standard_eval:
alpha = standard_eval
for move in board.legal_moves:
piece = str(board.piece_at(move.from_square))
if board.is_capture(move):
if piece == 'q' or piece == 'Q':
continue
board.push(move)
score = -quiescence_search(board, -beta, -alpha, max_depth-1)
board.pop()
if score >= beta:
return beta
if score > alpha:
alpha = score
return alpha
class Viridithas():
def __init__(
self,
human: bool = False,
fen: str = 'rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1',
pgn: str = '',
time_limit: float = 15,
fun: bool = False,
contempt: int = 3000,
book: bool = True,
advancedTC: list = [],
):
if pgn == '':
self.node = Board(fen)
else:
self.node = Board()
for move in pgn.split():
try:
self.node.push_san(move)
except Exception:
continue
self.time_limit = time_limit
if advancedTC:
self.endpoint = time.time()+advancedTC[0]*60
self.increment = advancedTC[1]
else:
self.endpoint = 0
self.increment = 0
self.fun = fun
self.contempt = contempt
self.human = human
self.nodes = 0
self.advancedTC = advancedTC
self.hashtable: dict[Hashable, TTEntry] = dict()
self.inbook = book
def set_position(self, fen):
self.node = Board(fen)
def __repr__(self) -> str:
return str(self.node) + '\n' + self.__class__.__name__+"-engine at position " + str(self.node.fen())
def __str__(self) -> str:
return self.__class__.__name__
def user_setup(self):
if input("Do you want to configure the bot? (Y/N) ").upper() != 'Y':
return
myname = self.__class__.__name__.upper()
print(f"BEGINNING USER CONFIGURATION OF {myname}-BOT")
datadict = get_engine_parameters()
self.__init__(
human=datadict["human"],
fen=datadict["fen"],
pgn=datadict["pgn"],
time_limit=datadict["time_limit"],
fun=datadict["fun"],
contempt=datadict["contempt"],
book=datadict["book"],
advancedTC=datadict["advancedTC"]
)
def gameover_check_info(self):
checkmate = self.node.is_checkmate()
draw = self.node.is_stalemate() or \
self.node.is_insufficient_material( ) or \
self.node.is_repetition(2) or self.node.is_seventyfive_moves() or not any(self.node.generate_legal_moves())
return checkmate or draw, checkmate, draw
# @profile
def evaluate(self, depth: float, checkmate: bool, draw: bool) -> float:
self.nodes += 1
if checkmate:
return MATE_VALUE * int(max(depth+1, 1)) * (1 if self.node.turn else -1)
if draw:
return -self.contempt * (1 if self.node.turn else -1)
rating: float = 0
rating += pst_eval(self.node)
# rating += see_eval(self.node)
# rating += mobility(self.node) * MOBILITY_FACTOR
# rating += piece_attack_counts(self.node) * ATTACK_FACTOR
# rating += king_safety(self.node) * KING_SAFETY_FACTOR
# rating += space(self.node) * SPACE_FACTOR
return rating
def single_hash_iterator(self, best):
yield best
def captures_piece(self, p): # concentrate on MVV, then LVA
return itertools.chain(
self.node.generate_pseudo_legal_moves(self.node.pawns, p),
self.node.generate_pseudo_legal_moves(self.node.knights, p),
self.node.generate_pseudo_legal_moves(self.node.bishops, p),
self.node.generate_pseudo_legal_moves(self.node.rooks, p),
self.node.generate_pseudo_legal_moves(self.node.queens, p),
self.node.generate_pseudo_legal_moves(self.node.kings, p),
)
#@profile
def captures(self): # (MVV/LVA)
return (m for m in itertools.chain(
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.queens),
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.rooks),
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.bishops),
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.knights),
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.pawns),
) if self.node.is_legal(m))
def winning_captures(self): # (MVV/LVA)
target_all = self.node.occupied_co[not self.node.turn]
target_3 = target_all & ~self.node.pawns
target_5 = target_3 & (~self.node.bishops | ~self.node.knights)
target_9 = target_5 & ~self.node.rooks
return itertools.chain(
self.node.generate_pseudo_legal_moves(self.node.pawns, target_all),
