[jira] [Resolved] (RNG-129) Performance improvement for UnitSphereSampler

["Alex Herbert (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/RNG-129?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Alex Herbert resolved RNG-129.
------------------------------
    Fix Version/s: 1.4
         Assignee: Alex Herbert
       Resolution: Implemented

In git master.

Commit: 5692fe119d9f5866e4448a47472ba20a4ea7a813

> Performance improvement for UnitSphereSampler
> ---------------------------------------------
>
>                 Key: RNG-129
>                 URL: https://issues.apache.org/jira/browse/RNG-129
>             Project: Commons RNG
>          Issue Type: Improvement
>          Components: sampling
>    Affects Versions: 1.3
>            Reporter: Alex Herbert
>            Assignee: Alex Herbert
>            Priority: Minor
>             Fix For: 1.4
>
>          Time Spent: 0.5h
>  Remaining Estimate: 0h
>
> The UnitSphereSampler accepts a dimension argument and creates the sample using iteration over an array of the given dimension creating n Gaussian samples. This can be optimised for low order dimensions to remove the use of array iteration, e.g.
> {code:java}
> final double[] v = new double[dimension];
> double sum = 0;
> for (int i = 0; i < dimension; i++) {
>     final double x = sampler.sample();
>     v[i] = x;
>     sum += x * x;
> }
> {code}
> becomes for 3D:
> {code:java}
> final double x = sampler.sample();
> final double y = sampler.sample();
> final double z = sampler.sample();
> final double sum = x * x + y * y + z * z;
> {code}
> The special case of 1D sampling can be handled by returning either 1 or -1 in a vector based on a single bit of the random source.
> Optimised versions can be created by adding a factory method to the class:
> {code:java}
> public static UnitSphereSampler of(int dimension, UniformRandomProvider rng) {
>     // ...
> }
> {code}



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