/** * @file GPHIKClassifier.h * @author Erik Rodner, Alexander Freytag * @brief Main interface for our GP HIK classifier (similar to the feature pool classifier interface in vislearning) (Interface) * @date 02/01/2012 */ #ifndef _NICE_GPHIKCLASSIFIERINCLUDE #define _NICE_GPHIKCLASSIFIERINCLUDE #include #include #include #include #include "FMKGPHyperparameterOptimization.h" #include "gp-hik-core/parameterizedFunctions/ParameterizedFunction.h" namespace NICE { /** * @class GPHIKClassifier * @brief Main interface for our GP HIK classifier (similar to the feature pool classifier interface in vislearning) * @author Erik Rodner, Alexander Freytag */ class GPHIKClassifier { protected: std::string confSection; double noise; enum VarianceApproximation{ APPROXIMATE_ROUGH, APPROXIMATE_FINE, EXACT, NONE }; VarianceApproximation varianceApproximation; /**compute the uncertainty prediction during classification?*/ bool uncertaintyPredictionForClassification; NICE::Config *confCopy; NICE::ParameterizedFunction *pf; NICE::FMKGPHyperparameterOptimization *gphyper; /** verbose flag for useful output*/ bool verbose; /** debug flag for several outputs useful for debugging*/ bool debug; /** * @brief classify a given example with the previously learnt model * @param pe example to be classified given in a sparse representation */ void init(const NICE::Config *conf, const std::string & confSection); public: /** simple constructor */ GPHIKClassifier( const NICE::Config *conf, const std::string & confSection = "GPHIKClassifier" ); /** simple destructor */ ~GPHIKClassifier(); /** * @brief classify a given example with the previously learnt model * @date 19-06-2012 (dd-mm-yyyy) * @author Alexander Freytag * @param example (SparseVector) to be classified given in a sparse representation * @param result (int) class number of most likely class * @param scores (SparseVector) classification scores for known classes */ void classify ( const NICE::SparseVector * example, int & result, NICE::SparseVector & scores ); /** * @brief classify a given example with the previously learnt model * @date 19-06-2012 (dd-mm-yyyy) * @author Alexander Freytag * @param example (SparseVector) to be classified given in a sparse representation * @param result (int) class number of most likely class * @param scores (SparseVector) classification scores for known classes * @param uncertainty (double*) predictive variance of the classification result, if computed */ void classify ( const NICE::SparseVector * example, int & result, NICE::SparseVector & scores, double & uncertainty ); /** * @brief train this classifier using a given set of examples and a given set of binary label vectors * @date 18-10-2012 (dd-mm-yyyy) * @author Alexander Freytag * @param examples (std::vector< NICE::SparseVector *>) training data given in a sparse representation * @param labels (Vector) class labels (multi-class) */ void train ( const std::vector< NICE::SparseVector *> & examples, const NICE::Vector & labels ); /** * @brief train this classifier using a given set of examples and a given set of binary label vectors * @date 19-06-2012 (dd-mm-yyyy) * @author Alexander Freytag * @param examples examples to use given in a sparse data structure * @param binLabels corresponding binary labels with class no. There is no need here that every examples has only on positive entry in this set (1,-1) */ void train ( const std::vector< NICE::SparseVector *> & examples, std::map & binLabels ); /** Persistent interface */ void restore ( std::istream & is, int format = 0 ); void store ( std::ostream & os, int format = 0 ) const; void clear (); GPHIKClassifier *clone () const; /** * @brief prediction of classification uncertainty * @date 19-06-2012 (dd-mm-yyyy) * @author Alexander Freytag * @param examples example for which the classification uncertainty shall be predicted, given in a sparse representation * @param uncertainties contains the resulting classification uncertainties (1 entry for standard setting, m entries for binary-balanced setting) */ void predictUncertainty( const NICE::SparseVector * example, NICE::Vector & uncertainties ); void addExample( const NICE::SparseVector * example, const double & label, const bool & performOptimizationAfterIncrement = true); void addMultipleExamples( const std::vector< const NICE::SparseVector * > & newExamples, const NICE::Vector & newLabels, const bool & performOptimizationAfterIncrement = true); }; } #endif