#include <cassert>
#include <cmath>
#include <cstring>
-#include <fstream>
#include <iomanip>
#include <iostream>
#include <sstream>
#include <vector>
+#include <algorithm>
#include "book.h"
#include "evaluate.h"
inline Value futility_margin(Depth d, int mn) {
- return d < 7 * ONE_PLY ? FutilityMargins[Max(d, 1)][Min(mn, 63)]
+ return d < 7 * ONE_PLY ? FutilityMargins[std::max(int(d), 1)][std::min(mn, 63)]
: 2 * VALUE_INFINITE;
}
template <bool PvNode> inline Depth reduction(Depth d, int mn) {
- return (Depth) Reductions[PvNode][Min(d / ONE_PLY, 63)][Min(mn, 63)];
+ return (Depth) Reductions[PvNode][std::min(int(d) / ONE_PLY, 63)][std::min(mn, 63)];
}
// Easy move margin. An easy move candidate must be at least this much
RootMoveList Rml;
// MultiPV mode
- int MultiPV, UCIMultiPV, MultiPVIteration;
+ int MultiPV, UCIMultiPV, MultiPVIdx;
// Time management variables
bool StopOnPonderhit, FirstRootMove, StopRequest, QuitRequest, AspirationFailLow;
TimeManager TimeMgr;
SearchLimits Limits;
- // Log file
- std::ofstream LogFile;
-
// Skill level adjustment
int SkillLevel;
bool SkillLevelEnabled;
bool connected_threat(const Position& pos, Move m, Move threat);
Value refine_eval(const TTEntry* tte, Value defaultEval, int ply);
void update_history(const Position& pos, Move move, Depth depth, Move movesSearched[], int moveCount);
- void update_gains(const Position& pos, Move move, Value before, Value after);
void do_skill_level(Move* best, Move* ponder);
int current_search_time(int set = 0);
if ( captureOrPromotion
&& type_of(pos.piece_on(move_to(m))) != PAWN
&& ( pos.non_pawn_material(WHITE) + pos.non_pawn_material(BLACK)
- - piece_value_midgame(pos.piece_on(move_to(m))) == VALUE_ZERO)
+ - PieceValueMidgame[pos.piece_on(move_to(m))] == VALUE_ZERO)
&& !is_special(m))
{
result += PawnEndgameExtension[PvNode];
*dangerous = true;
}
- return Min(result, ONE_PLY);
+ return std::min(result, ONE_PLY);
}
} // namespace
// Set best NodesBetweenPolls interval to avoid lagging under time pressure
if (Limits.maxNodes)
- NodesBetweenPolls = Min(Limits.maxNodes, 30000);
+ NodesBetweenPolls = std::min(Limits.maxNodes, 30000);
else if (Limits.time && Limits.time < 1000)
NodesBetweenPolls = 1000;
else if (Limits.time && Limits.time < 5000)
// Do we have to play with skill handicap? In this case enable MultiPV that
// we will use behind the scenes to retrieve a set of possible moves.
