1
0
Fork 0
mirror of https://github.com/sockspls/badfish synced 2025-04-29 16:23:09 +00:00
BadFish/src/evaluate.cpp
Stefan Geschwentner 48a3b7c0ee Simplify non-pawn material divisor to a constant
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
2024-04-28 21:43:46 +02:00

127 lines
4.8 KiB
C++

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