source: trunk/c++_tools/classifier/Kernel_SEV.h @ 608

Last change on this file since 608 was 608, checked in by Peter, 15 years ago

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1// $Id$
2
3#ifndef _theplu_classifier_kernel_sev_
4#define _theplu_classifier_kernel_sev_
5
6#include <c++_tools/classifier/Kernel.h>
7#include <c++_tools/gslapi/matrix.h>
8
9
10namespace theplu {
11namespace classifier {
12
13  class DataLookup1D;
14  class KernelFunction;
15
16  ///
17  ///   @brief Speed Efficient Kernel
18  ///   Class taking care of the \f$ NxN \f$ kernel matrix, where
19  ///   \f$ N \f$ is number of samples. Type of Kernel is defined by a
20  ///   KernelFunction. This Speed Efficient Version (SEV) calculated
21  ///   the kernel matrix once by construction and the kernel is stored in
22  ///   memory. When \f$ N \f$ is large and the kernel matrix cannot be
23  ///   stored in memory, use Kernel_MEV instead.
24  ///   
25  ///   @see also Kernel_MEV KernelWeighted_SEV
26  ///
27  class Kernel_SEV : public Kernel
28  {
29   
30  public:
31
32    ///
33    /// Constructor taking the data matrix and KernelFunction as
34    /// input. @note Can not handle NaNs. When dealing with missing values,
35    /// use KernelWeighted_SEV instead.
36    ///
37    Kernel_SEV(const MatrixLookup&, const KernelFunction&);
38   
39    ///
40    /// @todo remove
41    ///
42    Kernel_SEV(const Kernel_SEV& kernel, const std::vector<size_t>& index);
43
44    ///
45    /// @return element at position (\a row, \a column) in the Kernel
46    /// matrix
47    ///
48    inline double operator()(const size_t row,const size_t column) const
49    { return kernel_matrix_(row,column); }
50
51    ///
52    /// Calculates the scalar product using the KernelFunction between
53    /// data vector @a vec and column \f$ i \f$ in data matrix.
54    ///
55    /// @return kernel element between data @a vec and training sample @a i
56    ///
57    double element(const DataLookup1D& vec, const size_t i) const;
58
59    ///
60    /// Using the KernelFunction this function calculates the scalar
61    /// product between vector @a vec and the column \f$ i \f$ in data
62    /// matrix. The KernelFunction expects a weight vector for each of
63    /// the two data vectors and as this Kernel is non-weighted each
64    /// value in the data matrix is associated to a unity weight.
65    ///
66    /// @return weighted kernel element between data @a vec and
67    /// training sample @a i
68    ///
69    double element(const DataLookup1D& vec, const DataLookup1D& w, 
70                   const size_t i) const;
71
72    ///
73    /// @todo remove this function
74    ///
75    const Kernel* selected(const std::vector<size_t>& index) const;
76
77    ///
78    /// @return false
79    ///
80    inline bool weighted(void) const { return false; }
81
82  private:
83    ///
84    /// Copy constructor (not implemented)
85    ///
86    Kernel_SEV(const Kernel_SEV&);
87    const Kernel_SEV& operator=(const Kernel_SEV&);
88
89    void build_kernel(void);
90
91    gslapi::matrix kernel_matrix_;
92
93  }; // class Kernel_SEV
94
95}} // of namespace classifier and namespace theplu
96
97#endif
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