diff --git a/tests/analysis/fit/FitTest.cpp b/tests/analysis/fit/FitTest.cpp index d265ded01..68e141e76 100644 --- a/tests/analysis/fit/FitTest.cpp +++ b/tests/analysis/fit/FitTest.cpp @@ -1,353 +1,409 @@ /*************************************************************************** File : FitTest.cpp Project : LabPlot Description : Tests for data fitting -------------------------------------------------------------------- Copyright : (C) 2017 Alexander Semke (alexander.semke@web.de) Copyright : (C) 2018 Stefan Gerlach (stefan.gerlach@uni.kn) ***************************************************************************/ /*************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #include "FitTest.h" #include "backend/core/column/Column.h" #include "backend/worksheet/plots/cartesian/XYFitCurve.h" extern "C" { #include "backend/nsl/nsl_sf_stats.h" #include "backend/nsl/nsl_stats.h" } void FitTest::initTestCase() { // needed in order to have the signals triggered by SignallingUndoCommand, see LabPlot.cpp //TODO: redesign/remove this qRegisterMetaType("const AbstractAspect*"); qRegisterMetaType("const AbstractColumn*"); } //############################################################################## //################# linear regression with NIST datasets ###################### //############################################################################## void FitTest::testLinearNorris() { //NIST data for Norris dataset QVector xData = {0.2,337.4,118.2,884.6,10.1,226.5,666.3,996.3,448.6,777.0,558.2,0.4,0.6,775.5,666.9,338.0,447.5,11.6,556.0,228.1, 995.8,887.6,120.2,0.3,0.3,556.8,339.1,887.2,999.0,779.0,11.1,118.3,229.2,669.1,448.9,0.5}; QVector yData = {0.1,338.8,118.1,888.0,9.2,228.1,668.5,998.5,449.1,778.9,559.2,0.3,0.1,778.1,668.8,339.3,448.9,10.8,557.7,228.3, 998.0,888.8,119.6,0.3,0.6,557.6,339.3,888.0,998.5,778.9,10.2,117.6,228.9,668.4,449.2,0.2}; //data source columns Column xDataColumn("x", AbstractColumn::Numeric); xDataColumn.replaceValues(0, xData); Column yDataColumn("y", AbstractColumn::Numeric); yDataColumn.replaceValues(0, yData); XYFitCurve fitCurve("fit"); fitCurve.setXDataColumn(&xDataColumn); fitCurve.setYDataColumn(&yDataColumn); //prepare the fit XYFitCurve::FitData fitData = fitCurve.fitData(); fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_polynomial; fitData.degree = 1; XYFitCurve::initFitData(fitData); fitCurve.setFitData(fitData); //perform the fit fitCurve.recalculate(); const XYFitCurve::FitResult& fitResult = fitCurve.fitResult(); //check the results QCOMPARE(fitResult.available, true); QCOMPARE(fitResult.valid, true); const int np = fitData.paramNames.size(); QCOMPARE(np, 2); - QCOMPARE(fitResult.paramValues.at(0), -0.262323073774029); - QCOMPARE(fitResult.errorValues.at(0), 0.232818234301152); + QCOMPARE(fitResult.paramValues.at(0), -0.262323073774029); + QCOMPARE(fitResult.errorValues.at(0), 0.232818234301152); QCOMPARE(fitResult.paramValues.at(1), 1.00211681802045); - QCOMPARE(fitResult.errorValues.at(1), 0.429796848199937e-3); + QCOMPARE(fitResult.errorValues.at(1), 0.429796848199937e-3); QCOMPARE(fitResult.rsd, 0.884796396144373); QCOMPARE(fitResult.rsquare, 0.999993745883712); QCOMPARE(fitResult.sse, 26.6173985294224); QCOMPARE(fitResult.rms, 0.782864662630069); // DEBUG(std::setprecision(15) << fitResult.fdist_F); // result: 5436419.54079774 FuzzyCompare(fitResult.fdist_F, 5436385.54079785, 34.); } +void FitTest::testLinearPontius() { + //NIST data for Pontius dataset + QVector xData = {150000,300000,450000,600000,750000,900000,1050000,1200000,1350000,1500000,1650000,1800000,1950000,2100000, + 2250000,2400000,2550000,2700000,2850000,3000000,150000,300000,450000,600000,750000,900000,1050000,1200000,1350000,1500000, + 