SkillLevelEnabled = (SkillLevel < 20);
- MultiPV = (SkillLevelEnabled ? Max(UCIMultiPV, 4) : UCIMultiPV);
+ MultiPV = (SkillLevelEnabled ? std::max(UCIMultiPV, 4) : UCIMultiPV);
// Wake up needed threads and reset maxPly counter
for (int i = 0; i < Threads.size(); i++)
// Write to log file and keep it open to be accessed during the search
if (Options["Use Search Log"].value<bool>())
{
- string name = Options["Search Log Filename"].value<string>();
- LogFile.open(name.c_str(), std::ios::out | std::ios::app);
-
- if (LogFile.is_open())
- LogFile << "\nSearching: " << pos.to_fen()
- << "\ninfinite: " << Limits.infinite
- << " ponder: " << Limits.ponder
- << " time: " << Limits.time
- << " increment: " << Limits.increment
- << " moves to go: " << Limits.movesToGo
- << endl;
+ Log log(Options["Search Log Filename"].value<string>());
+ log << "\nSearching: " << pos.to_fen()
+ << "\ninfinite: " << Limits.infinite
+ << " ponder: " << Limits.ponder
+ << " time: " << Limits.time
+ << " increment: " << Limits.increment
+ << " moves to go: " << Limits.movesToGo
+ << endl;
}
// We're ready to start thinking. Call the iterative deepening loop function
Move bestMove = id_loop(pos, searchMoves, &ponderMove);
// Write final search statistics and close log file
- if (LogFile.is_open())
+ if (Options["Use Search Log"].value<bool>())
{
int t = current_search_time();
- LogFile << "Nodes: " << pos.nodes_searched()
- << "\nNodes/second: " << (t > 0 ? pos.nodes_searched() * 1000 / t : 0)
- << "\nBest move: " << move_to_san(pos, bestMove);
+ Log log(Options["Search Log Filename"].value<string>());
+ log << "Nodes: " << pos.nodes_searched()
+ << "\nNodes/second: " << (t > 0 ? pos.nodes_searched() * 1000 / t : 0)
+ << "\nBest move: " << move_to_san(pos, bestMove);
StateInfo st;
pos.do_move(bestMove, st);
- LogFile << "\nPonder move: " << move_to_san(pos, ponderMove) << endl;
+ log << "\nPonder move: " << move_to_san(pos, ponderMove) << endl;
pos.undo_move(bestMove); // Return from think() with unchanged position
- LogFile.close();
}
// This makes all the threads to go to sleep
*ponderMove = bestMove = easyMove = skillBest = skillPonder = MOVE_NONE;
depth = aspirationDelta = 0;
value = alpha = -VALUE_INFINITE, beta = VALUE_INFINITE;
- ss->currentMove = MOVE_NULL; // Hack to skip update_gains()
+ ss->currentMove = MOVE_NULL; // Hack to skip update gains
// Moves to search are verified and copied
Rml.init(pos, searchMoves);
// Iterative deepening loop until requested to stop or target depth reached
while (!StopRequest && ++depth <= PLY_MAX && (!Limits.maxDepth || depth <= Limits.maxDepth))
{
- // Save last iteration's scores, this needs to be done now, because in
- // the following MultiPV loop Rml moves could be reordered.
+ // Save now last iteration's scores, before Rml moves are reordered
for (size_t i = 0; i < Rml.size(); i++)
Rml[i].prevScore = Rml[i].score;
Rml.bestMoveChanges = 0;
- // MultiPV iteration loop
- for (MultiPVIteration = 0; MultiPVIteration < Min(MultiPV, (int)Rml.size()); MultiPVIteration++)
+ // MultiPV loop. We perform a full root search for each PV line
+ for (MultiPVIdx = 0; MultiPVIdx < std::min(MultiPV, (int)Rml.size()); MultiPVIdx++)
{
// Calculate dynamic aspiration window based on previous iterations
- if (depth >= 5 && abs(Rml[MultiPVIteration].prevScore) < VALUE_KNOWN_WIN)
+ if (depth >= 5 && abs(Rml[MultiPVIdx].prevScore) < VALUE_KNOWN_WIN)
{
int prevDelta1 = bestValues[depth - 1] - bestValues[depth - 2];
int prevDelta2 = bestValues[depth - 2] - bestValues[depth - 3];
- aspirationDelta = Min(Max(abs(prevDelta1) + abs(prevDelta2) / 2, 16), 24);
+ aspirationDelta = std::min(std::max(abs(prevDelta1) + abs(prevDelta2) / 2, 16), 24);
aspirationDelta = (aspirationDelta + 7) / 8 * 8; // Round to match grainSize
- alpha = Max(Rml[MultiPVIteration].prevScore - aspirationDelta, -VALUE_INFINITE);
- beta = Min(Rml[MultiPVIteration].prevScore + aspirationDelta, VALUE_INFINITE);
+ alpha = std::max(Rml[MultiPVIdx].prevScore - aspirationDelta, -VALUE_INFINITE);
+ beta = std::min(Rml[MultiPVIdx].prevScore + aspirationDelta, VALUE_INFINITE);
}
else
{
// Start with a small aspiration window and, in case of fail high/low,
// research with bigger window until not failing high/low anymore.
do {
- // Search starting from ss+1 to allow referencing (ss-1). This is
- // needed by update_gains() and ss copy when splitting at Root.
