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Removed obsolete documentation.
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Gilles 2016-05-29 18:08:41 +02:00
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@ -15,118 +15,19 @@
* limitations under the License.
*/
/**
* <p>Random Data Generation</p>
*
* <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 Fran&ccedil;ois 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>
* 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;