LogNormalSampler.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;
/**
* Sampling from a log-normal distribution.
*
* @since 1.1
*/
public class LogNormalSampler implements SharedStateContinuousSampler {
/** Mean of the natural logarithm of the distribution values. */
private final double mu;
/** Standard deviation of the natural logarithm of the distribution values. */
private final double sigma;
/** Gaussian sampling. */
private final NormalizedGaussianSampler gaussian;
/**
* Create an instance.
*
* @param gaussian N(0,1) generator.
* @param mu Mean of the natural logarithm of the distribution values.
* @param sigma Standard deviation of the natural logarithm of the distribution values.
* @throws IllegalArgumentException if {@code sigma <= 0}.
*/
public LogNormalSampler(NormalizedGaussianSampler gaussian,
double mu,
double sigma) {
// Validation before java.lang.Object constructor exits prevents partially initialized object
this(mu, InternalUtils.requireStrictlyPositive(sigma, "sigma"), gaussian);
}
/**
* @param mu Mean of the natural logarithm of the distribution values.
* @param sigma Standard deviation of the natural logarithm of the distribution values.
* @param gaussian N(0,1) generator.
*/
private LogNormalSampler(double mu,
double sigma,
NormalizedGaussianSampler gaussian) {
this.mu = mu;
this.sigma = sigma;
this.gaussian = gaussian;
}
/**
* @param rng Generator of uniformly distributed random numbers.
* @param source Source to copy.
*/
private LogNormalSampler(UniformRandomProvider rng,
LogNormalSampler source) {
this.mu = source.mu;
this.sigma = source.sigma;
this.gaussian = InternalUtils.newNormalizedGaussianSampler(source.gaussian, rng);
}
/** {@inheritDoc} */
@Override
public double sample() {
return Math.exp(mu + sigma * gaussian.sample());
}
/** {@inheritDoc} */
@Override
public String toString() {
return "Log-normal deviate [" + gaussian.toString() + "]";
}
/**
* {@inheritDoc}
*
* <p>Note: This function is available if the underlying {@link NormalizedGaussianSampler}
* is a {@link org.apache.commons.rng.sampling.SharedStateSampler SharedStateSampler}.
* Otherwise a run-time exception is thrown.</p>
*
* @throws UnsupportedOperationException if the underlying sampler is not a
* {@link org.apache.commons.rng.sampling.SharedStateSampler SharedStateSampler} or
* does not return a {@link NormalizedGaussianSampler} when sharing state.
*
* @since 1.3
*/
@Override
public SharedStateContinuousSampler withUniformRandomProvider(UniformRandomProvider rng) {
return new LogNormalSampler(rng, this);
}
/**
* Create a new log-normal distribution sampler.
*
* <p>Note: The shared-state functionality is available if the {@link NormalizedGaussianSampler}
* is a {@link org.apache.commons.rng.sampling.SharedStateSampler SharedStateSampler}.
* Otherwise a run-time exception will be thrown when the sampler is used to share state.</p>
*
* @param gaussian N(0,1) generator.
* @param mu Mean of the natural logarithm of the distribution values.
* @param sigma Standard deviation of the natural logarithm of the distribution values.
* @return the sampler
* @throws IllegalArgumentException if {@code sigma <= 0}.
* @see #withUniformRandomProvider(UniformRandomProvider)
* @since 1.3
*/
public static SharedStateContinuousSampler of(NormalizedGaussianSampler gaussian,
double mu,
double sigma) {
return new LogNormalSampler(gaussian, mu, sigma);
}
}