X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=src%2Fevaluate.cpp;h=12883fcc43e57a8a18f71e407f3604816a989321;hb=66bf45b99e2061c1ba74f9975bc5059ac0121dfd;hp=6db977a4c7e2c636c82b4417b423daf0c2203470;hpb=4f4e652ecaf8d42a9bd1092f72c3704435ddba12;p=stockfish

diff --git a/src/evaluate.cpp b/src/evaluate.cpp
index 6db977a4..12883fcc 100644
--- a/src/evaluate.cpp
+++ b/src/evaluate.cpp
@@ -36,7 +36,7 @@
 #include "timeman.h"
 #include "uci.h"
 #include "incbin/incbin.h"
-
+#include "nnue/evaluate_nnue.h"
 
 // Macro to embed the default efficiently updatable neural network (NNUE) file
 // data in the engine binary (using incbin.h, by Dale Weiler).
@@ -82,8 +82,6 @@ namespace Eval {
         eval_file = EvalFileDefaultName;
 
     #if defined(DEFAULT_NNUE_DIRECTORY)
-    #define stringify2(x) #x
-    #define stringify(x) stringify2(x)
     vector<string> dirs = { "<internal>" , "" , CommandLine::binaryDirectory , stringify(DEFAULT_NNUE_DIRECTORY) };
     #else
     vector<string> dirs = { "<internal>" , "" , CommandLine::binaryDirectory };
@@ -95,7 +93,7 @@ namespace Eval {
             if (directory != "<internal>")
             {
                 ifstream stream(directory + eval_file, ios::binary);
-                if (load_eval(eval_file, stream))
+                if (NNUE::load_eval(eval_file, stream))
                     currentEvalFileName = eval_file;
             }
 
@@ -111,7 +109,7 @@ namespace Eval {
                 (void) gEmbeddedNNUEEnd; // Silence warning on unused variable
 
                 istream stream(&buffer);
-                if (load_eval(eval_file, stream))
+                if (NNUE::load_eval(eval_file, stream))
                     currentEvalFileName = eval_file;
             }
         }
@@ -193,8 +191,8 @@ using namespace Trace;
 namespace {
 
   // Threshold for lazy and space evaluation
-  constexpr Value LazyThreshold1    =  Value(3631);
-  constexpr Value LazyThreshold2    =  Value(2084);
+  constexpr Value LazyThreshold1    =  Value(3622);
+  constexpr Value LazyThreshold2    =  Value(1962);
   constexpr Value SpaceThreshold    =  Value(11551);
 
   // KingAttackWeights[PieceType] contains king attack weights by piece type
@@ -388,10 +386,10 @@ namespace {
   template<Tracing T> template<Color Us, PieceType Pt>
   Score Evaluation<T>::pieces() {
 
-    constexpr Color     Them = ~Us;
-    constexpr Direction Down = -pawn_push(Us);
-    constexpr Bitboard OutpostRanks = (Us == WHITE ? Rank4BB | Rank5BB | Rank6BB
-                                                   : Rank5BB | Rank4BB | Rank3BB);
+    constexpr Color Them = ~Us;
+    [[maybe_unused]] constexpr Direction Down = -pawn_push(Us);
+    [[maybe_unused]] constexpr Bitboard OutpostRanks = (Us == WHITE ? Rank4BB | Rank5BB | Rank6BB
+                                                                    : Rank5BB | Rank4BB | Rank3BB);
     Bitboard b1 = pos.pieces(Us, Pt);
     Bitboard b, bb;
     Score score = SCORE_ZERO;
@@ -430,7 +428,7 @@ namespace {
         int mob = popcount(b & mobilityArea[Us]);
         mobility[Us] += MobilityBonus[Pt - 2][mob];
 
-        if (Pt == BISHOP || Pt == KNIGHT)
+        if constexpr (Pt == BISHOP || Pt == KNIGHT)
         {
             // Bonus if the piece is on an outpost square or can reach one
             // Bonus for knights (UncontestedOutpost) if few relevant targets
@@ -1063,7 +1061,7 @@ Value Eval::evaluate(const Position& pos, int* complexity) {
   else
   {
       int nnueComplexity;
-      int scale = 1076 + 96 * pos.non_pawn_material() / 5120;
+      int scale = 1001 + 5 * pos.count<PAWN>() + 61 * pos.non_pawn_material() / 4096;
 
       Color stm = pos.side_to_move();
       Value optimism = pos.this_thread()->optimism[stm];
@@ -1072,8 +1070,7 @@ Value Eval::evaluate(const Position& pos, int* complexity) {
 
       // Blend nnue complexity with (semi)classical complexity
       nnueComplexity = (  406 * nnueComplexity
-                        + 424 * abs(psq - nnue)
-                        + (optimism  > 0 ? int(optimism) * int(psq - nnue) : 0)
+                        + (424 + optimism) * abs(psq - nnue)
                         ) / 1024;
 
       // Return hybrid NNUE complexity to caller