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From er...@apache.org
Subject [2/6] [math] Javadoc.
Date Sun, 29 May 2016 17:29:39 GMT
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 <gilles@harfang.homelinux.org>
Authored: Sun May 29 18:08:41 2016 +0200
Committer: Gilles <gilles@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(-)
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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 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>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;


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