    001    /*
    002     * Licensed to the Apache Software Foundation (ASF) under one or more
    003     * contributor license agreements.  See the NOTICE file distributed with
    004     * this work for additional information regarding copyright ownership.
    005     * The ASF licenses this file to You under the Apache License, Version 2.0
    006     * (the "License"); you may not use this file except in compliance with
    007     * the License.  You may obtain a copy of the License at
    008     *
    009     *      http://www.apache.org/licenses/LICENSE-2.0
    010     *
    011     * Unless required by applicable law or agreed to in writing, software
    012     * distributed under the License is distributed on an "AS IS" BASIS,
    013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    014     * See the License for the specific language governing permissions and
    015     * limitations under the License.
    016     */
    017    package org.apache.commons.math.distribution;
    018
    019    import java.io.Serializable;
    020
    021    import org.apache.commons.math.MathException;
    022    import org.apache.commons.math.MathRuntimeException;
    023    import org.apache.commons.math.exception.util.LocalizedFormats;
    024    import org.apache.commons.math.special.Beta;
    025    import org.apache.commons.math.util.FastMath;
    026
    027    /**
    028     * The default implementation of {@link BinomialDistribution}.
    029     *
    030     * @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
    031     */
    032    public class BinomialDistributionImpl extends AbstractIntegerDistribution
    033            implements BinomialDistribution, Serializable {
    034
    035        /** Serializable version identifier */
    036        private static final long serialVersionUID = 6751309484392813623L;
    037
    038        /** The number of trials. */
    039        private int numberOfTrials;
    040
    041        /** The probability of success. */
    042        private double probabilityOfSuccess;
    043
    044        /**
    045         * Create a binomial distribution with the given number of trials and
    046         * probability of success.
    047         *
    048         * @param trials the number of trials.
    049         * @param p the probability of success.
    050         */
    051        public BinomialDistributionImpl(int trials, double p) {
    052            super();
    053            setNumberOfTrialsInternal(trials);
    054            setProbabilityOfSuccessInternal(p);
    055        }
    056
    057        /**
    058         * Access the number of trials for this distribution.
    059         *
    060         * @return the number of trials.
    061         */
    062        public int getNumberOfTrials() {
    063            return numberOfTrials;
    064        }
    065
    066        /**
    067         * Access the probability of success for this distribution.
    068         *
    069         * @return the probability of success.
    070         */
    071        public double getProbabilityOfSuccess() {
    072            return probabilityOfSuccess;
    073        }
    074
    075        /**
    076         * Change the number of trials for this distribution.
    077         *
    078         * @param trials the new number of trials.
    079         * @throws IllegalArgumentException if <code>trials</code> is not a valid
    080         *             number of trials.
    081         * @deprecated as of 2.1 (class will become immutable in 3.0)
    082         */
    083        @Deprecated
    084        public void setNumberOfTrials(int trials) {
    085            setNumberOfTrialsInternal(trials);
    086        }
    087
    088        /**
    089         * Change the number of trials for this distribution.
    090         *
    091         * @param trials the new number of trials.
    092         * @throws IllegalArgumentException if <code>trials</code> is not a valid
    093         *             number of trials.
    094         */
    095        private void setNumberOfTrialsInternal(int trials) {
    096            if (trials < 0) {
    097                throw MathRuntimeException.createIllegalArgumentException(
    098                        LocalizedFormats.NEGATIVE_NUMBER_OF_TRIALS, trials);
    099            }
    100            numberOfTrials = trials;
    101        }
    102
    103        /**
    104         * Change the probability of success for this distribution.
    105         *
    106         * @param p the new probability of success.
    107         * @throws IllegalArgumentException if <code>p</code> is not a valid
    108         *             probability.
    109         * @deprecated as of 2.1 (class will become immutable in 3.0)
    110         */
    111        @Deprecated
    112        public void setProbabilityOfSuccess(double p) {
    113            setProbabilityOfSuccessInternal(p);
    114        }
    115
    116        /**
    117         * Change the probability of success for this distribution.
    118         *
    119         * @param p the new probability of success.
    120         * @throws IllegalArgumentException if <code>p</code> is not a valid
    121         *             probability.
    122         */
    123        private void setProbabilityOfSuccessInternal(double p) {
    124            if (p < 0.0 || p > 1.0) {
    125                throw MathRuntimeException.createIllegalArgumentException(
    126                        LocalizedFormats.OUT_OF_RANGE_SIMPLE, p, 0.0, 1.0);
    127            }
    128            probabilityOfSuccess = p;
    129        }
    130
    131        /**
    132         * Access the domain value lower bound, based on <code>p</code>, used to
    133         * bracket a PDF root.
