src/main/java/org/apache/commons/math/distribution/ChiSquaredDistributionImpl.java - platform/external/apache-commons-math - Git at Google

 /*
  * 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: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
  */
 public class ChiSquaredDistributionImpl
     extends AbstractContinuousDistribution
     implements ChiSquaredDistribution, Serializable  {

     /**
      * Default inverse cumulative probability accuracy
      * @since 2.1
      */
     public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;

     /** Serializable version identifier */
     private static final long serialVersionUID = -8352658048349159782L;

     /** Internal Gamma distribution. */
     private GammaDistribution gamma;

     /** Inverse cumulative probability accuracy */
     private final double solverAbsoluteAccuracy;

     /**
      * 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
      * @deprecated as of 2.1 (to avoid possibly inconsistent state, the
      * "GammaDistribution" will be instantiated internally)
      */
     @Deprecated
     public ChiSquaredDistributionImpl(double df, GammaDistribution g) {
         super();
         setGammaInternal(g);
         setDegreesOfFreedomInternal(df);
         solverAbsoluteAccuracy = DEFAULT_INVERSE_ABSOLUTE_ACCURACY;
     }

     /**
      * Create a Chi-Squared distribution with the given degrees of freedom and
      * inverse cumulative probability accuracy.
      * @param df degrees of freedom.
      * @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates
      * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY})
      * @since 2.1
      */
     public ChiSquaredDistributionImpl(double df, double inverseCumAccuracy) {
         super();
         gamma = new GammaDistributionImpl(df / 2.0, 2.0);
         setDegreesOfFreedomInternal(df);
         solverAbsoluteAccuracy = inverseCumAccuracy;
     }

     /**
      * Modify the degrees of freedom.
      * @param degreesOfFreedom the new degrees of freedom.
      * @deprecated as of 2.1 (class will become immutable in 3.0)
      */
     @Deprecated
     public void setDegreesOfFreedom(double degreesOfFreedom) {
         setDegreesOfFreedomInternal(degreesOfFreedom);
     }
     /**
      * Modify the degrees of freedom.
      * @param degreesOfFreedom the new degrees of freedom.
      */
     private void setDegreesOfFreedomInternal(double degreesOfFreedom) {
         gamma.setAlpha(degreesOfFreedom / 2.0);
     }

     /**
      * Access the degrees of freedom.
      * @return the degrees of freedom.
      */
     public double getDegreesOfFreedom() {
         return gamma.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.
      * @deprecated
      */
     @Deprecated
     public double density(Double x) {
         return density(x.doubleValue());
     }

     /**
      * 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.
      * @since 2.1
      */
     @Override
     public double density(double x) {
         return gamma.density(x);
     }

     /**
      * For this distribution, X, this method returns P(X &lt; 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 gamma.cumulativeProbability(x);
     }

     /**
      * For this distribution, X, this method returns the critical point x, such
      * that P(X &lt; x) = <code>p</code>.
      * <p>
      * Returns 0 for p=0 and <code>Double.POSITIVE_INFINITY</code> for p=1.</p>
      *
      * @param p the desired probability
      * @return x, such that P(X &lt; x) = <code>p</code>
      * @throws MathException if the inverse cumulative probability can not be
      *         computed due to convergence or other numerical errors.
      * @throws IllegalArgumentException if <code>p</code> 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 <code>p</code>, 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 &lt; <i>lower bound</i>) &lt; <code>p</code>
      */
     @Override
     protected double getDomainLowerBound(double p) {
         return Double.MIN_VALUE * gamma.getBeta();
     }

     /**
      * Access the domain value upper bound, based on <code>p</code>, 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 &lt; <i>upper bound</i>) &gt; <code>p</code>
      */
     @Override
     protected double getDomainUpperBound(double p) {
         // NOTE: chi squared is skewed to the left
         // NOTE: therefore, P(X < &mu;) > .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 <code>p</code>, 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 < &mu;) > .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
      * @deprecated as of 2.1 (class will become immutable in 3.0)
      */
     @Deprecated
     public void setGamma(GammaDistribution g) {
         setGammaInternal(g);
     }
     /**
      * 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
      */
     private void setGammaInternal(GammaDistribution g) {
         this.gamma = g;

     }


     /**
      * Return the absolute accuracy setting of the solver used to estimate
      * inverse cumulative probabilities.
      *
      * @return the solver absolute accuracy
      * @since 2.1
      */
     @Override
     protected double getSolverAbsoluteAccuracy() {
         return solverAbsoluteAccuracy;
     }

     /**
      * Returns the lower bound of the support for the distribution.
      *
      * The lower bound of the support is always 0 no matter the
      * degrees of freedom.
      *
      * @return lower bound of the support (always 0)
      * @since 2.2
      */
     public double getSupportLowerBound() {
         return 0;
     }

     /**
      * Returns the upper bound for the support for the distribution.
      *
      * The upper bound of the support is always positive infinity no matter the
      * degrees of freedom.
      *
      * @return upper bound of the support (always Double.POSITIVE_INFINITY)
      * @since 2.2
      */
     public double getSupportUpperBound() {
         return Double.POSITIVE_INFINITY;
     }

     /**
      * Returns the mean of the distribution.
      *
      * For <code>k</code> degrees of freedom, the mean is
      * <code>k</code>
      *
      * @return the mean
      * @since 2.2
      */
     public double getNumericalMean() {
         return getDegreesOfFreedom();
     }

