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Use same nnue divisor for both nets
Passed non-regression STC: https://tests.stockfishchess.org/tests/view/6643ceeabc537f56194506f6 LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 224800 W: 57910 L: 57896 D: 108994 Ptnml(0-2): 673, 26790, 57519, 26686, 732 Passed non-regression LTC: https://tests.stockfishchess.org/tests/view/6643ff15bc537f5619451719 LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 347658 W: 87574 L: 87688 D: 172396 Ptnml(0-2): 207, 39004, 95488, 38956, 174 closes https://github.com/official-stockfish/Stockfish/pull/5250 Bench: 1804704
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1 changed files with 4 additions and 4 deletions
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@ -68,10 +68,10 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks,
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smallNet = false;
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smallNet = false;
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}
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}
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const auto adjustEval = [&](int nnueDiv, int pawnCountMul, int shufflingConstant) {
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const auto adjustEval = [&](int pawnCountMul, int shufflingConstant) {
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// Blend optimism and eval with nnue complexity and material imbalance
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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)) / 584;
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optimism += optimism * (nnueComplexity + std::abs(simpleEval - nnue)) / 584;
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nnue -= nnue * (nnueComplexity * 5 / 3) / nnueDiv;
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nnue -= nnue * (nnueComplexity * 5 / 3) / 32395;
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int npm = pos.non_pawn_material() / 64;
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int npm = pos.non_pawn_material() / 64;
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v = (nnue * (npm + 943 + pawnCountMul * pos.count<PAWN>()) + optimism * (npm + 140)) / 1058;
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v = (nnue * (npm + 943 + pawnCountMul * pos.count<PAWN>()) + optimism * (npm + 140)) / 1058;
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@ -82,9 +82,9 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks,
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};
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};
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if (!smallNet)
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if (!smallNet)
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adjustEval(32395, 11, 178);
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adjustEval(11, 178);
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else
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else
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adjustEval(32793, 9, 206);
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adjustEval(9, 206);
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// Guarantee evaluation does not hit the tablebase range
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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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v = std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
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