source: trunk/yat/utility/PCA.h @ 2119

Last change on this file since 2119 was 2119, checked in by Peter, 13 years ago

converted files to utf-8. fixes #577

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 3.9 KB
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1#ifndef _theplu_yat_utility_pca_
2#define _theplu_yat_utility_pca_
3
4// $Id: PCA.h 2119 2009-12-12 23:11:43Z peter $
5
6/*
7  Copyright (C) 2003 Daniel Dalevi
8  Copyright (C) 2004 Jari Häkkinen
9  Copyright (C) 2005 Jari Häkkinen, Peter Johansson
10  Copyright (C) 2006 Jari Häkkinen
11  Copyright (C) 2007, 2008 Jari Häkkinen, Peter Johansson
12
13  This file is part of the yat library, http://dev.thep.lu.se/yat
14
15  The yat library is free software; you can redistribute it and/or
16  modify it under the terms of the GNU General Public License as
17  published by the Free Software Foundation; either version 3 of the
18  License, or (at your option) any later version.
19
20  The yat library is distributed in the hope that it will be useful,
21  but WITHOUT ANY WARRANTY; without even the implied warranty of
22  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
23  General Public License for more details.
24
25  You should have received a copy of the GNU General Public License
26  along with yat. If not, see <http://www.gnu.org/licenses/>.
27*/
28
29#include "Matrix.h"
30#include "Vector.h"
31
32namespace theplu {
33namespace yat {
34namespace utility {
35
36  /**
37     @brief Principal Component Analysis
38
39     Class performing PCA using SVD. This class assumes that
40     the columns corresponds to the dimenension of the problem.
41     That means if data has dimension NxM (M=columns) the number
42     of principal-axes will equal M-1. When projecting data into
43     this space, all Nx1 vectors will have dimension Mx1. Hence
44     the projection will have dimension MxM where each column is
45     a point in the new space. Also, it assumes that M>N. The opposite
46     problem is added in the functions: process_transposed_problem and
47     projection_transposed()...
48  */
49  class PCA
50  {
51  public:
52    /**
53       Constructor taking the data-matrix as input. No row-centering
54       should have been performed and no products.
55     */
56    explicit PCA(const utility::Matrix&);
57 
58    /**
59       If M<N use this method instead. Using the same format as before
60       where rows in the matrix corresponds to the dimensional coordinate.
61       The only difference is in the SVD step where the matrix V is used
62       after running the transposed matrix. For projections, see
63       projection_transposed() method.
64     */
65    //    void process_transposed_problem(void);
66
67    /**
68       \brief Returns eigenvalues in a utility::vector.
69
70       \return A const reference to the internal vector containing all
71       eigenvalues.
72    */
73    const utility::Vector& eigenvalues(void) const;
74
75    /**
76       \brief Get all eigenvectors in a utility::matrix.
77
78       \return A const reference to the internal matrix containing all
79       eigenvectors.
80    */
81    const utility::Matrix& eigenvectors(void) const;
82
83    /**
84       This function will project data onto the new coordinate-system
85       where the axes are the calculated eigenvectors. This means that
86       PCA must have been run before this function can be used!
87       Output is presented as coordinates in the N-dimensional room
88       spanned by the eigenvectors.
89    */
90    utility::Matrix projection( const utility::Matrix& ) const;
91
92    /**
93       Same as projection() but works when used
94       process_transposed_problem().
95    */
96    //    utility::matrix projection_transposed( const utility::matrix& ) const;
97
98
99  private:
100
101    /**
102       Will perform PCA according to the following scheme: \n
103       1: Rowcenter A  \n
104       2: SVD(A)  --> USV' \n
105       3: Calculate eigenvalues according to \n
106          \f$ \lambda_{ii} = s_{ii}/N_{rows} \f$ \n
107       4: Sort eigenvectors (from matrix V) according to descending eigenvalues\n
108    */
109    void process(void);
110
111    /**
112       Private function that will row-center the matrix A,
113       that is, A = A - M, where M is a matrix
114       with the meanvalues of each row
115    */
116    void row_center( utility::Matrix& A_center );
117
118    utility::Matrix A_; 
119    utility::Vector eigenvalues_;
120    utility::Matrix eigenvectors_;
121    utility::Vector meanvalues_;
122  };
123
124}}} // of namespace utility, yat, and theplu
125
126#endif
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