/usr/include/shogun/kernel/AUCKernel.h is in libshogun-dev 3.2.0-7.3build4.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | /*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 1999-2008 Gunnar Raetsch
* Written (W) 2009 Soeren Sonnnenburg
* Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#ifndef _AUCKERNEL_H___
#define _AUCKERNEL_H___
#include <shogun/lib/common.h>
#include <shogun/kernel/DotKernel.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/labels/Labels.h>
namespace shogun
{
class CLabels;
template <class T> class CDenseFeatures;
/** @brief The AUC kernel can be used to maximize the area under the receiver operator
* characteristic curve (AUC) instead of margin in SVM training.
*
* It takes as argument a sub-kernel and Labels based on which number of
* positive labels times number of negative labels many ``virtual'' examples
* are created that ensure that all positive examples get a higher score than
* all negative examples in training.
*/
class CAUCKernel: public CDotKernel
{
void init();
public:
/** default constructor */
CAUCKernel();
/** constructor
*
* @param size cache size
* @param subkernel the subkernel
*/
CAUCKernel(int32_t size, CKernel* subkernel);
/** destructor */
virtual ~CAUCKernel();
/** initialize kernel based on current labeling and subkernel
*
* @param labels - current labeling
* @return new label object to be used together with this kernel in SVM
* training for AUC maximization
*/
CLabels* setup_auc_maximization(CLabels* labels);
/** initialize kernel
*
* @param l features of left-hand side
* @param r features of right-hand side
* @return if initializing was successful
*/
virtual bool init(CFeatures* l, CFeatures* r);
/** return what type of kernel we are
*
* @return kernel type AUC
*/
virtual EKernelType get_kernel_type() { return K_AUC; }
/** return the kernel's name
*
* @return name AUC
*/
virtual const char* get_name() const { return "AUCKernel" ; }
/** return feature class the kernel can deal with
*
* @return feature class SIMPLE
*/
virtual EFeatureClass get_feature_class() { return C_DENSE; }
/** return feature type the kernel can deal with
*
* @return word feature type
*/
virtual EFeatureType get_feature_type() { return F_WORD; }
protected:
/** compute kernel function for features a and b
* idx_{a,b} denote the index of the feature vectors
* in the corresponding feature object
*
* @param idx_a index a
* @param idx_b index b
* @return computed kernel function at indices a,b
*/
virtual float64_t compute(int32_t idx_a, int32_t idx_b);
protected:
/** the subkernel */
CKernel* subkernel;
};
}
#endif /* _AUCKERNEL_H__ */
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