     /**
      * Returns the variance of the distribution.
      *
      * For <code>k</code> degrees of freedom, the variance is
      * <code>2 * k</code>
      *
      * @return the variance
      * @since 2.2
      */
     public double getNumericalVariance() {
         return 2*getDegreesOfFreedom();
     }
 }
	/*
	* 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: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
	*/
	public class ChiSquaredDistributionImpl
	extends AbstractContinuousDistribution
	implements ChiSquaredDistribution, Serializable {

	/**
	* Default inverse cumulative probability accuracy
	* @since 2.1
	*/
	public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;

	/** Serializable version identifier */
	private static final long serialVersionUID = -8352658048349159782L;

	/** Internal Gamma distribution. */
	private GammaDistribution gamma;

	/** Inverse cumulative probability accuracy */
	private final double solverAbsoluteAccuracy;

	/**
	* 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
	* @deprecated as of 2.1 (to avoid possibly inconsistent state, the
	* "GammaDistribution" will be instantiated internally)
	*/
	@Deprecated
	public ChiSquaredDistributionImpl(double df, GammaDistribution g) {
	super();
	setGammaInternal(g);
	setDegreesOfFreedomInternal(df);
	solverAbsoluteAccuracy = DEFAULT_INVERSE_ABSOLUTE_ACCURACY;
	}

	/**
	* Create a Chi-Squared distribution with the given degrees of freedom and
	* inverse cumulative probability accuracy.
	* @param df degrees of freedom.
	* @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates
	* (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY})
	* @since 2.1
	*/
	public ChiSquaredDistributionImpl(double df, double inverseCumAccuracy) {
	super();
	gamma = new GammaDistributionImpl(df / 2.0, 2.0);
	setDegreesOfFreedomInternal(df);
	solverAbsoluteAccuracy = inverseCumAccuracy;
	}

	/**
	* Modify the degrees of freedom.
	* @param degreesOfFreedom the new degrees of freedom.
	* @deprecated as of 2.1 (class will become immutable in 3.0)
	*/
	@Deprecated
	public void setDegreesOfFreedom(double degreesOfFreedom) {
	setDegreesOfFreedomInternal(degreesOfFreedom);
	}
	/**
	* Modify the degrees of freedom.
	* @param degreesOfFreedom the new degrees of freedom.
	*/
	private void setDegreesOfFreedomInternal(double degreesOfFreedom) {
	gamma.setAlpha(degreesOfFreedom / 2.0);
	}

	/**
	* Access the degrees of freedom.
	* @return the degrees of freedom.
	*/
	public double getDegreesOfFreedom() {
	return gamma.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.
	* @deprecated
	*/
	@Deprecated
	public double density(Double x) {
	return density(x.doubleValue());
	}

	/**
	* 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.
	* @since 2.1
	*/
	@Override
	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 gamma.cumulativeProbability(x);
	}

	/**
	* For this distribution, X, this method returns the critical point x, such
	* that P(X < x) = <code>p</code>.
	* <p>
	* Returns 0 for p=0 and <code>Double.POSITIVE_INFINITY</code> for p=1.</p>
	*
	* @param p the desired probability
	* @return x, such that P(X < x) = <code>p</code>
	* @throws MathException if the inverse cumulative probability can not be
	* computed due to convergence or other numerical errors.
	* @throws IllegalArgumentException if <code>p</code> 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 <code>p</code>, 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 < <i>lower bound</i>) < <code>p</code>
	*/
	@Override
	protected double getDomainLowerBound(double p) {
	return Double.MIN_VALUE * gamma.getBeta();
	}

	/**
	* Access the domain value upper bound, based on <code>p</code>, 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 < <i>upper bound</i>) > <code>p</code>
	*/
	@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 <code>p</code>, 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
	* @deprecated as of 2.1 (class will become immutable in 3.0)
	*/
	@Deprecated
	public void setGamma(GammaDistribution g) {
	setGammaInternal(g);
	}
	/**
	* 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
	*/
	private void setGammaInternal(GammaDistribution g) {
	this.gamma = g;

	}


	/**
	* Return the absolute accuracy setting of the solver used to estimate
	* inverse cumulative probabilities.
	*
	* @return the solver absolute accuracy
	* @since 2.1
	*/
	@Override
	protected double getSolverAbsoluteAccuracy() {
	return solverAbsoluteAccuracy;
	}

	/**
	* Returns the lower bound of the support for the distribution.
	*
	* The lower bound of the support is always 0 no matter the
	* degrees of freedom.
	*
	* @return lower bound of the support (always 0)
	* @since 2.2
	*/
	public double getSupportLowerBound() {
	return 0;
	}

	/**
	* Returns the upper bound for the support for the distribution.
	*
	* The upper bound of the support is always positive infinity no matter the
	* degrees of freedom.
	*
	* @return upper bound of the support (always Double.POSITIVE_INFINITY)
	* @since 2.2
	*/
	public double getSupportUpperBound() {
	return Double.POSITIVE_INFINITY;
	}

	/**
	* Returns the mean of the distribution.
	*
	* For <code>k</code> degrees of freedom, the mean is
	* <code>k</code>
	*
	* @return the mean
	* @since 2.2
	*/
	public double getNumericalMean() {
	return getDegreesOfFreedom();
	}

	/**
	* Returns the variance of the distribution.
	*
	* For <code>k</code> degrees of freedom, the variance is
	* <code>2 * k</code>
	*
	* @return the variance
	* @since 2.2
	*/
	public double getNumericalVariance() {
	return 2*getDegreesOfFreedom();
	}
	}