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Parallel Colt is a multithreaded version of Colt - a library for high performance scientific computing in Java. It contains efficient algorithms for data analysis, linear algebra, multi-dimensional arrays, Fourier transforms, statistics and histogramming.
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/*
Copyright (C) 1999 CERN - European Organization for Nuclear Research.
Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose 
is hereby granted without fee, provided that the above copyright notice appear in all copies and 
that both that copyright notice and this permission notice appear in supporting documentation. 
CERN makes no representations about the suitability of this software for any purpose. 
It is provided "as is" without expressed or implied warranty.
 */
package cern.jet.random.tdouble.sampling;

import cern.colt.list.tboolean.BooleanArrayList;
import cern.jet.random.tdouble.DoubleUniform;
import cern.jet.random.tdouble.engine.DoubleRandomEngine;

/**
 * Conveniently computes a stable subsequence of elements from a given input
 * sequence; Picks (samples) exactly one random element from successive blocks
 * of weight input elements each. For example, if weight==2 (a block is
 * 2 elements), and the input is 5*2=10 elements long, then picks 5 random
 * elements from the 10 elements such that one element is randomly picked from
 * the first block, one element from the second block, ..., one element from the
 * last block. weight == 1.0 --> all elements are picked (sampled). weight ==
 * 10.0 --> Picks one random element from successive blocks of 10 elements each.
 * Etc. The subsequence is guaranteed to be stable, i.e. elements never
 * change position relative to each other.
 * 
 * @author [email protected]
 * @version 1.0, 02/05/99
 */
public class WeightedDoubleRandomSampler extends cern.colt.PersistentObject {
    /**
     * 
     */
    private static final long serialVersionUID = 1L;

    // public class BlockedRandomSampler extends Object implements
    // java.io.Serializable {
    protected int skip;

    protected int nextTriggerPos;

    protected int nextSkip;

    protected int weight;

    protected DoubleUniform generator;

    static final int UNDEFINED = -1;

    /**
     * Calls BlockedRandomSampler(1,null).
     */
    public WeightedDoubleRandomSampler() {
        this(1, null);
    }

    /**
     * Chooses exactly one random element from successive blocks of
     * weight input elements each. For example, if weight==2, and the
     * input is 5*2=10 elements long, then chooses 5 random elements from the 10
     * elements such that one is chosen from the first block, one from the
     * second, ..., one from the last block. weight == 1.0 --> all elements are
     * consumed (sampled). 10.0 --> Consumes one random element from successive
     * blocks of 10 elements each. Etc.
     * 
     * @param weight
     *            the weight.
     * @param randomGenerator
     *            a random number generator. Set this parameter to null
     *            to use the default random number generator.
     */
    public WeightedDoubleRandomSampler(int weight, DoubleRandomEngine randomGenerator) {
        if (randomGenerator == null)
            randomGenerator = cern.jet.random.tdouble.AbstractDoubleDistribution.makeDefaultGenerator();
        this.generator = new DoubleUniform(randomGenerator);
        setWeight(weight);
    }

    /**
     * Returns a deep copy of the receiver.
     */

    public Object clone() {
        WeightedDoubleRandomSampler copy = (WeightedDoubleRandomSampler) super.clone();
        copy.generator = (DoubleUniform) this.generator.clone();
        return copy;
    }

    public int getWeight() {
        return this.weight;
    }

    /**
     * Chooses exactly one random element from successive blocks of
     * weight input elements each. For example, if weight==2, and the
     * input is 5*2=10 elements long, then chooses 5 random elements from the 10
     * elements such that one is chosen from the first block, one from the
     * second, ..., one from the last block.
     * 
     * @return true if the next element shall be sampled (picked),
     *         false otherwise.
     */
    public boolean sampleNextElement() {
        if (skip > 0) { // reject
            skip--;
            return false;
        }

        if (nextTriggerPos == UNDEFINED) {
            if (weight == 1)
                nextTriggerPos = 0; // tuned for speed
            else
                nextTriggerPos = generator.nextIntFromTo(0, weight - 1);

            nextSkip = weight - 1 - nextTriggerPos;
        }

        if (nextTriggerPos > 0) { // reject
            nextTriggerPos--;
            return false;
        }

        // accept
        nextTriggerPos = UNDEFINED;
        skip = nextSkip;

        return true;
    }

    /**
     * Not yet commented.
     * 
     * @param weight
     *            int
     */
    public void setWeight(int weight) {
        if (weight < 1)
            throw new IllegalArgumentException("bad weight");
        this.weight = weight;
        this.skip = 0;
        this.nextTriggerPos = UNDEFINED;
        this.nextSkip = 0;
    }

    /**
     * Not yet commented.
     */
    public static void test(int weight, int size) {
        WeightedDoubleRandomSampler sampler = new WeightedDoubleRandomSampler();
        sampler.setWeight(weight);

        cern.colt.list.tint.IntArrayList sample = new cern.colt.list.tint.IntArrayList();
        for (int i = 0; i < size; i++) {
            if (sampler.sampleNextElement())
                sample.add(i);
        }

        System.out.println("Sample = " + sample);
    }

    /**
     * Chooses exactly one random element from successive blocks of
     * weight input elements each. For example, if weight==2, and the
     * input is 5*2=10 elements long, then chooses 5 random elements from the 10
     * elements such that one is chosen from the first block, one from the
     * second, ..., one from the last block.
     * 
     * @param acceptList
     *            a bitvector which will be filled with true where
     *            sampling shall occur and false where it shall not
     *            occur.
     */
    private void xsampleNextElements(BooleanArrayList acceptList) {
        // manually inlined
        int length = acceptList.size();
        boolean[] accept = acceptList.elements();
        for (int i = 0; i < length; i++) {
            if (skip > 0) { // reject
                skip--;
                accept[i] = false;
                continue;
            }

            if (nextTriggerPos == UNDEFINED) {
                if (weight == 1)
                    nextTriggerPos = 0; // tuned for speed
                else
                    nextTriggerPos = generator.nextIntFromTo(0, weight - 1);

                nextSkip = weight - 1 - nextTriggerPos;
            }

            if (nextTriggerPos > 0) { // reject
                nextTriggerPos--;
                accept[i] = false;
                continue;
            }

            // accept
            nextTriggerPos = UNDEFINED;
            skip = nextSkip;
            accept[i] = true;
        }
    }
}