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/LeastSquaresProblem.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));
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