X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=src%2Fevaluate.cpp;h=3aae9f1c6ec3d337d5a9c953e5e528cf3a341439;hb=31acd6bab70f4661316986c2c93163d39736fd61;hp=eaad4d556722cb0b2f99d8e0984865f2c549f8c1;hpb=15ac117ac492e3147b391aa0ee3665fe8876be63;p=stockfish

diff --git a/src/evaluate.cpp b/src/evaluate.cpp
index eaad4d55..3aae9f1c 100644
--- a/src/evaluate.cpp
+++ b/src/evaluate.cpp
@@ -1,6 +1,6 @@
 /*
   Stockfish, a UCI chess playing engine derived from Glaurung 2.1
-  Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
+  Copyright (C) 2004-2023 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
@@ -159,24 +159,24 @@ namespace Trace {
 
   Score scores[TERM_NB][COLOR_NB];
 
-  double to_cp(Value v) { return double(v) / PawnValueEg; }
+  static double to_cp(Value v) { return double(v) / UCI::NormalizeToPawnValue; }
 
-  void add(int idx, Color c, Score s) {
+  static void add(int idx, Color c, Score s) {
     scores[idx][c] = s;
   }
 
-  void add(int idx, Score w, Score b = SCORE_ZERO) {
+  static void add(int idx, Score w, Score b = SCORE_ZERO) {
     scores[idx][WHITE] = w;
     scores[idx][BLACK] = b;
   }
 
-  std::ostream& operator<<(std::ostream& os, Score s) {
+  static std::ostream& operator<<(std::ostream& os, Score s) {
     os << std::setw(5) << to_cp(mg_value(s)) << " "
        << std::setw(5) << to_cp(eg_value(s));
     return os;
   }
 
-  std::ostream& operator<<(std::ostream& os, Term t) {
+  static std::ostream& operator<<(std::ostream& os, Term t) {
 
     if (t == MATERIAL || t == IMBALANCE || t == WINNABLE || t == TOTAL)
         os << " ----  ----"    << " | " << " ----  ----";
@@ -981,7 +981,7 @@ namespace {
     // Initialize score by reading the incrementally updated scores included in
     // the position object (material + piece square tables) and the material
     // imbalance. Score is computed internally from the white point of view.
-    Score score = pos.psq_score() + me->imbalance() + pos.this_thread()->trend;
+    Score score = pos.psq_score() + me->imbalance();
 
     // Probe the pawn hash table
     pe = Pawns::probe(pos);
@@ -1051,41 +1051,47 @@ make_v:
 Value Eval::evaluate(const Position& pos, int* complexity) {
 
   Value v;
-  Color stm = pos.side_to_move();
   Value psq = pos.psq_eg_stm();
 
-  // Deciding between classical and NNUE eval: for high PSQ imbalance we use classical,
-  // but we switch to NNUE during long shuffling or with high material on the board.
-  bool useClassical = !useNNUE ||
-                      ((pos.count<ALL_PIECES>() > 7)
-                       && abs(psq) * 5 > (856 + pos.non_pawn_material() / 64) * (10 + pos.rule50_count()));
+  // We use the much less accurate but faster Classical eval when the NNUE
+  // option is set to false. Otherwise we use the NNUE eval unless the
+  // PSQ advantage is decisive and several pieces remain. (~3 Elo)
+  bool useClassical = !useNNUE || (pos.count<ALL_PIECES>() > 7 && abs(psq) > 1781);
 
   if (useClassical)
       v = Evaluation<NO_TRACE>(pos).value();
   else
   {
       int nnueComplexity;
-      int scale = 1064 + 106 * pos.non_pawn_material() / 5120;
+      int scale = 1076 + 96 * pos.non_pawn_material() / 5120;
+
+      Color stm = pos.side_to_move();
       Value optimism = pos.this_thread()->optimism[stm];
 
       Value nnue = NNUE::evaluate(pos, true, &nnueComplexity);
+
       // Blend nnue complexity with (semi)classical complexity
-      nnueComplexity = (104 * nnueComplexity + 131 * abs(nnue - psq)) / 256;
-      if (complexity) // Return hybrid NNUE complexity to caller
+      nnueComplexity = (  406 * nnueComplexity
+                        + 424 * abs(psq - nnue)
+                        + (optimism  > 0 ? int(optimism) * int(psq - nnue) : 0)
+                        ) / 1024;
+
+      // Return hybrid NNUE complexity to caller
+      if (complexity)
           *complexity = nnueComplexity;
 
-      optimism = optimism * (269 + nnueComplexity) / 256;
-      v = (nnue * scale + optimism * (scale - 754)) / 1024;
+      optimism = optimism * (272 + nnueComplexity) / 256;
+      v = (nnue * scale + optimism * (scale - 748)) / 1024;
   }
 
   // Damp down the evaluation linearly when shuffling
-  v = v * (195 - pos.rule50_count()) / 211;
+  v = v * (200 - pos.rule50_count()) / 214;
 
   // 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);
 
   // When not using NNUE, return classical complexity to caller
-  if (complexity && (!useNNUE || useClassical))
+  if (complexity && useClassical)
       *complexity = abs(v - psq);
 
   return v;
@@ -1109,7 +1115,6 @@ std::string Eval::trace(Position& pos) {
   std::memset(scores, 0, sizeof(scores));
 
   // Reset any global variable used in eval
-  pos.this_thread()->trend           = SCORE_ZERO;
   pos.this_thread()->bestValue       = VALUE_ZERO;
   pos.this_thread()->optimism[WHITE] = VALUE_ZERO;
   pos.this_thread()->optimism[BLACK] = VALUE_ZERO;