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/*
* 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.math3.random;
import org.apache.commons.math3.util.FastMath;
/**
* 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. */
private final RandomGenerator rand;
/** Space dimension. */
private final int dimension;
/**
* @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 ({@link MersenneTwister}), in order to generate
* the individual components.
*
* @param dimension Space dimension.
*/
public UnitSphereRandomVectorGenerator(final int dimension) {
this(dimension, new MersenneTwister());
}
/** {@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;
}
}
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