// better than the second best move.
const Value EasyMoveMargin = Value(0x200);
- // Last seconds noise filtering (LSN)
- const bool UseLSNFiltering = true;
- const int LSNTime = 100; // In milliseconds
- const Value LSNValue = value_from_centipawns(200);
- bool loseOnTime = false;
-
/// Global variables
bool connected_moves(const Position& pos, Move m1, Move m2);
bool value_is_mate(Value value);
+ Value value_to_tt(Value v, int ply);
+ Value value_from_tt(Value v, int ply);
bool move_is_killer(Move m, SearchStack* ss);
bool ok_to_use_TT(const TTEntry* tte, Depth depth, Value beta, int ply);
bool connected_threat(const Position& pos, Move m, Move threat);
void update_gains(const Position& pos, Move move, Value before, Value after);
int current_search_time();
+ std::string value_to_uci(Value v);
int nps();
void poll();
void ponderhit();
}
}
- // Reset loseOnTime flag at the beginning of a new game
- if (button_was_pressed("New Game"))
- loseOnTime = false;
-
// Read UCI option values
TT.set_size(get_option_value_int("Hash"));
if (button_was_pressed("Clear Hash"))
<< " increment: " << myIncrement
<< " moves to go: " << movesToGo << endl;
- // LSN filtering. Used only for developing purposes, disabled by default
- if ( UseLSNFiltering
- && loseOnTime)
- {
- // Step 2. If after last move we decided to lose on time, do it now!
- while (SearchStartTime + myTime + 1000 > get_system_time())
- /* wait here */;
- }
-
// We're ready to start thinking. Call the iterative deepening loop function
- Value v = id_loop(pos, searchMoves);
-
- if (UseLSNFiltering)
- {
- // Step 1. If this is sudden death game and our position is hopeless,
- // decide to lose on time.
- if ( !loseOnTime // If we already lost on time, go to step 3.
- && myTime < LSNTime
- && myIncrement == 0
- && movesToGo == 0
- && v < -LSNValue)
- {
- loseOnTime = true;
- }
- else if (loseOnTime)
- {
- // Step 3. Now after stepping over the time limit, reset flag for next match.
- loseOnTime = false;
- }
- }
+ id_loop(pos, searchMoves);
if (UseLogFile)
LogFile.close();
// so to output information also for iteration 1.
cout << "info depth " << 1
<< "\ninfo depth " << 1
- << " score " << value_to_string(rml.get_move_score(0))
+ << " score " << value_to_uci(rml.get_move_score(0))
<< " time " << current_search_time()
<< " nodes " << TM.nodes_searched()
<< " nps " << nps()
for (int j = 0; j < Min(MultiPV, rml.move_count()); j++)
{
cout << "info multipv " << j + 1
- << " score " << value_to_string(rml.get_move_score(j))
+ << " score " << value_to_uci(rml.get_move_score(j))
<< " depth " << (j <= i ? Iteration : Iteration - 1)
<< " time " << current_search_time()
<< " nodes " << TM.nodes_searched()
if (nullValue >= value_mate_in(PLY_MAX))
nullValue = beta;
- // Do zugzwang verification search at high depths
if (depth < 6 * OnePly)
return nullValue;
+ // Do verification search at high depths
ss->skipNullMove = true;
- Value v = search<NonPV>(pos, ss, alpha, beta, depth-5*OnePly, ply);
+ Value v = search<NonPV>(pos, ss, alpha, beta, depth-R*OnePly, ply);
ss->skipNullMove = false;
if (v >= beta)
}
- // value_is_mate() checks if the given value is a mate one
- // eventually compensated for the ply.
+ // value_is_mate() checks if the given value is a mate one eventually
+ // compensated for the ply.
bool value_is_mate(Value value) {
}
- // move_is_killer() checks if the given move is among the
- // killer moves of that ply.
+ // value_to_tt() adjusts a mate score from "plies to mate from the root" to
+ // "plies to mate from the current ply". Non-mate scores are unchanged.
+ // The function is called before storing a value to the transposition table.
+
+ Value value_to_tt(Value v, int ply) {
+
+ if (v >= value_mate_in(PLY_MAX))
+ return v + ply;
+
+ if (v <= value_mated_in(PLY_MAX))
+ return v - ply;
+
+ return v;
+ }
+
+
+ // value_from_tt() is the inverse of value_to_tt(): It adjusts a mate score from
+ // the transposition table to a mate score corrected for the current ply.
+
+ Value value_from_tt(Value v, int ply) {
+
+ if (v >= value_mate_in(PLY_MAX))
+ return v - ply;
+
+ if (v <= value_mated_in(PLY_MAX))
+ return v + ply;
+
+ return v;
+ }
+
+
+ // move_is_killer() checks if the given move is among the killer moves
bool move_is_killer(Move m, SearchStack* ss) {
}
+ // value_to_uci() converts a value to a string suitable for use with the UCI protocol
+
+ std::string value_to_uci(Value v) {
+
+ std::stringstream s;
+
+ if (abs(v) < VALUE_MATE - PLY_MAX * OnePly)
+ s << "cp " << int(v) * 100 / int(PawnValueMidgame); // Scale to pawn = 100
+ else
+ s << "mate " << (v > 0 ? (VALUE_MATE - v + 1) / 2 : -(VALUE_MATE + v) / 2 );
+
+ return s.str();
+ }
+
// nps() computes the current nodes/second count.
int nps() {
void print_pv_info(const Position& pos, Move pv[], Value alpha, Value beta, Value value) {
cout << "info depth " << Iteration
- << " score " << value_to_string(value)
+ << " score " << value_to_uci(value)
<< (value >= beta ? " lowerbound" : value <= alpha ? " upperbound" : "")
<< " time " << current_search_time()
<< " nodes " << TM.nodes_searched()
projects / stockfish / blobdiff