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Use global RNG for training RTrees.

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This commit is contained in:
Peter Würtz 2019-12-28 13:34:08 +01:00
parent 9572895da0
commit 8aebef2459
1 changed files with 2 additions and 2 deletions
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4
modules/ml/src/rtrees.cpp
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@ -90,12 +90,12 @@ public:
CV_TRACE_FUNCTION();
DTreesImpl::clear();
oobError = 0.;
rng = RNG((uint64)-1);
}
const vector<int>& getActiveVars() CV_OVERRIDE
{
CV_TRACE_FUNCTION();
RNG &rng = theRNG();
int i, nvars = (int)allVars.size(), m = (int)activeVars.size();
for( i = 0; i < nvars; i++ )
{
@ -134,6 +134,7 @@ public:
bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
RNG &rng = theRNG();
CV_Assert(!trainData.empty());
startTraining(trainData, flags);
int treeidx, ntrees = (rparams.termCrit.type & TermCriteria::COUNT) != 0 ?
@ -424,7 +425,6 @@ public:
double oobError;
vector<float> varImportance;
vector<int> allVars, activeVars;
RNG rng;
};