/usr/include/shogun/preprocessor/PCA.h is in libshogun-dev 1.1.0-4ubuntu2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 | /*
* 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) 1999-2008,2011 Soeren Sonnenburg
* Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
* Copyright (C) 2011 Berlin Institute of Technology
*/
#ifndef PCA_H_
#define PCA_H_
#include <shogun/lib/config.h>
#ifdef HAVE_LAPACK
#include <shogun/mathematics/lapack.h>
#include <stdio.h>
#include <shogun/preprocessor/DimensionReductionPreprocessor.h>
#include <shogun/features/Features.h>
#include <shogun/lib/common.h>
namespace shogun
{
/** mode of pca */
enum EPCAMode
{
/** cut by threshold */
THRESHOLD,
/** variance explained */
VARIANCE_EXPLAINED,
/** keep fixed number of features */
FIXED_NUMBER
};
/** @brief Preprocessor PCACut performs principial component analysis on the input
* vectors and keeps only the n eigenvectors with eigenvalues above a certain
* threshold.
*
* On preprocessing the stored covariance matrix is used to project
* vectors into eigenspace only returning vectors of reduced dimension n.
* Optional whitening is performed.
*
* This is only useful if the dimensionality of the data is rather low, as the
* covariance matrix is of size num_feat*num_feat. Note that vectors don't have
* to have zero mean as it is substracted.
*/
class CPCA: public CDimensionReductionPreprocessor
{
public:
/** constructor
* @param do_whitening do whitening
* @param mode mode of pca
* @param thresh threshold
*/
CPCA(bool do_whitening=false, EPCAMode mode=FIXED_NUMBER, float64_t thresh=1e-6);
/** destructor */
virtual ~CPCA();
/** initialize preprocessor from features
* @param features
*/
virtual bool init(CFeatures* features);
/** cleanup */
virtual void cleanup();
/** apply preprocessor to feature matrix
* @param features features
* @return processed feature matrix
*/
virtual SGMatrix<float64_t> apply_to_feature_matrix(CFeatures* features);
/** apply preprocessor to feature vector
* @param vector feature vector
* @return processed feature vector
*/
virtual SGVector<float64_t> apply_to_feature_vector(SGVector<float64_t> vector);
/** get transformation matrix, i.e. eigenvectors (potentially scaled if
* do_whitening is true)
*/
SGMatrix<float64_t> get_transformation_matrix();
/** get eigenvalues of PCA
*/
SGVector<float64_t> get_eigenvalues();
/** get mean vector of original data
*/
SGVector<float64_t> get_mean();
/** @return object name */
virtual inline const char* get_name() const { return "PCA"; }
/** @return a type of preprocessor */
virtual inline EPreprocessorType get_type() const { return P_PCA; }
protected:
void init();
protected:
/** transformation matrix */
SGMatrix<float64_t> m_transformation_matrix;
/** num dim */
int32_t num_dim;
/** num old dim */
int32_t num_old_dim;
/** mean vector */
SGVector<float64_t> m_mean_vector;
/** eigenvalues vector */
SGVector<float64_t> m_eigenvalues_vector;
/** initialized */
bool m_initialized;
/** whitening */
bool m_whitening;
/** PCA mode */
EPCAMode m_mode;
/** thresh */
float64_t thresh;
};
}
#endif
#endif
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