moved basic polynomial functions to jkqtpmathtools.h

renamed jkqtptoolsdebugging.h to jkqtpdebuggingtools.h
added jkqtpstatWeightedCoefficientOfDetermination()

doc/dox/jkqtplotter.dox

@@ -17,6 +17,7 @@ functionaly groups.
 \defgroup jkqtptools_math_basic Mathematical Functions & Tools
 \ingroup jkqtptools_math
 
+
 This group assembles a variety of mathematical tool functions that are used in different places.
 
 \defgroup jkqtptools_math_array Data Array Tools

examples/simpletest_datastore_regression/README.md

@@ -314,10 +314,10 @@ To demonstrate this function we first generate data from a poylnomial model (wit
     size_t colPolyY=datastore1->addColumn("polynomial data, y");
     for (double x=-10; x<=10; x++) {
         datastore1->appendToColumn(colPolyX, x);
-        datastore1->appendToColumn(colPolyY, jkqtpstatPolyEval(x, pPoly.begin(), pPoly.end())+d1(gen));
+        datastore1->appendToColumn(colPolyY, jkqtp_polyEval(x, pPoly.begin(), pPoly.end())+d1(gen));
     }
 ```
-The function `jkqtpstatPolyEval()` is used to evaluate a given polynomial (coefficients in `pPoly`) at a position `x`.
+The function `jkqtp_polyEval()` is used to evaluate a given polynomial (coefficients in `pPoly`) at a position `x`.
 
 The generated data is visualized with scatter-plots:
 ```.cpp
@@ -325,9 +325,9 @@ The generated data is visualized with scatter-plots:
     plot6->addGraph(graphP=new JKQTPXYLineGraph(plot6));
     graphP->setXYColumns(colPolyX, colPolyY);
     graphP->setDrawLine(false);
-    graphP->setTitle(QString("data $%1+\\mathcal{N}(0,50)$").arg(jkqtpstatPolynomialModel2Latex(pPoly.begin(), pPoly.end())));
+    graphP->setTitle(QString("data $%1+\\mathcal{N}(0,50)$").arg(jkqtp_polynomialModel2Latex(pPoly.begin(), pPoly.end())));
 ```
-Here the function `jkqtpstatPolynomialModel2Latex()` generates a string from a polynomial model.
+Here the function `jkqtp_polynomialModel2Latex()` generates a string from a polynomial model.
 
 Now we can call `jkqtpstatPolyFit()` to fit different polynomial regression models to the data:
 ```.cpp
@@ -336,11 +336,11 @@ Now we can call `jkqtpstatPolyFit()` to fit different polynomial regression mode
         JKQTPXFunctionLineGraph* gPoly;
         jkqtpstatPolyFit(datastore1->begin(colPolyX), datastore1->end(colPolyX), datastore1->begin(colPolyY), datastore1->end(colPolyY), p, std::back_inserter(pFit));
         plot6->addGraph(gPoly=new JKQTPXFunctionLineGraph(plot6));
-        gPoly->setPlotFunctionFunctor(jkqtpstatGeneratePolynomialModel(pFit.begin(), pFit.end()));
-        gPoly->setTitle(QString("regression: $%1$").arg(jkqtpstatPolynomialModel2Latex(pFit.begin(), pFit.end())));
+        gPoly->setPlotFunctionFunctor(jkqtp_generatePolynomialModel(pFit.begin(), pFit.end()));
+        gPoly->setTitle(QString("regression: $%1$").arg(jkqtp_polynomialModel2Latex(pFit.begin(), pFit.end())));
     }
 ```
-Each model is also ploted using a `JKQTPXFunctionLineGraph`. The plot function assigned to these `JKQTPXFunctionLineGraph` is generated by calling `jkqtpstatGeneratePolynomialModel()`, which returns a C++-functor for a polynomial.
+Each model is also ploted using a `JKQTPXFunctionLineGraph`. The plot function assigned to these `JKQTPXFunctionLineGraph` is generated by calling `jkqtp_generatePolynomialModel()`, which returns a C++-functor for a polynomial.
