src/main/java/org/apache/commons/math/distribution/BinomialDistributionImpl.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;
 import org.apache.commons.math.MathRuntimeException;
 import org.apache.commons.math.exception.util.LocalizedFormats;
 import org.apache.commons.math.special.Beta;
 import org.apache.commons.math.util.FastMath;

 /**
  * The default implementation of {@link BinomialDistribution}.
  *
  * @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
  */
 public class BinomialDistributionImpl extends AbstractIntegerDistribution
         implements BinomialDistribution, Serializable {

     /** Serializable version identifier */
     private static final long serialVersionUID = 6751309484392813623L;

     /** The number of trials. */
     private int numberOfTrials;

     /** The probability of success. */
     private double probabilityOfSuccess;

     /**
      * Create a binomial distribution with the given number of trials and
      * probability of success.
      *
      * @param trials the number of trials.
      * @param p the probability of success.
      */
     public BinomialDistributionImpl(int trials, double p) {
         super();
         setNumberOfTrialsInternal(trials);
         setProbabilityOfSuccessInternal(p);
     }

     /**
      * Access the number of trials for this distribution.
      *
      * @return the number of trials.
      */
     public int getNumberOfTrials() {
         return numberOfTrials;
     }

     /**
      * Access the probability of success for this distribution.
      *
      * @return the probability of success.
      */
     public double getProbabilityOfSuccess() {
         return probabilityOfSuccess;
     }

     /**
      * Change the number of trials for this distribution.
      *
      * @param trials the new number of trials.
      * @throws IllegalArgumentException if <code>trials</code> is not a valid
      *             number of trials.
      * @deprecated as of 2.1 (class will become immutable in 3.0)
      */
     @Deprecated
     public void setNumberOfTrials(int trials) {
         setNumberOfTrialsInternal(trials);
     }

     /**
      * Change the number of trials for this distribution.
      *
      * @param trials the new number of trials.
      * @throws IllegalArgumentException if <code>trials</code> is not a valid
      *             number of trials.
      */
     private void setNumberOfTrialsInternal(int trials) {
         if (trials < 0) {
             throw MathRuntimeException.createIllegalArgumentException(
                     LocalizedFormats.NEGATIVE_NUMBER_OF_TRIALS, trials);
         }
         numberOfTrials = trials;
     }

     /**
      * Change the probability of success for this distribution.
      *
      * @param p the new probability of success.
      * @throws IllegalArgumentException if <code>p</code> is not a valid
      *             probability.
      * @deprecated as of 2.1 (class will become immutable in 3.0)
      */
     @Deprecated
     public void setProbabilityOfSuccess(double p) {
         setProbabilityOfSuccessInternal(p);
     }

     /**
      * Change the probability of success for this distribution.
      *
      * @param p the new probability of success.
      * @throws IllegalArgumentException if <code>p</code> is not a valid
      *             probability.
      */
     private void setProbabilityOfSuccessInternal(double p) {
         if (p < 0.0 || p > 1.0) {
             throw MathRuntimeException.createIllegalArgumentException(
                     LocalizedFormats.OUT_OF_RANGE_SIMPLE, p, 0.0, 1.0);
         }
         probabilityOfSuccess = p;
     }

     /**
      * Access the domain value lower bound, based on <code>p</code>, used to
      * bracket a PDF root.
      *
      * @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 int getDomainLowerBound(double p) {
         return -1;
     }

     /**
      * Access the domain value upper bound, based on <code>p</code>, used to
      * bracket a PDF root.
      *
      * @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 int getDomainUpperBound(double p) {
         return numberOfTrials;
     }

     /**
      * For this distribution, X, this method returns P(X &le; x).
      *
      * @param x the value at which the PDF is evaluated.
      * @return PDF for this distribution.
      * @throws MathException if the cumulative probability can not be computed
      *             due to convergence or other numerical errors.
      */
     @Override
     public double cumulativeProbability(int x) throws MathException {
         double ret;
         if (x < 0) {
             ret = 0.0;
         } else if (x >= numberOfTrials) {
             ret = 1.0;
         } else {
             ret = 1.0 - Beta.regularizedBeta(getProbabilityOfSuccess(),
                     x + 1.0, numberOfTrials - x);
         }
         return ret;
     }

     /**
      * For this distribution, X, this method returns P(X = x).
      *
      * @param x the value at which the PMF is evaluated.
      * @return PMF for this distribution.
      */
     public double probability(int x) {
         double ret;
         if (x < 0 || x > numberOfTrials) {
             ret = 0.0;
         } else {
             ret = FastMath.exp(SaddlePointExpansion.logBinomialProbability(x,
                     numberOfTrials, probabilityOfSuccess,
                     1.0 - probabilityOfSuccess));
         }
         return ret;
     }

