1 | #ifndef _theplu_yat_classifier_kernel_ |
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2 | #define _theplu_yat_classifier_kernel_ |
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3 | |
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4 | // $Id$ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2005 Jari Häkkinen, Peter Johansson |
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8 | Copyright (C) 2006 Jari Häkkinen, Peter Johansson, Markus Ringnér |
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9 | Copyright (C) 2007 Jari Häkkinen, Peter Johansson |
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10 | Copyright (C) 2008 Jari Häkkinen, Peter Johansson, Markus Ringnér |
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11 | |
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12 | This file is part of the yat library, http://dev.thep.lu.se/yat |
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13 | |
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14 | The yat library is free software; you can redistribute it and/or |
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15 | modify it under the terms of the GNU General Public License as |
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16 | published by the Free Software Foundation; either version 3 of the |
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17 | License, or (at your option) any later version. |
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18 | |
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19 | The yat library is distributed in the hope that it will be useful, |
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20 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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21 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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22 | General Public License for more details. |
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23 | |
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24 | You should have received a copy of the GNU General Public License |
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25 | along with yat. If not, see <http://www.gnu.org/licenses/>. |
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26 | */ |
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27 | |
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28 | #include "KernelFunction.h" |
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29 | |
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30 | #include <cstddef> |
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31 | #include <vector> |
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32 | |
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33 | namespace theplu { |
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34 | namespace yat { |
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35 | namespace classifier { |
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36 | |
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37 | class MatrixLookup; |
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38 | class MatrixLookupWeighted; |
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39 | |
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40 | /// |
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41 | /// @brief Interface Class for Kernels. |
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42 | /// |
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43 | /// Class taking care of the \f$ NxN \f$ kernel matrix, where \f$ N \f$ |
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44 | /// is number of samples. Each element in the Kernel corresponds to |
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45 | /// the scalar product of the corresponding pair of samples. At the |
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46 | /// time being there are two kinds of kernels. Kernel_SEV that is |
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47 | /// optimized to be fast and Kernel_MEV that is preferable when |
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48 | /// dealing with many samples and memory might be a |
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49 | /// bottleneck. A |
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50 | /// KernelFunction defines what kind of scalar product the Kernel |
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51 | /// represents, e.g. a Polynomial Kernel of degree 1 means we are |
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52 | /// dealing with the ordinary linear scalar product. |
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53 | /// |
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54 | /// @note If the KernelFunction is destroyed, the Kernel is no |
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55 | /// longer defined. |
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56 | /// |
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57 | class Kernel |
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58 | { |
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59 | |
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60 | public: |
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61 | |
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62 | /// |
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63 | /// Constructor taking the @a data matrix and KernelFunction as |
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64 | /// input. Each column in the data matrix corresponds to one |
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65 | /// sample and the Kernel matrix is built applying the |
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66 | /// KernelFunction on each pair of columns in the data matrix. |
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67 | /// If @a own is set to true, Kernel is owner of underlying data. |
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68 | /// |
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69 | /// @note Can not handle NaNs. To deal with missing values use |
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70 | /// constructor taking MatrixLookupWeighted. |
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71 | /// |
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72 | Kernel(const MatrixLookup& data, const KernelFunction& kf, |
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73 | const bool own=false); |
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74 | |
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75 | /// |
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76 | /// Constructor taking the @a data matrix (with weights) and |
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77 | /// KernelFunction as |
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78 | /// input. Each column in the data matrix corresponds to one |
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79 | /// sample and the Kernel matrix is built applying the |
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80 | /// KernelFunction on each pair of columns in the data matrix. |
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81 | /// If @a own is set to true, Kernel is owner of underlying data. |
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82 | /// |
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83 | Kernel(const MatrixLookupWeighted& data, const KernelFunction& kf, |
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84 | const bool own=false); |
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85 | |
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86 | /// |
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87 | /// The new kernel is created using selected features @a |
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88 | /// index. Kernel will own its underlying data |
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89 | /// |
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90 | Kernel(const Kernel& kernel, const std::vector<size_t>& index); |
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91 | |
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92 | /// |
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93 | /// @brief Destructor |
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94 | /// |
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95 | /// If Kernel is owner of underlying data and Kernel is the last |
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96 | /// owner, underlying data is deleted. |
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97 | /// |
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98 | virtual ~Kernel(void); |
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99 | |