 
 The resulting plots look like this (without added gaussian noise):
 

examples/simpletest_datastore_regression/jkqtplotter_simpletest_datastore_regression.cpp

@@ -274,22 +274,22 @@ int main(int argc, char* argv[])
     size_t colPolyY=datastore1->addColumn("polynomial data, y");
     for (double x=-10; x<=10; x++) {
         datastore1->appendToColumn(colPolyX, x);
-        datastore1->appendToColumn(colPolyY, jkqtpstatPolyEval(x, pPoly.begin(), pPoly.end())+d1(gen)*50.0);
+        datastore1->appendToColumn(colPolyY, jkqtp_polyEval(x, pPoly.begin(), pPoly.end())+d1(gen)*50.0);
     }
     //     we visualize this data with a simple scatter graph:
     JKQTPXYLineGraph* graphP;
     plot6->addGraph(graphP=new JKQTPXYLineGraph(plot6));
     graphP->setXYColumns(colPolyX, colPolyY);
     graphP->setDrawLine(false);
-    graphP->setTitle(QString("data $%1+\\mathcal{N}(0,50)$").arg(jkqtpstatPolynomialModel2Latex(pPoly.begin(), pPoly.end())));
+    graphP->setTitle(QString("data $%1+\\mathcal{N}(0,50)$").arg(jkqtp_polynomialModel2Latex(pPoly.begin(), pPoly.end())));
     // 6.2. now we can fit polynomials with different number of coefficients:
     for (size_t p=0; p<=5; p++) {
         std::vector<double> pFit;
         JKQTPXFunctionLineGraph* gPoly;
         jkqtpstatPolyFit(datastore1->begin(colPolyX), datastore1->end(colPolyX), datastore1->begin(colPolyY), datastore1->end(colPolyY), p, std::back_inserter(pFit));
         plot6->addGraph(gPoly=new JKQTPXFunctionLineGraph(plot6));
-        gPoly->setPlotFunctionFunctor(jkqtpstatGeneratePolynomialModel(pFit.begin(), pFit.end()));
-        gPoly->setTitle(QString("regression: $%1$").arg(jkqtpstatPolynomialModel2Latex(pFit.begin(), pFit.end())));
+        gPoly->setPlotFunctionFunctor(jkqtp_generatePolynomialModel(pFit.begin(), pFit.end()));
+        gPoly->setTitle(QString("regression: $%1$").arg(jkqtp_polynomialModel2Latex(pFit.begin(), pFit.end())));
     }
     // 6.3. of course also the "adaptor" shortcuts are available:
     //for (size_t p=0; p<=5; p++) {

lib/jkqtcommon/jkqtpdebuggingtools.cpp

@@ -21,7 +21,7 @@ Copyright (c) 2008-2019 Jan W. Krieger (<jan@jkrieger.de>)
 
 
 
-#include "jkqtcommon/jkqtptoolsdebugging.h"
+#include "jkqtcommon/jkqtpdebuggingtools.h"
 #include <QDebug>
 #include <QApplication>
 

lib/jkqtcommon/jkqtpmathtools.h

@@ -22,11 +22,14 @@
 #ifndef jkqtpmathtools_H_INCLUDED
 #define jkqtpmathtools_H_INCLUDED
 #include "jkqtcommon/jkqtp_imexport.h"
+#include "jkqtcommon/jkqtpstringtools.h"
 #include <cmath>
 #include <limits>
 #include <QPoint>
 #include <QPointF>
 #include <vector>
+#include <QString>
+#include <functional>
 
 
 
@@ -279,4 +282,74 @@ inline double jkqtp_gaussdist(double x, double mu=0.0, double sigma=1.0) {
     return exp(-0.5*jkqtp_sqr(x-mu)/jkqtp_sqr(sigma))/sqrt(2.0*M_PI*sigma*sigma);
 }
 
+/*! \brief evaluate a polynomial \f$ f(x)=\sum\limits_{i=0}^Pp_ix^i \f$ with \f$ p_i \f$ taken from the range \a firstP ... \a lastP
+    \ingroup jkqtptools_math_basic
+
+    \tparam PolyItP iterator for the polynomial coefficients
+    \param x where to evaluate
+    \param firstP points to the first polynomial coefficient \f$ p_1 \f$ (i.e. the offset with \f$ x^0 \f$ )
+    \param lastP points behind the last polynomial coefficient  \f$ p_P \f$
+    \return value of polynomial \f$ f(x)=\sum\limits_{i=0}^Pp_ix^i \f$ at location \a x
+
+*/
+template <class PolyItP>
+inline double jkqtp_polyEval(double x, PolyItP firstP, PolyItP lastP) {
