minor change: moved algorithm type to the end of params
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@ -230,12 +230,12 @@ public:
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class CV_EXPORTS_W_MAP Params
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{
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public:
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Params(int algorithmType_=BRUTE_FORCE, int defaultK=10, bool isclassifier_=true, int Emax_=INT_MAX);
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Params(int defaultK=10, bool isclassifier_=true, int Emax_=INT_MAX, int algorithmType_=BRUTE_FORCE);
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CV_PROP_RW int algorithmType;
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CV_PROP_RW int defaultK;
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CV_PROP_RW bool isclassifier;
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CV_PROP_RW int Emax; // for implementation with KDTree
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CV_PROP_RW int algorithmType;
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};
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virtual void setParams(const Params& p) = 0;
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virtual Params getParams() const = 0;
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@ -50,11 +50,11 @@
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namespace cv {
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namespace ml {
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KNearest::Params::Params(int algorithmType_, int k, bool isclassifier_, int Emax_) :
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algorithmType(algorithmType_),
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KNearest::Params::Params(int k, bool isclassifier_, int Emax_, int algorithmType_) :
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defaultK(k),
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isclassifier(isclassifier_),
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Emax(Emax_)
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Emax(Emax_),
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algorithmType(algorithmType_)
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{
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}
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@ -330,7 +330,7 @@ void CV_KNearestTest::run( int /*start_from*/ )
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}
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// KNearest KDTree implementation
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Ptr<KNearest> knearestKdt = KNearest::create(ml::KNearest::Params(ml::KNearest::KDTREE));
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Ptr<KNearest> knearestKdt = KNearest::create(ml::KNearest::Params(10, true, INT_MAX, ml::KNearest::KDTREE));
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knearestKdt->train(trainData, ml::ROW_SAMPLE, trainLabels);
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knearestKdt->findNearest(testData, 4, bestLabels);
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if( !calcErr( bestLabels, testLabels, sizes, err, true ) )
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