1 | // $Id: LinearWeighted.cc 682 2006-10-11 22:06:38Z jari $ |
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2 | |
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3 | /* |
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4 | Copyright (C) The authors contributing to this file. |
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5 | |
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6 | This file is part of the yat library, http://lev.thep.lu.se/trac/yat |
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7 | |
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8 | The yat library is free software; you can redistribute it and/or |
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9 | modify it under the terms of the GNU General Public License as |
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10 | published by the Free Software Foundation; either version 2 of the |
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11 | License, or (at your option) any later version. |
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12 | |
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13 | The yat library is distributed in the hope that it will be useful, |
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14 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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15 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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16 | General Public License for more details. |
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17 | |
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18 | You should have received a copy of the GNU General Public License |
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19 | along with this program; if not, write to the Free Software |
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20 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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21 | 02111-1307, USA. |
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22 | */ |
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23 | |
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24 | #include "LinearWeighted.h" |
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25 | #include "yat/statistics/AveragerPairWeighted.h" |
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26 | #include "yat/utility/vector.h" |
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27 | |
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28 | #include <gsl/gsl_fit.h> |
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29 | |
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30 | namespace theplu { |
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31 | namespace yat { |
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32 | namespace regression { |
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33 | |
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34 | void LinearWeighted::fit(const utility::vector& x, |
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35 | const utility::vector& y, |
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36 | const utility::vector& w) |
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37 | { |
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38 | // AveragerPairWeighted requires 2 weights but works only on the |
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39 | // product wx*wy, so we can send in w and a dummie to get what we |
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40 | // want. |
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41 | utility::vector dummie(w.size(),1); |
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42 | statistics::AveragerPairWeighted ap; |
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43 | ap.add_values(x,y,w,dummie); |
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44 | |
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45 | double m_x = ap.x_averager().mean(); |
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46 | double m_y = ap.y_averager().mean(); |
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47 | |
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48 | double sxy = ap.sum_xy_centered(); |
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49 | |
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50 | double sxx = ap.x_averager().sum_xx_centered(); |
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51 | double syy = ap.y_averager().sum_xx_centered(); |
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52 | |
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53 | // estimating the noise level. see attached document for motivation |
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54 | // of the expression. |
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55 | s2_= (syy-sxy*sxy/sxx)/(w.sum()-2*(w*w)/w.sum()) ; |
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56 | |
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57 | alpha_ = m_y; |
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58 | beta_ = sxy/sxx; |
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59 | alpha_var_ = ap.y_averager().standard_error() * |
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60 | ap.y_averager().standard_error(); |
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61 | beta_var_ = s2_/sxx; |
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62 | m_x_=m_x; |
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63 | } |
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64 | |
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65 | }}} // of namespaces regression, yat, and theplu |
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