Javadoc. Removed obsolete documentation.
Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/34b96986 Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/34b96986 Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/34b96986 Branch: refs/heads/develop Commit: 34b96986624a87da2f0f2b3d2878c7be4d0eb25c Parents: 156dfa6 Author: Gilles <gil...@harfang.homelinux.org> Authored: Sun May 29 18:08:41 2016 +0200 Committer: Gilles <gil...@harfang.homelinux.org> Committed: Sun May 29 18:08:41 2016 +0200 ---------------------------------------------------------------------- .../commons/math4/random/package-info.java | 127 ++----------------- 1 file changed, 14 insertions(+), 113 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/commons-math/blob/34b96986/src/main/java/org/apache/commons/math4/random/package-info.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/random/package-info.java b/src/main/java/org/apache/commons/math4/random/package-info.java index 4d42815..45d810a 100644 --- a/src/main/java/org/apache/commons/math4/random/package-info.java +++ b/src/main/java/org/apache/commons/math4/random/package-info.java @@ -15,118 +15,19 @@ * limitations under the License. */ /** - * - * <p>Random number and random data generators.</p> - * <p>Commons-math provides a few pseudo random number generators. The top level interface is RandomGenerator. - * It is implemented by three classes: - * <ul> - * <li>{@link org.apache.commons.math4.random.JDKRandomGenerator JDKRandomGenerator} - * that extends the JDK provided generator</li> - * <li>AbstractRandomGenerator as a helper for users generators</li> - * <li>BitStreamGenerator which is an abstract class for several generators and - * which in turn is extended by: - * <ul> - * <li>{@link org.apache.commons.math4.random.MersenneTwister MersenneTwister}</li> - * <li>{@link org.apache.commons.math4.random.Well512a Well512a}</li> - * <li>{@link org.apache.commons.math4.random.Well1024a Well1024a}</li> - * <li>{@link org.apache.commons.math4.random.Well19937a Well19937a}</li> - * <li>{@link org.apache.commons.math4.random.Well19937c Well19937c}</li> - * <li>{@link org.apache.commons.math4.random.Well44497a Well44497a}</li> - * <li>{@link org.apache.commons.math4.random.Well44497b Well44497b}</li> - * </ul> - * </li> - * </ul> - * </p> - * - * <p> - * The JDK provided generator is a simple one that can be used only for very simple needs. - * The Mersenne Twister is a fast generator with very good properties well suited for - * Monte-Carlo simulation. It is equidistributed for generating vectors up to dimension 623 - * and has a huge period: 2<sup>19937</sup> - 1 (which is a Mersenne prime). This generator - * is described in a paper by Makoto Matsumoto and Takuji Nishimura in 1998: <a - * href="http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/ARTICLES/mt.pdf";>Mersenne Twister: - * A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator</a>, ACM - * Transactions on Modeling and Computer Simulation, Vol. 8, No. 1, January 1998, pp 3--30. - * The WELL generators are a family of generators with period ranging from 2<sup>512</sup> - 1 - * to 2<sup>44497</sup> - 1 (this last one is also a Mersenne prime) with even better properties - * than Mersenne Twister. These generators are described in a paper by François Panneton, - * Pierre L'Ecuyer and Makoto Matsumoto <a - * href="http://www.iro.umontreal.ca/~lecuyer/myftp/papers/wellrng.pdf";>Improved Long-Period - * Generators Based on Linear Recurrences Modulo 2</a> ACM Transactions on Mathematical Software, - * 32, 1 (2006). The errata for the paper are in <a - * href="http://www.iro.umontreal.ca/~lecuyer/myftp/papers/wellrng-errata.txt";>wellrng-errata.txt</a>. - * </p> - * - * <p> - * For simple sampling, any of these generators is sufficient. For Monte-Carlo simulations the - * JDK generator does not have any of the good mathematical properties of the other generators, - * so it should be avoided. The Mersenne twister and WELL generators have equidistribution properties - * proven according to their bits pool size which is directly linked to their period (all of them - * have maximal period, i.e. a generator with size n pool has a period 2<sup>n</sup>-1). They also - * have equidistribution properties for 32 bits blocks up to s/32 dimension where s is their pool size. - * So WELL19937c for exemple is equidistributed up to dimension 623 (19937/32). This means a Monte-Carlo - * simulation generating a vector of n variables at each iteration has some guarantees on the properties - * of the vector as long as its dimension does not exceed the