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();
// 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);
// 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);
}