PolynomialRegression.java


Below is the syntax highlighted version of PolynomialRegression.java from §9.7 Data Analysis.


/*************************************************************************
 *  Compilation:  javac -cp .:jama.jar PolynomialRegression.java
 *  Execution:    java  -cp .:jama.jar PolynomialRegression
 *
 *  % java -cp .:jama.jar PolynomialRegression
 *  0.01 N^3 + -1.64 N^2 + 168.92 N + -2113.73  (R^2 = 0.997)
 *
 *************************************************************************/

import Jama.Matrix;
import Jama.QRDecomposition;

public class PolynomialRegression {
    private final int N;
    private final int degree;
    private final Matrix beta;
    private double SSE;
    private double SST;

    public PolynomialRegression(double[] x, double[] y, int degree) {
        this.degree = degree;
        N = x.length;

        // build Vandermonde matrix
        double[][] vandermonde = new double[N][degree+1];
        for (int i = 0; i < N; i++) {
            for (int j = 0; j <= degree; j++) {
                vandermonde[i][j] = Math.pow(x[i], j);
            }
        }
        Matrix X = new Matrix(vandermonde);

        // create matrix from vector
        Matrix Y = new Matrix(y, N);

        // find least squares solution
        QRDecomposition qr = new QRDecomposition(X);
        beta = qr.solve(Y);


        // mean of y[] values
        double sum = 0.0;
        for (int i = 0; i < N; i++)
            sum += y[i];
        double mean = sum / N;

        // total variation to be accounted for
        for (int i = 0; i < N; i++) {
            double dev = y[i] - mean;
            SST += dev*dev;
        }

        // variation not accounted for
        Matrix residuals = X.times(beta).minus(Y);
        SSE = residuals.norm2() * residuals.norm2();

    }

    public double beta(int j) {
        return beta.get(j, 0);
    }

    public int degree() {
        return degree;
    }

    public double R2() {
        return 1.0 - SSE/SST;
    }

    // predicted y value corresponding to x
    public double predict(double x) {
        // horner's method
        double y = 0.0;
        for (int j = degree; j >= 0; j--)
            y = beta(j) + (x * y);
        return y;
    }

    public String toString() {
        String s = "";
        int j = degree;

        // ignoring leading zero coefficients
        while (Math.abs(beta(j)) < 1E-5)
            j--;

        // create remaining terms
        for (j = j; j >= 0; j--) {
            if      (j == 0) s += String.format("%.2f ", beta(j));
            else if (j == 1) s += String.format("%.2f N + ", beta(j));
            else             s += String.format("%.2f N^%d + ", beta(j), j);
        }
        return s + "  (R^2 = " + String.format("%.3f", R2()) + ")";
    }

    public static void main(String[] args) {
        double[] x = { 10, 20, 40, 80, 160, 200 };
        double[] y = { 100, 350, 1500, 6700, 20160, 40000 };
        PolynomialRegression regression = new PolynomialRegression(x, y, 3);
        StdOut.println(regression);
    }
}


Copyright © 2000–2011, Robert Sedgewick and Kevin Wayne.
Last updated: Mon Apr 4 12:02:50 EDT 2011.