/usr/include/shogun/features/TOPFeatures.h is in libshogun-dev 1.1.0-4ubuntu2.
This file is owned by root:root, with mode 0o644.
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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 _CTOPFEATURES__H__
#define _CTOPFEATURES__H__
#include <shogun/features/SimpleFeatures.h>
#include <shogun/distributions/HMM.h>
namespace shogun
{
template <class T> class CSimpleFeatures;
class CHMM;
#ifndef DOXYGEN_SHOULD_SKIP_THIS
/** HMM indices */
struct T_HMM_INDIZES
{
/** index p */
int32_t* idx_p;
/** index q */
int32_t* idx_q;
/** index a rows */
int32_t* idx_a_rows;
/** index a cols */
int32_t* idx_a_cols;
/** index b rows */
int32_t* idx_b_rows;
/** index b cols */
int32_t* idx_b_cols;
/** number p */
int32_t num_p;
/** number q */
int32_t num_q;
/** number a */
int32_t num_a;
/** number b */
int32_t num_b;
};
#endif // DOXYGEN_SHOULD_SKIP_THIS
/** @brief The class TOPFeatures implements TOP 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 TOP-features are computed on the fly, so to be effective feature
* caching should be enabled.
*
* It inherits its functionality from CSimpleFeatures, which should be
* consulted for further reference.
*/
class CTOPFeatures : public CSimpleFeatures<float64_t>
{
public:
/** default constructor */
CTOPFeatures();
/** constructor
*
* @param size cache size
* @param p positive HMM
* @param n negative HMM
* @param neglin if negative HMM is of linear shape
* @param poslin if positive HMM is of linear shape
*/
CTOPFeatures(int32_t size, CHMM* p, CHMM* n, bool neglin, bool poslin);
/** copy constructor */
CTOPFeatures(const CTOPFeatures &orig);
virtual ~CTOPFeatures();
/** set HMMs
*
* @param p positive HMM
* @param n negative HMM
*/
void set_models(CHMM* p, CHMM* n);
/** set feature matrix
*
* @return something floaty
*/
virtual float64_t* set_feature_matrix();
/** compute number of features
*
* @return number of features
*/
int32_t compute_num_features();
/** compute relevant indices
*
* @param hmm HMM to compute for
* @param hmm_idx HMM index
* @return if computing was successful
*/
bool compute_relevant_indizes(CHMM* hmm, T_HMM_INDIZES* hmm_idx);
/** @return object name */
inline virtual const char* get_name() const { return "TOPFeatures"; }
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);
private:
void init();
protected:
/** positive HMM */
CHMM* pos;
/** negative HMM */
CHMM* neg;
/** if negative HMM is a LinearHMM */
bool neglinear;
/** if positive HMM is a LinearHMM */
bool poslinear;
/** positive relevant indices */
T_HMM_INDIZES pos_relevant_indizes;
/** negative relevant indices */
T_HMM_INDIZES neg_relevant_indizes;
};
}
#endif
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