package org.zouzias.samplers;

import java.util.ArrayDeque;
import java.util.ArrayList;
import java.util.Deque;
import java.util.List;
import java.util.Random;

/******************************************************************************
 * File: AliasMethod.java
 * Author: Keith Schwarz (htiek@cs.stanford.edu)
 *
 * An implementation of the alias method implemented using Vose's algorithm.
 * The alias method allows for efficient sampling of random values from a
 * discrete probability distribution (i.e. rolling a loaded die) in O(1) time
 * each after O(n) preprocessing time.
 *
 * For a complete writeup on the alias method, including the intuition and
 * important proofs, please see the article "Darts, Dice, and Coins: Smpling
 * from a Discrete Distribution" at
 *
 *                 http://www.keithschwarz.com/darts-dice-coins/
 */

public final class AliasMethod {
    /* The random number generator used to sample from the distribution. */
    private final Random random;

    /* The probability and alias tables. */
    private final int[] alias;
    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 AliasMethod(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 AliasMethod(List<Double> probabilities, Random random) {
        /* Begin by doing basic structural checks on the inputs. */
        if (probabilities == null || random == null)
            throw new NullPointerException();
        if (probabilities.isEmpty())
            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];
    }
}

@@ -2606,16 +2606,146 @@
 mpty.%22);
+%0A        %0A        /* check if it is a probability distribution, if not normalize it*/%0A        scaleProbabilityMass(probabilities);
 %0A%0A      
@@ -5841,24 +5841,354 @@
 = 1.0;%0A    %7D
+%0A    %0A    public void scaleProbabilityMass(List%3CDouble%3E inputProb)%7B%0A        double sum = 0;%0A        for (double s : inputProb)%0A            sum += s;%0A        %0A        if ( Math.abs(sum - 1) %3E 10e-2 )%0A            for(int i = 0, size = inputProb.size(); i %3C size ; i++)%0A                inputProb.set(i, inputProb.get(i) / sum);%0A    %7D
 %0A%0A    /**%0A  

Add normalization to input distribution