+ // Search starts from ss+1 to allow referencing (ss-1). This is
+ // needed by update gains and ss copy when splitting at Root.
value = search<Root>(pos, ss+1, alpha, beta, depth * ONE_PLY);
- // It is critical that sorting is done with a stable algorithm
- // because all the values but the first are usually set to
- // -VALUE_INFINITE and we want to keep the same order for all
- // the moves but the new PV that goes to head.
- sort<RootMove>(Rml.begin() + MultiPVIteration, Rml.end());
-
- // In case we have found an exact score reorder the PV moves
- // before leaving the fail high/low loop, otherwise leave the
- // last PV move in its position so to be searched again.
- if (value > alpha && value < beta)
- sort<RootMove>(Rml.begin(), Rml.begin() + MultiPVIteration);
+ // Bring to front the best move. It is critical that sorting is
+ // done with a stable algorithm because all the values but the first
+ // and eventually the new best one are set to -VALUE_INFINITE and
+ // we want to keep the same order for all the moves but the new
+ // PV that goes to the front. Note that in case of MultiPV search
+ // the already searched PV lines are preserved.
+ sort<RootMove>(Rml.begin() + MultiPVIdx, Rml.end());
+
+ // In case we have found an exact score and we are going to leave
+ // the fail high/low loop then reorder the PV moves, otherwise
+ // leave the last PV move in its position so to be searched again.
+ // Of course this is needed only in MultiPV search.
+ if (MultiPVIdx && value > alpha && value < beta)
+ sort<RootMove>(Rml.begin(), Rml.begin() + MultiPVIdx);
// Write PV back to transposition table in case the relevant entries
// have been overwritten during the search.
- for (int i = 0; i <= MultiPVIteration; i++)
+ for (int i = 0; i <= MultiPVIdx; i++)
Rml[i].insert_pv_in_tt(pos);
- // Value cannot be trusted. Break out immediately!
+ // If search has been stopped exit the aspiration window loop,
+ // note that sorting and writing PV back to TT is safe becuase
+ // Rml is still valid, although refers to the previous iteration.
if (StopRequest)
break;
// Send full PV info to GUI if we are going to leave the loop or
- // if we have a fail high/low and we are deep in the search.
+ // if we have a fail high/low and we are deep in the search. UCI
+ // protocol requires to send all the PV lines also if are still
+ // to be searched and so refer to the previous search's score.
if ((value > alpha && value < beta) || current_search_time() > 2000)
- for (int i = 0; i < Min(UCIMultiPV, MultiPVIteration + 1); i++)
+ for (int i = 0; i < std::min(UCIMultiPV, (int)Rml.size()); i++)
+ {
+ bool updated = (i <= MultiPVIdx);
+
+ if (depth == 1 && !updated)
+ continue;
+
+ Depth d = (updated ? depth : depth - 1) * ONE_PLY;
+ Value s = (updated ? Rml[i].score : Rml[i].prevScore);
+
cout << "info"
- << depth_to_uci(depth * ONE_PLY)
- << (i == MultiPVIteration ? score_to_uci(Rml[i].score, alpha, beta) :
- score_to_uci(Rml[i].score))
+ << depth_to_uci(d)
+ << (i == MultiPVIdx ? score_to_uci(s, alpha, beta) : score_to_uci(s))
<< speed_to_uci(pos.nodes_searched())
<< pv_to_uci(&Rml[i].pv[0], i + 1, pos.is_chess960())
<< endl;
+ }
- // In case of failing high/low increase aspiration window and research,
- // otherwise exit the fail high/low loop.
+ // In case of failing high/low increase aspiration window and
+ // research, otherwise exit the fail high/low loop.
if (value >= beta)
{
- beta = Min(beta + aspirationDelta, VALUE_INFINITE);
+ beta = std::min(beta + aspirationDelta, VALUE_INFINITE);
aspirationDelta += aspirationDelta / 2;
}
else if (value <= alpha)
AspirationFailLow = true;
StopOnPonderhit = false;
- alpha = Max(alpha - aspirationDelta, -VALUE_INFINITE);
+ alpha = std::max(alpha - aspirationDelta, -VALUE_INFINITE);
aspirationDelta += aspirationDelta / 2;
}
else
bestValues[depth] = value;
bestMoveChanges[depth] = Rml.bestMoveChanges;
- // Do we need to pick now the best and the ponder moves ?
