本文實例為大家分享了java隨機(jī)數(shù)生成代碼，供大家參考，具體內(nèi)容如下
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package com.gonvan.common.utils; import java.util.*; /** * 隨機(jī)數(shù)工具 * * @author yuerzm * */public final class LotteryAliasMethod { /** * The random number generator used to sample from the distribution. */ private final Random random; /** * The alias tables. */ private final int[] alias; /** * The probability tables. */ private final double[] probability; /** * Constructs a new AliasMethod to sample from a discrete distribution and * hand back outcomes based on the probability distribution. * <p/> * Given as input a list of probabilities corresponding to outcomes 0, 1, * ..., n - 1, this constructor creates the probability and alias tables * needed to efficiently sample from this distribution. * * @param probabilities * The list of probabilities. */ public LotteryAliasMethod(List<Double> probabilities) { this(probabilities, new Random()); } /** * Constructs a new AliasMethod to sample from a discrete distribution and * hand back outcomes based on the probability distribution. * <p/> * Given as input a list of probabilities corresponding to outcomes 0, 1, * ..., n - 1, along with the random number generator that should be used as * the underlying generator, this constructor creates the probability and * alias tables needed to efficiently sample from this distribution. * * @param probabilities * The list of probabilities. * @param random * The random number generator */ public LotteryAliasMethod(List<Double> probabilities, Random random) { /* Begin by doing basic structural checks on the inputs. */ if (probabilities == null || random == null) throw new NullPointerException(); if (probabilities.size() == 0) throw new IllegalArgumentException("Probability vector must be nonempty."); /* Allocate space for the probability and alias tables. */ probability = new double[probabilities.size()]; alias = new int[probabilities.size()]; /* Store the underlying generator. */ this.random = random; /* Compute the average probability and cache it for later use. */ final double average = 1.0 / probabilities.size(); /* * Make a copy of the probabilities list, since we will be making * changes to it. */ probabilities = new ArrayList<Double>(probabilities); /* Create two stacks to act as worklists as we populate the tables. */ Deque<Integer> small = new ArrayDeque<Integer>(); Deque<Integer> large = new ArrayDeque<Integer>(); /* Populate the stacks with the input probabilities. */ for (int i = 0; i < probabilities.size(); ++i) { /* * If the probability is below the average probability, then we add * it to the small list; otherwise we add it to the large list. */ if (probabilities.get(i) >= average) large.add(i); else small.add(i); } /* * As a note: in the mathematical specification of the algorithm, we * will always exhaust the small list before the big list. However, * due to floating point inaccuracies, this is not necessarily true. * Consequently, this inner loop (which tries to pair small and large * elements) will have to check that both lists aren't empty. */ while (!small.isEmpty() && !large.isEmpty()) { /* Get the index of the small and the large probabilities. */ int less = small.removeLast(); int more = large.removeLast(); /* * These probabilities have not yet been scaled up to be such that * 1/n is given weight 1.0. We do this here instead. */ probability[less] = probabilities.get(less) * probabilities.size(); alias[less] = more; /* * Decrease the probability of the larger one by the appropriate * amount. */ probabilities.set(more, (probabilities.get(more) + probabilities.get(less)) - average); /* * If the new probability is less than the average, add it into the * small list; otherwise add it to the large list. */ if (probabilities.get(more) >= 1.0 / probabilities.size()) large.add(more); else small.add(more); } /* * At this point, everything is in one list, which means that the * remaining probabilities should all be 1/n. Based on this, set them * appropriately. Due to numerical issues, we can't be sure which * stack will hold the entries, so we empty both. */ while (!small.isEmpty()) probability[small.removeLast()] = 1.0; while (!large.isEmpty()) probability[large.removeLast()] = 1.0; } /** * Samples a value from the underlying distribution. * * @return A random value sampled from the underlying distribution. */ public int next() { /* Generate a fair die roll to determine which column to inspect. */ int column = random.nextInt(probability.length); /* Generate a biased coin toss to determine which option to pick. */ boolean coinToss = random.nextDouble() < probability[column]; /* Based on the outcome, return either the column or its alias. */ return coinToss ? column : alias[column]; } } |
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