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diff --git a/src/main/java/org/apache/commons/math/random/package.html b/src/main/java/org/apache/commons/math/random/package.html new file mode 100644 index 0000000..9978ca0 --- /dev/null +++ b/src/main/java/org/apache/commons/math/random/package.html @@ -0,0 +1,132 @@ +<html> +<!-- + Licensed to the Apache Software Foundation (ASF) under one or more + contributor license agreements. See the NOTICE file distributed with + this work for additional information regarding copyright ownership. + The ASF licenses this file to You under the Apache License, Version 2.0 + (the "License"); you may not use this file except in compliance with + the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + --> + <!-- $Revision: 1054186 $ $Date: 2011-01-01 03:28:46 +0100 (sam. 01 janv. 2011) $ --> + <body> + <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.math.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.math.random.MersenneTwister MersenneTwister}</li> + <li>{@link org.apache.commons.math.random.Well512a Well512a}</li> + <li>{@link org.apache.commons.math.random.Well1024a Well1024a}</li> + <li>{@link org.apache.commons.math.random.Well19937a Well19937a}</li> + <li>{@link org.apache.commons.math.random.Well19937c Well19937c}</li> + <li>{@link org.apache.commons.math.random.Well44497a Well44497a}</li> + <li>{@link org.apache.commons.math.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.math.random.MersenneTwister MersenneTwister}</td><td>1</td></tr> + <tr><td>{@link org.apache.commons.math.random.JDKRandomGenerator JDKRandomGenerator}</td><td>between 0.96 and 1.16</td></tr> + <tr><td>{@link org.apache.commons.math.random.Well512a Well512a}</td><td>between 0.85 and 0.88</td></tr> + <tr><td>{@link org.apache.commons.math.random.Well1024a Well1024a}</td><td>between 0.63 and 0.73</td></tr> + <tr><td>{@link org.apache.commons.math.random.Well19937a Well19937a}</td><td>between 0.70 and 0.71</td></tr> + <tr><td>{@link org.apache.commons.math.random.Well19937c Well19937c}</td><td>between 0.57 and 0.71</td></tr> + <tr><td>{@link org.apache.commons.math.random.Well44497a Well44497a}</td><td>between 0.69 and 0.71</td></tr> + <tr><td>{@link org.apache.commons.math.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.math.random.Well19937c Well19937c} and {@link + org.apache.commons.math.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> + + </body> +</html> |