1 | //#--------------------------------------------------------------------------- |
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2 | //# SDFitter.cc: A Fitter class for spectra |
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3 | //#-------------------------------------------------------------------------- |
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4 | //# Copyright (C) 2004 |
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5 | //# Malte Marquarding, ATNF |
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6 | //# |
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7 | //# This program is free software; you can redistribute it and/or modify it |
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8 | //# under the terms of the GNU General Public License as published by the Free |
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9 | //# Software Foundation; either version 2 of the License, or (at your option) |
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10 | //# any later version. |
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11 | //# |
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12 | //# This program is distributed in the hope that it will be useful, but |
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13 | //# WITHOUT ANY WARRANTY; without even the implied warranty of |
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14 | //# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General |
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15 | //# Public License for more details. |
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16 | //# |
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17 | //# You should have received a copy of the GNU General Public License along |
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18 | //# with this program; if not, write to the Free Software Foundation, Inc., |
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19 | //# 675 Massachusetts Ave, Cambridge, MA 02139, USA. |
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20 | //# |
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21 | //# Correspondence concerning this software should be addressed as follows: |
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22 | //# Internet email: Malte.Marquarding@csiro.au |
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23 | //# Postal address: Malte Marquarding, |
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24 | //# Australia Telescope National Facility, |
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25 | //# P.O. Box 76, |
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26 | //# Epping, NSW, 2121, |
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27 | //# AUSTRALIA |
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28 | //# |
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29 | //# $Id: |
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30 | //#--------------------------------------------------------------------------- |
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31 | #include <casa/Arrays/ArrayMath.h> |
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32 | #include <casa/Arrays/ArrayLogical.h> |
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33 | #include <scimath/Fitting.h> |
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34 | #include <scimath/Fitting/LinearFit.h> |
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35 | #include <scimath/Functionals/CompiledFunction.h> |
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36 | #include <scimath/Functionals/CompoundFunction.h> |
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37 | #include <scimath/Functionals/Gaussian1D.h> |
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38 | #include <scimath/Functionals/Polynomial.h> |
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39 | #include <scimath/Mathematics/AutoDiff.h> |
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40 | #include <scimath/Mathematics/AutoDiffMath.h> |
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41 | #include <scimath/Fitting/NonLinearFitLM.h> |
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42 | #include <components/SpectralComponents/SpectralEstimate.h> |
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43 | |
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44 | #include "SDFitter.h" |
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45 | using namespace asap; |
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46 | |
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47 | SDFitter::SDFitter() |
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48 | { |
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49 | } |
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50 | |
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51 | SDFitter::~SDFitter() |
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52 | { |
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53 | reset(); |
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54 | } |
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55 | |
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56 | void SDFitter::clear() |
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57 | { |
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58 | for (uInt i=0;i< funcs_.nelements();++i) { |
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59 | delete funcs_[i]; funcs_[i] = 0; |
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60 | }; |
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61 | funcs_.resize(0, True); |
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62 | parameters_.resize(); |
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63 | error_.resize(); |
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64 | thefit_.resize(); |
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65 | estimate_.resize(); |
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66 | chisquared_ = 0.0; |
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67 | } |
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68 | void SDFitter::reset() |
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69 | { |
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70 | clear(); |
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71 | x_.resize(); |
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72 | y_.resize(); |
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73 | m_.resize(); |
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74 | } |
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75 | |
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76 | |
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77 | bool SDFitter::computeEstimate() { |
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78 | if (x_.nelements() == 0 || y_.nelements() == 0) |
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79 | throw (AipsError("No x/y data specified.")); |
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80 | |
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81 | if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) == 0) |
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82 | return false; |
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83 | uInt n = funcs_.nelements(); |
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84 | SpectralEstimate estimator(n); |
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85 | estimator.setQ(5); |
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86 | //estimator.setWindowing(True); |
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87 | SpectralList listGauss = estimator.estimate(x_, y_); |
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88 | Gaussian1D<Float>* g; |
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89 | parameters_.resize(n*3); |
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90 | uInt count = 0; |
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91 | cout << "n = " << n << endl; |
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92 | for (uInt i=0; i<n;i++) { |
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93 | g = dynamic_cast<Gaussian1D<Float>* >(funcs_[i]); |
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94 | if (g) { |
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95 | (*g)[0] = listGauss[i].getAmpl(); |
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96 | (*g)[1] = listGauss[i].getCenter(); |
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97 | (*g)[2] = listGauss[i].getFWHM(); |
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98 | ++count; |
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99 | } |
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100 | } |
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101 | estimate_.resize(); |
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102 | listGauss.evaluate(estimate_,x_); |
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103 | return true; |
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104 | } |
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105 | |
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106 | std::vector<float> SDFitter::getEstimate() const |
