quadratic.java

import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression;

public class PolynomialRegressionExample {
    public static void main(String[] args) {
        // Define your input data (x and y values)
        double[][] data = {
            {1.0, 2.3},
            {2.0, 4.5},
            {3.0, 6.7},
            // Add more data points as needed
        };

        // Create arrays to hold the independent variables (x values) and the dependent variable (y values)
        double[] x = new double[data.length];
        double[] y = new double[data.length];

        for (int i = 0; i < data.length; i++) {
            x[i] = data[i][0];
            y[i] = data[i][1];
        }

        // Perform the polynomial regression
        int degree = 2; // Degree of the polynomial (second order)
        double[][] xPoly = createPolynomialMatrix(x, degree); // Create the polynomial matrix
        OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
        regression.newSampleData(y, xPoly); // Fit the model

        // Get the estimated parameters
        double[] parameters = regression.estimateRegressionParameters();

        // Print the fitted parameters
        System.out.println("Fitted Parameters:");
        for (int i = 0; i < parameters.length; i++) {
            System.out.println("Parameter " + i + ": " + parameters[i]);
        }
    }

    private static double[][] createPolynomialMatrix(double[] x, int degree) {
        int numDataPoints = x.length;
        double[][] polyMatrix = new double[numDataPoints][degree + 1];

        for (int i = 0; i < numDataPoints; i++) {
            double xi = x[i];
            for (int j = 0; j <= degree; j++) {
                polyMatrix[i][j] = Math.pow(xi, j);
            }
        }

        return polyMatrix;
    }
}