+    double v=0.0;
+    double xx=1.0;
+    for (auto itP=firstP; itP!=lastP; ++itP) {
+        v=v+(*itP)*xx;
+        xx=xx*x;
+    }
+    return v;
+}
+
+/*! \brief a C++-functor, which evaluates a polynomial
+    \ingroup jkqtptools_math_basic
+*/
+struct JKQTPPolynomialFunctor {
+        std::vector<double> P;
+        template <class PolyItP>
+        inline JKQTPPolynomialFunctor(PolyItP firstP, PolyItP lastP) {
+            for (auto itP=firstP; itP!=lastP; ++itP) {
+                P.push_back(*itP);
+            }
+        }
+        inline double operator()(double x) const { return jkqtp_polyEval(x, P.begin(), P.end()); }
+
+};
+
+/*! \brief returns a C++-functor, which evaluates a polynomial
+    \ingroup jkqtptools_math_basic
+
+    \tparam PolyItP iterator for the polynomial coefficients
+    \param firstP points to the first polynomial coefficient \f$ p_1 \f$ (i.e. the offset with \f$ x^0 \f$ )
+    \param lastP points behind the last polynomial coefficient  \f$ p_P \f$
+*/
+template <class PolyItP>
+inline std::function<double(double)> jkqtp_generatePolynomialModel(PolyItP firstP, PolyItP lastP) {
+    return JKQTPPolynomialFunctor(firstP, lastP);
+}
+
+/*! \brief Generates a LaTeX string for the polynomial model with the coefficients \a firstP ... \a lastP
+    \ingroup jkqtptools_math_basic
+
+    \tparam PolyItP iterator for the polynomial coefficients
+    \param firstP points to the first polynomial coefficient \f$ p_1 \f$ (i.e. the offset with \f$ x^0 \f$ )
+    \param lastP points behind the last polynomial coefficient  \f$ p_P \f$
+    */
+template <class PolyItP>
+QString jkqtp_polynomialModel2Latex(PolyItP firstP, PolyItP lastP) {
+    QString str="f(x)=";
+    size_t p=0;
+    for (auto itP=firstP; itP!=lastP; ++itP) {
+        if (p==0) str+=jkqtp_floattolatexqstr(*itP, 3);
+        else {
+            if (*itP>=0) str+="+";
+            str+=QString("%2{\\cdot}x^{%1}").arg(p).arg(jkqtp_floattolatexqstr(*itP, 3));
+        }
+        p++;
+    }
+    return str;
+}
+
 #endif // jkqtpmathtools_H_INCLUDED

lib/jkqtcommon/jkqtpstatisticstools.cpp

@@ -132,7 +132,7 @@ std::function<double (double)> jkqtpStatGenerateRegressionModel(JKQTPStatRegress
 
 std::pair<std::function<double (double)>, std::function<double (double)> > jkqtpStatGenerateTransformation(JKQTPStatRegressionModelType type) {
     auto logF=[](double x)->double { return log(x); };
-    auto expF=[](double x)->double { return exp(x); };
+    //auto expF=[](double x)->double { return exp(x); };
     auto idF=&jkqtp_identity<double>;
     switch(type) {
         case JKQTPStatRegressionModelType::Linear: return std::pair<std::function<double(double)>,std::function<double(double)> >(idF, idF);

lib/jkqtcommon/jkqtpstatisticstools.h

@@ -37,7 +37,7 @@
 #include "jkqtcommon/jkqtp_imexport.h"
 #include "jkqtcommon/jkqtplinalgtools.h"
 #include "jkqtcommon/jkqtparraytools.h"
-#include "jkqtcommon/jkqtptoolsdebugging.h"
+#include "jkqtcommon/jkqtpdebuggingtools.h"
 
 
 
@@ -2209,6 +2209,7 @@ inline void jkqtpstatWeightedRegression(JKQTPStatRegressionModelType type, Input
 
 /*! \brief fits (in a least-squares sense) a polynomial \f$ f(x)=\sum\limits_{i=0}^Pp_ix^i \f$ of order P to a set of N data pairs \f$ (x_i,y_i) \f$
     \ingroup jkqtptools_math_statistics_poly
+    \ingroup jkqtptools_math_statistics_regression
 
     \tparam InputItX standard iterator type of \a firstX and \a lastX.