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107 | { |
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108 | if (estimate_.nelements() == 0) |
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109 | throw (AipsError("No estimate set.")); |
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110 | std::vector<float> stlout; |
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111 | estimate_.tovector(stlout); |
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112 | return stlout; |
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113 | } |
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114 | |
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115 | |
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116 | bool SDFitter::setExpression(const std::string& expr, int ncomp) |
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117 | { |
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118 | clear(); |
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119 | if (expr == "gauss") { |
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120 | if (ncomp < 1) throw (AipsError("Need at least one gaussian to fit.")); |
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121 | funcs_.resize(ncomp); |
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122 | for (Int k=0; k<ncomp; ++k) { |
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123 | funcs_[k] = new Gaussian1D<Float>(); |
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124 | } |
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125 | } else if (expr == "poly") { |
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126 | funcs_.resize(1); |
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127 | funcs_[0] = new Polynomial<Float>(ncomp); |
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128 | } else { |
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129 | cerr << " compiled functions not yet implemented" << endl; |
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130 | //funcs_.resize(1); |
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131 | //funcs_[0] = new CompiledFunction<Float>(); |
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132 | //funcs_[0]->setFunction(String(expr)); |
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133 | return false; |
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134 | }; |
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135 | return true; |
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136 | } |
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137 | |
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138 | bool SDFitter::setData(std::vector<float> absc, std::vector<float> spec, |
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139 | std::vector<bool> mask) |
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140 | { |
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141 | x_.resize(); |
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142 | y_.resize(); |
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143 | m_.resize(); |
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144 | // convert std::vector to casa Vector |
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145 | Vector<Float> tmpx(absc); |
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146 | Vector<Float> tmpy(spec); |
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147 | Vector<Bool> tmpm(mask); |
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148 | AlwaysAssert(tmpx.nelements() == tmpy.nelements(), AipsError); |
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149 | x_ = tmpx; |
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150 | y_ = tmpy; |
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151 | m_ = tmpm; |
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152 | return true; |
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153 | } |
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154 | |
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155 | std::vector<float> SDFitter::getResidual() const |
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156 | { |
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157 | if (residual_.nelements() == 0) |
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158 | throw (AipsError("Function not yet fitted.")); |
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159 | std::vector<float> stlout; |
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160 | residual_.tovector(stlout); |
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161 | return stlout; |
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162 | } |
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163 | |
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164 | std::vector<float> SDFitter::getFit() const |
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165 | { |
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166 | Vector<Float> out = thefit_; |
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167 | std::vector<float> stlout; |
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168 | out.tovector(stlout); |
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169 | return stlout; |
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170 | |
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171 | } |
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172 | |
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173 | std::vector<float> SDFitter::getErrors() const |
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174 | { |
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175 | Vector<Float> out = error_; |
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176 | std::vector<float> stlout; |
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177 | out.tovector(stlout); |
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178 | return stlout; |
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179 | } |
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180 | |
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181 | bool SDFitter::setParameters(std::vector<float> params) |
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182 | { |
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183 | Vector<Float> tmppar(params); |
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184 | if (funcs_.nelements() == 0) |
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185 | throw (AipsError("Function not yet set.")); |
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186 | if (parameters_.nelements() > 0 && tmppar.nelements() != parameters_.nelements()) |
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187 | throw (AipsError("Number of parameters inconsistent with function.")); |
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188 | if (parameters_.nelements() == 0) |
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189 | parameters_.resize(tmppar.nelements()); |
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190 | fixedpar_.resize(tmppar.nelements()); |
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191 | fixedpar_ = False; |
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192 | if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) != 0) { |
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193 | uInt count = 0; |
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194 | for (uInt j=0; j < funcs_.nelements(); ++j) { |
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195 | for (uInt i=0; i < funcs_[j]->nparameters(); ++i) { |
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196 | (funcs_[j]->parameters())[i] = tmppar[count]; |
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197 | parameters_[count] = tmppar[count]; |
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198 | ++count; |
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199 | } |
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200 | } |
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201 | } else if (dynamic_cast<Polynomial<Float>* >(funcs_[0]) != 0) { |
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202 | for (uInt i=0; i < funcs_[0]->nparameters(); ++i) { |
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203 | parameters_[i] = tmppar[i]; |
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204 | (funcs_[0]->parameters())[i] = tmppar[i]; |
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205 | } |
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206 | } |
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207 | return true; |
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208 | } |
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209 | |
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210 | bool SDFitter::setFixedParameters(std::vector<bool> fixed) |
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211 | { |
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212 | Vector<Bool> tmp(fixed); |
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213 | if (funcs_.nelements() == 0) |
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214 | throw (AipsError("Function not yet set.")); |
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215 | if (fixedpar_.nelements() > 0 && tmp.nelements() != fixedpar_.nelements()) |
