/usr/include/shogun/preprocessor/KernelPCA.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) 2011 Soeren Sonnenburg
* Copyright (C) 2011 Berlin Institute of Technology
*/
#ifndef KERNELPCA_H__
#define KERNELPCA_H__
#include <shogun/lib/config.h>
#ifdef HAVE_LAPACK
#include <shogun/preprocessor/DimensionReductionPreprocessor.h>
#include <shogun/features/Features.h>
#include <shogun/kernel/Kernel.h>
#include <shogun/lib/common.h>
namespace shogun
{
class CFeatures;
class CKernel;
/** @brief Preprocessor KernelPCA performs kernel principal component analysis
*
* Schoelkopf, B., Smola, A. J., & Mueller, K. R. (1999).
* Kernel Principal Component Analysis.
* Advances in kernel methods support vector learning, 1327(3), 327-352. MIT Press.
* Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.8744
*
*/
class CKernelPCA: public CDimensionReductionPreprocessor
{
public:
/** default constructor
*/
CKernelPCA();
/** constructor
* @param k kernel to be used
*/
CKernelPCA(CKernel* k);
virtual ~CKernelPCA();
/// initialize preprocessor from features
virtual bool init(CFeatures* features);
/// cleanup
virtual void cleanup();
/// apply preproc on feature matrix
/// result in feature matrix
/// return pointer to feature_matrix, i.e. f->get_feature_matrix();
virtual SGMatrix<float64_t> apply_to_feature_matrix(CFeatures* features);
/// apply preproc on single feature vector
/// result in feature matrix
virtual SGVector<float64_t> apply_to_feature_vector(SGVector<float64_t> vector);
/** apply to string features
* @param features
*/
virtual CSimpleFeatures<float64_t>* apply_to_string_features(CFeatures* features);
/** get transformation matrix, i.e. eigenvectors
*
*/
SGMatrix<float64_t> get_transformation_matrix() const
{
return m_transformation_matrix;
}
/** get bias of KPCA
*
*/
SGVector<float64_t> get_bias_vector() const
{
return m_bias_vector;
}
/** @return object name */
virtual inline const char* get_name() const { return "KernelPCA"; }
/** @return the type of preprocessor */
virtual inline EPreprocessorType get_type() const { return P_KERNELPCA; }
protected:
/** default init */
void init();
protected:
/** features used by init. needed for apply */
CFeatures* m_init_features;
/** transformation matrix */
SGMatrix<float64_t> m_transformation_matrix;
/** bias vector */
SGVector<float64_t> m_bias_vector;
/** true when already initialized */
bool m_initialized;
};
}
#endif
#endif
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