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halfka.py
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halfka.py
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import chess
import torch
import feature_block
from collections import OrderedDict
from feature_block import *
NUM_SQ = 64
NUM_PT = 12
NUM_PLANES = (NUM_SQ * NUM_PT + 1)
def orient(is_white_pov: bool, sq: int):
return (56 * (not is_white_pov)) ^ sq
def halfka_idx(is_white_pov: bool, king_sq: int, sq: int, p: chess.Piece):
p_idx = (p.piece_type - 1) * 2 + (p.color != is_white_pov)
return 1 + orient(is_white_pov, sq) + p_idx * NUM_SQ + king_sq * NUM_PLANES
def halfka_psqts():
# values copied from stockfish, in stockfish internal units
piece_values = {
chess.PAWN : 126,
chess.KNIGHT : 781,
chess.BISHOP : 825,
chess.ROOK : 1276,
chess.QUEEN : 2538
}
values = [0] * (NUM_PLANES * NUM_SQ)
for ksq in range(64):
for s in range(64):
for pt, val in piece_values.items():
idxw = halfka_idx(True, ksq, s, chess.Piece(pt, chess.WHITE))
idxb = halfka_idx(True, ksq, s, chess.Piece(pt, chess.BLACK))
values[idxw] = val
values[idxb] = -val
return values
class Features(FeatureBlock):
def __init__(self):
super(Features, self).__init__('HalfKA', 0x5f134cb8, OrderedDict([('HalfKA', NUM_PLANES * NUM_SQ)]))
def get_active_features(self, board: chess.Board):
def piece_features(turn):
indices = torch.zeros(NUM_PLANES * NUM_SQ)
for sq, p in board.piece_map().items():
indices[halfka_idx(turn, orient(turn, board.king(turn)), sq, p)] = 1.0
return indices
return (piece_features(chess.WHITE), piece_features(chess.BLACK))
def get_initial_psqt_features(self):
return halfka_psqts()
class FactorizedFeatures(FeatureBlock):
def __init__(self):
super(FactorizedFeatures, self).__init__('HalfKA^', 0x5f134cb8, OrderedDict([('HalfKA', NUM_PLANES * NUM_SQ), ('A', NUM_SQ * NUM_PT)]))
def get_active_features(self, board: chess.Board):
raise Exception('Not supported yet, you must use the c++ data loader for factorizer support during training')
def get_feature_factors(self, idx):
if idx >= self.num_real_features:
raise Exception('Feature must be real')
a_idx = idx % NUM_PLANES - 1
return [idx, self.get_factor_base_feature('A') + a_idx]
def get_initial_psqt_features(self):
return halfka_psqts() + [0] * (NUM_SQ * NUM_PT)
'''
This is used by the features module for discovery of feature blocks.
'''
def get_feature_block_clss():
return [Features, FactorizedFeatures]