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216 | throw (AipsError("Number of mask elements inconsistent with function.")); |
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217 | if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) != 0) { |
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218 | uInt count = 0; |
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219 | for (uInt j=0; j < funcs_.nelements(); ++j) { |
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220 | for (uInt i=0; i < funcs_[j]->nparameters(); ++i) { |
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221 | funcs_[j]->mask(i) = !tmp[count]; |
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222 | fixedpar_[count] = !tmp[count]; |
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223 | ++count; |
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224 | } |
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225 | } |
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226 | } else if (dynamic_cast<Polynomial<Float>* >(funcs_[0]) != 0) { |
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227 | for (uInt i=0; i < funcs_[0]->nparameters(); ++i) { |
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228 | fixedpar_[i] = tmp[i]; |
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229 | funcs_[0]->mask(i) = tmp[i]; |
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230 | } |
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231 | } |
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232 | //fixedpar_ = !tmpmsk; |
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233 | return true; |
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234 | } |
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235 | |
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236 | std::vector<float> SDFitter::getParameters() const { |
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237 | Vector<Float> out = parameters_; |
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238 | std::vector<float> stlout; |
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239 | out.tovector(stlout); |
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240 | return stlout; |
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241 | } |
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242 | |
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243 | std::vector<bool> SDFitter::getFixedParameters() const { |
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244 | if (fixedpar_.nelements() == 0) |
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245 | throw (AipsError("No parameter mask set.")); |
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246 | Vector<Bool> out = fixedpar_; |
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247 | std::vector<bool> stlout; |
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248 | out.tovector(stlout); |
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249 | return stlout; |
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250 | } |
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251 | |
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252 | float SDFitter::getChisquared() const { |
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253 | return chisquared_; |
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254 | } |
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255 | |
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256 | bool SDFitter::fit() { |
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257 | NonLinearFitLM<Float> fitter; |
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258 | //CompiledFunction<AutoDiff<Float> > comp; |
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259 | //Polynomial<AutoDiff<Float> > poly; |
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260 | CompoundFunction<AutoDiff<Float> > func; |
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261 | if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) != 0) { |
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262 | //computeEstimates(); |
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263 | for (uInt i=0; i<funcs_.nelements(); i++) { |
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264 | Gaussian1D<AutoDiff<Float> > gauss; |
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265 | for (uInt j=0; j<funcs_[i]->nparameters(); j++) { |
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266 | gauss[j] = AutoDiff<Float>((*funcs_[i])[j], gauss.nparameters(), j); |
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267 | gauss.mask(j) = funcs_[i]->mask(j); |
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268 | } |
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269 | func.addFunction(gauss); |
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270 | } |
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271 | } else if (dynamic_cast<Polynomial<Float>* >(funcs_[0]) != 0) { |
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272 | Polynomial<AutoDiff<Float> > poly(funcs_[0]->nparameters()-1); |
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273 | for (uInt j=0; j<funcs_[0]->nparameters(); j++) { |
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274 | poly[j] = AutoDiff<Float>(0, poly.nparameters(), j); |
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275 | poly.mask(j) = funcs_[0]->mask(j); |
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276 | } |
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277 | func.addFunction(poly); |
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278 | } else if (dynamic_cast<CompiledFunction<Float>* >(funcs_[0]) != 0) { |
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279 | |
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280 | // CompiledFunction<AutoDiff<Float> > comp; |
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281 | // for (uInt j=0; j<funcs_[0]->nparameters(); j++) { |
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282 | // comp[j] = AutoDiff<Float>(0, comp.nparameters(), j); |
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283 | // comp.mask(j) = funcs_[0]->mask(j); |
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284 | // } |
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285 | // func.addFunction(comp); |
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286 | |
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287 | cout << "NYI." << endl; |
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288 | } else { |
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289 | throw (AipsError("Fitter not set up correctly.")); |
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290 | } |
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291 | fitter.setFunction(func); |
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292 | fitter.setMaxIter(50+funcs_.nelements()*10); |
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293 | // Convergence criterium |
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294 | fitter.setCriteria(0.001); |
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295 | // Fit |
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296 | Vector<Float> sigma(x_.nelements()); |
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297 | sigma = 1.0; |
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298 | //Vector<Float> sol; |
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299 | parameters_.resize(); |
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300 | //Vector<Float> err; |
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301 | error_.resize(); |
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302 | parameters_ = fitter.fit(x_, y_, sigma, &m_); |
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303 | /* |
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304 | CompoundFunction<Float> f; |
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305 | for (uInt i=0; i<funcs_.nelements(); i++) { |
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306 | f.addFunction(*funcs_[i]); |
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307 | } |
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308 | f.parameters().setParameters(parameters_); |
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309 | */ |
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310 | error_ = fitter.errors(); |
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311 | chisquared_ = fitter.getChi2(); |
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312 | residual_.resize(); |
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313 | residual_ = y_; |
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314 | fitter.residual(residual_,x_); |
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315 | // use fitter.residual(model=True) to get the model |
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316 | thefit_.resize(x_.nelements()); |
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317 | fitter.residual(thefit_,x_,True); |
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318 | return true; |
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319 | } |
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