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Subject [35/57] [abbrv] [math] MATH-1383
Date Tue, 20 Sep 2016 18:20:20 GMT

Update userguide: removing reference to deleted package "o.a.c.math4.rng".


Branch: refs/heads/master
Commit: d52a020ef7bdc98ba2ba88a8e2bdd412d30a930c
Parents: e2423a4
Author: Gilles <>
Authored: Sun Aug 28 00:06:51 2016 +0200
Committer: Gilles <>
Committed: Sun Aug 28 00:06:51 2016 +0200

 src/site/site.xml                  |  1 -
 src/site/xdoc/userguide/random.xml | 45 +++++++++++++--------------------
 2 files changed, 18 insertions(+), 28 deletions(-)
diff --git a/src/site/site.xml b/src/site/site.xml
index 22fefe6..b6e0f7a 100644
--- a/src/site/site.xml
+++ b/src/site/site.xml
@@ -76,7 +76,6 @@
       <item name="Filters"                 href="/userguide/filter.html"/>
       <item name="Machine Learning"        href="/userguide/ml.html"/>
       <item name="Exceptions"              href="/userguide/exceptions.html"/>
-      <item name="Random Number Generators" href="/userguide/rng.html"/>
diff --git a/src/site/xdoc/userguide/random.xml b/src/site/xdoc/userguide/random.xml
index 8ef9f84..868c96a 100644
--- a/src/site/xdoc/userguide/random.xml
+++ b/src/site/xdoc/userguide/random.xml
@@ -49,14 +49,8 @@
       These utilities rely on an underlying "source of randomness", which in most
       cases is a pseudo-random number generator (PRNG) that produces sequences
       of numbers that are uniformly distributed within their range.
-      Commons Math provides many PRNG implementations that share a common
-      interface:
-      <a href="../apidocs/org/apache/commons/math4/rng/UniformRandomProvider.html">
-	UniformRandomProvider</a> (for more details about this interface and the
-      available RNG algorithms, please refer to the Javadoc of package
-      <a href="../apidocs/org/apache/commons/math4/rng/package-summary.html">
-	    org.apache.commons.math4.rng</a> and <a href="../userguide/rng.html">this
-      of the userguide.
+      Commons Math depends on <a href="">Commons Rng</a>
+      for the PRNG implementations.
       A PRNG algorithm is often deterministic, i.e. it produces the same sequence
@@ -96,23 +90,20 @@
     It is possible for a sequence of numbers to appear random, but
     nonetheless to be predictable based on the algorithm used to generate the
-    sequence. If in addition to randomness, strong unpredictability is
-    required, it is best to use a  
+    sequence.
+    When in addition to randomness, strong unpredictability is
+    required, a
     <a href="">
-      secure random number generator</a> to generate values (or strings).
-    Most PRNG implemented in this library are not secure in that sense, except
-    perhaps the <a href="../apidocs/org/apache/commons/math4/rng/internal/source32/ISAACRandom.html">
-      ISAAC generator</a>.
-    An alternative is to use an instance of the JDK-provided <code>SecureRandom</code>
-    generator.
+    secure random number generator</a>
+    should be used to generate values (or strings), for example an instance of
+    the JDK-provided <code>SecureRandom</code> generator.
     In general, such secure generator produce sequence based on a source of
     true randomness, and sequences started with the same seed will diverge.
     The <a href="../apidocs/org/apache/commons/math4/random/RandomUtils.html">RandomUtils</a>
-    class provides a "factory" method to wrap <code>java.util.Random</code> or
-    <code></code> instances in an object that implements
-    the <a href="../apidocs/org/apache/commons/math4/rng/UniformRandomProvider.html">
+    class provides a method for wrapping a <code>java.util.Random</code> or
+    <code></code> instance in an object that implements
+    the <a href="">
     UniformRandomProvider</a> interface:
 UniformRandomProvider rg = RandomUtils.asUniformRandomProvider(new;
@@ -151,21 +142,21 @@ UniformRandomProvider rg = RandomUtils.asUniformRandomProvider(new
     <dt>Generating random vectors from a bivariate normal distribution</dt><dd>
 // Import common PRNG interface and factory class that instantiates the PRNG.
-import org.apache.commons.math4.rng.UniformRandomProvider;
-import org.apache.commons.math4.rng.RandomSource;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.RandomSource;
-// Create (and possibly seed) a PRNG (could use any of the CM-provided generators)
+// Create (and possibly seed) a PRNG (could use any of the CM-provided generators).
 long seed = 17399225432L; // Fixed seed means same results every time 
 UniformRandomProvider rg = RandomSource.create(RandomSource.MT, seed);
-// Create a GassianRandomGenerator using rg as its source of randomness
+// Create a GaussianRandomGenerator using "rg" as its source of randomness.
 GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
-// Create a CorrelatedRandomVectorGenerator using rawGenerator for the components
+// Create a CorrelatedRandomVectorGenerator using "rawGenerator" for the components.
 CorrelatedRandomVectorGenerator generator = 
     new CorrelatedRandomVectorGenerator(mean, covariance, 1.0e-12 * covariance.getNorm(),
-// Use the generator to generate correlated vectors
+// Use the generator to generate correlated vectors.
 double[] randomVector = generator.nextVector();
 ... </source>
@@ -181,7 +172,7 @@ double[] randomVector = generator.nextVector();
 double[] mean = {1, 2};
 double[][] cov = {{9, c}, {c, 16}};
 RealMatrix covariance = MatrixUtils.createRealMatrix(cov); </source>
-    where c is the desired covariance. If you are starting with a desired correlation,
+    where "c" is the desired covariance. If you are starting with a desired correlation,
     you need to translate this to a covariance by multiplying it by the product of the
     standard deviations.  For example, if you want to generate data that will give Pearson's
     R of 0.5, you would use c = 3 * 4 * 0.5 = 6.

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