1650000,1800000,1950000,2100000,2250000,2400000,2550000,2700000,2850000,3000000}; + QVector yData = {.11019,.21956,.32949,.43899,.54803,.65694,.76562,.87487,.98292,1.09146,1.20001,1.30822,1.41599,1.52399, + 1.63194,1.73947,1.84646,1.95392,2.06128,2.16844,.11052,.22018,.32939,.43886,.54798,.65739,.76596,.87474, + .98300,1.09150,1.20004,1.30818,1.41613,1.52408,1.63159,1.73965,1.84696,1.95445,2.06177,2.16829}; + + //data source columns + Column xDataColumn("x", AbstractColumn::Integer); + xDataColumn.replaceInteger(0, xData); + + Column yDataColumn("y", AbstractColumn::Numeric); + yDataColumn.replaceValues(0, yData); + + XYFitCurve fitCurve("fit"); + fitCurve.setXDataColumn(&xDataColumn); + fitCurve.setYDataColumn(&yDataColumn); + + //prepare the fit + XYFitCurve::FitData fitData = fitCurve.fitData(); + fitData.modelCategory = nsl_fit_model_basic; + fitData.modelType = nsl_fit_model_polynomial; + fitData.degree = 2; + XYFitCurve::initFitData(fitData); + fitCurve.setFitData(fitData); + + //perform the fit + fitCurve.recalculate(); + const XYFitCurve::FitResult& fitResult = fitCurve.fitResult(); + + //check the results + QCOMPARE(fitResult.available, true); + QCOMPARE(fitResult.valid, true); + + const int np = fitData.paramNames.size(); + QCOMPARE(np, 3); + + QCOMPARE(fitResult.paramValues.at(0), 0.673565789473684e-3); + QCOMPARE(fitResult.errorValues.at(0), 0.107938612033077e-3); + QCOMPARE(fitResult.paramValues.at(1), 0.732059160401003e-6); + QCOMPARE(fitResult.errorValues.at(1), 0.157817399981659e-9); + QCOMPARE(fitResult.paramValues.at(2), -0.316081871345029e-14); + QCOMPARE(fitResult.errorValues.at(2), 0.486652849992036e-16); + + QCOMPARE(fitResult.rsd, 0.205177424076185e-3); + QCOMPARE(fitResult.rsquare, 0.999999900178537); + QCOMPARE(fitResult.sse, 0.155761768796992e-5); + QCOMPARE(fitResult.rms, 0.420977753505385e-7); + DEBUG(std::setprecision(15) << fitResult.fdist_F); // result: 370661768.991551 + //FuzzyCompare(fitResult.fdist_F, 185330865.995752, 1.); +} + void FitTest::testLinearWampler1() { + // TODO: remove when finished +// qFatal("STOP!"); //NIST data for Wampler1 dataset QVector xData = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}; QVector yData = {1,6,63,364,1365,3906,9331,19608,37449,66430,111111, 177156,271453,402234,579195,813616,1118481,1508598,2000719,2613660,3368421}; //data source columns Column xDataColumn("x", AbstractColumn::Integer); xDataColumn.replaceInteger(0, xData); Column yDataColumn("y", AbstractColumn::Integer); yDataColumn.replaceInteger(0, yData); XYFitCurve fitCurve("fit"); fitCurve.setXDataColumn(&xDataColumn); fitCurve.setYDataColumn(&yDataColumn); //prepare the fit XYFitCurve::FitData fitData = fitCurve.fitData(); fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_polynomial; fitData.degree = 5; XYFitCurve::initFitData(fitData); fitCurve.setFitData(fitData); //perform the fit fitCurve.recalculate(); const XYFitCurve::FitResult& fitResult = fitCurve.fitResult(); //check the results QCOMPARE(fitResult.available, true); QCOMPARE(fitResult.valid, true); const int np = fitData.paramNames.size(); QCOMPARE(np, 6); for (int i = 0; i < np; i++) { const double paramValue = fitResult.paramValues.at(i); QCOMPARE(paramValue, 1.0); } QCOMPARE(fitResult.rsd, 0.0); QCOMPARE(fitResult.rsquare, 1.0); QCOMPARE(fitResult.sse, 0.0); QCOMPARE(fitResult.rms, 0.0); } void FitTest::testLinearWampler2() { //NIST data for Wampler2 dataset QVector xData = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}; QVector yData = {1.00000,1.11111,1.24992,1.42753,1.65984,1.96875,2.38336,2.94117,3.68928,4.68559, 6.00000,7.71561,9.92992,12.75603,16.32384,20.78125,26.29536,33.05367,41.26528,51.16209,63.00000}; //data source columns Column