def evaluate(board: chess.Board):
'''
returns a value for the board based on a simple evaluation function
'''
ret = 0
player = not not ((board.board().contents.flags) & 0b10000
) # True if black, False if white
for rk in range(0, 8):
for offs in range(0, 8):
pc = (board.board().contents.ranks[rk] >> (offs << 2)) & 0xf
# if (pc / 6) == player:
if (pc >= BPAWN and pc <= BKING) == player:
ret += pcval(pc)
else:
ret -= pcval(pc)
return ret
def board_tensor(board: chess.Board,
one_hot=False,
unsqueeze=False,
half=False):
'''
Returns an (8,8) shape torch.tensor form of the board's ranks array
'''
t = ((torch.tensor(board.board().contents.ranks,
dtype=torch.int64).reshape(8, 1) & masks) >> shift)
if (one_hot):
t = F.one_hot(t, num_classes=13) # 13 = 6 white + 6 black + 1 empty
if (unsqueeze):
t = t.unsqueeze(0)
if (half):
t = t.half()
else:
t = t.float()
return t
