mirror of
https://github.com/sockspls/badfish
synced 2025-04-29 16:23:09 +00:00
48a3b7c0ee
Passed STC: https://tests.stockfishchess.org/tests/view/662942603fe04ce4cefc7aba LLR: 2.93 (-2.94,2.94) <-1.75,0.25> Total: 272832 W: 70456 L: 70497 D: 131879 Ptnml(0-2): 1020, 32619, 69154, 32628, 995 Passed LTC: https://tests.stockfishchess.org/tests/view/662dfe3b6115ff6764c829eb LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 100254 W: 25446 L: 25303 D: 49505 Ptnml(0-2): 121, 11292, 27166, 11419, 129 closes https://github.com/official-stockfish/Stockfish/pull/5198 Bench: 1544645
127 lines
4.8 KiB
C++
127 lines
4.8 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2024 The Stockfish developers (see AUTHORS file)
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Stockfish is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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Stockfish is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "evaluate.h"
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#include <algorithm>
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#include <cassert>
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#include <cmath>
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#include <cstdlib>
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#include <iomanip>
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#include <iostream>
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#include <sstream>
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#include <memory>
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#include "nnue/network.h"
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#include "nnue/nnue_misc.h"
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#include "position.h"
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#include "types.h"
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#include "uci.h"
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#include "nnue/nnue_accumulator.h"
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namespace Stockfish {
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// Returns a static, purely materialistic evaluation of the position from
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// the point of view of the given color. It can be divided by PawnValue to get
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// an approximation of the material advantage on the board in terms of pawns.
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int Eval::simple_eval(const Position& pos, Color c) {
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return PawnValue * (pos.count<PAWN>(c) - pos.count<PAWN>(~c))
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+ (pos.non_pawn_material(c) - pos.non_pawn_material(~c));
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}
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// Evaluate is the evaluator for the outer world. It returns a static evaluation
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// of the position from the point of view of the side to move.
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Value Eval::evaluate(const Eval::NNUE::Networks& networks,
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const Position& pos,
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Eval::NNUE::AccumulatorCaches& caches,
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int optimism) {
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assert(!pos.checkers());
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int simpleEval = simple_eval(pos, pos.side_to_move());
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bool smallNet = std::abs(simpleEval) > SmallNetThreshold;
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bool psqtOnly = std::abs(simpleEval) > PsqtOnlyThreshold;
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int nnueComplexity;
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int v;
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Value nnue = smallNet
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? networks.small.evaluate(pos, &caches.small, true, &nnueComplexity, psqtOnly)
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: networks.big.evaluate(pos, &caches.big, true, &nnueComplexity, false);
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const auto adjustEval = [&](int optDiv, int nnueDiv, int pawnCountConstant, int pawnCountMul,
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int npmConstant, int evalDiv, int shufflingConstant,
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int shufflingDiv) {
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// Blend optimism and eval with nnue complexity and material imbalance
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optimism += optimism * (nnueComplexity + std::abs(simpleEval - nnue)) / optDiv;
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nnue -= nnue * (nnueComplexity * 5 / 3) / nnueDiv;
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int npm = pos.non_pawn_material() / 64;
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v = (nnue * (npm + pawnCountConstant + pawnCountMul * pos.count<PAWN>())
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+ optimism * (npmConstant + npm))
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/ evalDiv;
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// Damp down the evaluation linearly when shuffling
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int shuffling = pos.rule50_count();
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v = v * (shufflingConstant - shuffling) / shufflingDiv;
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};
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if (!smallNet)
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adjustEval(524, 32395, 942, 11, 139, 1058, 178, 204);
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else if (psqtOnly)
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adjustEval(517, 32857, 908, 7, 155, 1006, 224, 238);
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else
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adjustEval(515, 32793, 944, 9, 140, 1067, 206, 206);
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// Guarantee evaluation does not hit the tablebase range
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v = std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
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return v;
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}
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// Like evaluate(), but instead of returning a value, it returns
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// a string (suitable for outputting to stdout) that contains the detailed
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// descriptions and values of each evaluation term. Useful for debugging.
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// Trace scores are from white's point of view
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std::string Eval::trace(Position& pos, const Eval::NNUE::Networks& networks) {
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auto caches = std::make_unique<Eval::NNUE::AccumulatorCaches>(networks);
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if (pos.checkers())
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return "Final evaluation: none (in check)";
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std::stringstream ss;
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ss << std::showpoint << std::noshowpos << std::fixed << std::setprecision(2);
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ss << '\n' << NNUE::trace(pos, networks, *caches) << '\n';
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ss << std::showpoint << std::showpos << std::fixed << std::setprecision(2) << std::setw(15);
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Value v = networks.big.evaluate(pos, &caches->big, false);
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v = pos.side_to_move() == WHITE ? v : -v;
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ss << "NNUE evaluation " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)\n";
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v = evaluate(networks, pos, *caches, VALUE_ZERO);
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v = pos.side_to_move() == WHITE ? v : -v;
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ss << "Final evaluation " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)";
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ss << " [with scaled NNUE, ...]";
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ss << "\n";
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return ss.str();
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}
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} // namespace Stockfish
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