source: trunk/src/SDFitter.cc@ 124

Last change on this file since 124 was 108, checked in by mar637, 20 years ago

added fixed parameters
  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 10.2 KB
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1//#---------------------------------------------------------------------------
2//# SDFitter.cc: A Fitter class for spectra
3//#--------------------------------------------------------------------------
4//# Copyright (C) 2004
5//# Malte Marquarding, ATNF
6//#
7//# This program is free software; you can redistribute it and/or modify it
8//# under the terms of the GNU General Public License as published by the Free
9//# Software Foundation; either version 2 of the License, or (at your option)
10//# any later version.
11//#
12//# This program is distributed in the hope that it will be useful, but
13//# WITHOUT ANY WARRANTY; without even the implied warranty of
14//# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
15//# Public License for more details.
16//#
17//# You should have received a copy of the GNU General Public License along
18//# with this program; if not, write to the Free Software Foundation, Inc.,
19//# 675 Massachusetts Ave, Cambridge, MA 02139, USA.
20//#
21//# Correspondence concerning this software should be addressed as follows:
22//# Internet email: Malte.Marquarding@csiro.au
23//# Postal address: Malte Marquarding,
24//# Australia Telescope National Facility,
25//# P.O. Box 76,
26//# Epping, NSW, 2121,
27//# AUSTRALIA
28//#
29//# $Id:
30//#---------------------------------------------------------------------------
31#include <casa/Arrays/ArrayMath.h>
32#include <casa/Arrays/ArrayLogical.h>
33#include <scimath/Fitting.h>
34#include <scimath/Fitting/LinearFit.h>
35#include <scimath/Functionals/CompiledFunction.h>
36#include <scimath/Functionals/CompoundFunction.h>
37#include <scimath/Functionals/Gaussian1D.h>
38#include <scimath/Functionals/Polynomial.h>
39#include <scimath/Mathematics/AutoDiff.h>
40#include <scimath/Mathematics/AutoDiffMath.h>
41#include <scimath/Fitting/NonLinearFitLM.h>
42#include <components/SpectralComponents/SpectralEstimate.h>
43
44#include "SDFitter.h"
45using namespace asap;
46
47SDFitter::SDFitter()
48{
49}
50
51SDFitter::~SDFitter()
52{
53 reset();
54}
55
56void SDFitter::clear()
57{
58 for (uInt i=0;i< funcs_.nelements();++i) {
59 delete funcs_[i]; funcs_[i] = 0;
60 };
61 funcs_.resize(0, True);
62 parameters_.resize();
63 error_.resize();
64 thefit_.resize();
65 estimate_.resize();
66 chisquared_ = 0.0;
67}
68void SDFitter::reset()
69{
70 clear();
71 x_.resize();
72 y_.resize();
73 m_.resize();
74}
75
76
77bool SDFitter::computeEstimate() {
78 if (x_.nelements() == 0 || y_.nelements() == 0)
79 throw (AipsError("No x/y data specified."));
80
81 if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) == 0)
82 return false;
83 uInt n = funcs_.nelements();
84 SpectralEstimate estimator(n);
85 estimator.setQ(5);
86 Int mn,mx;
87 mn = 0;
88 mx = m_.nelements()-1;
89 for (uInt i=0; i<m_.nelements();++i) {
90 if (m_[i]) {
91 mn = i;
92 break;
93 }
94 }
95 for (uInt j=m_.nelements()-1; j>=0;--j) {
96 if (m_[j]) {
97 mx = j;
98 break;
99 }
100 }
101 //mn = 0+x_.nelements()/10;
102 //mx = x_.nelements()-x_.nelements()/10;
103 estimator.setRegion(mn,mx);
104 //estimator.setWindowing(True);
105 SpectralList listGauss = estimator.estimate(x_, y_);
106 Gaussian1D<Float>* g;
107 parameters_.resize(n*3);
