1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17
18 package org.apache.commons.rng.sampling;
19
20 import org.apache.commons.rng.UniformRandomProvider;
21 import org.apache.commons.rng.sampling.distribution.NormalizedGaussianSampler;
22 import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler;
23
24 /**
25 * Generate vectors <a href="http://mathworld.wolfram.com/SpherePointPicking.html">
26 * isotropically located on the surface of a sphere</a>.
27 *
28 * @since 1.1
29 */
30 public class UnitSphereSampler {
31 /** Sampler used for generating the individual components of the vectors. */
32 private final NormalizedGaussianSampler sampler;
33 /** Space dimension. */
34 private final int dimension;
35
36 /**
37 * @param dimension Space dimension.
38 * @param rng Generator for the individual components of the vectors.
39 * A shallow copy will be stored in this instance.
40 */
41 public UnitSphereSampler(int dimension,
42 UniformRandomProvider rng) {
43 if (dimension <= 0) {
44 throw new IllegalArgumentException("Dimension must be strictly positive");
45 }
46
47 this.dimension = dimension;
48 sampler = new ZigguratNormalizedGaussianSampler(rng);
49 }
50
51 /**
52 * @return a random normalized Cartesian vector.
53 */
54 public double[] nextVector() {
55 final double[] v = new double[dimension];
56
57 // Pick a point by choosing a standard Gaussian for each element,
58 // and then normalize to unit length.
59 double normSq = 0;
60 for (int i = 0; i < dimension; i++) {
61 final double comp = sampler.sample();
62 v[i] = comp;
63 normSq += comp * comp;
64 }
65
66 if (normSq == 0) {
67 // Zero-norm vector is discarded.
68 // Using recursion as it is highly unlikely to generate more
69 // than a few such vectors. It also protects against infinite
70 // loop (in case a buggy generator is used), by eventually
71 // raising a "StackOverflowError".
72 return nextVector();
73 }
74
75 final double f = 1 / Math.sqrt(normSq);
76 for (int i = 0; i < dimension; i++) {
77 v[i] *= f;
78 }
79
80 return v;
81 }
82 }