1 | #ifndef _theplu_yat_regression_naive_ |
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2 | #define _theplu_yat_regression_naive_ |
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3 | |
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4 | // $Id: Naive.h 865 2007-09-10 19:41:04Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2004 Peter Johansson |
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8 | Copyright (C) 2005, 2006 Jari Häkkinen, Peter Johansson |
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9 | Copyright (C) 2007 Peter Johansson |
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10 | |
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11 | This file is part of the yat library, http://trac.thep.lu.se/trac/yat |
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12 | |
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13 | The yat library is free software; you can redistribute it and/or |
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14 | modify it under the terms of the GNU General Public License as |
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15 | published by the Free Software Foundation; either version 2 of the |
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16 | License, or (at your option) any later version. |
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17 | |
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18 | The yat library is distributed in the hope that it will be useful, |
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19 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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20 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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21 | General Public License for more details. |
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22 | |
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23 | You should have received a copy of the GNU General Public License |
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24 | along with this program; if not, write to the Free Software |
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25 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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26 | 02111-1307, USA. |
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27 | */ |
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28 | |
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29 | #include "OneDimensional.h" |
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30 | |
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31 | #include <iostream> |
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32 | #include <utility> |
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33 | |
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34 | namespace theplu { |
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35 | namespace yat { |
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36 | namespace utility { |
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37 | class vector; |
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38 | } |
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39 | namespace regression { |
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40 | |
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41 | /** |
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42 | @brief Naive Regression |
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43 | |
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44 | Data are modeled as \f$ y_i = \alpha + \epsilon_i \f$ |
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45 | |
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46 | */ |
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47 | class Naive : public OneDimensional |
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48 | { |
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49 | |
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50 | public: |
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51 | /// |
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52 | /// @brief The default constructor |
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53 | /// |
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54 | Naive(void); |
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55 | |
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56 | /// |
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57 | /// @brief The destructor |
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58 | /// |
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59 | virtual ~Naive(void); |
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60 | |
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61 | /// |
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62 | /// This function computes the best-fit for the naive model \f$ y |
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63 | /// = m \f$ from vectors \a x and \a y, by minimizing \f$ |
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64 | /// \sum{(y_i-m)^2} \f$. |
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65 | /// |
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66 | void fit(const utility::vector& x, const utility::vector& y); |
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67 | |
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68 | /// |
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69 | /// The predicted value is the average \f$ m \f$ |
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70 | /// |
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71 | double predict(const double x) const; |
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72 | |
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73 | /** |
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74 | \f$ \frac{\sum \epsilon_i^2}{N-1} \f$ |
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75 | |
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76 | @return Conditional variance |
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77 | */ |
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78 | double s2(void) const; |
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79 | |
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80 | /// |
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81 | /// \f$ \frac{s^2}{N} \f$ |
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82 | /// |
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83 | /// @return squared standard error |
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84 | /// |
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85 | /// @see statistics::Averager |
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86 | /// |
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87 | double standard_error2(const double x) const; |
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88 | |
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89 | private: |
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90 | /// |
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91 | /// @brief The copy constructor (not implemented). |
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92 | /// |
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93 | Naive(const Naive&); |
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94 | |
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95 | double mse_; |
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96 | }; |
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97 | |
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98 | }}} // of namespaces regression, yat, and theplu |
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99 | |
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100 | #endif |
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