source: trunk/yat/classifier/Kernel.h @ 865

Last change on this file since 865 was 865, checked in by Peter, 14 years ago

changing URL to http://trac.thep.lu.se/trac/yat

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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/trac/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 DataLookup2D;
39  class MatrixLookup;
40  class MatrixLookupWeighted;
41
42  ///
43  ///  @brief Interface Class for Kernels.
44  ///
45  ///  Class taking care of the \f$ NxN \f$ kernel matrix, where \f$ N \f$
46  ///  is number of samples. Each element in the Kernel corresponds to
47  ///  the scalar product of the corresponding pair of samples. At the
48  ///  time being there are two kinds of kernels. Kernel_SEV that is
49  ///  optimized to be fast and Kernel_MEV that is preferable when
50  ///  dealing with many samples and memory might be a
51  ///  bottleneck. A
52  ///  KernelFunction defines what kind of scalar product the Kernel
53  ///  represents, e.g. a Polynomial Kernel of degree 1 means we are
54  ///  dealing with the ordinary linear scalar product.
55  ///
56  /// @note If the KernelFunction is destroyed, the Kernel is no
57  /// longer defined.
58  ///
59  class Kernel
60  {
61
62  public:
63
64    ///
65    /// Constructor taking the @a data matrix and KernelFunction as
66    /// input. Each column in the data matrix corresponds to one
67    /// sample and the Kernel matrix is built applying the
68    /// KernelFunction on each pair of columns in the data matrix.
69    /// If @a own is set to true, Kernel is owner of underlying data.
70    ///
71    /// @note Can not handle NaNs. To deal with missing values use
72    /// constructor taking MatrixLookupWeighted.
73    ///
74    Kernel(const MatrixLookup& data, const KernelFunction& kf, 
75           const bool own=false); 
76
77    ///
78    /// Constructor taking the @a data matrix (with weights) and
79    /// KernelFunction as
80    /// input. Each column in the data matrix corresponds to one
81    /// sample and the Kernel matrix is built applying the
82    /// KernelFunction on each pair of columns in the data matrix.
83    /// If @a own is set to true, Kernel is owner of underlying data.
84    ///
85    Kernel(const MatrixLookupWeighted& data, const KernelFunction& kf, 
86           const bool own=false); 
87
88    ///
89    /// The new kernel is created using selected features @a
90    /// index. Kernel will own its underlying data
91    ///
92    Kernel(const Kernel& kernel, const std::vector<size_t>& index);
93
94    ///
95    /// @brief Destructor
96    ///
97    /// If Kernel is owner of underlying data and Kernel is the last
98    /// owner, underlying data is deleted.
99    ///
100    virtual ~Kernel(void);
101
102    ///
103    /// @return element at position (\a row, \a column) of the Kernel
104    /// matrix
105    ///
106    virtual double operator()(const size_t row, const size_t column) const=0;
107
108    ///
109    /// @return const reference to the underlying data.
110    ///
111    const DataLookup2D& data(void) const;
112
113    ///
114    /// Calculates the scalar product (using the KernelFunction)
115    /// between vector @a vec and the \f$ i \f$ th column in the data
116    /// matrix.
117    ///   
118    double element(const DataLookup1D& vec, const size_t i) const;
119
120    ///
121    /// Calculates the weighted scalar product (using the
122    /// KernelFunction) between vector @a vec and the \f$ i \f$ th column
123    /// in the data matrix. Using a weight vector with all elements
124    /// equal to unity yields same result as the non-weighted version
125    /// above.
126    ///
127    double element(const DataLookupWeighted1D& vec, const size_t i) const;
128
129    ///
130    /// An interface for making new classifier objects. This function
131    /// allows for specification at run-time of which kernel to
132    /// instatiate (see 'Prototype' in Design Patterns).
133    ///
134    /// @Note Returns a dynamically allocated Kernel, which has
135    /// to be deleted by the caller to avoid memory leaks.
136    ///
137    virtual const Kernel* make_kernel(const MatrixLookup&, const bool) const=0;
138
139
140    ///
141    /// An interface for making new classifier objects. This function
142    /// allows for specification at run-time of which kernel to
143    /// instatiate (see 'Prototype' in Design Patterns).
144    ///
145    /// @Note Returns a dynamically allocated Kernel, which has
146    /// to be deleted by the caller to avoid memory leaks.
147    ///
148    virtual const Kernel* make_kernel(const MatrixLookupWeighted&, 
149                                      const bool own=false) const=0;
150
151
152    /**
153       \brief number of samples
154    */
155    size_t size(void) const;
156
157    ///
158    /// @return true if kernel is calculated using weights
159    ///
160    bool weighted(void) const;
161
162  protected:
163    /// underlying data
164    const DataLookup2D* data_;
165    /// same as data_ if weifghted otherwise a NULL pointer
166    const MatrixLookupWeighted* data_w_;
167    /// type of Kernel Function e.g. Gaussian (aka RBF)
168    const KernelFunction* kf_;
169
170    ///
171    /// pointer telling how many owners to underlying data
172    /// (data_). NULL if this is not an owner.
173    ///
174    u_int* ref_count_;
175
176    ///
177    /// pointer telling how many owners to underlying weights
178    /// (data_w_). NULL if this is not an owner.
179    ///
180    u_int* ref_count_w_;
181
182  private:
183    ///
184    /// Copy constructor (not implemented)
185    ///
186    Kernel(const Kernel&);
187
188    const Kernel& operator=(const Kernel&);
189
190  }; // class Kernel
191
192}}} // of namespace classifier, yat, and theplu
193
194#endif
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