RootMoveList Rml;
// MultiPV mode
- int MultiPV, UCIMultiPV, MultiPVIdx;
+ size_t MultiPV, UCIMultiPV, MultiPVIdx;
// Time management variables
TimeManager TimeMgr;
TT.clear();
}
- UCIMultiPV = Options["MultiPV"].value<int>();
- SkillLevel = Options["Skill Level"].value<int>();
+ UCIMultiPV = Options["MultiPV"].value<size_t>();
+ SkillLevel = Options["Skill Level"].value<size_t>();
// 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 ? std::max(UCIMultiPV, 4) : UCIMultiPV);
+ MultiPV = (SkillLevelEnabled ? std::max(UCIMultiPV, (size_t)4) : UCIMultiPV);
// Write current search header to log file
if (Options["Use Search Log"].value<bool>())
Rml.bestMoveChanges = 0;
// MultiPV loop. We perform a full root search for each PV line
- for (MultiPVIdx = 0; MultiPVIdx < std::min(MultiPV, (int)Rml.size()); MultiPVIdx++)
+ for (MultiPVIdx = 0; MultiPVIdx < std::min(MultiPV, Rml.size()); MultiPVIdx++)
{
// Calculate dynamic aspiration window based on previous iterations
if (depth >= 5 && abs(Rml[MultiPVIdx].prevScore) < VALUE_KNOWN_WIN)
// Write PV back to transposition table in case the relevant entries
// have been overwritten during the search.
- for (int i = 0; i <= MultiPVIdx; i++)
+ for (size_t i = 0; i <= MultiPVIdx; i++)
Rml[i].insert_pv_in_tt(pos);
// If search has been stopped exit the aspiration window loop,
// 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 ((bestValue > alpha && bestValue < beta) || elapsed_time() > 2000)
- for (int i = 0; i < std::min(UCIMultiPV, (int)Rml.size()); i++)
+ for (size_t i = 0; i < std::min(UCIMultiPV, Rml.size()); i++)
{
bool updated = (i <= MultiPVIdx);
{
lock_grab(&(sp->lock));
bestValue = sp->bestValue;
+ moveCount = sp->moveCount;
+
+ assert(bestValue > -VALUE_INFINITE && moveCount > 0);
}
// Step 11. Loop through moves
if (futilityValue < beta)
{
if (SpNode)
- {
lock_grab(&(sp->lock));
- if (futilityValue > sp->bestValue)
- sp->bestValue = bestValue = futilityValue;
- }
- else if (futilityValue > bestValue)
- bestValue = futilityValue;
continue;
}
// 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)
+ if (!moveCount)
return excludedMove ? oldAlpha : inCheck ? value_mated_in(ss->ply) : VALUE_DRAW;
+ // We have pruned all the moves, so return a fail-low score
+ if (bestValue == -VALUE_INFINITE)
+ {
+ assert(!playedMoveCount);
+
+ bestValue = alpha;
+ }
+
// Step 21. Update tables
// If the search is not aborted, update the transposition table,
// history counters, and killer moves.
// Rml list is already sorted by score in descending order
int s;
+ size_t size = std::min(MultiPV, Rml.size());
int max_s = -VALUE_INFINITE;
- int size = std::min(MultiPV, (int)Rml.size());
int max = Rml[0].score;
int var = std::min(max - Rml[size - 1].score, int(PawnValueMidgame));
int wk = 120 - 2 * SkillLevel;
// Choose best move. For each move's score we add two terms both dependent
// on wk, one deterministic and bigger for weaker moves, and one random,
// then we choose the move with the resulting highest score.
- for (int i = 0; i < size; i++)
+ for (size_t i = 0; i < size; i++)
{
s = Rml[i].score;