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Code Issues Releases Wiki Activity

Retire outdated aspiration search code

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Signed-off-by: Marco Costalba <mcostalba@gmail.com>
This commit is contained in:
Joona Kiiski 2010-02-02 19:21:49 +02:00 • committed by Marco Costalba
parent b5a4edd86f
commit 95d33aef9f
1 changed files with 14 additions and 61 deletions
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75
src/search.cpp
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@ -53,26 +53,6 @@ namespace {
/// 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
@ -213,7 +193,7 @@ namespace {
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
@ -716,7 +696,7 @@ namespace {
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 ?
@ -740,16 +720,16 @@ namespace {
// 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
{
@ -768,32 +748,7 @@ namespace {
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)
@ -813,15 +768,13 @@ namespace {
// 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)
@ -983,7 +936,7 @@ namespace {
// 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;
@ -1132,7 +1085,7 @@ namespace {
// 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
@ -1366,7 +1319,7 @@ namespace {
// (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;
}
@ -2224,7 +2177,7 @@ namespace {
// (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));