PolynomialRegression.java


Below is the syntax highlighted version of PolynomialRegression.java from §1.4 Analysis of Algorithms.


/******************************************************************************
 *  Compilation:  javac -cp .:jama.jar PolynomialRegression.java
 *  Execution:    java  -cp .:jama.jar PolynomialRegression
 *  Dependencies: jama.jar
 * 
 *  % 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;

/**
 *  The {@code PolynomialRegression} class performs a polynomial regression
 *  on an set of <em>N</em> data points (<em>y<sub>i</sub></em>, <em>x<sub>i</sub></em>).
 *  That is, it fits a polynomial
 *  <em>y</em> = &beta;<sub>0</sub> +  &beta;<sub>1</sub> <em>x</em> +
 *  &beta;<sub>2</sub> <em>x</em><sup>2</sup> + ... +
 *  &beta;<sub><em>d</em></sub> <em>x</em><sup><em>d</em></sup>
 *  (where <em>y</em> is the response variable, <em>x</em> is the predictor variable,
 *  and the &beta;<sub><em>i</em></sub> are the regression coefficients)
 *  that minimizes the sum of squared residuals of the multiple regression model.
 *  It also computes associated the coefficient of determination <em>R</em><sup>2</sup>.
 *  <p>
 *  This implementation performs a QR-decomposition of the underlying
 *  Vandermonde matrix, so it is neither the fastest nor the most numerically
 *  stable way to perform the polynomial regression.
 *
 *  @author Robert Sedgewick
 *  @author Kevin Wayne
 */
public class PolynomialRegression implements Comparable<PolynomialRegression> {
    private final String variableName;  // name of the predictor variable
    private int degree;                 // degree of the polynomial regression
    private Matrix beta;                // the polynomial regression coefficients
    private double sse;                 // sum of squares due to error
    private double sst;                 // total sum of squares


  /**
     * Performs a polynomial reggression on the data points {@code (y[i], x[i])}.
     * Uses n as the name of the predictor variable.
      *
     * @param  x the values of the predictor variable
     * @param  y the corresponding values of the response variable
     * @param  degree the degree of the polynomial to fit
     * @throws IllegalArgumentException if the lengths of the two arrays are not equal
     */
    public PolynomialRegression(double[] x, double[] y, int degree) {
        this(x, y, degree, "n");
    }

  /**
     * Performs a polynomial reggression on the data points {@code (y[i], x[i])}.
     *
     * @param  x the values of the predictor variable
     * @param  y the corresponding values of the response variable
     * @param  degree the degree of the polynomial to fit
     * @param  variableName the name of the predictor variable
     * @throws IllegalArgumentException if the lengths of the two arrays are not equal
     */
    public PolynomialRegression(double[] x, double[] y, int degree, String variableName) {
        this.degree = degree;
        this.variableName = variableName;

        int n = x.length;
        QRDecomposition qr = null;
        Matrix matrixX = null;

        // in case Vandermonde matrix does not have full rank, reduce degree until it does
        while (true) {

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

            // find least squares solution
            qr = new QRDecomposition(matrixX);
            if (qr.isFullRank()) break;

            // decrease degree and try again
            this.degree--;
        }

        // create matrix from vector
        Matrix matrixY = new Matrix(y, n);

        // linear regression coefficients
        beta = qr.solve(matrixY);

        // 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 = matrixX.times(beta).minus(matrixY);
        sse = residuals.norm2() * residuals.norm2();
    }

   /**
     * Returns the {@code j}th regression coefficient.
     *
     * @param  j the index
     * @return the {@code j}th regression coefficient
     */
    public double beta(int j) {
        // to make -0.0 print as 0.0
        if (Math.abs(beta.get(j, 0)) < 1E-4) return 0.0;
        return beta.get(j, 0);
    }

   /**
     * Returns the degree of the polynomial to fit.
     *
     * @return the degree of the polynomial to fit
     */
    public int degree() {
        return degree;
    }

   /**
     * Returns the coefficient of determination <em>R</em><sup>2</sup>.
     *
     * @return the coefficient of determination <em>R</em><sup>2</sup>,
     *         which is a real number between 0 and 1
     */
    public double R2() {
        if (sst == 0.0) return 1.0;   // constant function
        return 1.0 - sse/sst;
    }

   /**
     * Returns the expected response {@code y} given the value of the predictor
     *    variable {@code x}.
     *
     * @param  x the value of the predictor variable
     * @return the expected response {@code y} given the value of the predictor
     *         variable {@code 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;
    }

   /**
     * Returns a string representation of the polynomial regression model.
     *
     * @return a string representation of the polynomial regression model,
     *         including the best-fit polynomial and the coefficient of
     *         determination <em>R</em><sup>2</sup>
     */
    public String toString() {
        StringBuilder s = new StringBuilder();
        int j = degree;

        // ignoring leading zero coefficients
        while (j >= 0 && Math.abs(beta(j)) < 1E-5)
            j--;

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

        // replace "+ -2n" with "- 2n"
        return s.toString().replace("+ -", "- ");
    }

   /**
     * Compare lexicographically.
     */
    public int compareTo(PolynomialRegression that) {
        double EPSILON = 1E-5;
        int maxDegree = Math.max(this.degree(), that.degree());
        for (int j = maxDegree; j >= 0; j--) {
            double term1 = 0.0;
            double term2 = 0.0;
            if (this.degree() >= j) term1 = this.beta(j);
            if (that.degree() >= j) term2 = that.beta(j);
            if (Math.abs(term1) < EPSILON) term1 = 0.0;
            if (Math.abs(term2) < EPSILON) term2 = 0.0;
            if      (term1 < term2) return -1;
            else if (term1 > term2) return +1;
        }
        return 0;
    }

   /**
     * Unit tests the {@code PolynomialRegression} data type.
     *
     * @param args the command-line arguments
     */
    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);

        // Use System.out.println() so that it works with either stdlib.jar or algs4.jar version
        System.out.println(regression);
    }
}


Copyright © 2000–2017, Robert Sedgewick and Kevin Wayne.
Last updated: Fri Oct 20 12:50:46 EDT 2017.