Alex Herbert created RNG-128:
--------------------------------
Summary: UnitBallSampler
Key: RNG-128
URL: https://issues.apache.org/jira/browse/RNG-128
Project: Commons RNG
Issue Type: Improvement
Components: sampling
Reporter: Alex Herbert
The *UnitSphereSampler* can generate isotropically distributed coordinates *on
the surface* of a hypersphere of a given dimension.
I propose to create a *UnitBallSampler* to generate isotropically distributed
coordinates *within* a hypersphere of a given dimension.
Wolfram describes a method that will work with any dimension and a method for 2
dimensions:
[Ball Point Picking|https://mathworld.wolfram.com/BallPointPicking.html]
[Disk Point Picking|https://mathworld.wolfram.com/DiskPointPicking.html]
It also suggests that a simple rejection method when sampling inside an n-cube
of length 2 may be faster for small dimensions.
Creating an abstract class with a factory method would allow the best
performing sampler to be created for each dimension:
{code:java}
public abstract class UnitBallSampler implements
SharedStateSampler<UnitBallSampler> {
/**
* @return a random Cartesian coordinate within the unit n-ball.
*/
public double[] next() {
// ...
}
/**
* Create a unit n-ball sampler for the given dimension.
*
* @param dimension Space dimension.
* @param rng Generator for the individual components of the coordinates.
* A shallow copy will be stored in this instance.
* @throws IllegalArgumentException If {@code dimension <= 0}
*/
public static UnitBallSampler of(int dimension,
UniformRandomProvider rng) {
// ...
}
}
{code}
The UnitSphereSampler sample method is named {{nextVector}}. The corresponding
method here could be:
* next
* nextVector
* nextCoordinate
* nextPoint
* sample
* ...
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