self.node.generate_pseudo_legal_moves(self.node.knights, target_3),
self.node.generate_pseudo_legal_moves(self.node.bishops, target_3),
self.node.generate_pseudo_legal_moves(self.node.rooks, target_5),
self.node.generate_pseudo_legal_moves(self.node.queens, target_9),
self.node.generate_pseudo_legal_moves(self.node.kings, target_9),
)
def losing_captures(self): # (MVV/LVA)
target_pawns = self.node.pawns
target_pnb = target_pawns | self.node.bishops | self.node.knights
target_pnbr = target_pnb | self.node.rooks
return itertools.chain(
self.node.generate_pseudo_legal_moves(self.node.knights, target_pawns),
self.node.generate_pseudo_legal_moves(self.node.bishops, target_pawns),
self.node.generate_pseudo_legal_moves(self.node.rooks, target_pnb),
self.node.generate_pseudo_legal_moves(self.node.queens, target_pnbr),
self.node.generate_pseudo_legal_moves(self.node.kings, target_pnbr),
)
def ordered_moves(self):
return (m for m in itertools.chain(
# self.winning_captures(),
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.queens),
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.rooks),
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.bishops),
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.knights),
self.captures_piece(
self.node.occupied_co[not self.node.turn] & self.node.pawns),
self.node.generate_pseudo_legal_moves(
0xffff_ffff_ffff_ffff, ~self.node.occupied_co[not self.node.turn]),
# self.losing_captures()
) if self.node.is_legal(m))
def pass_turn(self) -> None:
self.node.push(Move.from_uci("0000"))
#@profile
def qsearch(self, alpha: float, beta: float, depth: float, colour: int, gameover: bool, checkmate: bool, draw: bool) -> float:
val = self.evaluate(1, checkmate, draw) * colour
if gameover:
return val
if val >= beta:
return beta
if (val < alpha - QUEEN_VALUE):
return alpha
alpha = max(val, alpha)
for capture in self.captures():
self.node.push(capture)
gameover, checkmate, draw = self.gameover_check_info()
score = -self.qsearch(-beta, -alpha, depth - 1, -colour, gameover, checkmate, draw)
self.node.pop()
if score >= beta:
return score
alpha = max(score, alpha)
return alpha
def tt_lookup(self, board: Board) -> "TTEntry":
key = board._transposition_key()
return self.hashtable.get(key, TTEntry.default())
def tt_store(self, board: Board, entry: TTEntry):
key = board._transposition_key()
self.hashtable[key] = entry
#@profile
def negamax(self, depth: float, colour: int, alpha: float, beta: float) -> float:
initial_alpha = alpha
# (* Transposition Table Lookup; self.node is the lookup key for ttEntry *)
tt_entry = self.tt_lookup(self.node)
if not tt_entry.is_null() and tt_entry.depth >= depth:
if tt_entry.type == EXACT:
return tt_entry.value
elif tt_entry.type == LOWERBOUND:
alpha = max(alpha, tt_entry.value)
elif tt_entry.type == UPPERBOUND:
beta = min(beta, tt_entry.value)
if alpha >= beta:
return tt_entry.value
if self.node.is_legal(tt_entry.best):
moves = itertools.chain([tt_entry.best], filter(lambda x: x != tt_entry.best, self.ordered_moves()))
else:
moves = self.ordered_moves()
else:
moves = self.ordered_moves()
gameover, checkmate, draw = self.gameover_check_info()
if gameover:
return colour * self.evaluate(depth, checkmate, draw)
if depth < 1:
return self.qsearch(alpha, beta, depth, colour, gameover, checkmate, draw)
current_pos_is_check = self.node.is_check()
if not current_pos_is_check and depth >= 3 and abs(alpha) < MATE_VALUE and abs(beta) < MATE_VALUE:
# MAKE A NULL MOVE
self.node.push(Move.null())
# PERFORM A LIMITED SEARCH
value = - self.negamax(depth - 3, -colour, -beta, -beta + 1)
# UNMAKE NULL MOVE
self.node.pop()
if value >= beta:
return beta
do_prune = self.pruning(depth, colour, alpha, beta, current_pos_is_check)
best_move = Move.null()
search_pv = True
value = float("-inf")
for move_idx, move in enumerate(moves):
if move_idx == 0:
best_move = move