     \tparam InputItY standard iterator type of \a firstY and \a lastY.
@@ -2322,82 +2323,9 @@ inline void jkqtpstatPolyFit(InputItX firstX, InputItX lastX, InputItY firstY, I
 
 }
 
-/*! \brief evaluate a polynomial \f$ f(x)=\sum\limits_{i=0}^Pp_ix^i \f$ with \f$ p_i \f$ taken from the range \a firstP ... \a lastP
-    \ingroup jkqtptools_math_statistics_poly
-
-    \tparam PolyItP iterator for the polynomial coefficients
-    \param x where to evaluate
-    \param firstP points to the first polynomial coefficient \f$ p_1 \f$ (i.e. the offset with \f$ x^0 \f$ )
-    \param lastP points behind the last polynomial coefficient  \f$ p_P \f$
-    \return value of polynomial \f$ f(x)=\sum\limits_{i=0}^Pp_ix^i \f$ at location \a x
-
-*/
-template <class PolyItP>
-inline double jkqtpstatPolyEval(double x, PolyItP firstP, PolyItP lastP) {
-    double v=0.0;
-    double xx=1.0;
-    for (auto itP=firstP; itP!=lastP; ++itP) {
-        v=v+(*itP)*xx;
-        xx=xx*x;
-    }
-    return v;
-}
-
-/*! \brief a C++-functor, which evaluates a polynomial
-    \ingroup jkqtptools_math_statistics_poly
-*/
-struct JKQTPStatPolynomialFunctor {
-        std::vector<double> P;
-        template <class PolyItP>
-        inline JKQTPStatPolynomialFunctor(PolyItP firstP, PolyItP lastP) {
-            for (auto itP=firstP; itP!=lastP; ++itP) {
-                P.push_back(*itP);
-            }
-        }
-        inline double operator()(double x) const { return jkqtpstatPolyEval(x, P.begin(), P.end()); }
-
-};
-
-/*! \brief returns a C++-functor, which evaluates a polynomial
-    \ingroup jkqtptools_math_statistics_poly
-
-    \tparam PolyItP iterator for the polynomial coefficients
-    \param firstP points to the first polynomial coefficient \f$ p_1 \f$ (i.e. the offset with \f$ x^0 \f$ )
-    \param lastP points behind the last polynomial coefficient  \f$ p_P \f$
-*/
-template <class PolyItP>
-inline std::function<double(double)> jkqtpstatGeneratePolynomialModel(PolyItP firstP, PolyItP lastP) {
-    return JKQTPStatPolynomialFunctor(firstP, lastP);
-}
-
-/*! \brief Generates a LaTeX string for the polynomial model with the coefficients \a firstP ... \a lastP
-    \ingroup jkqtptools_math_statistics_regression
-
-    \tparam PolyItP iterator for the polynomial coefficients
-    \param firstP points to the first polynomial coefficient \f$ p_1 \f$ (i.e. the offset with \f$ x^0 \f$ )
-    \param lastP points behind the last polynomial coefficient  \f$ p_P \f$
-    */
-template <class PolyItP>
-QString jkqtpstatPolynomialModel2Latex(PolyItP firstP, PolyItP lastP) {
-    QString str="f(x)=";
-    size_t p=0;
-    for (auto itP=firstP; itP!=lastP; ++itP) {
-        if (p==0) str+=jkqtp_floattolatexqstr(*itP, 3);
-        else {
-            if (*itP>=0) str+="+";
-            str+=QString("%2{\\cdot}x^{%1}").arg(p).arg(jkqtp_floattolatexqstr(*itP, 3));
-        }
-        p++;
-    }
-    return str;
-}
-
-
-
-
 
 /*! \brief calculates the coefficient of determination \f$ R^2 \f$ for a set of measurements \f$ (x_i,y_i) \f$ with a fit function \f$ f(x) \f$
-    \ingroup jkqtptools_math_statistics_poly
+    \ingroup jkqtptools_math_statistics_regression
 
     \tparam InputItX standard iterator type of \a firstX and \a lastX.
