| /* |
| * 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.statistics.distribution; |
| |
| import org.apache.commons.numbers.gamma.Gamma; |
| import org.apache.commons.numbers.gamma.RegularizedGamma; |
| |
| /** |
| * This class implements the <a href="http://en.wikipedia.org/wiki/Nakagami_distribution">Nakagami distribution</a>. |
| */ |
| public class NakagamiDistribution extends AbstractContinuousDistribution { |
| /** Support lower bound. */ |
| private static final double SUPPORT_LO = 0; |
| /** Support upper bound. */ |
| private static final double SUPPORT_HI = Double.POSITIVE_INFINITY; |
| /** The minimum allowed for the shape parameter. */ |
| private static final double MIN_SHAPE = 0.5; |
| |
| /** The shape parameter. */ |
| private final double mu; |
| /** The scale parameter. */ |
| private final double omega; |
| |
| /** |
| * Creates a distribution. |
| * |
| * @param mu shape parameter |
| * @param omega scale parameter (must be positive) |
| * @throws IllegalArgumentException if {@code mu < 0.5} or if |
| * {@code omega <= 0}. |
| */ |
| public NakagamiDistribution(double mu, |
| double omega) { |
| if (mu < MIN_SHAPE) { |
| throw new DistributionException(DistributionException.TOO_SMALL, mu, MIN_SHAPE); |
| } |
| if (omega <= 0) { |
| throw new DistributionException(DistributionException.NOT_STRICTLY_POSITIVE, omega); |
| } |
| |
| this.mu = mu; |
| this.omega = omega; |
| } |
| |
| /** |
| * Access the shape parameter, {@code mu}. |
| * |
| * @return the shape parameter. |
| */ |
| public double getShape() { |
| return mu; |
| } |
| |
| /** |
| * Access the scale parameter, {@code omega}. |
| * |
| * @return the scale parameter. |
| */ |
| public double getScale() { |
| return omega; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double density(double x) { |
| if (x <= SUPPORT_LO || |
| x >= SUPPORT_HI) { |
| return 0; |
| } |
| |
| return 2.0 * Math.pow(mu, mu) / (Gamma.value(mu) * Math.pow(omega, mu)) * |
| Math.pow(x, 2 * mu - 1) * Math.exp(-mu * x * x / omega); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double cumulativeProbability(double x) { |
| if (x <= SUPPORT_LO) { |
| return 0; |
| } else if (x >= SUPPORT_HI) { |
| return 1; |
| } |
| |
| return RegularizedGamma.P.value(mu, mu * x * x / omega); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double getMean() { |
| return Gamma.value(mu + 0.5) / Gamma.value(mu) * Math.sqrt(omega / mu); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double getVariance() { |
| final double v = Gamma.value(mu + 0.5) / Gamma.value(mu); |
| return omega * (1 - 1 / mu * v * v); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double getSupportLowerBound() { |
| return SUPPORT_LO; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double getSupportUpperBound() { |
| return SUPPORT_HI; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public boolean isSupportConnected() { |
| return true; |
| } |
| |
| } |