    134         *
    135         * @param p the desired probability for the critical value
    136         * @return domain value lower bound, i.e. P(X &lt; <i>lower bound</i>) &lt;
    137         *         <code>p</code>
    138         */
    139        @Override
    140        protected int getDomainLowerBound(double p) {
    141            return -1;
    142        }
    143
    144        /**
    145         * Access the domain value upper bound, based on <code>p</code>, used to
    146         * bracket a PDF root.
    147         *
    148         * @param p the desired probability for the critical value
    149         * @return domain value upper bound, i.e. P(X &lt; <i>upper bound</i>) &gt;
    150         *         <code>p</code>
    151         */
    152        @Override
    153        protected int getDomainUpperBound(double p) {
    154            return numberOfTrials;
    155        }
    156
    157        /**
    158         * For this distribution, X, this method returns P(X &le; x).
    159         *
    160         * @param x the value at which the PDF is evaluated.
    161         * @return PDF for this distribution.
    162         * @throws MathException if the cumulative probability can not be computed
    163         *             due to convergence or other numerical errors.
    164         */
    165        @Override
    166        public double cumulativeProbability(int x) throws MathException {
    167            double ret;
    168            if (x < 0) {
    169                ret = 0.0;
    170            } else if (x >= numberOfTrials) {
    171                ret = 1.0;
    172            } else {
    173                ret = 1.0 - Beta.regularizedBeta(getProbabilityOfSuccess(),
    174                        x + 1.0, numberOfTrials - x);
    175            }
    176            return ret;
    177        }
    178
    179        /**
    180         * For this distribution, X, this method returns P(X = x).
    181         *
    182         * @param x the value at which the PMF is evaluated.
    183         * @return PMF for this distribution.
    184         */
    185        public double probability(int x) {
    186            double ret;
    187            if (x < 0 || x > numberOfTrials) {
    188                ret = 0.0;
    189            } else {
    190                ret = FastMath.exp(SaddlePointExpansion.logBinomialProbability(x,
    191                        numberOfTrials, probabilityOfSuccess,
    192                        1.0 - probabilityOfSuccess));
    193            }
    194            return ret;
    195        }
    196
    197        /**
    198         * For this distribution, X, this method returns the largest x, such that
    199         * P(X &le; x) &le; <code>p</code>.
    200         * <p>
    201         * Returns <code>-1</code> for p=0 and <code>Integer.MAX_VALUE</code> for
    202         * p=1.
    203         * </p>
    204         *
    205         * @param p the desired probability
    206         * @return the largest x such that P(X &le; x) <= p
    207         * @throws MathException if the inverse cumulative probability can not be
    208         *             computed due to convergence or other numerical errors.
    209         * @throws IllegalArgumentException if p < 0 or p > 1
    210         */
    211        @Override
    212        public int inverseCumulativeProbability(final double p)
    213                throws MathException {
    214            // handle extreme values explicitly
    215            if (p == 0) {
    216                return -1;
    217            }
    218            if (p == 1) {
    219                return Integer.MAX_VALUE;
    220            }
    221
    222            // use default bisection impl
    223            return super.inverseCumulativeProbability(p);
    224        }
    225
    226        /**
    227         * Returns the lower bound of the support for the distribution.
    228         *
    229         * The lower bound of the support is always 0 no matter the number of trials
    230         * and probability parameter.
    231         *
    232         * @return lower bound of the support (always 0)
    233         * @since 2.2
    234         */
    235        public int getSupportLowerBound() {
    236            return 0;
    237        }
    238
    239        /**
    240         * Returns the upper bound of the support for the distribution.
    241         *
    242         * The upper bound of the support is the number of trials.
    243         *
    244         * @return upper bound of the support (equal to number of trials)
    245         * @since 2.2
    246         */
    247        public int getSupportUpperBound() {
    248            return getNumberOfTrials();
    249        }
    250
    251        /**
    252         * Returns the mean.
    253         *
    254         * For <code>n</code> number of trials and
    255         * probability parameter <code>p</code>, the mean is
    256         * <code>n * p</code>
    257         *
    258         * @return the mean
    259         * @since 2.2
    260         */
    261        public double getNumericalMean() {
    262            return (double)getNumberOfTrials() * getProbabilityOfSuccess();
    263        }
    264
    265        /**
    266         * Returns the variance.
    267         *
    268         * For <code>n</code> number of trials and
    269         * probability parameter <code>p</code>, the variance is
    270         * <code>n * p * (1 - p)</code>
    271         *
    272         * @return the variance
    273         * @since 2.2
    274         */
    275        public double getNumericalVariance() {
    276            final double p = getProbabilityOfSuccess();
    277            return (double)getNumberOfTrials() * p * (1 - p);
    278        }
    279    }