     /**
      * For this distribution, X, this method returns the largest x, such that
      * P(X &le; x) &le; <code>p</code>.
      * <p>
      * Returns <code>-1</code> for p=0 and <code>Integer.MAX_VALUE</code> for
      * p=1.
      * </p>
      *
      * @param p the desired probability
      * @return the largest x such that P(X &le; x) <= p
      * @throws MathException if the inverse cumulative probability can not be
      *             computed due to convergence or other numerical errors.
      * @throws IllegalArgumentException if p < 0 or p > 1
      */
     @Override
     public int inverseCumulativeProbability(final double p)
             throws MathException {
         // handle extreme values explicitly
         if (p == 0) {
             return -1;
         }
         if (p == 1) {
             return Integer.MAX_VALUE;
         }

         // use default bisection impl
         return super.inverseCumulativeProbability(p);
     }

     /**
      * Returns the lower bound of the support for the distribution.
      *
      * The lower bound of the support is always 0 no matter the number of trials
      * and probability parameter.
      *
      * @return lower bound of the support (always 0)
      * @since 2.2
      */
     public int getSupportLowerBound() {
         return 0;
     }

     /**
      * Returns the upper bound of the support for the distribution.
      *
      * The upper bound of the support is the number of trials.
      *
      * @return upper bound of the support (equal to number of trials)
      * @since 2.2
      */
     public int getSupportUpperBound() {
         return getNumberOfTrials();
     }

     /**
      * Returns the mean.
      *
      * For <code>n</code> number of trials and
      * probability parameter <code>p</code>, the mean is
      * <code>n * p</code>
      *
      * @return the mean
      * @since 2.2
      */
     public double getNumericalMean() {
         return (double)getNumberOfTrials() * getProbabilityOfSuccess();
     }

     /**
      * Returns the variance.
      *
      * For <code>n</code> number of trials and
      * probability parameter <code>p</code>, the variance is
      * <code>n * p * (1 - p)</code>
      *
      * @return the variance
      * @since 2.2
      */
     public double getNumericalVariance() {
         final double p = getProbabilityOfSuccess();
         return (double)getNumberOfTrials() * p * (1 - p);
     }
 }
	/*
	* 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;
	import org.apache.commons.math.MathRuntimeException;
	import org.apache.commons.math.exception.util.LocalizedFormats;
	import org.apache.commons.math.special.Beta;
	import org.apache.commons.math.util.FastMath;

	/**
	* The default implementation of {@link BinomialDistribution}.
	*
	* @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
	*/
	public class BinomialDistributionImpl extends AbstractIntegerDistribution
	implements BinomialDistribution, Serializable {

	/** Serializable version identifier */
	private static final long serialVersionUID = 6751309484392813623L;

	/** The number of trials. */
	private int numberOfTrials;

	/** The probability of success. */
	private double probabilityOfSuccess;

	/**
	* Create a binomial distribution with the given number of trials and
	* probability of success.
	*
	* @param trials the number of trials.
	* @param p the probability of success.
	*/
	public BinomialDistributionImpl(int trials, double p) {
	super();
	setNumberOfTrialsInternal(trials);
	setProbabilityOfSuccessInternal(p);
	}

	/**
	* Access the number of trials for this distribution.
	*
	* @return the number of trials.
	*/
	public int getNumberOfTrials() {
	return numberOfTrials;
	}

	/**
	* Access the probability of success for this distribution.
	*
	* @return the probability of success.
	*/
	public double getProbabilityOfSuccess() {
	return probabilityOfSuccess;
	}

	/**
	* Change the number of trials for this distribution.
	*
	* @param trials the new number of trials.
	* @throws IllegalArgumentException if <code>trials</code> is not a valid
	* number of trials.
	* @deprecated as of 2.1 (class will become immutable in 3.0)
	*/
	@Deprecated
	public void setNumberOfTrials(int trials) {
	setNumberOfTrialsInternal(trials);
	}

	/**
	* Change the number of trials for this distribution.
	*
	* @param trials the new number of trials.
	* @throws IllegalArgumentException if <code>trials</code> is not a valid
	* number of trials.
	*/
	private void setNumberOfTrialsInternal(int trials) {
	if (trials < 0) {
	throw MathRuntimeException.createIllegalArgumentException(
	LocalizedFormats.NEGATIVE_NUMBER_OF_TRIALS, trials);
	}
	numberOfTrials = trials;
	}