+ // Skills: Do we need to pick now the best and the ponder moves ?
if (SkillLevelEnabled && depth == 1 + SkillLevel)
do_skill_level(&skillBest, &skillPonder);
- if (LogFile.is_open())
- LogFile << pretty_pv(pos, depth, value, current_search_time(), &Rml[0].pv[0]) << endl;
+ if (Options["Use Search Log"].value<bool>())
+ {
+ Log log(Options["Search Log Filename"].value<string>());
+ log << pretty_pv(pos, depth, value, current_search_time(), &Rml[0].pv[0]) << endl;
+ }
- // Init easyMove after first iteration or drop if differs from the best move
+ // Init easyMove at first iteration or drop it if differs from the best move
if (depth == 1 && (Rml.size() == 1 || Rml[0].score > Rml[1].score + EasyMoveMargin))
easyMove = bestMove;
else if (bestMove != easyMove)
// Check for some early stop condition
if (!StopRequest && Limits.useTimeManagement())
{
- // Stop search early if one move seems to be much better than the
- // others or if there is only a single legal move. Also in the latter
- // case we search up to some depth anyway to get a proper score.
+ // Easy move: Stop search early if one move seems to be much better
+ // than the others or if there is only a single legal move. Also in
+ // the latter case search to some depth anyway to get a proper score.
if ( depth >= 7
&& easyMove == bestMove
&& ( Rml.size() == 1
// Step 3. Mate distance pruning
if (!RootNode)
{
- alpha = Max(value_mated_in(ss->ply), alpha);
- beta = Min(value_mate_in(ss->ply+1), beta);
+ alpha = std::max(value_mated_in(ss->ply), alpha);
+ beta = std::min(value_mate_in(ss->ply+1), beta);
if (alpha >= beta)
return alpha;
}
excludedMove = ss->excludedMove;
posKey = excludedMove ? pos.get_exclusion_key() : pos.get_key();
tte = TT.probe(posKey);
- ttMove = RootNode ? Rml[MultiPVIteration].pv[0] : tte ? tte->move() : MOVE_NONE;
+ ttMove = RootNode ? Rml[MultiPVIdx].pv[0] : tte ? tte->move() : MOVE_NONE;
// At PV nodes we check for exact scores, while at non-PV nodes we check for
// a fail high/low. Biggest advantage at probing at PV nodes is to have a
TT.store(posKey, VALUE_NONE, VALUE_TYPE_NONE, DEPTH_NONE, MOVE_NONE, ss->eval, ss->evalMargin);
}
- // Save gain for the parent non-capture move
- update_gains(pos, (ss-1)->currentMove, (ss-1)->eval, ss->eval);
+ // Update gain for the parent non-capture move given the static position
+ // evaluation before and after the move.
+ if ( (move = (ss-1)->currentMove) != MOVE_NULL
+ && (ss-1)->eval != VALUE_NONE
+ && ss->eval != VALUE_NONE
+ && pos.captured_piece_type() == PIECE_TYPE_NONE
+ && !is_special(move))
+ {
+ Square to = move_to(move);
+ H.update_gain(pos.piece_on(to), to, -(ss-1)->eval - ss->eval);
+ }
// Step 6. Razoring (is omitted in PV nodes)
if ( !PvNode
if (refinedValue - PawnValueMidgame > beta)
R++;
- pos.do_null_move(st);
+ pos.do_null_move<true>(st);
(ss+1)->skipNullMove = true;
nullValue = depth-R*ONE_PLY < ONE_PLY ? -qsearch<NonPV>(pos, ss+1, -beta, -alpha, DEPTH_ZERO)
: - search<NonPV>(pos, ss+1, -beta, -alpha, depth-R*ONE_PLY);
(ss+1)->skipNullMove = false;
- pos.undo_null_move();
+ pos.do_null_move<false>(st);
if (nullValue >= beta)
{
if (move == excludedMove)
continue;
- // At root obey the "searchmoves" option and skip moves not listed in Root Move List.
- // Also in MultiPV mode we skip moves which already have got an exact score
- // in previous MultiPV Iteration. Finally any illegal move is skipped here.
