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;
}
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);
}
final PolynomialFunction polynomial = getPolynomialFitter(trainData);
System.out.printf("nPolynimailCurveFitter R-Square %f", getRSquare(polynomial, trainData));