Return-Path: X-Original-To: apmail-commons-commits-archive@minotaur.apache.org Delivered-To: apmail-commons-commits-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 94E1F1094C for ; Mon, 16 Feb 2015 22:40:08 +0000 (UTC) Received: (qmail 54046 invoked by uid 500); 16 Feb 2015 22:39:36 -0000 Delivered-To: apmail-commons-commits-archive@commons.apache.org Received: (qmail 52380 invoked by uid 500); 16 Feb 2015 22:39:35 -0000 Mailing-List: contact commits-help@commons.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@commons.apache.org Delivered-To: mailing list commits@commons.apache.org Received: (qmail 47215 invoked by uid 99); 16 Feb 2015 22:39:32 -0000 Received: from git1-us-west.apache.org (HELO git1-us-west.apache.org) (140.211.11.23) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 16 Feb 2015 22:39:32 +0000 Received: by git1-us-west.apache.org (ASF Mail Server at git1-us-west.apache.org, from userid 33) id A050EE0663; Mon, 16 Feb 2015 22:39:31 +0000 (UTC) Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit From: tn@apache.org To: commits@commons.apache.org Date: Mon, 16 Feb 2015 22:40:04 -0000 Message-Id: <36fb0d73afc7462d9b5239fad826871c@git.apache.org> In-Reply-To: References: X-Mailer: ASF-Git Admin Mailer Subject: [34/82] [partial] [math] Update for next development iteration: commons-math4 http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/distribution/AbstractRealDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/distribution/AbstractRealDistribution.java b/src/main/java/org/apache/commons/math3/distribution/AbstractRealDistribution.java deleted file mode 100644 index e3b1fac..0000000 --- a/src/main/java/org/apache/commons/math3/distribution/AbstractRealDistribution.java +++ /dev/null @@ -1,307 +0,0 @@ -/* - * 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.math3.distribution; - -import java.io.Serializable; - -import org.apache.commons.math3.analysis.UnivariateFunction; -import org.apache.commons.math3.analysis.solvers.UnivariateSolverUtils; -import org.apache.commons.math3.exception.NotStrictlyPositiveException; -import org.apache.commons.math3.exception.NumberIsTooLargeException; -import org.apache.commons.math3.exception.OutOfRangeException; -import org.apache.commons.math3.exception.util.LocalizedFormats; -import org.apache.commons.math3.random.RandomGenerator; -import org.apache.commons.math3.random.RandomDataImpl; -import org.apache.commons.math3.util.FastMath; - -/** - * Base class for probability distributions on the reals. - * Default implementations are provided for some of the methods - * that do not vary from distribution to distribution. - * - * @since 3.0 - */ -public abstract class AbstractRealDistribution -implements RealDistribution, Serializable { - /** Default accuracy. */ - public static final double SOLVER_DEFAULT_ABSOLUTE_ACCURACY = 1e-6; - /** Serializable version identifier */ - private static final long serialVersionUID = -38038050983108802L; - /** - * RandomData instance used to generate samples from the distribution. - * @deprecated As of 3.1, to be removed in 4.0. Please use the - * {@link #random} instance variable instead. - */ - @Deprecated - protected RandomDataImpl randomData = new RandomDataImpl(); - - /** - * RNG instance used to generate samples from the distribution. - * @since 3.1 - */ - protected final RandomGenerator random; - - /** Solver absolute accuracy for inverse cumulative computation */ - private double solverAbsoluteAccuracy = SOLVER_DEFAULT_ABSOLUTE_ACCURACY; - - /** - * @deprecated As of 3.1, to be removed in 4.0. Please use - * {@link #AbstractRealDistribution(RandomGenerator)} instead. - */ - @Deprecated - protected AbstractRealDistribution() { - // Legacy users are only allowed to access the deprecated "randomData". - // New users are forbidden to use this constructor. - random = null; - } - /** - * @param rng Random number generator. - * @since 3.1 - */ - protected AbstractRealDistribution(RandomGenerator rng) { - random = rng; - } - - /** - * {@inheritDoc} - * - * The default implementation uses the identity - *

{@code P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)}

- * - * @deprecated As of 3.1 (to be removed in 4.0). Please use - * {@link #probability(double,double)} instead. - */ - @Deprecated - public double cumulativeProbability(double x0, double x1) throws NumberIsTooLargeException { - return probability(x0, x1); - } - - /** - * For a random variable {@code X} whose values are distributed according - * to this distribution, this method returns {@code P(x0 < X <= x1)}. - * - * @param x0 Lower bound (excluded). - * @param x1 Upper bound (included). - * @return the probability that a random variable with this distribution - * takes a value between {@code x0} and {@code x1}, excluding the lower - * and including the upper endpoint. - * @throws NumberIsTooLargeException if {@code x0 > x1}. - * - * The default implementation uses the identity - * {@code P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)} - * - * @since 3.1 - */ - public double probability(double x0, - double x1) { - if (x0 > x1) { - throw new NumberIsTooLargeException(LocalizedFormats.LOWER_ENDPOINT_ABOVE_UPPER_ENDPOINT, - x0, x1, true); - } - return cumulativeProbability(x1) - cumulativeProbability(x0); - } - - /** - * {@inheritDoc} - * - * The default implementation returns - *
    - *
  • {@link #getSupportLowerBound()} for {@code p = 0},
    • - *
  • {@link #getSupportUpperBound()} for {@code p = 1}.