     \tparam InputItY standard iterator type of \a firstY and \a lastY.
@@ -2434,10 +2362,58 @@ inline double jkqtpstatCoefficientOfDetermination(InputItX firstX, InputItX last
 }
 
 
+/*! \brief calculates the weightedcoefficient of determination \f$ R^2 \f$ for a set of measurements \f$ (x_i,y_i,w_i) \f$ with a fit function \f$ f(x) \f$
+    \ingroup jkqtptools_math_statistics_regression
+
+    \tparam InputItX standard iterator type of \a firstX and \a lastX.
+    \tparam InputItY standard iterator type of \a firstY and \a lastY.
+    \tparam InputItW standard iterator type of \a firstW and \a lastW.
+    \param firstX iterator pointing to the first item in the x-dataset to use \f$ x_1 \f$
+    \param lastX iterator pointing behind the last item in the x-dataset to use \f$ x_N \f$
+    \param firstY iterator pointing to the first item in the y-dataset to use \f$ y_1 \f$
+    \param lastY iterator pointing behind the last item in the y-dataset to use \f$ y_N \f$
+    \param firstW iterator pointing to the first item in the weight-dataset to use \f$ w_1 \f$
+    \param lastW iterator pointing behind the last item in the weight-dataset to use \f$ w_N \f$
+    \param f function \f$ f(x) \f$, result of a fit to the data
+    \param fWeightDataToWi an optional function, which is applied to the data from \a firstW ... \a lastW to convert them to weight, i.e. \c wi=fWeightDataToWi(*itW)
+                           e.g. if you use data used to draw error bars, you can use jkqtp_inversePropSaveDefault(). The default is jkqtp_identity(), which just returns the values.
+                           In the case of jkqtp_inversePropSaveDefault(), a datapoint x,y, has a large weight, if it's error is small and in the case if jkqtp_identity() it's weight
+                           is directly proportional to the given value.
+    \return weighted coeffcicient of determination \f[ R^2=1-\frac{\sum_iw_i^2\bigl[y_i-f(x_i)\bigr]^2}{\sum_iw_i^2\bigl[y_i-\overline{y}\bigr]^2} \f] where \f[ \overline{y}=\frac{1}{N}\cdot\sum_iw_iy_i \f]
+            with \f[ \sum_iw_i=1 \f]
+
+
+
+    \see https://en.wikipedia.org/wiki/Coefficient_of_determination
+*/
+template <class InputItX, class InputItY, class InputItW>
+inline double jkqtpstatWeightedCoefficientOfDetermination(InputItX firstX, InputItX lastX, InputItY firstY, InputItY lastY, InputItW firstW, InputItW lastW, std::function<double(double)> f, std::function<double(double)> fWeightDataToWi=&jkqtp_identity<double>) {
+
+    auto itX=firstX;
+    auto itY=firstY;
+    auto itW=firstW;
+
+    const double yMean=jkqtpstatWeightedAverage(firstX,lastX,firstW);
+    double SSres=0;
+    double SStot=0;
+    for (; itX!=lastX && itY!=lastY && itW!=lastW; ++itX, ++itY, ++itW) {
+        const double fit_x=jkqtp_todouble(*itX);
+        const double fit_y=jkqtp_todouble(*itY);
+        const double fit_w2=jkqtp_sqr(fWeightDataToWi(jkqtp_todouble(*itW)));
+        if (JKQTPIsOKFloat(fit_x) && JKQTPIsOKFloat(fit_y) && JKQTPIsOKFloat(fit_w2)) {
+            SSres+=(fit_w2*jkqtp_sqr(fit_y-f(fit_x)));
+            SStot+=(fit_w2*jkqtp_sqr(fit_y-yMean));
+        }
+    }
+
+    return 1.0-SSres/SStot;
+}
+
+
 
 
 /*! \brief calculates the sum of deviations \f$ \chi^2 \f$ for a set of measurements \f$ (x_i,y_i) \f$ with a fit function \f$ f(x) \f$
-    \ingroup jkqtptools_math_statistics_poly
+    \ingroup jkqtptools_math_statistics_regression
 
     \tparam InputItX standard iterator type of \a firstX and \a lastX.
