/usr/include/shogun/features/FKFeatures.h is in libshogun-dev 3.2.0-7.5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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* 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) 1999-2009 Soeren Sonnenburg
* Written (W) 1999-2008 Gunnar Raetsch
* Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
*/
#ifndef _CFKFEATURES__H__
#define _CFKFEATURES__H__
#include <shogun/features/DenseFeatures.h>
#include <shogun/distributions/HMM.h>
namespace shogun
{
template <class T> class CDenseFeatures;
class CHMM;
/** @brief The class FKFeatures implements Fischer kernel features obtained from
* two Hidden Markov models.
*
* It was used in
*
* K. Tsuda, M. Kawanabe, G. Raetsch, S. Sonnenburg, and K.R. Mueller. A new
* discriminative kernel from probabilistic models. Neural Computation,
* 14:2397-2414, 2002.
*
* which also has the details.
*
* Note that FK-features are computed on the fly, so to be effective feature
* caching should be enabled.
*
* It inherits its functionality from CDenseFeatures, which should be
* consulted for further reference.
*/
class CFKFeatures: public CDenseFeatures<float64_t>
{
public:
/** default constructor */
CFKFeatures();
/** constructor
*
* @param size cache size
* @param p positive HMM
* @param n negative HMM
*/
CFKFeatures(int32_t size, CHMM* p, CHMM* n);
/** copy constructor */
CFKFeatures(const CFKFeatures &orig);
virtual ~CFKFeatures();
/** set HMMs
*
* @param p positive HMM
* @param n negative HMM
*/
void set_models(CHMM* p, CHMM* n);
/** set weight a
*
* @param a weight a
*/
inline void set_a(float64_t a)
{
weight_a=a;
}
/** get weight a
*
* @return weight a
*/
inline float64_t get_a()
{
return weight_a;
}
/** set feature matrix
*
* @return something floaty
*/
virtual float64_t* set_feature_matrix();
/** set opt a
*
* @param a a
* @return something floaty
*/
float64_t set_opt_a(float64_t a=-1);
/** get weight_a
*
* @return weight_a
*/
inline float64_t get_weight_a() { return weight_a; };
/** @return object name */
virtual const char* get_name() const { return "FKFeatures"; }
protected:
/** compute feature vector
*
* @param num num
* @param len len
* @param target
* @return something floaty
*/
virtual float64_t* compute_feature_vector(
int32_t num, int32_t& len, float64_t* target=NULL);
/** computes the feature vector to the address addr
*
* @param addr address
* @param num num
* @param len len
*/
void compute_feature_vector(float64_t* addr, int32_t num, int32_t& len);
/** deriv a
*
* @param a a
* @param dimension dimension
*/
float64_t deriv_a(float64_t a, int32_t dimension=-1) ;
private:
void init();
protected:
/** positive HMM */
CHMM* pos;
/** negative HMM */
CHMM* neg;
/** positive prob */
float64_t* pos_prob;
/** negative prob */
float64_t* neg_prob;
/** weight a */
float64_t weight_a;
};
}
#endif
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