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100 | /// |
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101 | /// @return element at position (\a row, \a column) of the Kernel |
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102 | /// matrix |
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103 | /// |
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104 | virtual double operator()(const size_t row, const size_t column) const=0; |
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105 | |
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106 | /// |
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107 | /// @return const reference to the underlying data. |
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108 | /// |
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109 | /// \throw if data is weighted |
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110 | /// |
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111 | const MatrixLookup& data(void) const; |
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112 | |
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113 | /// |
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114 | /// @return const reference to the underlying data. |
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115 | /// |
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116 | /// \throw if data is unweighted |
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117 | /// |
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118 | const MatrixLookupWeighted& data_weighted(void) const; |
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119 | |
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120 | /// |
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121 | /// Calculates the scalar product (using the KernelFunction) |
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122 | /// between vector @a vec and the \f$ i \f$ th column in the data |
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123 | /// matrix. |
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124 | /// |
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125 | double element(const DataLookup1D& vec, const size_t i) const; |
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126 | |
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127 | /// |
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128 | /// Calculates the weighted scalar product (using the |
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129 | /// KernelFunction) between vector @a vec and the \f$ i \f$ th column |
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130 | /// in the data matrix. Using a weight vector with all elements |
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131 | /// equal to unity yields same result as the non-weighted version |
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132 | /// above. |
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133 | /// |
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134 | double element(const DataLookupWeighted1D& vec, const size_t i) const; |
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135 | |
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136 | /// |
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137 | /// An interface for making new classifier objects. This function |
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138 | /// allows for specification at run-time of which kernel to |
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139 | /// instatiate (see 'Prototype' in Design Patterns). |
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140 | /// |
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141 | /// @note Returns a dynamically allocated Kernel, which has |
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142 | /// to be deleted by the caller to avoid memory leaks. |
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143 | /// |
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144 | virtual const Kernel* make_kernel(const MatrixLookup&, const bool) const=0; |
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145 | |
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146 | |
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147 | /// |
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148 | /// An interface for making new classifier objects. This function |
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149 | /// allows for specification at run-time of which kernel to |
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150 | /// instatiate (see 'Prototype' in Design Patterns). |
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151 | /// |
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152 | /// @note Returns a dynamically allocated Kernel, which has |
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153 | /// to be deleted by the caller to avoid memory leaks. |
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154 | /// |
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155 | virtual const Kernel* make_kernel(const MatrixLookupWeighted&, |
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156 | const bool own=false) const=0; |
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157 | |
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158 | |
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159 | /** |
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160 | \brief number of samples |
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161 | */ |
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162 | size_t size(void) const; |
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163 | |
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164 | /// |
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165 | /// @return true if kernel is calculated using weights |
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166 | /// |
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167 | bool weighted(void) const; |
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168 | |
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169 | protected: |
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170 | /// underlying data |
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171 | const MatrixLookup* ml_; |
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172 | /// same as data_ if weifghted otherwise a NULL pointer |
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173 | const MatrixLookupWeighted* mlw_; |
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174 | /// type of Kernel Function e.g. Gaussian (aka RBF) |
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175 | const KernelFunction* kf_; |
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176 | |
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177 | /// |
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178 | /// pointer telling how many owners to underlying data |
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179 | /// (data_). NULL if this is not an owner. |
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180 | /// |
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181 | unsigned int* ref_count_; |
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182 | |
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183 | /// |
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184 | /// pointer telling how many owners to underlying weights |
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185 | /// (data_w_). NULL if this is not an owner. |
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186 | /// |
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187 | unsigned int* ref_count_w_; |
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188 | |
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189 | private: |
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190 | /// |
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191 | /// Copy constructor (not implemented) |
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192 | /// |
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193 | Kernel(const Kernel&); |
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194 | |
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195 | const Kernel& operator=(const Kernel&); |
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196 | |
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197 | }; // class Kernel |
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198 | |
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199 | }}} // of namespace classifier, yat, and theplu |
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200 | |
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201 | #endif |
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