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The Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* 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.math.distribution;
import java.io.Serializable;
import org.apache.commons.math.MathException;
/**
* The default implementation of {@link ChiSquaredDistribution}
*
* @version $Revision: 762087 $ $Date: 2009-04-05 10:20:18 -0400 (Sun, 05 Apr 2009) $
*/
public class ChiSquaredDistributionImpl
extends AbstractContinuousDistribution
implements ChiSquaredDistribution, Serializable {
/** Serializable version identifier */
private static final long serialVersionUID = -8352658048349159782L;
/** Internal Gamma distribution. */
private GammaDistribution gamma;
/**
* Create a Chi-Squared distribution with the given degrees of freedom.
* @param df degrees of freedom.
*/
public ChiSquaredDistributionImpl(double df) {
this(df, new GammaDistributionImpl(df / 2.0, 2.0));
}
/**
* Create a Chi-Squared distribution with the given degrees of freedom.
* @param df degrees of freedom.
* @param g the underlying gamma distribution used to compute probabilities.
* @since 1.2
*/
public ChiSquaredDistributionImpl(double df, GammaDistribution g) {
super();
setGamma(g);
setDegreesOfFreedom(df);
}
/**
* Modify the degrees of freedom.
* @param degreesOfFreedom the new degrees of freedom.
*/
public void setDegreesOfFreedom(double degreesOfFreedom) {
getGamma().setAlpha(degreesOfFreedom / 2.0);
}
/**
* Access the degrees of freedom.
* @return the degrees of freedom.
*/
public double getDegreesOfFreedom() {
return getGamma().getAlpha() * 2.0;
}
/**
* Return the probability density for a particular point.
*
* @param x The point at which the density should be computed.
* @return The pdf at point x.
*/
public double density(Double x) {
return gamma.density(x);
}
/**
* For this distribution, X, this method returns P(X < x).
* @param x the value at which the CDF is evaluated.
* @return CDF for this distribution.
* @throws MathException if the cumulative probability can not be
* computed due to convergence or other numerical errors.
*/
public double cumulativeProbability(double x) throws MathException {
return getGamma().cumulativeProbability(x);
}
/**
* For this distribution, X, this method returns the critical point x, such
* that P(X < x) = p.
*
* Returns 0 for p=0 and Double.POSITIVE_INFINITY for p=1.
*
* @param p the desired probability
* @return x, such that P(X < x) = p
* @throws MathException if the inverse cumulative probability can not be
* computed due to convergence or other numerical errors.
* @throws IllegalArgumentException if p is not a valid
* probability.
*/
@Override
public double inverseCumulativeProbability(final double p)
throws MathException {
if (p == 0) {
return 0d;
}
if (p == 1) {
return Double.POSITIVE_INFINITY;
}
return super.inverseCumulativeProbability(p);
}
/**
* Access the domain value lower bound, based on p, used to
* bracket a CDF root. This method is used by
* {@link #inverseCumulativeProbability(double)} to find critical values.
*
* @param p the desired probability for the critical value
* @return domain value lower bound, i.e.
* P(X < lower bound) < p
*/
@Override
protected double getDomainLowerBound(double p) {
return Double.MIN_VALUE * getGamma().getBeta();
}
/**
* Access the domain value upper bound, based on p, used to
* bracket a CDF root. This method is used by
* {@link #inverseCumulativeProbability(double)} to find critical values.
*
* @param p the desired probability for the critical value
* @return domain value upper bound, i.e.
* P(X < upper bound) > p
*/
@Override
protected double getDomainUpperBound(double p) {
// NOTE: chi squared is skewed to the left
// NOTE: therefore, P(X < μ) > .5
double ret;
if (p < .5) {
// use mean
ret = getDegreesOfFreedom();
} else {
// use max
ret = Double.MAX_VALUE;
}
return ret;
}
/**
* Access the initial domain value, based on p, used to
* bracket a CDF root. This method is used by
* {@link #inverseCumulativeProbability(double)} to find critical values.
*
* @param p the desired probability for the critical value
* @return initial domain value
*/
@Override
protected double getInitialDomain(double p) {
// NOTE: chi squared is skewed to the left
// NOTE: therefore, P(X < μ) > .5
double ret;
if (p < .5) {
// use 1/2 mean
ret = getDegreesOfFreedom() * .5;
} else {
// use mean
ret = getDegreesOfFreedom();
}
return ret;
}
/**
* Modify the underlying gamma distribution. The caller is responsible for
* insuring the gamma distribution has the proper parameter settings.
* @param g the new distribution.
* @since 1.2 made public
*/
public void setGamma(GammaDistribution g) {
this.gamma = g;
}
/**
* Access the Gamma distribution.
* @return the internal Gamma distribution.
*/
private GammaDistribution getGamma() {
return gamma;
}
}
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