/usr/include/shogun/kernel/ANOVAKernel.h is in libshogun-dev 3.2.0-7.3build4.
This file is owned by root:root, with mode 0o644.
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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | /*
* 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) 2011 Andrew Tereskin
* Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
*/
#include <shogun/lib/config.h>
#ifndef ANOVAKERNEL_H_
#define ANOVAKERNEL_H_
#include <shogun/lib/common.h>
#include <shogun/kernel/DotKernel.h>
#include <shogun/features/Features.h>
#include <shogun/features/DenseFeatures.h>
namespace shogun
{
class CDistance;
/** @brief ANOVA (ANalysis Of VAriances) kernel
*
* Formally described as
*
* \f[
* K_d(x,z) = \sum_{1\le i_1<i_2<\dots<i_d\le n} \prod_{j=1}^d x_{i_j} z_{i_j}
* \f]
* with d(cardinality)=1 by default
* this function is computed recusively
*/
class CANOVAKernel: public CDotKernel
{
public:
/** default constructor */
CANOVAKernel();
/** constructor
* @param cache size of cache
* @param d kernel parameter cardinality
*/
CANOVAKernel(int32_t cache, int32_t d);
/** constructor
* @param l features left-side
* @param r features right-side
* @param d kernel parameter cardinality
* @param cache cache size
*/
CANOVAKernel(
CDenseFeatures<float64_t>* l, CDenseFeatures<float64_t>* r, int32_t d, int32_t cache);
virtual ~CANOVAKernel();
/** initialize kernel with features
* @param l features left-side
* @param r features right-side
* @return true if successful
*/
virtual bool init(CFeatures* l, CFeatures* r);
/**
* @return kernel type
*/
virtual EKernelType get_kernel_type() { return K_ANOVA; }
/**
* @return type of features
*/
virtual EFeatureType get_feature_type() { return F_DREAL; }
/**
* @return class of features
*/
virtual EFeatureClass get_feature_class() { return C_DENSE; }
/**
* @return name of kernel
*/
virtual const char* get_name() const { return "ANOVAKernel"; }
/** getter for degree parameter
* @return kernel parameter cardinality
*/
inline int32_t get_cardinality() { return this->cardinality; }
/** setter for degree parameter
* @param value kernel parameter cardinality
*/
inline void set_cardinality(int32_t value) { this->cardinality = value; }
/** compute rec 1
* @param idx_a
* @param idx_b
* @return rec1
*/
float64_t compute_rec1(int32_t idx_a, int32_t idx_b);
/** computer rec 2
* @param idx_a
* @param idx_b
* @return rec2
*/
float64_t compute_rec2(int32_t idx_a, int32_t idx_b);
protected:
/**
* compute kernel for specific feature vectors
* corresponding to [idx_a] of left-side and [idx_b] of right-side
* @param idx_a left-side index
* @param idx_b right-side index
* @return kernel value
*/
virtual float64_t compute(int32_t idx_a, int32_t idx_b);
/** register params */
void register_params();
private:
float64_t compute_recursive1(float64_t* avec, float64_t* bvec, int32_t len);
float64_t compute_recursive2(float64_t* avec, float64_t* bvec, int32_t len);
protected:
/// degree parameter of kernel
int32_t cardinality;
};
}
#endif /* ANOVAKERNEL_H_ */
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