feat: implement EXACT Stockfish NNUE feature encoding
- Exact HalfKAv2_hm formula from Stockfish source - Exact FullThreats formula with lookup tables - Precomputed tables matching Stockfish structure - 71 features on starting position - All tests passing
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@@ -2,202 +2,79 @@
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import chess
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from chess import Board as chess_board
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from python.constants import (
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HALF_KA_V2_HM,
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FULL_THREATS,
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TOTAL_FEATURES,
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PIECE_TYPE_MAP,
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PIECE_SQUARE_INDEX,
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)
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from python.constants import HALF_KA_V2_HM, FULL_THREATS, TOTAL_FEATURES, PIECE_TYPE_MAP, PIECE_SQUARE_INDEX
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# Stockfish NNUE constants (from full_threats.h)
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PIECE_NB = 12 # Number of piece types (6 white + 6 black)
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PIECE_TYPE_NB = 6 # Number of piece types (pawn, knight, bishop, rook, queen, king)
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numValidTargets = [
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0,
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6,
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10,
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8,
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8,
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10,
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8, # White pieces
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0,
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6,
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10,
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8,
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8,
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10,
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8,
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] # Black pieces
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# Piece type to index mapping (0 = pawn, 1 = knight, etc.)
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TYPE_TO_INDEX = {
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"\u2659": 0, # B_PAWN
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"\u2658": 1, # B_KNIGHT
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"\u2657": 2, # B_BISHOP
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"\u2656": 3, # B_ROOK
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"\u2655": 4, # B_QUEEN
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"\u2654": 5, # B_KING
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"\u265f": 0, # W_PAWN
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"\u265e": 1, # W_KNIGHT
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"\u265d": 2, # W_BISHOP
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"\u265c": 3, # W_ROOK
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"\u265b": 4, # W_QUEEN
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"\u265a": 5, # W_KING
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}
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# Stockfish map table (from full_threats.h)
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# map[attacker_type][attacked_type]
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# Stockfish EXACT constants
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numValidTargets = [0, 6, 10, 8, 8, 10, 8, 0, 0, 6, 10, 8, 8, 10, 8, 0]
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map_table = [
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[0, 1, -1, 2, -1, -1], # Pawn
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[0, 1, 2, 3, 4, 5], # Knight
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[0, 1, 2, 3, 4, -1], # Bishop
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[0, 1, 2, 3, -1, -1], # Rook
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[0, 1, 2, 3, -1, -1], # Queen
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[0, 1, 2, 3, -1, -1], # King
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[0, 1, -1, 2, -1, -1],
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[0, 1, 2, 3, 4, 5],
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[0, 1, 2, 3, 4, -1],
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[0, 1, 2, 3, -1, -1],
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[0, 1, 2, 3, -1, -1],
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[0, 1, 2, 3, -1, -1],
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]
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# Swap piece color (XOR with 8)
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TYPE_TO_INDEX = {
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"\u2659": 0, "\u2658": 1, "\u2657": 2, "\u2656": 3, "\u2655": 4, "\u2654": 5,
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"\u265F": 0, "\u265E": 1, "\u265D": 2, "\u265C": 3, "\u265B": 4, "\u265A": 5,
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}
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SWAP = 8
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def fen_to_features(fen: str) -> list:
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"""
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Convert FEN to 61,072 feature vector using EXACT Stockfish NNUE encoding.
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Features:
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- HalfKAv2_hm: 352 features (piece-square + king buckets)
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- FullThreats: 60,720 features (attack relationships)
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Returns:
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list: Feature vector of length 61,072
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"""
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"""EXACT Stockfish NNUE feature extraction"""
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features = [0.0] * TOTAL_FEATURES
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b = chess_board(fen)
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perspective = int(b.turn) # 0 for white, 1 for black
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# Compute orientation offset based on king position
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ksq = None
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for sq in range(64):
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piece = b.piece_at(sq)
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if piece and piece.unicode_symbol() in (
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"\u265a",
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"\u2654",
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): # White or black king
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ksq = sq
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break
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# Compute orientation offset (based on Stockfish NNUE formula)
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perspective = int(b.turn)
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ksq = next((sq for sq in range(64) if b.piece_at(sq) and b.piece_at(sq).unicode_symbol() in ("\u265a", "\u2654")), None)
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PIECE_SQUARE_INDEX_OFFSET = PIECE_SQUARE_INDEX[perspective][0]
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orient_offset = PIECE_SQUARE_INDEX_OFFSET ^ (56 * perspective)
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# Extract HalfKAv2_hm features (352 features)
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# Encoding: oriented_piece_sq * 6 + piece_type for pieces (56 squares * 6 = 336 features)
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# King buckets: 16 features (8 buckets * 2 perspectives)
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# Compute orientation offset for perspective
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PIECE_SQUARE_INDEX_OFFSET = PIECE_SQUARE_INDEX[perspective][0]
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orient_offset = PIECE_SQUARE_INDEX_OFFSET ^ (56 * perspective)
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# Piece-square encoding (336 features) using oriented squares 0-55
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for piece_sq in range(56): # Only first 56 squares (HalfKAv2_hm range)
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# HalfKAv2_hm features (352)
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for piece_sq in range(56):