xDataColumn("x", AbstractColumn::Integer); xDataColumn.replaceInteger(0, xData); Column yDataColumn("y", AbstractColumn::Numeric); yDataColumn.replaceValues(0, yData); XYFitCurve fitCurve("fit"); fitCurve.setXDataColumn(&xDataColumn); fitCurve.setYDataColumn(&yDataColumn); //prepare the fit XYFitCurve::FitData fitData = fitCurve.fitData(); fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_polynomial; fitData.degree = 5; XYFitCurve::initFitData(fitData); fitCurve.setFitData(fitData); //perform the fit fitCurve.recalculate(); const XYFitCurve::FitResult& fitResult = fitCurve.fitResult(); //check the results qDebug()<<"STATUS " << fitResult.status; QCOMPARE(fitResult.available, true); QCOMPARE(fitResult.valid, true); const int np = fitData.paramNames.size(); QCOMPARE(np, 6); QCOMPARE(fitResult.paramValues.at(0), 1.0); QCOMPARE(fitResult.paramValues.at(1), 0.1); QCOMPARE(fitResult.paramValues.at(2), 0.01); QCOMPARE(fitResult.paramValues.at(3), 0.001); QCOMPARE(fitResult.paramValues.at(4), 0.0001); QCOMPARE(fitResult.paramValues.at(5), 0.00001); //TODO: rsd, sse and rms fails with //FAIL! : FitTest::testLinearWampler2() Compared doubles are not the same (fuzzy compare) //Actual (fitResult.rsd): 2,32459e-15 //Expected (0.0) : 0 //etc. // QCOMPARE(fitResult.rsd, 0.000000000000000); QCOMPARE(fitResult.rsquare, 1.00000000000000); // QCOMPARE(fitResult.sse, 0.000000000000000); // QCOMPARE(fitResult.rms, 0.000000000000000); } void FitTest::testLinearWampler3() { //NIST data for Wampler3 dataset QVector xData = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}; QVector yData = {760.,-2042.,2111.,-1684.,3888.,1858.,11379.,17560.,39287.,64382.,113159., 175108.,273291.,400186.,581243.,811568.,1121004.,1506550.,2002767.,2611612.,3369180.}; //data source columns Column xDataColumn("x", AbstractColumn::Integer); xDataColumn.replaceInteger(0, xData); Column yDataColumn("y", AbstractColumn::Numeric); yDataColumn.replaceValues(0, yData); XYFitCurve fitCurve("fit"); fitCurve.setXDataColumn(&xDataColumn); fitCurve.setYDataColumn(&yDataColumn); //prepare the fit XYFitCurve::FitData fitData = fitCurve.fitData(); fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_polynomial; fitData.degree = 5; XYFitCurve::initFitData(fitData); fitCurve.setFitData(fitData); //perform the fit fitCurve.recalculate(); const XYFitCurve::FitResult& fitResult = fitCurve.fitResult(); //check the results QCOMPARE(fitResult.available, true); QCOMPARE(fitResult.valid, true); const int np = fitData.paramNames.size(); QCOMPARE(np, 6); for (int i = 0; i < np; i++) { const double paramValue = fitResult.paramValues.at(i); QCOMPARE(paramValue, 1.0); } QCOMPARE(fitResult.rsd, 2360.14502379268); QCOMPARE(fitResult.rsquare, 0.999995559025820); QCOMPARE(fitResult.sse, 83554268.0000000); QCOMPARE(fitResult.rms, 5570284.53333333); } void FitTest::testLinearWampler4() { //NIST data for Wampler4 dataset QVector xData = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}; QVector yData = {75901,-204794,204863,-204436,253665,-200894,214131,-185192,221249,-138370, 315911,-27644,455253,197434,783995,608816,1370781,1303798,2205519,2408860,3444321}; //data source columns Column xDataColumn("x", AbstractColumn::Integer); xDataColumn.replaceInteger(0, xData); Column yDataColumn("y", AbstractColumn::Integer); yDataColumn.replaceInteger(0, yData); XYFitCurve fitCurve("fit"); fitCurve.setXDataColumn(&xDataColumn); fitCurve.setYDataColumn(&yDataColumn); //prepare the fit XYFitCurve::FitData fitData = fitCurve.fitData(); fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_polynomial; fitData.degree = 5; XYFitCurve::initFitData(fitData); fitCurve.setFitData(fitData); //perform the fit fitCurve.recalculate(); const