def minimax(board, turn):
"""
Depth:
depth = 1 Max --> return
depth = 2 Max --> Mini --> return
depth = 3 Max --> Mini --> Max --> return
depth = 4 Max --> Mini --> Max --> Mini --> return
"""
depth = 3
# Set turn
if turn == 1:
color = 'white'
best_score = float('-inf')
else:
color = 'black'
best_score = float('inf')
# Check for checkmate oppenents checkmate
if board.checkmate(color):
output_board_file = open('gameResult.txt', 'w')
if color == 'black':
print('STALEMATE! {} beats {}'.format(color, Board.op_color(color)), file = output_board_file)
print(board, file = output_board_file)
print('STALEMATE! {} beats {}'.format(color, Board.op_color(color)))
print(board)
sys.exit()
print('CHECKMATE! {} beats {}'.format(color, Board.op_color(color)), file = output_board_file)
print(board, file = output_board_file)
print('CHECKMATE! {} beats {}'.format(color, Board.op_color(color)))
print(board)
sys.exit()
# Check for checkmate oppenents checkmate
if board.checkmate(Board.op_color(color)):
output_board_file = open('gameResult.txt', 'w')
if color == 'white':
print('STALEMATE! {1} beats {0}'.format(color, Board.op_color(color)), file = output_board_file)
print(board, file = output_board_file)
print('STALEMATE! {1} beats {0}'.format(color, Board.op_color(color)))
print(board)
sys.exit()
print('CHECKMATE! {1} beats {0}'.format(color, Board.op_color(color)), file = output_board_file)
print(board, file = output_board_file)
print('CHECKMATE! {1} beats {0}'.format(color, Board.op_color(color)))
print(board)
sys.exit()
# Get possible legal moves
moves = board.possible_moves(color)
best_move = moves[0]
# Initialize variables
alpha = float('-inf')
beta = float('inf')
for move in moves:
clone = deepcopy(board)
clone.squares = deepcopy(board.squares)
step = deepcopy(move)
clone.move_piece(step[0], step[1])
if color == 'white':
# score = heuristicY(clone, depth - 1, turn)
score = heuristicY(clone, depth - 1, turn, alpha, beta)
if score > best_score:
best_move = step
best_score = score
# print(best_score)
else:
# score = heuristicX(clone, depth - 1, turn)
score = heuristicX(clone, depth - 1, turn, alpha, beta)
if score < best_score:
best_move = step
best_score = score
# print(best_score)
return best_move
