// Pointer to pawn hash table entry
PawnInfo* pi;
- // updateKingTables[color] is set to true if we have enough material
- // to trigger the opponent's king safety calculation. When is false we
- // skip the time consuming update of the king attackers tables.
- bool updateKingTables[2];
-
// attackedBy[color][piece type] is a bitboard representing all squares
// attacked by a given color and piece type, attackedBy[color][0] contains
// all squares attacked by the given color.
Score evaluate_pieces_of_color(const Position& pos, EvalInfo& ei, Score& mobility);
template<Color Us, bool HasPopCnt>
- Score evaluate_king(const Position& pos, EvalInfo& ei, Value& margin);
+ Score evaluate_king(const Position& pos, EvalInfo& ei, Value margins[]);
template<Color Us>
Score evaluate_threats(const Position& pos, EvalInfo& ei);
template<Color Us>
Score evaluate_passed_pawns(const Position& pos, EvalInfo& ei);
- Score apply_weight(Score v, Score weight);
+ inline Score apply_weight(Score v, Score weight);
Value scale_by_game_phase(const Score& v, Phase ph, ScaleFactor sf);
Score weight_option(const std::string& mgOpt, const std::string& egOpt, Score internalWeight);
void init_safety();
Value do_evaluate(const Position& pos, Value& margin) {
EvalInfo ei;
+ Value margins[2];
Score mobilityWhite, mobilityBlack;
assert(pos.is_ok());
// in the position object (material + piece square tables).
Score bonus = pos.value();
- // margin is the uncertainty estimation of position's evaluation
- // and typically is used by the search for pruning decisions.
- margin = VALUE_ZERO;
+ // margins[] store the uncertainty estimation of position's evaluation
+ // that typically is used by the search for pruning decisions.
+ margins[WHITE] = margins[BLACK] = VALUE_ZERO;
// Probe the material hash table
MaterialInfo* mi = MaterialTable[pos.thread()]->get_material_info(pos);
// If we have a specialized evaluation function for the current material
// configuration, call it and return.
if (mi->specialized_eval_exists())
+ {
+ margin = VALUE_ZERO;
return mi->evaluate(pos);
+ }
// Probe the pawn hash table
ei.pi = PawnTable[pos.thread()]->get_pawn_info(pos);
// Evaluate kings after all other pieces because we need complete attack
// information when computing the king safety evaluation.
- bonus += evaluate_king<WHITE, HasPopCnt>(pos, ei, margin)
- - evaluate_king<BLACK, HasPopCnt>(pos, ei, margin);
+ bonus += evaluate_king<WHITE, HasPopCnt>(pos, ei, margins)
+ - evaluate_king<BLACK, HasPopCnt>(pos, ei, margins);
// Evaluate tactical threats, we need full attack information including king
bonus += evaluate_threats<WHITE>(pos, ei)
}
// Scale winning side if position is more drawish that what it appears
- ScaleFactor sf = eg_value(bonus) > VALUE_ZERO ? mi->scale_factor(pos, WHITE)
+ ScaleFactor sf = eg_value(bonus) > VALUE_DRAW ? mi->scale_factor(pos, WHITE)
: mi->scale_factor(pos, BLACK);
Phase phase = mi->game_phase();
}
// Interpolate between the middle game and the endgame score
+ margin = margins[pos.side_to_move()];
Value v = scale_by_game_phase(bonus, phase, sf);
return pos.side_to_move() == WHITE ? v : -v;
}
// If running in analysis mode, make sure we use symmetrical king safety. We do this
// by replacing both Weights[kingDangerUs] and Weights[kingDangerThem] by their average.
- if (get_option_value_bool("UCI_AnalyseMode"))
+ if (Options["UCI_AnalyseMode"].value<bool>())
Weights[kingDangerUs] = Weights[kingDangerThem] = (Weights[kingDangerUs] + Weights[kingDangerThem]) / 2;
init_safety();
const Color Them = (Us == WHITE ? BLACK : WHITE);
Bitboard b = ei.attackedBy[Them][KING] = pos.attacks_from<KING>(pos.king_square(Them));
- ei.kingZone[Us] = (b | (Us == WHITE ? b >> 8 : b << 8));
ei.attackedBy[Us][PAWN] = ei.pi->pawn_attacks(Us);
- ei.updateKingTables[Us] = pos.piece_count(Us, QUEEN) && pos.non_pawn_material(Us) >= QueenValueMidgame + RookValueMidgame;
- if (ei.updateKingTables[Us])
+
+ // Init king safety tables only if we are going to use them
+ if ( pos.piece_count(Us, QUEEN)
+ && pos.non_pawn_material(Us) >= QueenValueMidgame + RookValueMidgame)
{
+ ei.kingZone[Us] = (b | (Us == WHITE ? b >> 8 : b << 8));
b &= ei.attackedBy[Us][PAWN];
- ei.kingAttackersCount[Us] = b ? count_1s<Max15>(b) / 2 : EmptyBoardBB;
- ei.kingAdjacentZoneAttacksCount[Us] = ei.kingAttackersWeight[Us] = EmptyBoardBB;
- }
+ ei.kingAttackersCount[Us] = b ? count_1s<Max15>(b) / 2 : 0;
+ ei.kingAdjacentZoneAttacksCount[Us] = ei.kingAttackersWeight[Us] = 0;
+ } else
+ ei.kingZone[Us] = ei.kingAttackersCount[Us] = 0;
}
ei.attackedBy[Us][Piece] |= b;
// King attacks
- if (ei.updateKingTables[Us] && (b & ei.kingZone[Us]))
+ if (b & ei.kingZone[Us])
{
ei.kingAttackersCount[Us]++;
ei.kingAttackersWeight[Us] += KingAttackWeights[Piece];
// evaluate_king<>() assigns bonuses and penalties to a king of a given color
template<Color Us, bool HasPopCnt>
- Score evaluate_king(const Position& pos, EvalInfo& ei, Value& margin) {
+ Score evaluate_king(const Position& pos, EvalInfo& ei, Value margins[]) {
const BitCountType Max15 = HasPopCnt ? CNT_POPCNT : CpuIs64Bit ? CNT64_MAX15 : CNT32_MAX15;
const Color Them = (Us == WHITE ? BLACK : WHITE);
// King safety. This is quite complicated, and is almost certainly far
// from optimally tuned.
- if ( ei.updateKingTables[Them]
- && ei.kingAttackersCount[Them] >= 2
+ if ( ei.kingAttackersCount[Them] >= 2
&& ei.kingAdjacentZoneAttacksCount[Them])
{
// Find the attacked squares around the king which has no defenders
// be very big, and so capturing a single attacking piece can therefore
// result in a score change far bigger than the value of the captured piece.
bonus -= KingDangerTable[Us][attackUnits];
- if (pos.side_to_move() == Us)
- margin += mg_value(KingDangerTable[Us][attackUnits]);
+ margins[Us] += mg_value(KingDangerTable[Us][attackUnits]);
}
return bonus;
}
Score weight_option(const std::string& mgOpt, const std::string& egOpt, Score internalWeight) {
// Scale option value from 100 to 256
- int mg = get_option_value_int(mgOpt) * 256 / 100;
- int eg = get_option_value_int(egOpt) * 256 / 100;
+ int mg = Options[mgOpt].value<int>() * 256 / 100;
+ int eg = Options[egOpt].value<int>() * 256 / 100;
return apply_weight(make_score(mg, eg), internalWeight);
}