gives_check = self.node.gives_check(move)
is_capture = self.node.is_capture(move)
is_promo = bool(move.promotion)
depth_reduction = search_reduction_factor(
move_idx, current_pos_is_check, gives_check, is_capture, is_promo, depth)
if do_prune:
if not gives_check and not is_capture:
continue
self.node.push(move)
if search_pv:
r = -self.negamax(depth - depth_reduction, -colour, -beta, -alpha)
value = max(value, r)
else:
r = -self.negamax(depth - depth_reduction, -colour, -alpha-1, -alpha)
if (r > alpha): # // in fail-soft ... & & value < beta) is common
r = -self.negamax(depth - depth_reduction, -colour, -beta, -alpha) #// re-search
value = max(value, r)
self.node.pop()
if value > alpha:
alpha = value
best_move = move
alpha = max(alpha, value)
if alpha >= beta:
break
search_pv = False
# (* Transposition Table Store; self.node is the lookup key for ttEntry *)
# ttEntry = TTEntry()
tt_entry.value = value
if value <= initial_alpha:
tt_entry.type = UPPERBOUND
elif value >= beta:
tt_entry.type = LOWERBOUND
else:
tt_entry.type = EXACT
tt_entry.depth = depth
tt_entry.best = best_move
tt_entry.null = False
self.tt_store(self.node, tt_entry)
return value
def pruning(self, depth, colour, alpha, beta, current_pos_is_check):
if not (not current_pos_is_check and abs(
alpha) < MATE_VALUE / 2 and abs(beta) < MATE_VALUE / 2) or depth > 2:
return False
see = see_eval(self.node) * colour
DO_D1_PRUNING = depth <= 1 and see + FUTILITY_MARGIN < alpha
DO_D2_PRUNING = depth <= 2 and see + FUTILITY_MARGIN_2 < alpha
return DO_D1_PRUNING or DO_D2_PRUNING
def move_sort(self, moves: list, ratings: list):
pairs = zip(*sorted(zip(moves, ratings), key=operator.itemgetter(1)))
moves, ratings = [list(pair) for pair in pairs]
return moves, ratings
def pv_string(self):
count = 0
moves = []
while True:
e = self.tt_lookup(self.node)
if e.is_null() or not self.node.is_legal(e.best):
break
# print(self.node.__str__())
# print(self.node.__repr__())
# print(e.best)
# print(self.node.san(e.best))
moves.append(self.node.san(e.best))
self.node.push(e.best)
count += 1
for _ in moves:
self.node.pop()
if count == 0: return ""
return " ".join(moves)
def turnmod(self) -> int:
return -1 if self.node.turn else 1
def show_iteration_data(self, moves: list, values: list, depth: float, start: float) -> tuple:
t = round(time.time()-start, 2)
print(f"{self.node.san(moves[0])} | {-round((self.turnmod()*values[0])/1000, 3)} | {str(t)}s at depth {str(depth + 1)}, {str(self.nodes)} nodes processed, at {str(int(self.nodes / (t+0.00001)))}NPS.\n", f"PV: {self.pv_string()}\n", end="")
return (self.node.san(moves[0]), self.turnmod()*values[0], self.nodes, depth+1, t)
def search(self, ponder: bool = False, readout: bool = True):
val = float("-inf")
start_time = time.time()
self.nodes = 0
moves = [next(self.ordered_moves())]
saved_position = deepcopy(self.node)
alpha, beta = float("-inf"), float("inf")
valWINDOW = PAWN_VALUE / 4
WINDOW_FAILED = False
try:
depth = 1
while depth < 40:
best = self.tt_lookup(self.node).best
time_elapsed = time.time() - start_time
# check if we aren't going to finish the next search in time
if time_elapsed > 0.5 * self.time_limit and not ponder and not WINDOW_FAILED:
return best, val
val = self.negamax(
depth, self.turnmod(), alpha=alpha, beta=beta)
# print(val)
if ((val <= alpha) or (val >= beta)):
# We fell outside the window, so try again with a
# full-width window (and the same depth).
alpha = float("-inf")
beta = float("inf")
WINDOW_FAILED = True
continue
WINDOW_FAILED = False
best = self.tt_lookup(self.node).best
# check if we've run out of time
if time_elapsed > self.time_limit and not ponder:
return best, val
moves = [self.tt_lookup(self.node).best]
values = [self.tt_lookup(self.node).value]
if readout:
self.show_iteration_data(moves, values, depth, start_time)
alpha = val - valWINDOW # Set up the window for the next iteration.
