Class BoxSampler
- java.lang.Object
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- org.apache.commons.rng.sampling.shape.BoxSampler
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- All Implemented Interfaces:
ObjectSampler<double[]>
,SharedStateObjectSampler<double[]>
,SharedStateSampler<SharedStateObjectSampler<double[]>>
public abstract class BoxSampler extends Object implements SharedStateObjectSampler<double[]>
Generate points uniformly distributed within a n-dimension box (hyperrectangle).Sampling uses:
- Since:
- 1.4
- See Also:
- Hyperrectangle (Wikipedia)
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Method Summary
All Methods Static Methods Instance Methods Abstract Methods Concrete Methods Modifier and Type Method Description static BoxSampler
of(UniformRandomProvider rng, double[] a, double[] b)
Create a box sampler with boundsa
andb
.abstract double[]
sample()
Create an object sample.abstract BoxSampler
withUniformRandomProvider(UniformRandomProvider rng)
Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface org.apache.commons.rng.sampling.ObjectSampler
samples, samples
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Method Detail
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sample
public abstract double[] sample()
Description copied from interface:ObjectSampler
Create an object sample.- Specified by:
sample
in interfaceObjectSampler<double[]>
- Returns:
- a random Cartesian coordinate within the box.
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withUniformRandomProvider
public abstract BoxSampler withUniformRandomProvider(UniformRandomProvider rng)
Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.- Specified by:
withUniformRandomProvider
in interfaceSharedStateSampler<SharedStateObjectSampler<double[]>>
- Parameters:
rng
- Generator of uniformly distributed random numbers.- Returns:
- the sampler
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of
public static BoxSampler of(UniformRandomProvider rng, double[] a, double[] b)
Create a box sampler with boundsa
andb
. Sampled points are uniformly distributed within the box defined by the bounds.Sampling is supported in dimensions of 2 or above. Single dimension sampling can be performed using a
LineSampler
.Note: There is no requirement that
a <= b
. The samples will be uniformly distributed in the rangea
tob
for each dimension.- Parameters:
rng
- Source of randomness.a
- Bound a.b
- Bound b.- Returns:
- the sampler
- Throws:
IllegalArgumentException
- If the bounds do not have the same dimension; the dimension is less than 2; or bounds have non-finite coordinates.
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