[91] | 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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