regressionline.java

来自「一个一元曲线多项式数值演示例子」· Java 代码 · 共 160 行

package numbercruncher.mathutils;

/**
 * A least-squares regression line function.
 */
public class RegressionLine
    implements Evaluatable {
  /** sum of x */
  private double sumX;
  /** sum of y */
  private double sumY;
  /** sum of x*x */
  private double sumXX;
  /** sum of x*y */
  private double sumXY;

  /** line coefficient a0 */
  private float a0;
  /** line coefficient a1 */
  private float a1;

  /** number of data points */
  private int n;
  /** true if coefficients valid */
  private boolean coefsValid;

  /**
   * Constructor.
   */
  public RegressionLine() {}

  /**
   * Constructor.
   * @param data the array of data points
   */
  public RegressionLine(DataPoint data[]) {
    for (int i = 0; i < data.length; ++i) {
      addDataPoint(data[i]);
    }
  }

  /**
   * Return the current number of data points.
   * @return the count
   */
  public int getDataPointCount() {
    return n;
  }

  /**
   * Return the coefficient a0.
   * @return the value of a0
   */
  public float getA0() {
    validateCoefficients();
    return a0;
  }

  /**
   * Return the coefficient a1.
   * @return the value of a1
   */
  public float getA1() {
    validateCoefficients();
    return a1;
  }

  /**
   * Return the sum of the x values.
   * @return the sum
   */
  public double getSumX() {
    return sumX;
  }

  /**
   * Return the sum of the y values.
   * @return the sum
   */
  public double getSumY() {
    return sumY;
  }

  /**
   * Return the sum of the x*x values.
   * @return the sum
   */
  public double getSumXX() {
    return sumXX;
  }

  /**
   * Return the sum of the x*y values.
   * @return the sum
   */
  public double getSumXY() {
    return sumXY;
  }

  /**
   * Add a new data point: Update the sums.
   * @param dataPoint the new data point
   */
  public void addDataPoint(DataPoint dataPoint) {
    sumX += dataPoint.x;
    sumY += dataPoint.y;
    sumXX += dataPoint.x * dataPoint.x;
    sumXY += dataPoint.x * dataPoint.y;

    ++n;
    coefsValid = false;
  }

  /**
   * Return the value of the regression line function at x.
   * (Implementation of Evaluatable.)
   * @param x the value of x
   * @return the value of the function at x
   */
  public float at(float x) {
    if (n < 2) {
      return Float.NaN;
    }

    validateCoefficients();
    return a0 + a1 * x;
  }

  /**
   * Reset.
   */
  public void reset() {
    n = 0;
    sumX = sumY = sumXX = sumXY = 0;
    coefsValid = false;
  }

  /**
   * Validate the coefficients.
   */
  private void validateCoefficients() {
    if (coefsValid) {
      return;
    }

    if (n >= 2) {
      float xBar = (float) sumX / n;
      float yBar = (float) sumY / n;

      a1 = (float) ( (n * sumXY - sumX * sumY)
                    / (n * sumXX - sumX * sumX));
      a0 = (float) (yBar - a1 * xBar);
    }
    else {
      a0 = a1 = Float.NaN;
    }

    coefsValid = true;
  }
}