     \tparam InputItY standard iterator type of \a firstY and \a lastY.
@@ -2474,7 +2450,7 @@ inline double jkqtpstatSumOfDeviations(InputItX firstX, InputItX lastX, InputItY
 
 
 /*! \brief calculates the weighted sum of deviations \f$ \chi^2 \f$ for a set of measurements \f$ (x_i,y_i,w_i) \f$ with a fit function \f$ f(x) \f$
-    \ingroup jkqtptools_math_statistics_poly
+    \ingroup jkqtptools_math_statistics_regression
 
     \tparam InputItX standard iterator type of \a firstX and \a lastX.
     \tparam InputItY standard iterator type of \a firstY and \a lastY.

lib/jkqtpcommon.pri

@@ -17,7 +17,7 @@ isEmpty(JKQTP_COMMON_PRI_INCLUDED) {
     }
 
     HEADERS += $$PWD/jkqtcommon/jkqtp_imexport.h \
-               $$PWD/jkqtcommon/jkqtptoolsdebugging.h \
+               $$PWD/jkqtcommon/jkqtpdebuggingtools.h \
                $$PWD/jkqtcommon/jkqtpmathtools.h \
                $$PWD/jkqtcommon/jkqtpalgorithms.h \
                $$PWD/jkqtcommon/jkqtpstringtools.h  \
@@ -33,7 +33,7 @@ isEmpty(JKQTP_COMMON_PRI_INCLUDED) {
                $$PWD/jkqtcommon/jkqtpstatisticstools.h
 
 
-    SOURCES += $$PWD/jkqtcommon/jkqtptoolsdebugging.cpp \
+    SOURCES += $$PWD/jkqtcommon/jkqtpdebuggingtools.cpp \
                $$PWD/jkqtcommon/jkqtpmathtools.cpp \
                $$PWD/jkqtcommon/jkqtpalgorithms.cpp \
                $$PWD/jkqtcommon/jkqtpstringtools.cpp  \

lib/jkqtplotter/jkqtpdatastorage.h

@@ -21,7 +21,7 @@
 
 #include "jkqtcommon/jkqtp_imexport.h"
 #include "jkqtplotter/jkqtptools.h"
-#include "jkqtcommon/jkqtptoolsdebugging.h"
+#include "jkqtcommon/jkqtpdebuggingtools.h"
 #include <vector>
 #include <cmath>
 #include <iostream>

lib/jkqtplotter/jkqtpgraphsstatisticsadaptors.h

@@ -20,7 +20,7 @@
 
 #include "jkqtcommon/jkqtp_imexport.h"
 #include "jkqtcommon/jkqtpstatisticstools.h"
-#include "jkqtcommon/jkqtptoolsdebugging.h"
+#include "jkqtcommon/jkqtpdebuggingtools.h"
 #include "jkqtplotter/jkqtpgraphsbase.h"
 #include "jkqtplotter/jkqtpgraphsbaseerrors.h"
 #include "jkqtplotter/jkqtpgraphsboxplot.h"
@@ -853,7 +853,7 @@ inline JKQTPXFunctionLineGraph* jkqtpstatAddLinearWeightedRegression(JKQTBasePlo
     JKQTPXFunctionLineGraph* g=new JKQTPXFunctionLineGraph(plotter);
     g->setSpecialFunction(JKQTPXFunctionLineGraph::SpecialFunction::Line);
     g->setParamsV(cA, cB);
-    g->setTitle(QString("weighted regression: $f(x) = %1%2{\\cdot}x, \\chi^2=%4, R^2=%3$").arg(jkqtp_floattolatexqstr(cA, 2, true, 1e-16,1e-2, 1e4,false)).arg(jkqtp_floattolatexqstr(cB, 2, true, 1e-16,1e-2, 1e4,true)).arg(jkqtp_floattolatexqstr(jkqtpstatCoefficientOfDetermination(firstX,lastX,firstY,lastY,jkqtpStatGenerateRegressionModel(JKQTPStatRegressionModelType::Linear, cA, cB)),3)).arg(jkqtp_floattolatexqstr(jkqtpstatWeightedSumOfDeviations(firstX,lastX,firstY,lastY,firstW,lastW,jkqtpStatGenerateRegressionModel(JKQTPStatRegressionModelType::Linear, cA, cB),fWeightDataToWi),3)));