- if (RootNode && !Rml.find(move, MultiPVIteration))
+ // At root obey the "searchmoves" option and skip moves not listed in Root
+ // Move List, as a consequence any illegal move is also skipped. In MultiPV
+ // mode we also skip PV moves which have been already searched.
+ if (RootNode && !Rml.find(move, MultiPVIdx))
continue;
// At PV and SpNode nodes we want all moves to be legal since the beginning
if (pos.thread() == 0 && current_search_time() > 2000)
cout << "info" << depth_to_uci(depth)
<< " currmove " << move
- << " currmovenumber " << moveCount + MultiPVIteration << endl;
+ << " currmovenumber " << moveCount + MultiPVIdx << endl;
}
// At Root and at first iteration do a PV search on all the moves to score root moves
}
// Step 20. Check for mate and stalemate
- // All legal moves have been searched and if there are
- // no legal moves, it must be mate or stalemate.
- // If one move was excluded return fail low score.
+ // All legal moves have been searched and if there are no legal moves, it
+ // must be mate or stalemate. Note that we can have a false positive in
+ // case of StopRequest or thread.cutoff_occurred() are set, but this is
+ // harmless because return value is discarded anyhow in the parent nodes.
+ // If we are in a singular extension search then return a fail low score.
if (!SpNode && !moveCount)
return excludedMove ? oldAlpha : inCheck ? value_mated_in(ss->ply) : VALUE_DRAW;
&& !pos.is_passed_pawn_push(move))
{
futilityValue = futilityBase
- + piece_value_endgame(pos.piece_on(move_to(move)))
+ + PieceValueEndgame[pos.piece_on(move_to(move))]
+ (is_enpassant(move) ? PawnValueEndgame : VALUE_ZERO);
if (futilityValue < beta)
while (b)
{
victimSq = pop_1st_bit(&b);
- futilityValue = futilityBase + piece_value_endgame(pos.piece_on(victimSq));
+ futilityValue = futilityBase + PieceValueEndgame[pos.piece_on(victimSq)];
// Note that here we generate illegal "double move"!
if ( futilityValue >= beta
// Case 2: If the threatened piece has value less than or equal to the
// value of the threatening piece, don't prune moves which defend it.
if ( pos.is_capture(threat)
- && ( piece_value_midgame(pos.piece_on(tfrom)) >= piece_value_midgame(pos.piece_on(tto))
+ && ( PieceValueMidgame[pos.piece_on(tfrom)] >= PieceValueMidgame[pos.piece_on(tto)]
|| type_of(pos.piece_on(tfrom)) == KING)
&& pos.move_attacks_square(m, tto))
return true;
Value v = value_from_tt(tte->value(), ply);
return ( tte->depth() >= depth
- || v >= Max(VALUE_MATE_IN_PLY_MAX, beta)
- || v < Min(VALUE_MATED_IN_PLY_MAX, beta))
+ || v >= std::max(VALUE_MATE_IN_PLY_MAX, beta)
+ || v < std::min(VALUE_MATED_IN_PLY_MAX, beta))
&& ( ((tte->type() & VALUE_TYPE_LOWER) && v >= beta)
|| ((tte->type() & VALUE_TYPE_UPPER) && v < beta));
}
- // update_gains() updates the gains table of a non-capture move given
- // the static position evaluation before and after the move.
-
- void update_gains(const Position& pos, Move m, Value before, Value after) {
-
- if ( m != MOVE_NULL
- && before != VALUE_NONE
- && after != VALUE_NONE
- && pos.captured_piece_type() == PIECE_TYPE_NONE
- && !is_special(m))
- H.update_gain(pos.piece_on(move_to(m)), move_to(m), -(before + after));
- }
-
-
// current_search_time() returns the number of milliseconds which have passed
// since the beginning of the current search.
// Rml list is already sorted by score in descending order
int s;
int max_s = -VALUE_INFINITE;
- int size = Min(MultiPV, (int)Rml.size());
+ int size = std::min(MultiPV, (int)Rml.size());
int max = Rml[0].score;
- int var = Min(max - Rml[size - 1].score, PawnValueMidgame);
+ int var = std::min(max - Rml[size - 1].score, int(PawnValueMidgame));
int wk = 120 - 2 * SkillLevel;
// PRNG sequence should be non deterministic
projects / stockfish / blobdiff