BoxMullerNormalizedGaussianSampler.java
- /*
- * 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.distribution;
- import org.apache.commons.rng.UniformRandomProvider;
- /**
- * <a href="https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform">
- * Box-Muller algorithm</a> for sampling from Gaussian distribution with
- * mean 0 and standard deviation 1.
- *
- * <p>Sampling uses:</p>
- *
- * <ul>
- * <li>{@link UniformRandomProvider#nextDouble()}
- * <li>{@link UniformRandomProvider#nextLong()}
- * </ul>
- *
- * @since 1.1
- */
- public class BoxMullerNormalizedGaussianSampler
- implements NormalizedGaussianSampler, SharedStateContinuousSampler {
- /** Next gaussian. */
- private double nextGaussian = Double.NaN;
- /** Underlying source of randomness. */
- private final UniformRandomProvider rng;
- /**
- * Create an instance.
- *
- * @param rng Generator of uniformly distributed random numbers.
- */
- public BoxMullerNormalizedGaussianSampler(UniformRandomProvider rng) {
- this.rng = rng;
- }
- /** {@inheritDoc} */
- @Override
- public double sample() {
- final double random;
- if (Double.isNaN(nextGaussian)) {
- // Generate a pair of Gaussian numbers.
- // Avoid zero for the uniform deviate y.
- // The extreme tail of the sample is:
- // y = 2^-53
- // r = 8.57167
- final double x = rng.nextDouble();
- final double y = InternalUtils.makeNonZeroDouble(rng.nextLong());
- final double alpha = 2 * Math.PI * x;
- final double r = Math.sqrt(-2 * Math.log(y));
- // Return the first element of the generated pair.
- random = r * Math.cos(alpha);
- // Keep second element of the pair for next invocation.
- nextGaussian = r * Math.sin(alpha);
- } else {
- // Use the second element of the pair (generated at the
- // previous invocation).
- random = nextGaussian;
- // Both elements of the pair have been used.
- nextGaussian = Double.NaN;
- }
- return random;
- }
- /** {@inheritDoc} */
- @Override
- public String toString() {
- return "Box-Muller normalized Gaussian deviate [" + rng.toString() + "]";
- }
- /**
- * {@inheritDoc}
- *
- * @since 1.3
- */
- @Override
- public SharedStateContinuousSampler withUniformRandomProvider(UniformRandomProvider rng) {
- return new BoxMullerNormalizedGaussianSampler(rng);
- }
- /**
- * Create a new normalised Gaussian sampler.
- *
- * @param <S> Sampler type.
- * @param rng Generator of uniformly distributed random numbers.
- * @return the sampler
- * @since 1.3
- */
- @SuppressWarnings("unchecked")
- public static <S extends NormalizedGaussianSampler & SharedStateContinuousSampler> S
- of(UniformRandomProvider rng) {
- return (S) new BoxMullerNormalizedGaussianSampler(rng);
- }
- }