+    g->setTitle(QString("weighted regression: $f(x) = %1%2{\\cdot}x, \\chi^2=%4, R^2=%3$").arg(jkqtp_floattolatexqstr(cA, 2, true, 1e-16,1e-2, 1e4,false)).arg(jkqtp_floattolatexqstr(cB, 2, true, 1e-16,1e-2, 1e4,true)).arg(jkqtp_floattolatexqstr(jkqtpstatWeightedCoefficientOfDetermination(firstX,lastX,firstY,lastY,firstW,lastW,jkqtpStatGenerateRegressionModel(JKQTPStatRegressionModelType::Linear, cA, cB),fWeightDataToWi),3)).arg(jkqtp_floattolatexqstr(jkqtpstatWeightedSumOfDeviations(firstX,lastX,firstY,lastY,firstW,lastW,jkqtpStatGenerateRegressionModel(JKQTPStatRegressionModelType::Linear, cA, cB),fWeightDataToWi),3)));
     plotter->addGraph(g);
     if (coeffA) *coeffA=cA;
     if (coeffB) *coeffB=cB;
@@ -1175,8 +1175,8 @@ inline JKQTPXFunctionLineGraph* jkqtpstatAddPolyFit(JKQTBasePlotter* plotter, In
     std::vector<double> pFit;
     JKQTPXFunctionLineGraph* gPoly=new JKQTPXFunctionLineGraph(plotter);
     jkqtpstatPolyFit(firstX,lastX,firstY,lastY,P,std::back_inserter(pFit));
-    gPoly->setPlotFunctionFunctor(jkqtpstatGeneratePolynomialModel(pFit.begin(), pFit.end()));
-    gPoly->setTitle(QString("regression: $%1, \\chi^2=%3, R^2=%2$").arg(jkqtpstatPolynomialModel2Latex(pFit.begin(), pFit.end())).arg(jkqtp_floattolatexqstr(jkqtpstatCoefficientOfDetermination(firstX,lastX,firstY,lastY,jkqtpstatGeneratePolynomialModel(pFit.begin(), pFit.end())),3)).arg(jkqtp_floattolatexqstr(jkqtpstatSumOfDeviations(firstX,lastX,firstY,lastY,jkqtpstatGeneratePolynomialModel(pFit.begin(), pFit.end())),3)));
+    gPoly->setPlotFunctionFunctor(jkqtp_generatePolynomialModel(pFit.begin(), pFit.end()));
+    gPoly->setTitle(QString("regression: $%1, \\chi^2=%3, R^2=%2$").arg(jkqtp_polynomialModel2Latex(pFit.begin(), pFit.end())).arg(jkqtp_floattolatexqstr(jkqtpstatCoefficientOfDetermination(firstX,lastX,firstY,lastY,jkqtp_generatePolynomialModel(pFit.begin(), pFit.end())),3)).arg(jkqtp_floattolatexqstr(jkqtpstatSumOfDeviations(firstX,lastX,firstY,lastY,jkqtp_generatePolynomialModel(pFit.begin(), pFit.end())),3)));
     std::copy(pFit.begin(), pFit.end(), firstRes);
     plotter->addGraph(gPoly);
     return gPoly;

lib/jkqtplotter/jkqtptools.h

@@ -44,7 +44,7 @@
 #include <stdexcept>
 #include <cctype>
 #include "jkqtcommon/jkqtpstringtools.h"
-#include "jkqtcommon/jkqtptoolsdebugging.h"
+#include "jkqtcommon/jkqtpdebuggingtools.h"
 #include "jkqtcommon/jkqtpmathtools.h"
 #include "jkqtcommon/jkqtpalgorithms.h"
 #include "jkqtcommon/jkqtpcodestructuring.h"