gaussianrandomgenerator.java

JGAP是一种遗传算法和遗传规划的组成部分提供了一个Java框架。它提供了基本的遗传机制

/*
 * This file is part of JGAP.
 *
 * JGAP offers a dual license model containing the LGPL as well as the MPL.
 *
 * For licensing information please see the file license.txt included with JGAP
 * or have a look at the top of class org.jgap.Chromosome which representatively
 * includes the JGAP license policy applicable for any file delivered with JGAP.
 */
package org.jgap.impl;

import java.util.*;
import java.io.*;
import org.jgap.*;

/**
 * Gaussian deviation serving as basis for randomly finding a number.
 *
 * @author Klaus Meffert
 * @since 1.1
 */
public class GaussianRandomGenerator
    implements RandomGenerator {
  /** String containing the CVS revision. Read out via reflection!*/
  private final static String CVS_REVISION = "$Revision: 1.21 $";

  //delta for distinguishing whether a value is to be interpreted as zero
  private static final double DELTA = 0.0000001;

  private Random m_rn;

  /**
   * Standard deviation of the gaussian deviation
   */
  private double m_standardDeviation;

  public GaussianRandomGenerator() {
    this(1.0d);
  }

  /**
   * Constructor speicifying the (obliagtory) standard deviation.
   *
   * @param a_standardDeviation the standard deviation to use
   */
  public GaussianRandomGenerator(final double a_standardDeviation) {
    super();
    init();
    setGaussianStdDeviation(a_standardDeviation);
  }

  /**
   * Initializations on construction.
   */
  private void init() {
    m_rn = new Random();
  }

  public void setGaussianStdDeviation(final double a_standardDeviation) {
    if (a_standardDeviation <= DELTA) {
      throw new IllegalArgumentException(
          "Standard deviation must be greater 0!");
    }
    m_standardDeviation = a_standardDeviation;
  }

  public double getGaussianStdDeviation() {
    return m_standardDeviation;
  }

  /**
   * @return positive integer value
   */
  public int nextInt() {
    return Math.abs(Math.min(Integer.MAX_VALUE - 1,
                             (int) Math.round(nextGaussian()
        * Integer.MAX_VALUE)));
  }

  /**
   * @param a_ceiling the upper boundary excluded
   * @return positive integer value between 0 and (ceiling - 1)
   */
  public int nextInt(final int a_ceiling) {
    return Math.min(a_ceiling - 1,
                    (int) Math.round(nextGaussian() * a_ceiling / (5.8d * 2)));
  }

  /**
   * @return positive long value
   */
  public long nextLong() {
    long result = Math.min(Long.MAX_VALUE,
                           (long) (nextGaussian() * Long.MAX_VALUE / (5.8d * 2)));
    return result;
  }

  public double nextDouble() {
    return nextGaussian();
  }

  public float nextFloat() {
    return (float) (nextGaussian());
  }

  public boolean nextBoolean() {
    return nextGaussian() >= 0.5d;
  }

  /**
   * @return the next randomly distributed gaussian with current standard
   * deviation, will be greater/equal zero
   */
  private double nextGaussian() {
    //scale to [0..1[
    double r = (m_rn.nextGaussian() + 5.8d) / (5.8d * 2.0d);
    return r;
  }

  /**
   * When deserializing, initialize the seed because otherwise we could get
   * duplicate evolution results when doing distributed computing!
   *
   * @param a_inputStream the ObjectInputStream provided for deserialzation
   *
   * @throws IOException
   * @throws ClassNotFoundException
   *
   * @author Klaus Meffert
   * @since 3.01
   */
  private void readObject(ObjectInputStream a_inputStream)
      throws IOException, ClassNotFoundException {
    //always perform the default de-serialization first
    a_inputStream.defaultReadObject();
    m_rn.setSeed(System.currentTimeMillis());
  }
}