beta = val + valWINDOW
depth += 1
except KeyboardInterrupt:
self.node = saved_position
pass
return moves[0], val
def ponder(self) -> None:
self.origin = self.node.copy()
self.search(ponder=True)
def get_book_move(self):
# book = chess.polyglot.open_reader(
# r"ProDeo292/ProDeo292/books/elo2500.bin")
book = chess.polyglot.open_reader(
r"books/elo2500.bin")
main_entry = book.find(self.node)
choice = book.weighted_choice(self.node)
book.close()
return main_entry.move, choice.move
def engine_move(self) -> Move:
# add flag_func for egtb mode
if self.advancedTC:
self.time_limit = (self.endpoint-time.time())/20
print("Time for move: " + str(round(self.time_limit, 2)) + "s")
if self.inbook:
try:
best, choice = self.get_book_move()
if self.fun:
self.node.push(choice)
return choice
else:
self.node.push(best)
print(chess.pgn.Game.from_board(self.node)[-1])
except IndexError:
self.time_limit = self.time_limit*2
best, _ = self.search()
self.node.push(best)
print(chess.pgn.Game.from_board(self.node)[-1])
self.inbook = False
self.time_limit = self.time_limit/2
else:
best, _ = self.search()
self.node.push(best)
print(chess.pgn.Game.from_board(self.node)[-1])
# self.record_stack()
self.endpoint += self.increment
return best
def user_move(self) -> str:
move = input("enter move: ")
while True:
try:
self.node.push_san(move)
break
except Exception:
move = input("enter move: ")
return move
def display_ending(self) -> None:
if self.node.is_stalemate():
print('END BY STALEMATE')
elif self.node.is_insufficient_material():
print('END BY INSUFFICIENT MATERIAL')
elif self.node.is_fivefold_repetition():
print('END BY FIVEFOLD REPETITION')
elif self.node.is_checkmate:
print("BLACK" if self.node.turn == BLACK else "WHITE", 'WINS ON TURN',
self.node.fullmove_number)
else:
print('END BY UNKNOWN REASON')
def run_game(self, indefinite=True) -> str:
while not self.node.is_game_over():
print(self.__repr__())
if self.human and self.node.turn:
try:
self.ponder()
except KeyboardInterrupt:
self.node = self.origin
self.user_move()
else: # SWAP THESE ASAP
self.engine_move()
if not indefinite:
break
self.display_ending()
try:
return str(chess.pgn.Game.from_board(self.node)[-1])
except Exception:
return "PGN ERROR"
def play_viri(self, fen=None):
player_colour = input(
"Enter the human player's colour in the form b/w\n--> ")
while player_colour not in ['b', 'w']:
player_colour = input(
"Enter the human player's colour in the form b/w\n--> ")
player_colour = WHITE if player_colour == 'w' else BLACK
timeControl = int(
input("how many seconds should viri get per move?\n--> "))
self.__init__(human=True, time_limit=timeControl, fen=fen, book=True, fun=False)
self.fun = False
while not self.node.is_game_over():
print(self.__repr__())
if player_colour == self.node.turn:
# try:
# self.ponder()
# except KeyboardInterrupt:
# self.node = self.origin
# self.user_move()
self.user_move()
else:
self.engine_move()
self.display_ending()
def perftx(self, n):
if n == 0:
self.nodes += 1
else:
for move in self.ordered_moves():
self.node.push(move)
self.perftx(n - 1)
self.node.pop()
def perft(self, n):
self.nodes = 0
self.perftx(n)
print(self.nodes)
def uci(self):
start = input()
while start != "uci":
start = input()
print("id", end="")
while True:
command = input()
if command == "ucinewgame":
board = Board()
elif command.split()[0] == "position":
fen = command[command.index(
"fen") + 3: command.index("moves") - 1]
moves = command[command.index("moves"):].split()
if fen == "startpos":
fen = 'rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1'
board = Board(fen)
for move in moves:
board.push(Move.from_uci(move))
def __init__(self, value: str, board: Board):
"""Helper class to parse moves on a given board status
The base class from python-chess miss information like piece value, capture and checkmate
Used across the library to standardize move parsing and evaluation
Args:
value (str): The input move as string (SAN, LAN) or chess Move object
board (Board): The board status at the right moment, moves and notations depend on the context
"""
# Parse the string input if input value is not already a move object
if isinstance(value, str):
self.move = board.parse_san(value)
else:
self.move = value
# Convert to real SAN value to have a uniformed string version
self.move_str = board.san(self.move)
# Store board and future board
self.board_from = board.deepcopy()
self.board_to = board.deepcopy()
self.board_to.push_san(self.move_str)
# Evaluate move characteristics, capture, from and to square
self.trajectory = (self.move.from_square, self.move.to_square)
self.is_capture = board.is_capture(self.move)
self.from_piece = parse_piece(board.piece_at(self.from_square))
# Prepare features for display and evaluation
self.color = self.from_piece["color"].upper()
self.color_factor = 1 if self.color == "WHITE" else -1
self.name = self.from_piece["name"].upper()
# Capture rules
# Parse piece value of the capture
if self.is_capture:
try:
self.to_piece = parse_piece(board.piece_at(self.to_square))
self.value = self.to_piece["value"]
except:
self.to_piece = {}
self.value = 0
else:
self.to_piece = {}
self.value = 0
# Use SAN to determine is the move is a checkmate
# Modify move value if there is a checkmate
self.checkmate = "#" in self.move_str
if self.checkmate:
self.value = CHECKMATE_VALUE
# Promotion rules
# Parsing the SAN name to get promotion piece (With a check the san value is appended by +)
# Move value increased by the promotion piece minus the pawn value we exchanged
if "=" in self.move_str:
self.promoted_piece = PROMOTED_PIECES[self.move_str.split(
"=")[-1].replace("+", "").replace("#", "")]
bonus_promotion = PIECE_VALUES_BY_NAME[self.promoted_piece] - 1
self.value += bonus_promotion
