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- /**
- * @file KCGPOneClass.h
- * @brief One-Class Gaussian Process Regression for Classification
- * @author Erik Rodner + Mi.Ke.
- * @date 12/03/2010
- */
- #ifndef KCGPONECLASSINCLUDE
- #define KCGPONECLASSINCLUDE
- #include "vislearning/classifier/classifierbase/KernelClassifier.h"
- #include "vislearning/math/kernels/ParameterizedKernel.h"
- #include "vislearning/regression/regressionbase/RegressionAlgorithmKernel.h"
- #undef ROADWORKS
- #define ROADWORKS fthrow(Exception, "Persistent interface not implemented!");
- #define VARIANCE_DETECTION_MODE 1
- #define MEAN_DETECTION_MODE 2
- namespace OBJREC {
-
- /** Gaussian Process Regression for One-Class Classification (actually same as binary, don't use parameter optimization!!!)*/
- class KCGPOneClass : public KernelClassifier
- {
- protected:
- RegressionAlgorithmKernel *regressionAlgorithm;
- NICE::Matrix InverseKernelMatrix; //only used when 'variance mode' is used and computeInverse=true
- KernelData *kernelData; ////only used when 'variance mode' is used and computeInverse=false
- NICE::Vector y;
- int mode;
- bool computeInverse;
- double staticNoise;
- public:
-
- /** simple constructor */
- KCGPOneClass( const NICE::Config *conf, Kernel *kernel = NULL, const std::string & section = "OneClassGP" );
- /** copy constructor */
- KCGPOneClass( const KCGPOneClass & src );
-
- /** simple destructor */
- virtual ~KCGPOneClass();
-
- /** teach the classifier with a kernel matrix and the corresponding class labels @param y ! */
- void teach ( KernelData *kernelData, const NICE::Vector & y );
-
- /** classify an example by using its kernel values with the training set,
- be careful with the order in @param kernelVector */
- virtual ClassificationResult classifyKernel ( const NICE::Vector & kernelVector, double kernelSelf ) const;
-
- /** clone this object */
- KCGPOneClass *clone() const;
- void restore(std::istream&, int) { ROADWORKS };
- void store(std::ostream&, int) const { ROADWORKS };
- void clear() { ROADWORKS };
- };
- }
- #undef ROADWORKS
- #endif
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