Random Points in an n-Dimensional Hypersphere

Randomly and uniformly distributes points throughout a hypersphere.

Description

This creates a set of random points defined by Cartesian coordinates and uniformly distributed over the interior of an n-dimensional hypersphere of radius r with center at the origin.

  The 'randn' function is first used to create independent multivariate normally distributed sets of n random variables, each representing points in n-dimensional space. The incomplete gamma function, 'gammainc', is then used to map these points radially to the interior of an n-dimensional hypersphere of finite radius r in such a way as to be spatially uniformly distributed.

• MATLAB release: MATLAB 5.2 (R10)

[Peter (PB) Bodin]

Simple, yet very clever solution.

[John D'Errico]

I'll admit it took me a minute or so to verify this code. Its
mathematically elegant. Its efficient. It even works in the
special case of n = 1.

[Peter Nave]

The compactness of the code is stunning.

[Lee Jones]

elegant procedure, surprisingly

[Steve Lord]

Very nice piece of code. I have just two small suggestions for enhancements to the M-file help:

1) Include the calling syntax early in the help. I will admit I sometimes forget the calling syntax for functions I don't use often; my first resort is to type "help <functionname>", either to refresh my memory or to copy and paste the syntax into my code. In a case like this, I can see users perhaps forgetting the order of the parameters.

2) Include an example. Having a example that users can copy and paste included in the help gives people who may not know how to use your function a "known good" state -- from there, they can tweak. For a straightforward function like this, it may not seem necessary, but I think it's a good habit. It could be as simple as the two lines of code to generate the sample image.

[Aree Witoelar]

Thanks a lot for the elegant code! I had trouble generating this distribution. Before this, my solution was to sample and reject; but for high dimensions it was too crude and consuming.

[MathMoose]

 

[molc morloc]

 

[Florian]

The elegancy is breathtaking! I wish you would post more functions!

[Jiaqi]

 

[chen ?]

Thanks a lot!
I have another question.If I wanna to get a uniform distribution on the surface of the hypersphere (radius equals 1), whether is it correct to normalize the generated vectors to be length 1. Thank you very much!

[Laurent]

The convenient use of the incomplete gamma function luckily provided by Matlab can be avoided, so as to make the function probably faster, more elegant and reliable. No non-elementary function is necessary here.

In the last line, simply replace
gammainc(s2/2,n/2)
by
rand(m,1)

This amount to sampling the radius randomly, independently of the point "X/sqrt(s2)" that is uniform on the surface of the sphere. Indeed, one can check that if u is uniform on [0,1] then r*u^(1/n) has same distribution as the norm of a vector chosen uniformly inside the ball B(0,r) in n-dimensional space.

I don't have matlab right here to check for speed compared to using gammainc, but in any case this method does not rely on some blackbox approximate integration, which makes it, to my taste, more elegant. It is also more portable into other programming languages.

[Vincent Dubourg]

Hi!

Thank you for sharing this neat function! I was wondering if you could give me any reference explaining the theoretical concepts underlying this script?

Thanks,
Vincent

[Vincent Dubourg]

Hi again,

I googled a little more and finally found a short explanation in the book by Rubinstein (2008, p. 69). I leave the link here in case it deserves someone else...
http://books.google.com/books?id=1-ffZVmazvwC&pg=PA69&lpg=PA69&dq=random+hypersphere+rubinstein&source=bl&ots=-q859Ytx3q&sig=q-PtG2P62_kTCgzi-J7QuqvPOZA&hl=fr&ei=zZ25TcfUJ4SChQeIzY3_Dg&sa=X&oi=book_result&ct=result&resnum=1&ved=0CBwQ6AEwAA

Cheers,
Vincent