108 uInt count = 0;
109 for (uInt i=0; i<n;i++) {
110 g = dynamic_cast<Gaussian1D<Float>* >(funcs_[i]);
111 if (g) {
112 (*g)[0] = listGauss[i].getAmpl();
113 (*g)[1] = listGauss[i].getCenter();
114 (*g)[2] = listGauss[i].getFWHM();
115 ++count;
116 }
117 }
118 estimate_.resize();
119 listGauss.evaluate(estimate_,x_);
120 return true;
121}
122
123std::vector<float> SDFitter::getEstimate() const
124{
125 if (estimate_.nelements() == 0)
126 throw (AipsError("No estimate set."));
127 std::vector<float> stlout;
128 estimate_.tovector(stlout);
129 return stlout;
130}
131
132
133bool SDFitter::setExpression(const std::string& expr, int ncomp)
134{
135 clear();
136 if (expr == "gauss") {
137 if (ncomp < 1) throw (AipsError("Need at least one gaussian to fit."));
138 funcs_.resize(ncomp);
139 for (Int k=0; k<ncomp; ++k) {
140 funcs_[k] = new Gaussian1D<Float>();
141 }
142 } else if (expr == "poly") {
143 funcs_.resize(1);
144 funcs_[0] = new Polynomial<Float>(ncomp);
145 } else {
146 cerr << " compiled functions not yet implemented" << endl;
147 //funcs_.resize(1);
148 //funcs_[0] = new CompiledFunction<Float>();
149 //funcs_[0]->setFunction(String(expr));
150 return false;
151 };
152 return true;
153}
154
155bool SDFitter::setData(std::vector<float> absc, std::vector<float> spec,
156 std::vector<bool> mask)
157{
158 x_.resize();
159 y_.resize();
160 m_.resize();
161 // convert std::vector to casa Vector
162 Vector<Float> tmpx(absc);
163 Vector<Float> tmpy(spec);
164 Vector<Bool> tmpm(mask);
165 AlwaysAssert(tmpx.nelements() == tmpy.nelements(), AipsError);
166 x_ = tmpx;
167 y_ = tmpy;
168 m_ = tmpm;
169 return true;
170}
171
172std::vector<float> SDFitter::getResidual() const
173{
174 if (residual_.nelements() == 0)
175 throw (AipsError("Function not yet fitted."));
176 std::vector<float> stlout;
177 residual_.tovector(stlout);
178 return stlout;
179}
180
181std::vector<float> SDFitter::getFit() const
182{
183 Vector<Float> out = thefit_;
184 std::vector<float> stlout;
185 out.tovector(stlout);
186 return stlout;
187
188}
189
190std::vector<float> SDFitter::getErrors() const
191{
192 Vector<Float> out = error_;
193 std::vector<float> stlout;
194 out.tovector(stlout);
195 return stlout;
196}
197
198bool SDFitter::setParameters(std::vector<float> params)
199{
200 Vector<Float> tmppar(params);
201 if (funcs_.nelements() == 0)
202 throw (AipsError("Function not yet set."));
203 if (parameters_.nelements() > 0 && tmppar.nelements() != parameters_.nelements())
204 throw (AipsError("Number of parameters inconsistent with function."));
205 if (parameters_.nelements() == 0)
206 parameters_.resize(tmppar.nelements());
207 fixedpar_.resize(tmppar.nelements());
208 fixedpar_ = False;
209 if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) != 0) {
210 uInt count = 0;
211 for (uInt j=0; j < funcs_.nelements(); ++j) {
212 for (uInt i=0; i < funcs_[j]->nparameters(); ++i) {
213 (funcs_[j]->parameters())[i] = tmppar[count];
214 parameters_[count] = tmppar[count];
215 ++count;
216 }
217 }
218 } else if (dynamic_cast<Polynomial<Float>* >(funcs_[0]) != 0) {
219 for (uInt i=0; i < funcs_[0]->nparameters(); ++i) {
220 parameters_[i] = tmppar[i];
221 (funcs_[0]->parameters())[i] = tmppar[i];
222 }
223 }
224 return true;
225}
226
227bool SDFitter::setFixedParameters(std::vector<bool> fixed)
228{
229 Vector<Bool> tmp(fixed);
230 if (funcs_.nelements() == 0)
231 throw (AipsError("Function not yet set."));
