/// Types
- // IterationInfoType stores search results for each iteration
- //
- // Because we use relatively small (dynamic) aspiration window,
- // there happens many fail highs and fail lows in root. And
- // because we don't do researches in those cases, "value" stored
- // here is not necessarily exact. Instead in case of fail high/low
- // we guess what the right value might be and store our guess
- // as a "speculated value" and then move on. Speculated values are
- // used just to calculate aspiration window width, so also if are
- // not exact is not big a problem.
-
- struct IterationInfoType {
-
- IterationInfoType(Value v = Value(0), Value sv = Value(0))
- : value(v), speculatedValue(sv) {}
-
- Value value, speculatedValue;
- };
-
-
// The BetaCounterType class is used to order moves at ply one.
// Apart for the first one that has its score, following moves
// normally have score -VALUE_INFINITE, so are ordered according
BetaCounterType BetaCounter;
// Scores and number of times the best move changed for each iteration
- IterationInfoType IterationInfo[PLY_MAX_PLUS_2];
+ Value ValueByIteration[PLY_MAX_PLUS_2];
int BestMoveChangesByIteration[PLY_MAX_PLUS_2];
// Search window management
TT.new_search();
H.clear();
init_ss_array(ss);
- IterationInfo[1] = IterationInfoType(rml.get_move_score(0), rml.get_move_score(0));
+ ValueByIteration[1] = rml.get_move_score(0);
Iteration = 1;
// Is one move significantly better than others after initial scoring ?
// Calculate dynamic search window based on previous iterations
Value alpha, beta;
- if (MultiPV == 1 && Iteration >= 6 && abs(IterationInfo[Iteration - 1].value) < VALUE_KNOWN_WIN)
+ if (MultiPV == 1 && Iteration >= 6 && abs(ValueByIteration[Iteration - 1]) < VALUE_KNOWN_WIN)
{
- int prevDelta1 = IterationInfo[Iteration - 1].speculatedValue - IterationInfo[Iteration - 2].speculatedValue;
- int prevDelta2 = IterationInfo[Iteration - 2].speculatedValue - IterationInfo[Iteration - 3].speculatedValue;
+ int prevDelta1 = ValueByIteration[Iteration - 1] - ValueByIteration[Iteration - 2];
+ int prevDelta2 = ValueByIteration[Iteration - 2] - ValueByIteration[Iteration - 3];
AspirationDelta = Max(abs(prevDelta1) + abs(prevDelta2) / 2, 16);
AspirationDelta = (AspirationDelta + 7) / 8 * 8; // Round to match grainSize
- alpha = Max(IterationInfo[Iteration - 1].value - AspirationDelta, -VALUE_INFINITE);
- beta = Min(IterationInfo[Iteration - 1].value + AspirationDelta, VALUE_INFINITE);
+ alpha = Max(ValueByIteration[Iteration - 1] - AspirationDelta, -VALUE_INFINITE);
+ beta = Min(ValueByIteration[Iteration - 1] + AspirationDelta, VALUE_INFINITE);
}
else
{
break; // Value cannot be trusted. Break out immediately!
//Save info about search result
- Value speculatedValue;
- bool fHigh = false;
- bool fLow = false;
- Value delta = value - IterationInfo[Iteration - 1].value;
-
- if (value >= beta)
- {
- assert(delta > 0);
-
- fHigh = true;
- speculatedValue = value + delta;
- BestMoveChangesByIteration[Iteration] += 2; // Allocate more time
- }
- else if (value <= alpha)
- {
- assert(value == alpha);
- assert(delta < 0);
-
- fLow = true;
- speculatedValue = value + delta;
- BestMoveChangesByIteration[Iteration] += 3; // Allocate more time
- } else
- speculatedValue = value;
-
- speculatedValue = Min(Max(speculatedValue, -VALUE_INFINITE), VALUE_INFINITE);
- IterationInfo[Iteration] = IterationInfoType(value, speculatedValue);
+ ValueByIterationInfo[Iteration] = value;
// Drop the easy move if it differs from the new best move
if (ss[0].pv[0] != EasyMove)
// Stop search early when the last two iterations returned a mate score
if ( Iteration >= 6
- && abs(IterationInfo[Iteration].value) >= abs(VALUE_MATE) - 100
- && abs(IterationInfo[Iteration-1].value) >= abs(VALUE_MATE) - 100)
+ && abs(ValueByIteration[Iteration]) >= abs(VALUE_MATE) - 100
+ && abs(ValueByIteration[Iteration-1]) >= abs(VALUE_MATE) - 100)
stopSearch = true;
// Stop search early if one move seems to be much better than the rest
int64_t nodes = nodes_searched();
if ( Iteration >= 8
- && !fLow
- && !fHigh
&& EasyMove == ss[0].pv[0]
&& ( ( rml.get_move_cumulative_nodes(0) > (nodes * 85) / 100
&& current_search_time() > MaxSearchTime / 16)
// for time managment: When Problem is true, we try to complete the
// current iteration before playing a move.
Problem = ( Iteration >= 2
- && value <= IterationInfo[Iteration - 1].value - ProblemMargin);
+ && value <= ValueByIteration[Iteration - 1] - ProblemMargin);
if (Problem && StopOnPonderhit)
StopOnPonderhit = false;
// Reset the global variable Problem to false if the value isn't too
// far below the final value from the last iteration.
- if (value > IterationInfo[Iteration - 1].value - NoProblemMargin)
+ if (value > ValueByIteration[Iteration - 1] - NoProblemMargin)
Problem = false;
}
else // MultiPV > 1
// (from the computer's point of view) since the previous iteration.
if ( ply == 1
&& Iteration >= 2
- && -value <= IterationInfo[Iteration-1].value - ProblemMargin)
+ && -value <= ValueByIteration[Iteration-1] - ProblemMargin)
Problem = true;
}
// (from the computer's point of view) since the previous iteration.
if ( sp->ply == 1
&& Iteration >= 2
- && -value <= IterationInfo[Iteration-1].value - ProblemMargin)
+ && -value <= ValueByIteration[Iteration-1] - ProblemMargin)
Problem = true;
}
lock_release(&(sp->lock));
projects / stockfish / blobdiff