Thanks Giles for the pointers. On Sun, Mar 18, 2018 at 10:29 PM, Gilles <gil...@harfang.homelinux.org> wrote:
> On Sun, 18 Mar 2018 20:52:27 +0530, Debraj Manna wrote: > >> Cross-posting from stackoverflow >> >> <https://stackoverflow.com/questions/49349580/calculate-r- >> square-for-polynomialcurvefitter-in-apache-commons-math3> >> . >> >> OLSMultipleLinearRegression, SimpleRegression provide a method that >> returns calculateRSquared(), >> getRSquare(). But I am not able to find any such method for >> PolynomialCurveFitter ? >> >> Right now I am doing it myself like below :- >> >> Is there any such method in common-math which does this? >> > > "PolynomialCurveFitter" is one of the syntactic sugar/wrapper > around the least-squares optimizers. > No state is maintained in the (immutable) instance. > > private PolynomialFunction getPolynomialFitter(List<List<Double>> >> pointlist) { >> final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(2); >> final WeightedObservedPoints obs = new WeightedObservedPoints(); >> for (List<Double> point : pointlist) { >> obs.add(point.get(0), point.get(1)); >> } >> >> double[] fit = fitter.fit(obs.toList()); >> System.out.printf("\nCoefficient %f, %f, %f", fit[0], fit[1], >> fit[2]); >> final PolynomialFunction fitted = new PolynomialFunction(fit); >> return fitted; >> } >> > > This is indeed one the intended use-cases. > > private double getRSquare(PolynomialFunction fitter, >> List<List<Double>> pointList) { >> final double[] coefficients = fitter.getCoefficients(); >> double[] predictedValues = new double[pointList.size()]; >> double residualSumOfSquares = 0; >> final DescriptiveStatistics descriptiveStatistics = new >> DescriptiveStatistics(); >> for (int i=0; i< pointList.size(); i++) { >> predictedValues[i] = predict(coefficients, >> pointList.get(i).get(0)); >> double actualVal = pointList.get(i).get(1); >> double t = Math.pow((predictedValues[i] - actualVal), 2); >> residualSumOfSquares += t; >> descriptiveStatistics.addValue(actualVal); >> } >> final double avgActualValues = descriptiveStatistics.getMean(); >> double totalSumOfSquares = 0; >> for (int i=0; i<pointList.size(); i++) { >> totalSumOfSquares += Math.pow( (predictedValues[i] - >> avgActualValues),2); >> } >> return 1.0 - (residualSumOfSquares/totalSumOfSquares); >> } >> > > The "predict" method is not shown here, but note that the argument > which you called "fitter" in the above, is actually a polynomial > function: > http://commons.apache.org/proper/commons-math/apidocs/org/ > apache/commons/math4/analysis/polynomials/PolynomialFunction.html > > Hence: > predictedValues[i] = fitter.value(pointList.get(i).get(0)); > > But otherwise, yes, the caller is responsible for choosing his > assessement of the quality of the model. > > You could directly use the least-squares suite of classes; then > the "Evaluation" object would allow to retrieve various measures > of the fit: > http://commons.apache.org/proper/commons-math/apidocs/org/ > apache/commons/math4/fitting/leastsquares/LeastSquaresProbl > em.Evaluation.html > > However, they might still not be what you are looking for... > > HTH, > Gilles > > final PolynomialFunction polynomial = getPolynomialFitter(trainData); >> System.out.printf("\nPolynimailCurveFitter R-Square %f", >> getRSquare(polynomial, trainData)); >> > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@commons.apache.org > For additional commands, e-mail: user-h...@commons.apache.org > >