232 if (fixedpar_.nelements() > 0 && tmp.nelements() != fixedpar_.nelements())
233 throw (AipsError("Number of mask elements inconsistent with function."));
234 if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) != 0) {
235 uInt count = 0;
236 for (uInt j=0; j < funcs_.nelements(); ++j) {
237 for (uInt i=0; i < funcs_[j]->nparameters(); ++i) {
238 funcs_[j]->mask(i) = !tmp[count];
239 fixedpar_[count] = !tmp[count];
240 ++count;
241 }
242 }
243 } else if (dynamic_cast<Polynomial<Float>* >(funcs_[0]) != 0) {
244 for (uInt i=0; i < funcs_[0]->nparameters(); ++i) {
245 fixedpar_[i] = tmp[i];
246 funcs_[0]->mask(i) = tmp[i];
247 }
248 }
249 //fixedpar_ = !tmpmsk;
250 return true;
251}
252
253std::vector<float> SDFitter::getParameters() const {
254 Vector<Float> out = parameters_;
255 std::vector<float> stlout;
256 out.tovector(stlout);
257 return stlout;
258}
259
260std::vector<bool> SDFitter::getFixedParameters() const {
261 Vector<Bool> out(parameters_.nelements());
262 if (fixedpar_.nelements() == 0) {
263 out = False;
264 //throw (AipsError("No parameter mask set."));
265 } else {
266 out = fixedpar_;
267 }
268 std::vector<bool> stlout;
269 out.tovector(stlout);
270 return stlout;
271}
272
273float SDFitter::getChisquared() const {
274 return chisquared_;
275}
276
277bool SDFitter::fit() {
278 NonLinearFitLM<Float> fitter;
279 //CompiledFunction<AutoDiff<Float> > comp;
280 //Polynomial<AutoDiff<Float> > poly;
281 CompoundFunction<AutoDiff<Float> > func;
282 if (dynamic_cast<Gaussian1D<Float>* >(funcs_[0]) != 0) {
283 //computeEstimates();
284 for (uInt i=0; i<funcs_.nelements(); i++) {
285 Gaussian1D<AutoDiff<Float> > gauss;
286 for (uInt j=0; j<funcs_[i]->nparameters(); j++) {
287 gauss[j] = AutoDiff<Float>((*funcs_[i])[j], gauss.nparameters(), j);
288 gauss.mask(j) = funcs_[i]->mask(j);
289 }
290 func.addFunction(gauss);
291 }
292 } else if (dynamic_cast<Polynomial<Float>* >(funcs_[0]) != 0) {
293 Polynomial<AutoDiff<Float> > poly(funcs_[0]->nparameters()-1);
294 for (uInt j=0; j<funcs_[0]->nparameters(); j++) {
295 poly[j] = AutoDiff<Float>(0, poly.nparameters(), j);
296 poly.mask(j) = funcs_[0]->mask(j);
297 }
298 func.addFunction(poly);
299 } else if (dynamic_cast<CompiledFunction<Float>* >(funcs_[0]) != 0) {
300
301// CompiledFunction<AutoDiff<Float> > comp;
302// for (uInt j=0; j<funcs_[0]->nparameters(); j++) {
303// comp[j] = AutoDiff<Float>(0, comp.nparameters(), j);
304// comp.mask(j) = funcs_[0]->mask(j);
305// }
306// func.addFunction(comp);
307
308 cout << "NYI." << endl;
309 } else {
310 throw (AipsError("Fitter not set up correctly."));
311 }
312 fitter.setFunction(func);
313 fitter.setMaxIter(50+funcs_.nelements()*10);
314 // Convergence criterium
315 fitter.setCriteria(0.001);
316 // Fit
317 Vector<Float> sigma(x_.nelements());
318 sigma = 1.0;
319 //Vector<Float> sol;
320 parameters_.resize();
321 //Vector<Float> err;
322 error_.resize();
323 parameters_ = fitter.fit(x_, y_, sigma, &m_);
324 /*
325 CompoundFunction<Float> f;
326 for (uInt i=0; i<funcs_.nelements(); i++) {
327 f.addFunction(*funcs_[i]);
328 }
329 f.parameters().setParameters(parameters_);
330 */
331 error_ = fitter.errors();
332 chisquared_ = fitter.getChi2();
333 residual_.resize();
334 residual_ = y_;
335 fitter.residual(residual_,x_);
336 // use fitter.residual(model=True) to get the model
337 thefit_.resize(x_.nelements());
338 fitter.residual(thefit_,x_,True);
339 return true;
340}
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