A GaussianHat is a random number generator that will give you numbers based on a population mean and standard deviation. : Random « Development Class « Java

1. Java
2. Development Class
3. Random

A GaussianHat is a random number generator that will give you numbers based on a population mean and standard deviation.

  
// $Id: GaussianHat.java 12 2009-11-09 22:58:47Z gabe.johnson $

//package org.six11.util.data;

import java.util.Random;

/**
 * A GaussianHat is a random number generator that will give you numbers based
 * on a population mean and standard deviation. You can ask it for integers or
 * probabilities. You may also specify a floor and ceiling (so in that case it's
 * not truely Gaussian).
 */
public class GaussianHat {

  // this is necessary because if you create a few GaussianHats
  // during the same millisecond and then use them in the exact
  // manner, they will yield the same 'random' numbers if you ask
  // for the same kinds of random numbers in the same order, which
  // is absolutely no good. So we'll create a single Random that
  // will then generate better Randoms from it.
  static Random staticRand;
  static {
    staticRand = new Random(System.currentTimeMillis());
  }

  double mean;
  double sd;
  Random rand;
  double floor;
  double ceiling;

  /**
   * Creates a copy of the input GaussianHat.
   */
  public GaussianHat(GaussianHat other) {
    this(other.mean, other.sd, other.floor, other.ceiling);
  }

  public GaussianHat(double mean, double sd, double floor, double ceiling) {
    this.mean = mean;
    this.sd = sd;
    this.floor = floor;
    this.ceiling = ceiling;
    this.rand = new Random(staticRand.nextLong());
  }

  public GaussianHat(double mean, double sd) {
    this(mean, sd, 0, Double.MAX_VALUE);
  }

  public GaussianHat(int mean, double sd) {
    this((double) mean, sd);
  }

  public GaussianHat(int mean, int sd) {
    this((double) mean, (double) sd);
  }

  public GaussianHat(int mean, int sd, int floor, int ceiling) {
    this((double) mean, (double) sd, (double) floor, (double) ceiling);
  }

  public GaussianHat(double mean) {
    this(mean, 1.0);
  }

  public void setMean(double mean) {
    this.mean = mean;
  }

  public void setStdDev(double sd) {
    this.sd = sd;
  }

  public int getInt() {
    // simply return getDouble() as an int.
    return (int) getDouble();
  }

  public double getDouble() {
    // based on the mean and standard deviation, pick a number from a normal
    // distribution, cast it to an integer
    double d = (rand.nextGaussian() * sd) + mean;
    if (d < floor)
      d = floor;
    if (d > ceiling)
      d = ceiling;
    return d;
  }

  public double getUniformDouble() {
    double d = rand.nextDouble();
    d = floor + (d * (ceiling - floor));
    return d;
  }

  public boolean getYesNo() {
    // get a random number from uniform distribution 0..1 and return true if
    // that number is less than or equal to the provided 'chance' parameter
    return (rand.nextDouble() <= mean);
  }

  public String toString() {
    return mean + " (" + sd + ")";
  }

  public static void main(String[] args) {
    // I have convinced myself that my randomness is OK using JMP
    GaussianHat[] intHats = new GaussianHat[] { new GaussianHat(10, 2),
        new GaussianHat(10, 4), new GaussianHat(20, 2),
        new GaussianHat(20, 4) };

    GaussianHat[] chanceHats = new GaussianHat[] {
        new GaussianHat(0.60, 0.05), new GaussianHat(0.60, 0.15),
        new GaussianHat(0.30, 0.05), new GaussianHat(0.30, 0.15) };
    for (int trial = 1; trial <= 100; trial++) {
      for (int i = 0; i < intHats.length; i++) {
        System.out.print(intHats[i].getInt() + "\t");
        if (i == (intHats.length - 1))
          System.out.println();
      }
    }
    for (int trial = 1; trial <= 100; trial++) {
      for (int i = 0; i < chanceHats.length; i++) {
        System.out.print(chanceHats[i].getYesNo() + "\t");
        if (i == (chanceHats.length - 1))
          System.out.println();
      }
    }
  }
}

   
    
  

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