source: trunk/yat/classifier/Kernel.h @ 1189

Last change on this file since 1189 was 1166, checked in by Markus Ringnér, 13 years ago

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1#ifndef _theplu_yat_classifier_kernel_
2#define _theplu_yat_classifier_kernel_
3
4// $Id$
5
6/*
7  Copyright (C) 2005 Jari Häkkinen, Peter Johansson
8  Copyright (C) 2006 Jari Häkkinen, Markus Ringnér, Peter Johansson
9  Copyright (C) 2007 Peter Johansson
10
11  This file is part of the yat library, http://trac.thep.lu.se/yat
12
13  The yat library is free software; you can redistribute it and/or
14  modify it under the terms of the GNU General Public License as
15  published by the Free Software Foundation; either version 2 of the
16  License, or (at your option) any later version.
17
18  The yat library is distributed in the hope that it will be useful,
19  but WITHOUT ANY WARRANTY; without even the implied warranty of
20  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
21  General Public License for more details.
22
23  You should have received a copy of the GNU General Public License
24  along with this program; if not, write to the Free Software
25  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
26  02111-1307, USA.
27*/
28
29#include "KernelFunction.h"
30
31#include <cctype>
32#include <vector>
33
34namespace theplu {
35namespace yat {
36namespace classifier {
37
38  class MatrixLookup;
39  class MatrixLookupWeighted;
40
41  ///
42  ///  @brief Interface Class for Kernels.
43  ///
44  ///  Class taking care of the \f$ NxN \f$ kernel matrix, where \f$ N \f$
45  ///  is number of samples. Each element in the Kernel corresponds to
46  ///  the scalar product of the corresponding pair of samples. At the
47  ///  time being there are two kinds of kernels. Kernel_SEV that is
48  ///  optimized to be fast and Kernel_MEV that is preferable when
49  ///  dealing with many samples and memory might be a
50  ///  bottleneck. A
51  ///  KernelFunction defines what kind of scalar product the Kernel
52  ///  represents, e.g. a Polynomial Kernel of degree 1 means we are
53  ///  dealing with the ordinary linear scalar product.
54  ///
55  /// @note If the KernelFunction is destroyed, the Kernel is no
56  /// longer defined.
57  ///
58  class Kernel
59  {
60
61  public:
62
63    ///
64    /// Constructor taking the @a data matrix and KernelFunction as
65    /// input. Each column in the data matrix corresponds to one
66    /// sample and the Kernel matrix is built applying the
67    /// KernelFunction on each pair of columns in the data matrix.
68    /// If @a own is set to true, Kernel is owner of underlying data.
69    ///
70    /// @note Can not handle NaNs. To deal with missing values use
71    /// constructor taking MatrixLookupWeighted.
72    ///
73    Kernel(const MatrixLookup& data, const KernelFunction& kf, 
74           const bool own=false); 
75
76    ///
77    /// Constructor taking the @a data matrix (with weights) and
78    /// KernelFunction as
79    /// input. Each column in the data matrix corresponds to one
80    /// sample and the Kernel matrix is built applying the
81    /// KernelFunction on each pair of columns in the data matrix.
82    /// If @a own is set to true, Kernel is owner of underlying data.
83    ///
84    Kernel(const MatrixLookupWeighted& data, const KernelFunction& kf, 
85           const bool own=false); 
86
87    ///
88    /// The new kernel is created using selected features @a
89    /// index. Kernel will own its underlying data
90    ///
91    Kernel(const Kernel& kernel, const std::vector<size_t>& index);
92
93    ///
94    /// @brief Destructor
95    ///
96    /// If Kernel is owner of underlying data and Kernel is the last
97    /// owner, underlying data is deleted.
98    ///
99    virtual ~Kernel(void);
100
101    ///
102    /// @return element at position (\a row, \a column) of the Kernel
103    /// matrix
104    ///
105    virtual double operator()(const size_t row, const size_t column) const=0;
106
107    ///
108    /// @return const reference to the underlying data.
109    ///
110    /// \throw if data is weighted
111    ///
112    const MatrixLookup& data(void) const;
113
114    ///
115    /// @return const reference to the underlying data.
116    ///
117    /// \throw if data is unweighted
118    ///
119    const MatrixLookupWeighted& data_weighted(void) const;
120
121    ///
122    /// Calculates the scalar product (using the KernelFunction)
123    /// between vector @a vec and the \f$ i \f$ th column in the data
124    /// matrix.
125    ///   
126    double element(const DataLookup1D& vec, const size_t i) const;
127
128    ///
129    /// Calculates the weighted scalar product (using the
130    /// KernelFunction) between vector @a vec and the \f$ i \f$ th column
131    /// in the data matrix. Using a weight vector with all elements
132    /// equal to unity yields same result as the non-weighted version
133    /// above.
134    ///
135    double element(const DataLookupWeighted1D& vec, const size_t i) const;
136
137    ///
138    /// An interface for making new classifier objects. This function
139    /// allows for specification at run-time of which kernel to
140    /// instatiate (see 'Prototype' in Design Patterns).
141    ///
142    /// @note Returns a dynamically allocated Kernel, which has
143    /// to be deleted by the caller to avoid memory leaks.
144    ///
145    virtual const Kernel* make_kernel(const MatrixLookup&, const bool) const=0;
146
147
148    ///
149    /// An interface for making new classifier objects. This function
150    /// allows for specification at run-time of which kernel to
151    /// instatiate (see 'Prototype' in Design Patterns).
152    ///
153    /// @note Returns a dynamically allocated Kernel, which has
154    /// to be deleted by the caller to avoid memory leaks.
155    ///
156    virtual const Kernel* make_kernel(const MatrixLookupWeighted&, 
157                                      const bool own=false) const=0;
158
159
160    /**
161       \brief number of samples
162    */
163    size_t size(void) const;
164
165    ///
166    /// @return true if kernel is calculated using weights
167    ///
168    bool weighted(void) const;
169
170  protected:
171    /// underlying data
172    const MatrixLookup* ml_;
173    /// same as data_ if weifghted otherwise a NULL pointer
174    const MatrixLookupWeighted* mlw_;
175    /// type of Kernel Function e.g. Gaussian (aka RBF)
176    const KernelFunction* kf_;
177
178    ///
179    /// pointer telling how many owners to underlying data
180    /// (data_). NULL if this is not an owner.
181    ///
182    u_int* ref_count_;
183
184    ///
185    /// pointer telling how many owners to underlying weights
186    /// (data_w_). NULL if this is not an owner.
187    ///
188    u_int* ref_count_w_;
189
190  private:
191    ///
192    /// Copy constructor (not implemented)
193    ///
194    Kernel(const Kernel&);
195
196    const Kernel& operator=(const Kernel&);
197
198  }; // class Kernel
199
200}}} // of namespace classifier, yat, and theplu
201
202#endif
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