XYFitCurve::FitResult& fitResult = fitCurve.fitResult(); //check the results QCOMPARE(fitResult.available, true); QCOMPARE(fitResult.valid, true); const int np = fitData.paramNames.size(); QCOMPARE(np, 6); for (int i = 0; i < np; i++) { const double paramValue = fitResult.paramValues.at(i); QCOMPARE(paramValue, 1.0); } QCOMPARE(fitResult.rsd, 236014.502379268); QCOMPARE(fitResult.rsquare, 0.957478440825662); QCOMPARE(fitResult.sse, 835542680000.000); QCOMPARE(fitResult.rms, 55702845333.3333); } void FitTest::testLinearWampler5() { //NIST data for Wampler5 dataset QVector xData = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}; QVector yData = {7590001,-20479994,20480063,-20479636,25231365,-20476094,20489331,-20460392,18417449,-20413570, 20591111,-20302844,18651453,-20077766,21059195,-19666384,26348481,-18971402,22480719,-17866340,10958421}; //data source columns Column xDataColumn("x", AbstractColumn::Integer); xDataColumn.replaceInteger(0, xData); Column yDataColumn("y", AbstractColumn::Integer); yDataColumn.replaceInteger(0, yData); XYFitCurve fitCurve("fit"); fitCurve.setXDataColumn(&xDataColumn); fitCurve.setYDataColumn(&yDataColumn); //prepare the fit XYFitCurve::FitData fitData = fitCurve.fitData(); fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_polynomial; fitData.degree = 5; XYFitCurve::initFitData(fitData); fitCurve.setFitData(fitData); //perform the fit fitCurve.recalculate(); const XYFitCurve::FitResult& fitResult = fitCurve.fitResult(); //check the results QCOMPARE(fitResult.available, true); QCOMPARE(fitResult.valid, true); const int np = fitData.paramNames.size(); QCOMPARE(np, 6); for (int i = 0; i < np; i++) { const double paramValue = fitResult.paramValues.at(i); QCOMPARE(paramValue, 1.0); } QCOMPARE(fitResult.rsd, 23601450.2379268); QCOMPARE(fitResult.rsquare, 0.224668921574940E-02); QCOMPARE(fitResult.sse, 0.835542680000000E+16); QCOMPARE(fitResult.rms, 557028453333333.); } //############################################################################## //############# non-linear regression with NIST datasets ##################### //############################################################################## //TODO //############################################################################## //######################### Fits with weights ################################# //############################################################################## //TODO QTEST_MAIN(FitTest) diff --git a/tests/analysis/fit/FitTest.h b/tests/analysis/fit/FitTest.h index 63130fae1..0b327d043 100644 --- a/tests/analysis/fit/FitTest.h +++ b/tests/analysis/fit/FitTest.h @@ -1,50 +1,53 @@ /*************************************************************************** File : FitTest.h Project : LabPlot Description : Tests for data fitting -------------------------------------------------------------------- Copyright : (C) 2017 Alexander Semke (alexander.semke@web.de) + Copyright : (C) 2018 Stefan Gerlach (stefan.gerlach@uni.kn) ***************************************************************************/ /*************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #include class FitTest : public QObject { Q_OBJECT private slots: void initTestCase(); // compare floats with given delta (could be useful for other tests too) static inline void FuzzyCompare(double actual, double expected, double delta = 0.000000000001) { QVERIFY(actual-delta <= expected && actual+delta >=expected); } //linear regression (see NIST/linear data) void testLinearNorris(); + void testLinearPontius(); + void testLinearWampler1(); void testLinearWampler2(); void testLinearWampler3(); void testLinearWampler4(); void testLinearWampler5(); //non-linear regression //fits with weights };