doEvenDeeperSearch + tuning

[stockfish] / src / evaluate.cpp

diff --git a/src/evaluate.cpp b/src/evaluate.cpp
index eaad4d556722cb0b2f99d8e0984865f2c549f8c1..93e665dfe859295dda5364904469f85cd4ad93eb 100644 (file)
--- a/src/evaluate.cpp
+++ b/src/evaluate.cpp
@@ -159,7 +159,7 @@ namespace Trace {
 
   Score scores[TERM_NB][COLOR_NB];
 
-  double to_cp(Value v) { return double(v) / PawnValueEg; }
+  double to_cp(Value v) { return double(v) / UCI::NormalizeToPawnValue; }
 
   void add(int idx, Color c, Score s) {
     scores[idx][c] = s;
@@ -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);
@@ -1051,35 +1051,41 @@ 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) > 1760);
 
   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 = (  412 * nnueComplexity
+                        + 428 * abs(psq - nnue)
+                        + (optimism  > 0 ? int(optimism) * int(psq - nnue) : 0)
+                        ) / 1026;
+
+      // Return hybrid NNUE complexity to caller
+      if (complexity)
           *complexity = nnueComplexity;
 
-      optimism = optimism * (269 + nnueComplexity) / 256;
-      v = (nnue * scale + optimism * (scale - 754)) / 1024;
+      optimism = optimism * (278 + nnueComplexity) / 256;
+      v = (nnue * scale + optimism * (scale - 755)) / 1024;
   }
 
   // Damp down the evaluation linearly when shuffling
-  v = v * (195 - pos.rule50_count()) / 211;
+  v = v * (197 - 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);
@@ -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;