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Use same eval divisor for both nets

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Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/66428f146577e9d2c8a29cf8
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 241024 W: 62173 L: 62177 D: 116674
Ptnml(0-2): 904, 28648, 61407, 28654, 899

Passed non-regression LTC:
https://tests.stockfishchess.org/tests/view/6643ae6f1f32a966da74977b
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 193710 W: 48762 L: 48717 D: 96231
Ptnml(0-2): 70, 21599, 53481, 21626, 79

closes https://github.com/official-stockfish/Stockfish/pull/5246

Bench: 1700680
This commit is contained in:
Linmiao Xu 2024-05-13 18:06:38 -04:00 • committed by Disservin
parent 9b90cd88f0
commit 1f3a0fda2e
1 changed files with 4 additions and 5 deletions
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9
src/evaluate.cpp
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@ -68,14 +68,13 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks,
smallNet = false;
}
const auto adjustEval = [&](int nnueDiv, int pawnCountMul, int evalDiv, int shufflingConstant) {
const auto adjustEval = [&](int nnueDiv, int pawnCountMul, int shufflingConstant) {
// Blend optimism and eval with nnue complexity and material imbalance
optimism += optimism * (nnueComplexity + std::abs(simpleEval - nnue)) / 584;
nnue -= nnue * (nnueComplexity * 5 / 3) / nnueDiv;
int npm = pos.non_pawn_material() / 64;
v = (nnue * (npm + 943 + pawnCountMul * pos.count<PAWN>()) + optimism * (npm + 140))
/ evalDiv;
v = (nnue * (npm + 943 + pawnCountMul * pos.count<PAWN>()) + optimism * (npm + 140)) / 1058;
// Damp down the evaluation linearly when shuffling
int shuffling = pos.rule50_count();
@ -83,9 +82,9 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks,
};
if (!smallNet)
adjustEval(32395, 11, 1058, 178);
adjustEval(32395, 11, 178);
else
adjustEval(32793, 9, 1067, 206);
adjustEval(32793, 9, 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);