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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.rng.sampling;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.sampling.distribution.NormalizedGaussianSampler;
import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler;
/**
* Generate vectors
* isotropically located on the surface of a sphere.
*
* Sampling uses:
*
*
* - {@link UniformRandomProvider#nextLong()}
*
- {@link UniformRandomProvider#nextDouble()}
*
*
* @since 1.1
*/
public class UnitSphereSampler {
/** Sampler used for generating the individual components of the vectors. */
private final NormalizedGaussianSampler sampler;
/** Space dimension. */
private final int dimension;
/**
* @param dimension Space dimension.
* @param rng Generator for the individual components of the vectors.
* A shallow copy will be stored in this instance.
* @throws IllegalArgumentException If {@code dimension <= 0}
*/
public UnitSphereSampler(int dimension,
UniformRandomProvider rng) {
if (dimension <= 0) {
throw new IllegalArgumentException("Dimension must be strictly positive");
}
this.dimension = dimension;
sampler = new ZigguratNormalizedGaussianSampler(rng);
}
/**
* @return a random normalized Cartesian vector.
*/
public double[] nextVector() {
final double[] v = new double[dimension];
// Pick a point by choosing a standard Gaussian for each element,
// and then normalize to unit length.
double normSq = 0;
for (int i = 0; i < dimension; i++) {
final double comp = sampler.sample();
v[i] = comp;
normSq += comp * comp;
}
if (normSq == 0) {
// Zero-norm vector is discarded.
// Using recursion as it is highly unlikely to generate more
// than a few such vectors. It also protects against infinite
// loop (in case a buggy generator is used), by eventually
// raising a "StackOverflowError".
return nextVector();
}
final double f = 1 / Math.sqrt(normSq);
for (int i = 0; i < dimension; i++) {
v[i] *= f;
}
return v;
}
}