1 | // $Id$ |
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2 | |
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3 | /* |
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4 | Copyright (C) 2004 Jari Häkkinen, Peter Johansson |
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5 | Copyright (C) 2005 Peter Johansson |
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6 | Copyright (C) 2006 Jari Häkkinen, Peter Johansson |
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7 | Copyright (C) 2007 Peter Johansson |
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8 | |
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9 | This file is part of the yat library, http://lev.thep.lu.se/trac/yat |
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10 | |
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11 | The yat library is free software; you can redistribute it and/or |
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12 | modify it under the terms of the GNU General Public License as |
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13 | published by the Free Software Foundation; either version 2 of the |
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14 | License, or (at your option) any later version. |
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15 | |
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16 | The yat library is distributed in the hope that it will be useful, |
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17 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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18 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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19 | General Public License for more details. |
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20 | |
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21 | You should have received a copy of the GNU General Public License |
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22 | along with this program; if not, write to the Free Software |
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23 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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24 | 02111-1307, USA. |
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25 | */ |
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26 | |
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27 | #include "GaussianKernelFunction.h" |
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28 | #include "KernelFunction.h" |
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29 | #include "DataLookup1D.h" |
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30 | #include "DataLookupWeighted1D.h" |
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31 | |
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32 | #include <cassert> |
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33 | #include <math.h> |
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34 | |
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35 | namespace theplu { |
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36 | namespace yat { |
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37 | namespace classifier { |
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38 | |
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39 | GaussianKernelFunction::GaussianKernelFunction(double sigma) |
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40 | : KernelFunction(), sigma2_(sigma*sigma) |
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41 | { |
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42 | } |
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43 | |
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44 | double GaussianKernelFunction::operator()(const DataLookup1D& x, |
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45 | const DataLookup1D& y) const |
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46 | { |
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47 | assert(x.size()==y.size()); |
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48 | double d2 = 0; |
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49 | for (size_t i=0; i<x.size(); i++){ |
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50 | double d = x(i)-y(i); |
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51 | d2 += d*d; |
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52 | } |
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53 | return exp(-d2/sigma2_); |
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54 | } |
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55 | |
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56 | |
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57 | double GaussianKernelFunction::operator()(const DataLookup1D& x, |
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58 | const DataLookupWeighted1D& y) const |
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59 | { |
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60 | assert(x.size()==y.size()); |
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61 | double d2 = 0; |
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62 | double normalization_factor = 0; |
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63 | for (size_t i=0; i<x.size(); i++) { |
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64 | // ignoring Nan with accompanied weight zero |
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65 | if (y.weight(i)){ |
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66 | d2 += y.weight(i) * (x(i)-y.data(i)) * (x(i)-y.data(i)); |
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67 | normalization_factor += y.weight(i); |
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68 | } |
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69 | } |
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70 | // to make it coherent with no weight case |
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71 | normalization_factor /= x.size(); |
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72 | return exp(d2/normalization_factor/sigma2_); |
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73 | } |
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74 | |
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75 | |
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76 | double GaussianKernelFunction::operator()(const DataLookupWeighted1D& x, |
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77 | const DataLookupWeighted1D& y) const |
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78 | { |
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79 | assert(x.size()==y.size()); |
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80 | double d2 = 0; |
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81 | double normalization_factor = 0; |
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82 | for (size_t i=0; i<x.size(); i++) { |
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83 | // ignoring Nan with accompanied weight zero |
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84 | if (x.weight(i) && y.weight(i)){ |
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85 | d2 += x.weight(i) * y.weight(i) * (x.data(i)-y.data(i)) * |
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86 | (x.data(i)-y.data(i)); |
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87 | normalization_factor += x.weight(i) * y.weight(i); |
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88 | } |
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89 | } |
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90 | // to make it coherent with no weight case |
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91 | normalization_factor /= x.size(); |
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92 | return exp(d2/normalization_factor/sigma2_); |
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93 | } |
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94 | |
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95 | |
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96 | }}} // of namespace svn, yat, and theplu |
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