	/**
	* Change the probability of success for this distribution.
	*
	* @param p the new probability of success.
	* @throws IllegalArgumentException if <code>p</code> is not a valid
	* probability.
	* @deprecated as of 2.1 (class will become immutable in 3.0)
	*/
	@Deprecated
	public void setProbabilityOfSuccess(double p) {
	setProbabilityOfSuccessInternal(p);
	}

	/**
	* Change the probability of success for this distribution.
	*
	* @param p the new probability of success.
	* @throws IllegalArgumentException if <code>p</code> is not a valid
	* probability.
	*/
	private void setProbabilityOfSuccessInternal(double p) {
	if (p < 0.0 \|\| p > 1.0) {
	throw MathRuntimeException.createIllegalArgumentException(
	LocalizedFormats.OUT_OF_RANGE_SIMPLE, p, 0.0, 1.0);
	}
	probabilityOfSuccess = p;
	}

	/**
	* Access the domain value lower bound, based on <code>p</code>, used to
	* bracket a PDF root.
	*
	* @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 int getDomainLowerBound(double p) {
	return -1;
	}

	/**
	* Access the domain value upper bound, based on <code>p</code>, used to
	* bracket a PDF root.
	*
	* @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 int getDomainUpperBound(double p) {
	return numberOfTrials;
	}

	/**
	* For this distribution, X, this method returns P(X ≤ x).
	*
	* @param x the value at which the PDF is evaluated.
	* @return PDF for this distribution.
	* @throws MathException if the cumulative probability can not be computed
	* due to convergence or other numerical errors.
	*/
	@Override
	public double cumulativeProbability(int x) throws MathException {
	double ret;
	if (x < 0) {
	ret = 0.0;
	} else if (x >= numberOfTrials) {
	ret = 1.0;
	} else {
	ret = 1.0 - Beta.regularizedBeta(getProbabilityOfSuccess(),
	x + 1.0, numberOfTrials - x);
	}
	return ret;
	}

	/**
	* For this distribution, X, this method returns P(X = x).
	*
	* @param x the value at which the PMF is evaluated.
	* @return PMF for this distribution.
	*/
	public double probability(int x) {
	double ret;
	if (x < 0 \|\| x > numberOfTrials) {
	ret = 0.0;
	} else {
	ret = FastMath.exp(SaddlePointExpansion.logBinomialProbability(x,
	numberOfTrials, probabilityOfSuccess,
	1.0 - probabilityOfSuccess));
	}
	return ret;
	}

	/**
	* For this distribution, X, this method returns the largest x, such that
	* P(X ≤ x) ≤ <code>p</code>.
	* <p>
	* Returns <code>-1</code> for p=0 and <code>Integer.MAX_VALUE</code> for
	* p=1.
	* </p>
	*
	* @param p the desired probability
	* @return the largest 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 < 0 or p > 1
	*/
	@Override
	public int inverseCumulativeProbability(final double p)
	throws MathException {
	// handle extreme values explicitly
	if (p == 0) {
	return -1;
	}
	if (p == 1) {
	return Integer.MAX_VALUE;
	}

	// use default bisection impl
	return super.inverseCumulativeProbability(p);
	}

	/**
	* Returns the lower bound of the support for the distribution.
	*
	* The lower bound of the support is always 0 no matter the number of trials
	* and probability parameter.
	*
	* @return lower bound of the support (always 0)
	* @since 2.2
	*/
	public int getSupportLowerBound() {
	return 0;
	}

	/**
	* Returns the upper bound of the support for the distribution.
	*
	* The upper bound of the support is the number of trials.
	*
	* @return upper bound of the support (equal to number of trials)
	* @since 2.2
	*/
	public int getSupportUpperBound() {
	return getNumberOfTrials();
	}

	/**
	* Returns the mean.
	*
	* For <code>n</code> number of trials and
	* probability parameter <code>p</code>, the mean is
	* <code>n * p</code>
	*
	* @return the mean
	* @since 2.2
	*/
	public double getNumericalMean() {
	return (double)getNumberOfTrials() * getProbabilityOfSuccess();
	}

	/**
	* Returns the variance.
	*
	* For <code>n</code> number of trials and
	* probability parameter <code>p</code>, the variance is
	* <code>n * p * (1 - p)</code>
	*
	* @return the variance
	* @since 2.2
	*/
	public double getNumericalVariance() {
	final double p = getProbabilityOfSuccess();
	return (double)getNumberOfTrials() * p * (1 - p);
	}
	}