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Posted to commits@commons.apache.org by er...@apache.org on 2016/05/29 17:29:39 UTC
[2/6] [math] Javadoc.
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/task-MATH-1366
Commit: 34b96986624a87da2f0f2b3d2878c7be4d0eb25c
Parents: 156dfa6
Author: Gilles <gi...@harfang.homelinux.org>
Authored: Sun May 29 18:08:41 2016 +0200
Committer: Gilles <gi...@harfang.homelinux.org>
Committed: Sun May 29 18:08:41 2016 +0200
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.../commons/math4/random/package-info.java | 127 ++-----------------
1 file changed, 14 insertions(+), 113 deletions(-)
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http://git-wip-us.apache.org/repos/asf/commons-math/blob/34b96986/src/main/java/org/apache/commons/math4/random/package-info.java
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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;