/*
 * 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
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 *      http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.commons.math3.random;

import org.apache.commons.math3.util.FastMath;


Generate random vectors isotropically located on the surface of a sphere.
Since:2.1
/** * Generate random vectors isotropically located on the surface of a sphere. * * @since 2.1 */
public class UnitSphereRandomVectorGenerator implements RandomVectorGenerator {
RNG used for generating the individual components of the vectors.
/** * RNG used for generating the individual components of the vectors. */
private final RandomGenerator rand;
Space dimension.
/** * Space dimension. */
private final int dimension;
Params:
  • dimension – Space dimension.
  • rand – RNG for the individual components of the vectors.
/** * @param dimension Space dimension. * @param rand RNG for the individual components of the vectors. */
public UnitSphereRandomVectorGenerator(final int dimension, final RandomGenerator rand) { this.dimension = dimension; this.rand = rand; }
Create an object that will use a default RNG (MersenneTwister), in order to generate the individual components.
Params:
  • dimension – Space dimension.
/** * Create an object that will use a default RNG ({@link MersenneTwister}), * in order to generate the individual components. * * @param dimension Space dimension. */
public UnitSphereRandomVectorGenerator(final int dimension) { this(dimension, new MersenneTwister()); }
{@inheritDoc}
/** {@inheritDoc} */
public double[] nextVector() { final double[] v = new double[dimension]; // See http://mathworld.wolfram.com/SpherePointPicking.html for example. // Pick a point by choosing a standard Gaussian for each element, and then // normalizing to unit length. double normSq = 0; for (int i = 0; i < dimension; i++) { final double comp = rand.nextGaussian(); v[i] = comp; normSq += comp * comp; } final double f = 1 / FastMath.sqrt(normSq); for (int i = 0; i < dimension; i++) { v[i] *= f; } return v; } }