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Posted to issues@commons.apache.org by "Luc Maisonobe (JIRA)" <ji...@apache.org> on 2013/04/07 11:19:21 UTC
[jira] [Closed] (MATH-931) Speed up UnitSphereRandomVectorGenerator
for high dimensions
[ https://issues.apache.org/jira/browse/MATH-931?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Luc Maisonobe closed MATH-931.
------------------------------
Closing issue as version 3.2 has been released on 2013-04-06.
> Speed up UnitSphereRandomVectorGenerator for high dimensions
> ------------------------------------------------------------
>
> Key: MATH-931
> URL: https://issues.apache.org/jira/browse/MATH-931
> Project: Commons Math
> Issue Type: Improvement
> Affects Versions: 3.1.1
> Reporter: Sean Owen
> Priority: Minor
> Fix For: 3.2
>
> Attachments: MATH-931.patch
>
>
> I have a small proposal to improve the speed of UnitSphereRandomVectorGenerator. This class picks a random point on the unit n-sphere -- a unit vector, chosen uniformly from all possible directions.
> It does so using a rejection process -- generates a random point in the unit n-cube (well, with side lengths 2) and rejects any points outside the unit n-sphere, then normalizes the length. This is correct and works well at low dimension. However the volume of the unit n-sphere compared to the unit n-cube drops exponentially. This method eventually takes an extraordinary amount of time when dimensions get past about 20, since virtually no samples are usable.
> For example, here is the time in milliseconds taken to make pick 10 points as a function of dimension up to 20:
> 1 : 11
> 2 : 1
> 3 : 0
> 4 : 1
> 5 : 0
> 6 : 1
> 7 : 1
> 8 : 17
> 9 : 4
> 10 : 3
> 11 : 13
> 12 : 32
> 13 : 15
> 14 : 41
> 15 : 220
> 16 : 897
> 17 : 1770
> 18 : 7426
> 19 : 48457
> 20 : 122647
> ...
> It's equally correct, and much faster, to generate these points by picking n standard Gaussians and normalizing. This method takes negligible time even into thousands of dimensions.
> Patch coming.
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