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Option to create random path in MarkovPath.

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This commit is contained in:
Thomas Lichtenegger
2021-07-29 09:44:45 +02:00
parent 67a2a7e90f
commit f6cf8daa95
2 changed files with 20 additions and 9 deletions
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23
src/recurrence/recPath/MarkovPath/MarkovPath.C
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@ -56,6 +56,7 @@ MarkovPath::MarkovPath
:
recPath(dict, base),
propsDict_(dict.subDict(typeName + "Props")),
searchMinimum_(propsDict_.lookupOrDefault<bool>("searchMinimum",true)),
correlationSteps_(readLabel(propsDict_.lookup("correlationSteps"))),
meanIntervalSteps_(propsDict_.lookupOrDefault<label>("meanIntervalSteps",-1)),
minIntervalSteps_(propsDict_.lookupOrDefault<label>("minIntervalSteps",0)),
@ -167,7 +168,7 @@ void MarkovPath::extendPath()
scalar randInterval = ranGen.scalar01();
label interval = numIntervals_-1;
for(int i = numIntervals_-2 ;i >= 0; i--)
for(int i = numIntervals_-2; i >= 0; i--)
{
if (randInterval < intervalWeightsCumulative_[i]) interval=i;
}
@ -176,16 +177,24 @@ void MarkovPath::extendPath()
if (interval > 0) startLoop = intervalSizesCumulative_[interval-1];
label endLoop = intervalSizesCumulative_[interval] - meanIntervalSteps_;
scalar nextMinimum(GREAT);
for (label j = startLoop; j <= endLoop; j++)
if (searchMinimum_)
{
if(abs(j - virtualTimeIndex) < correlationSteps_) continue;
if (recurrenceMatrix[j][virtualTimeIndex] < nextMinimum)
scalar nextMinimum(GREAT);
for (label j = startLoop; j <= endLoop; j++)
{
nextMinimum = recurrenceMatrix[j][virtualTimeIndex];
seqStart = j+1;
if(abs(j - virtualTimeIndex) < correlationSteps_) continue;
if (recurrenceMatrix[j][virtualTimeIndex] < nextMinimum)
{
nextMinimum = recurrenceMatrix[j][virtualTimeIndex];
seqStart = j+1;
}
}
}
else
{
scalar randIntStart = ranGen.scalar01();
seqStart = (int)(startLoop + randIntStart*(endLoop - startLoop));
}
virtualTimeIndex = seqStart;
}

6
src/recurrence/recPath/MarkovPath/MarkovPath.H
View File

@ -58,12 +58,14 @@ protected:
void weightsCumulation();
void weightsNormalization();
bool searchMinimum_;
// correlation steps determine which temporal neighborhood is excluded from nearest-neighbor search
label correlationSteps_;
label meanIntervalSteps_;
label minIntervalSteps_;
label numIntervals_;