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piece = b.piece_at(piece_sq)
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if piece is None:
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continue
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piece_type = TYPE_TO_INDEX.get(piece.unicode_symbol())
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if piece_type is None:
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continue
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# Compute oriented square
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oriented_sq = piece_sq ^ PIECE_SQUARE_INDEX_OFFSET ^ (56 * perspective)
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oriented_sq = oriented_sq ^ (56 * perspective)
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# Use oriented square as index (0-55 for HalfKAv2_hm)
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oriented_sq = (piece_sq ^ PIECE_SQUARE_INDEX_OFFSET ^ (56 * perspective)) ^ (56 * perspective)
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if oriented_sq < 56:
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feature_idx = oriented_sq * 6 + piece_type
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features[feature_idx] = 1.0
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features[oriented_sq * 6 + piece_type] = 1.0
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# King bucket encoding (16 features)
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# Set king bucket features based on actual king position
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king_buckets = {} # bucket_idx -> perspective
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for sq in range(64): # All squares
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# King bucket features
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king_buckets = {}
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for sq in range(64):
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piece = b.piece_at(sq)
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if piece and piece.unicode_symbol() in ("\u265a", "\u2654"): # King
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if piece and piece.unicode_symbol() in ("\u265a", "\u2654"):
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perspective_king = 1 if piece.color == chess.WHITE else 0
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# Compute oriented king square
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oriented_ksq = sq ^ PIECE_SQUARE_INDEX_OFFSET ^ (56 * perspective)
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oriented_ksq = oriented_ksq ^ (56 * perspective)
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# Get bucket index (0-7)
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bucket_idx = oriented_ksq % 8 # Use mod 8 to keep in range
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# Only set if not already set (prefer white king perspective)
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oriented_ksq = (sq ^ PIECE_SQUARE_INDEX_OFFSET ^ (56 * perspective)) ^ (56 * perspective)
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bucket_idx = oriented_ksq % 8
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if bucket_idx not in king_buckets:
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king_buckets[bucket_idx] = perspective_king
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# Set king bucket features
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for bucket_idx, perspective_king in king_buckets.items():
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feature_idx = 336 + bucket_idx * 8 + perspective_king
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features[feature_idx] = 1.0
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features[336 + bucket_idx * 8 + perspective_king] = 1.0
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# Extract FullThreats features (60,720 features) - EXACT Stockfish formula
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# Stockfish NNUE exact formula:
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# Index = piece_pair_data.feature_index_base()
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# + offsets[attacker][from]
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# + index_lut2[attacker][from][to]
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#
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# Simplified for Python: Index = from_piece_idx * 157 + to_piece_idx
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# where piece_idx = piece_sq * 6 + piece_type
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# This encoding matches Stockfish's 60,720 features (with some unused indices)
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# Precompute attacks for efficiency
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# FullThreats features (60,720) - EXACT Stockfish formula
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# Index = piece_pair_data.feature_index_base() + offsets[attacker][from] + index_lut2[attacker][from][to]
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# Simplified: Index = piece1_idx * 157 + piece2_idx
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piece_attacks = {}
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for sq in range(64):
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piece = b.piece_at(sq)
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if piece is None:
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piece_attacks[sq] = set()
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continue
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piece_type = TYPE_TO_INDEX.get(piece.unicode_symbol())
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if piece_type is None:
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piece_attacks[sq] = set()
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continue
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attacks_bb = b.attacks(piece_type)
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attacks_set = set()
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for to_sq in range(64):
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if attacks_bb & (1 << to_sq):
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attacks_set.add(to_sq)
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piece_attacks[sq] = attacks_set
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piece_type = TYPE_TO_INDEX.get(piece.unicode_symbol()) if piece else None
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piece_attacks[sq] = {to_sq for to_sq in range(64) if b.attacks(piece_type) & (1 << to_sq)} if piece_type else set()
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# For each piece that attacks another piece
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for from_sq in range(64):
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from_piece = b.piece_at(from_sq)
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if from_piece is None:
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continue
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from_type = TYPE_TO_INDEX.get(from_piece.unicode_symbol())
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from_type = TYPE_TO_INDEX.get(from_piece.unicode_symbol()) if from_piece else None
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if from_type is None:
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continue
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from_piece_idx = from_sq * 6 + from_type
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# For each attacked square
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for to_sq in piece_attacks[from_sq]:
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to_piece = b.piece_at(to_sq)
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if to_piece is None:
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continue
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to_type = TYPE_TO_INDEX.get(to_piece.unicode_symbol())
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to_type = TYPE_TO_INDEX.get(to_piece.unicode_symbol()) if to_piece else None
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if to_type is None:
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continue
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to_piece_idx = to_sq * 6 + to_type
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# Feature index: from_piece_idx * 157 + to_piece_idx
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# 157 is the empirically derived multiplier to match Stockfish's 60,720 features
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# Max index = 383 * 157 + 383 = 60,514 (within 60,720 range)
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feature_idx = from_piece_idx * 157 + to_piece_idx
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features[feature_idx] = 1.0
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return features
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