regression.java

一个一元曲线多项式数值演示例子

package numbercruncher.program10_2;

import numbercruncher.mathutils.*;
import numbercruncher.matrix.*;

/**
 * PROGRAM 10-2: Polynomial Regression
 *
 * Demonstrate polynomial regression by fitting a polynomial
 * to a set of data points.
 */
public class Regression {
  private static final int MAX_POINTS = 20;
  private static final float TWO_PI = (float) (2 * Math.PI);
  private static final float H = TWO_PI / MAX_POINTS;

  /**
   * Main program.
   * @param args the array of runtime arguments
   */
  public static void main(String args[]) {
    int degree = 3;
    float testX = (float) Math.PI;

    try {
      RegressionPolynomial poly =
          new RegressionPolynomial(degree, MAX_POINTS);

      // Compute MAX_POINTS data points along the sine curve
      // between 0 and 2*pi.
      for (int i = 0; i < MAX_POINTS; ++i) {
        float x = i * H;
        float y = (float) Math.sin(x);
        poly.addDataPoint(new DataPoint(x, y));
      }

      // Compute and print the regression polynomial.
      System.out.print("y = ");
      ColumnVector a = poly.getRegressionCoefficients();
      System.out.print(a.at(0) + " + " + a.at(1) + "x");
      for (int i = 2; i <= degree; ++i) {
        System.out.print(" + " + a.at(i) + "x^" + i);
      }
      System.out.println();

      // Compute an estimate.
      System.out.println("y(" + testX + ") = " +
                         poly.at(testX));

      // Print the warning if there is one.
      String warning = poly.getWarningMessage();
      if (warning != null) {
        System.out.println("WARNING: " + warning);
      }
    }
    catch (Exception ex) {
      System.out.println("\nERROR: " + ex.getMessage());
    }
  }
}

/*
 Output:
 y = -0.14296114 + 1.8568094x + -0.87079257x^2 + 0.09318722x^3
 y(3.1415927) = -0.014611721
 */