limit. However, since we use bits from two - * successive 32 bits generated integers to create one double, this limit is smaller when the variables are - * of type double. so for Monte-Carlo simulation where less the 16 doubles are generated at each round, - * WELL1024 may be sufficient. If a larger number of doubles are needed a generator with a larger pool - * would be useful. - * </p> - * - * <p> - * The WELL generators are more modern then MersenneTwister (the paper describing than has been published - * in 2006 instead of 1998) and fix some of its (few) drawbacks. If initialization array contains many - * zero bits, MersenneTwister may take a very long time (several hundreds of thousands of iterations to - * reach a steady state with a balanced number of zero and one in its bits pool). So the WELL generators - * are better to <i>escape zeroland</i> as explained by the WELL generators creators. The Well19937a and - * Well44497a generator are not maximally equidistributed (i.e. there are some dimensions or bits blocks - * size for which they are not equidistributed). The Well512a, Well1024a, Well19937c and Well44497b are - * maximally equidistributed for blocks size up to 32 bits (they should behave correctly also for double - * based on more than 32 bits blocks, but equidistribution is not proven at these blocks sizes). - * </p> - * - * <p> - * The MersenneTwister generator uses a 624 elements integer array, so it consumes less than 2.5 kilobytes. - * The WELL generators use 6 integer arrays with a size equal to the pool size, so for example the - * WELL44497b generator uses about 33 kilobytes. This may be important if a very large number of - * generator instances were used at the same time. - * </p> - * - * <p> - * All generators are quite fast. As an example, here are some comparisons, obtained on a 64 bits JVM on a - * linux computer with a 2008 processor (AMD phenom Quad 9550 at 2.2 GHz). The generation rate for - * MersenneTwister was about 27 millions doubles per second (remember we generate two 32 bits integers for - * each double). Generation rates for other PRNG, relative to MersenneTwister: - * </p> - * - * <p> - * <table border="1" align="center"> - * <tr BGCOLOR="#CCCCFF"><td colspan="2"><font size="+2">Example of performances</font></td></tr> - * <tr BGCOLOR="#EEEEFF"><font size="+1"><td>Name</td><td>generation rate (relative to MersenneTwister)</td></font></tr> - * <tr><td>{@link org.apache.commons.math4.random.MersenneTwister MersenneTwister}</td><td>1</td></tr> - * <tr><td>{@link org.apache.commons.math4.random.JDKRandomGenerator JDKRandomGenerator}</td><td>between 0.96 and 1.16</td></tr> - * <tr><td>{@link org.apache.commons.math4.random.Well512a Well512a}</td><td>between 0.85 and 0.88</td></tr> - * <tr><td>{@link org.apache.commons.math4.random.Well1024a Well1024a}</td><td>between 0.63 and 0.73</td></tr> - * <tr><td>{@link org.apache.commons.math4.random.Well19937a Well19937a}</td><td>between 0.70 and 0.71</td></tr> - * <tr><td>{@link org.apache.commons.math4.random.Well19937c Well19937c}</td><td>between 0.57 and 0.71</td></tr> - * <tr><td>{@link org.apache.commons.math4.random.Well44497a Well44497a}</td><td>between 0.69 and 0.71</td></tr> - * <tr><td>{@link org.apache.commons.math4.random.Well44497b Well44497b}</td><td>between 0.65 and 0.71</td></tr> - * </table> - * </p> - * - * <p> - * So for most simulation problems, the better generators like {@link - * org.apache.commons.math4.random.Well19937c Well19937c} and {@link - * org.apache.commons.math4.random.Well44497b Well44497b} are probably very good choices. - * </p> - * - * <p> - * Note that <em>none</em> of these generators are suitable for cryptography. They are devoted - * to simulation, and to generate very long series with strong properties on the series as a whole - * (equidistribution, no correlation ...). They do not attempt to create small series but with - * very strong properties of unpredictability as needed in cryptography. - * </p> - * - * + * <p>Random Data Generation</p> + * + * <p> + * Some of the utilities in this package use the pseudo-random number + * generators defined in package {@link org.apache.commons.math4.rng} + * to provide higher level functionality (such as random strings) based + * on an underlying source of randomness that provides sequences of + * uniformly distributed integers. + * </p> + * <p> + * Others are sources of pseudo-randomness that directly produce "compound" + * types such as {@link org.apache.commons.math4.random.RandomVectorGenerator + * random vectors}. + * </p> */ package org.apache.commons.math4.random;
