enum TimeType { OptimumTime, MaxTime };
const int MoveHorizon = 50; // Plan time management at most this many moves ahead
- const double MaxRatio = 7.0; // When in trouble, we can step over reserved time with this ratio
- const double StealRatio = 0.33; // However we must not steal time from remaining moves over this ratio
+ const double MaxRatio = 6.93; // When in trouble, we can step over reserved time with this ratio
+ const double StealRatio = 0.36; // However we must not steal time from remaining moves over this ratio
// move_importance() is a skew-logistic function based on naive statistical
double move_importance(int ply) {
- const double XScale = 9.3;
- const double XShift = 59.8;
- const double Skew = 0.172;
+ const double XScale = 8.27;
+ const double XShift = 59.;
+ const double Skew = 0.179;
return pow((1 + exp((ply - XShift) / XScale)), -Skew) + DBL_MIN; // Ensure non-zero
}
/// inc > 0 && movestogo == 0 means: x basetime + z increment
/// inc > 0 && movestogo != 0 means: x moves in y minutes + z increment
-void TimeManagement::init(Search::LimitsType& limits, Color us, int ply, TimePoint now)
+void TimeManagement::init(Search::LimitsType& limits, Color us, int ply)
{
int minThinkingTime = Options["Minimum Thinking Time"];
int moveOverhead = Options["Move Overhead"];
limits.npmsec = npmsec;
}
- start = now;
+ startTime = limits.startTime;
unstablePvFactor = 1;
optimumTime = maximumTime = std::max(limits.time[us], minThinkingTime);
if (Options["Ponder"])
optimumTime += optimumTime / 4;
-
- optimumTime = std::min(optimumTime, maximumTime);
}