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/***************************************************************************
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--------------------------------------------------------------------
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Copyright : (C) 2006 by Ion Vasilief, Tilman Hoener zu Siederdissen
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Email (use @ for *) : ion_vasilief*yahoo.fr, thzs*gmx.net
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Description : Fit base class
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***************************************************************************/
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/***************************************************************************
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* This program is free software; you can redistribute it and/or modify *
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* it under the terms of the GNU General Public License as published by *
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* the Free Software Foundation; either version 2 of the License, or *
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* (at your option) any later version. *
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* This program is distributed in the hope that it will be useful, *
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* but WITHOUT ANY WARRANTY; without even the implied warranty of *
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
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* GNU General Public License for more details. *
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* You should have received a copy of the GNU General Public License *
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* along with this program; if not, write to the Free Software *
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, *
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* Boston, MA 02110-1301 USA *
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***************************************************************************/
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#include "ApplicationWindow.h"
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#include <gsl/gsl_multifit_nlin.h>
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#include <gsl/gsl_multimin.h>
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class Fit : public Filter
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typedef double (*fit_function_simplex)(const gsl_vector *, void *);
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typedef int (*fit_function)(const gsl_vector *, void *, gsl_vector *);
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typedef int (*fit_function_df)(const gsl_vector *, void *, gsl_matrix *);
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typedef int (*fit_function_fdf)(const gsl_vector *, void *, gsl_vector *, gsl_matrix *);
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enum Algorithm{ScaledLevenbergMarquardt, UnscaledLevenbergMarquardt, NelderMeadSimplex};
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enum WeightingMethod{NoWeighting, Instrumental, Statistical, Dataset};
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Fit(ApplicationWindow *parent, Graph *g = 0, const char * name = 0);
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//! Actually does the fit. Should be reimplemented in derived classes.
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virtual bool run(){return false;};
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//! Sets the data set to be used for weighting
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bool setWeightingData(WeightingMethod w, const QString& colName = QString::null);
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void setDataCurve(int curve, double start, double end);
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QString formula(){return d_formula;};
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int numParameters() {return d_p;}
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void setInitialGuess(int parIndex, double val){gsl_vector_set(d_param_init, parIndex, val);};
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void setInitialGuesses(double *x_init);
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virtual void guessInitialValues(){};
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void setAlgorithm(Algorithm s){d_solver = s;};
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//! Specifies weather the result of the fit is a function curve
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void generateFunction(bool yes, int points = 100);
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//! Output string added to the plot as a new legend
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virtual QString legendInfo();
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//! Returns a vector with the fit results
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double* results(){return d_results;};
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//! Returns a vector with the standard deviations of the results
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//! Returns the sum of squares of the residuals from the best-fit line
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double chiSquare() {return chi_2;};
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//! Specifies wheather the errors must be scaled with sqrt(chi_2/dof)
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void scaleErrors(bool yes = true){d_scale_errors = yes;};
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Table* parametersTable(const QString& tableName);
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Matrix* covarianceMatrix(const QString& matrixName);
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//! Pointer to the GSL multifit minimizer (for simplex algorithm)
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gsl_multimin_fminimizer * fitSimplex(gsl_multimin_function f, int &iterations, int &status);
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//! Pointer to the GSL multifit solver
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gsl_multifit_fdfsolver * fitGSL(gsl_multifit_function_fdf f, int &iterations, int &status);
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//! Customs and stores the fit results according to the derived class specifications. Used by exponential fits.
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virtual void storeCustomFitResults(double *par);
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//! Adds the result curve as a FunctionCurve to the plot, if d_gen_function = true
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void insertFitFunctionCurve(const QString& name, double *x, double *y, int penWidth = 1);
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//! Adds the result curve to the plot
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virtual void generateFitCurve(double *par);
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//! Calculates the data for the output fit curve and store itin the X an Y vectors
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virtual void calculateFitCurveData(double *par, double *X, double *Y) { Q_UNUSED(par) Q_UNUSED(X) Q_UNUSED(Y) };
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//! Output string added to the result log
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virtual QString logFitInfo(double *par, int iterations, int status, const QString& plotName);
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fit_function_df d_df;
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fit_function_fdf d_fdf;
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fit_function_simplex d_fsimplex;
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//! Number of fit parameters
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//! Initial guesses for the fit parameters
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gsl_vector *d_param_init;
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/*! \brief Tells whether the fitter uses non-linear/simplex fitting
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* with an initial parameters set, that must be freed in the destructor.
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//! weighting data set used for the fit
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//! Names of the fit parameters
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QStringList d_param_names;
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//! Stores a list of short explanations for the significance of the fit parameters
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QStringList d_param_explain;
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//! Specifies weather the result curve is a FunctionCurve or a normal curve with the same x values as the fit data
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//! Covariance matrix
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//! The kind of weighting to be performed on the data
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WeightingMethod d_weihting;
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//! The name of the weighting dataset
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QString weighting_dataset;
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//! Stores the result parameters
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//! Stores standard deviations of the result parameters
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//! The sum of squares of the residuals from the best-fit line
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//! Specifies wheather the errors must be scaled with sqrt(chi_2/dof)
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qtiplot/src/Fit.h
