def main():
screen = p.display.set_mode((WIDTH, HEIGHT))
clock = p.time.Clock()
screen.fill(p.Color(WHITE))
gs = GameState() # gs is the game state
validMoves = gs.getValidMoves()
moveMade = False
loadImages() # we are only loading the images once in pygame
running = True
sqSelected = () # keeps track of the last user mouse clicks
playerClicks = [] # keeps track of the last player clicks
while running:
for e in p.event.get():
if e.type == p.QUIT:
running = False
# mouse key handlers
elif e.type == p.MOUSEBUTTONDOWN:
location = p.mouse.get_pos() # location of x and y
col = location[0] // SQ_SIZE
row = location[1] // SQ_SIZE
if sqSelected == (row, col):
sqSelected = ()
playerClicks = []
# if the player clicks on a chess piece
else:
sqSelected = (row, col)
playerClicks.append(
sqSelected
) # this appends both the 1st and the 2nd clicks
if len(playerClicks) == 2: # if the user made 2 mouse clicks
move = Move(playerClicks[0], playerClicks[1], gs.board)
print(
str(move.getChessNotation()) + ' id: ' +
str(move.moveID))
for i in range(len(validMoves)):
if move == validMoves[i]:
gs.makeMove(validMoves[i])
moveMade = True
sqSelected = ()
playerClicks = [] # resets user clicks
if not moveMade:
playerClicks = [sqSelected]
# keyboard handlers
elif e.type == p.KEYDOWN:
if e.key == p.K_z: # the undo key
gs.undoMove()
moveMade = True
if moveMade:
validMoves = gs.getValidMoves()
moveMade = False
drawGameState(screen, gs)
clock.tick(MAX_FPS)
p.display.flip()
def minimax(board: GameState, alpha: float, beta: float, maximizer: bool,
curDepth: int, max_depth: int) -> Tuple[float, Move]:
"""
returns an integer score and move which is the best current player can get
"""
if board.is_game_over():
if board.staleMate:
return 0, None
if board.checkMate:
return (-inf if maximizer else +inf), None
if curDepth == max_depth:
return evaluate(board, not (board.whiteToMove ^ maximizer)), None
# sending inf so that the branch is ignored by parent
if final_move is not None and time() - stime > timeout:
return +inf if maximizer else -inf, None
moves = list(board.getValidMoves())
# moves.sort(key=move_score, reverse=True)
assert moves != []
best_move = None
if maximizer:
best_score = -inf
def is_better_score(curr, currbest):
return curr >= currbest
def update_AB(score):
nonlocal alpha
alpha = max(alpha, score)
else:
best_score = +inf
def is_better_score(curr, currbest):
return curr <= currbest
def update_AB(score):
nonlocal beta
beta = min(beta, score)
for move in moves:
board.makeMove(move, by_AI=True)
global moves_cnt
moves_cnt += 1
curr_score, _ = minimax(board, alpha, beta, not maximizer,
curDepth + 1, max_depth)
board.undoMove()
if is_better_score(curr_score, best_score):
best_score = curr_score
best_move = move
update_AB(best_score)
if alpha >= beta:
break
return best_score, best_move
def minimax(board: GameState, moves: List[Move], alpha: float, beta: float, maximizer: bool, curDepth: int, max_depth: int, moves_line: List[Move]) -> Tuple[float, Move, List[Move]]:
"""
returns an integer score and move which is the best current player can get
"""
if board.is_game_over():
moves_line.append(None)
if board.staleMate:
return 0, None, moves_line
if board.checkMate:
return (-inf if maximizer else +inf), None, moves_line
if curDepth == max_depth:
return evaluate(board, not(board.whiteToMove ^ maximizer)), None, moves_line
# sending inf so that the branch is ignored by parent
if final_move is not None and time() - stime > timeout:
moves_line.append(None)
return +inf if maximizer else -inf, None, moves_line
# moves = list(board.getValidMoves())
assert moves != []
best_move = None
best_line = []
best_score = -inf if maximizer else +inf
for move in moves:
board.makeMove(move, by_AI=True)
moves_line.append(move)
global moves_cnt
moves_cnt += 1
curr_score, _, curr_line = minimax(
board, board.getValidMoves(), alpha, beta, not maximizer, curDepth+1, max_depth, moves_line[:])
board.undoMove()
moves_line.pop()
if maximizer:
if curr_score >= best_score:
best_score, best_move, best_line = curr_score, move, curr_line
alpha = max(alpha, best_score)
else:
if curr_score <= best_score:
best_score, best_move, best_line = curr_score, move, curr_line
beta = min(beta, best_score)
if alpha >= beta:
break
return best_score, best_move, best_line