/* * Artificial Intelligence for Humans * Volume 2: Nature Inspired Algorithms * Java Version * http://www.aifh.org * http://www.jeffheaton.com * * Code repository: * https://github.com/jeffheaton/aifh * * Copyright 2014 by Jeff Heaton * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * For more information on Heaton Research copyrights, licenses * and trademarks visit: * http://www.heatonresearch.com/copyright */ package com.heatonresearch.aifh.randomize; import com.heatonresearch.aifh.AIFHError; import java.io.Serializable; /** * Generate random choices unevenly. This class is used to select random * choices from a list, with a probability weight places on each item * in the list. * <p/> * This is often called a Roulette Wheel in Machine Learning texts. How it differs from * a Roulette Wheel that you might find in Las Vegas or Monte Carlo is that the * areas that can be selected are not of uniform size. However, you can be sure * that one will be picked. * <p/> * http://en.wikipedia.org/wiki/Fitness_proportionate_selection */ public class RandomChoice implements Serializable { /** * The probabilities of each item in the list. */ final private double[] probabilities; /** * Construct a list of probabilities. * * @param theProbabilities The probability of each item in the list. */ public RandomChoice(double[] theProbabilities) { this.probabilities = theProbabilities.clone(); double total = 0; for (final double probability : probabilities) { total += probability; } if (total == 0.0) { double prob = 1.0 / probabilities.length; for (int i = 0; i < probabilities.length; i++) { probabilities[i] = prob; } } else { double total2 = 0; double factor = 1.0 / total; for (int i = 0; i < probabilities.length; i++) { probabilities[i] = probabilities[i] * factor; total2 += probabilities[i]; } if (Math.abs(1.0 - total2) > 0.02) { double prob = 1.0 / probabilities.length; for (int i = 0; i < probabilities.length; i++) { probabilities[i] = prob; } } } } /** * Generate a random choice, based on the probabilities provided to the constructor. * * @return The random choice. */ public int generate(GenerateRandom theGenerator) { double r = theGenerator.nextDouble(); double sum = 0.0; for (int i = 0; i < probabilities.length; i++) { sum += probabilities[i]; if (r < sum) { return i; } } for (int i = 0; i < probabilities.length; i++) { if (probabilities[i] != 0.0) { return i; } } throw new AIFHError("Invalid probabilities."); } /** * Generate a random choice, but skip one of the choices. * * @param skip The choice to skip. * @return The random choice. */ public int generate(GenerateRandom theGenerator, int skip) { double totalProb = 1.0 - probabilities[skip]; double throwValue = theGenerator.nextDouble() * totalProb; double accumulator = 0.0; for (int i = 0; i < skip; i++) { accumulator += probabilities[i]; if (accumulator > throwValue) { return i; } } for (int i = skip + 1; i < probabilities.length; i++) { accumulator += probabilities[i]; if (accumulator > throwValue) { return i; } } for (int i = 0; i < skip; i++) { if (probabilities[i] != 0.0) { return i; } } for (int i = skip + 1; i < probabilities.length; i++) { if (probabilities[i] != 0.0) { return i; } } return -1; } }