    • - *
- */ - public double inverseCumulativeProbability(final double p) throws OutOfRangeException { - /* - * IMPLEMENTATION NOTES - * -------------------- - * Where applicable, use is made of the one-sided Chebyshev inequality - * to bracket the root. This inequality states that - * P(X - mu >= k * sig) <= 1 / (1 + k^2), - * mu: mean, sig: standard deviation. Equivalently - * 1 - P(X < mu + k * sig) <= 1 / (1 + k^2), - * F(mu + k * sig) >= k^2 / (1 + k^2). - * - * For k = sqrt(p / (1 - p)), we find - * F(mu + k * sig) >= p, - * and (mu + k * sig) is an upper-bound for the root. - * - * Then, introducing Y = -X, mean(Y) = -mu, sd(Y) = sig, and - * P(Y >= -mu + k * sig) <= 1 / (1 + k^2), - * P(-X >= -mu + k * sig) <= 1 / (1 + k^2), - * P(X <= mu - k * sig) <= 1 / (1 + k^2), - * F(mu - k * sig) <= 1 / (1 + k^2). - * - * For k = sqrt((1 - p) / p), we find - * F(mu - k * sig) <= p, - * and (mu - k * sig) is a lower-bound for the root. - * - * In cases where the Chebyshev inequality does not apply, geometric - * progressions 1, 2, 4, ... and -1, -2, -4, ... are used to bracket - * the root. - */ - if (p < 0.0 || p > 1.0) { - throw new OutOfRangeException(p, 0, 1); - } - - double lowerBound = getSupportLowerBound(); - if (p == 0.0) { - return lowerBound; - } - - double upperBound = getSupportUpperBound(); - if (p == 1.0) { - return upperBound; - } - - final double mu = getNumericalMean(); - final double sig = FastMath.sqrt(getNumericalVariance()); - final boolean chebyshevApplies; - chebyshevApplies = !(Double.isInfinite(mu) || Double.isNaN(mu) || - Double.isInfinite(sig) || Double.isNaN(sig)); - - if (lowerBound == Double.NEGATIVE_INFINITY) { - if (chebyshevApplies) { - lowerBound = mu - sig * FastMath.sqrt((1. - p) / p); - } else { - lowerBound = -1.0; - while (cumulativeProbability(lowerBound) >= p) { - lowerBound *= 2.0; - } - } - } - - if (upperBound == Double.POSITIVE_INFINITY) { - if (chebyshevApplies) { - upperBound = mu + sig * FastMath.sqrt(p / (1. - p)); - } else { - upperBound = 1.0; - while (cumulativeProbability(upperBound) < p) { - upperBound *= 2.0; - } - } - } - - final UnivariateFunction toSolve = new UnivariateFunction() { - - public double value(final double x) { - return cumulativeProbability(x) - p; - } - }; - - double x = UnivariateSolverUtils.solve(toSolve, - lowerBound, - upperBound, - getSolverAbsoluteAccuracy()); - - if (!isSupportConnected()) { - /* Test for plateau. */ - final double dx = getSolverAbsoluteAccuracy(); - if (x - dx >= getSupportLowerBound()) { - double px = cumulativeProbability(x); - if (cumulativeProbability(x - dx) == px) { - upperBound = x; - while (upperBound - lowerBound > dx) { - final double midPoint = 0.5 * (lowerBound + upperBound); - if (cumulativeProbability(midPoint) < px) { - lowerBound = midPoint; - } else { - upperBound = midPoint; - } - } - return upperBound; - } - } - } - return x; - } - - /** - * Returns the solver absolute accuracy for inverse cumulative computation. - * You can override this method in order to use a Brent solver with an - * absolute accuracy different from the default. - * - * @return the maximum absolute error in inverse cumulative probability estimates - */ - protected double getSolverAbsoluteAccuracy() { - return solverAbsoluteAccuracy; - } - - /** {@inheritDoc} */ - public void reseedRandomGenerator(long seed) { - random.setSeed(seed); - randomData.reSeed(seed); - } - - /** - * {@inheritDoc} - * - * The default implementation uses the - * - * inversion method. - * - */ - public double sample() { - return inverseCumulativeProbability(random.nextDouble()); - } - - /** - * {@inheritDoc} - * - * The default implementation generates the sample by calling - * {@link #sample()} in a loop. - */ - public double[] sample(int sampleSize) { - if (sampleSize <= 0) { - throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, - sampleSize); - } - double[] out = new double[sampleSize]; - for (int i = 0; i < sampleSize; i++) { - out[i] = sample(); - } - return out; - } - - /** - * {@inheritDoc} - * - * @return zero. - * @since 3.1 - */ - public double probability(double x) { - return 0d; - } - - /** - * Returns the natural logarithm of the probability density function (PDF) of this distribution - * evaluated at the specified point {@code x}. In general, the PDF is the derivative of the - * {@link #cumulativeProbability(double) CDF}. If the derivative does not exist at {@code x}, - * then an appropriate replacement should be returned, e.g. {@code Double.POSITIVE_INFINITY}, - * {@code Double.NaN}, or the limit inferior or limit superior of the difference quotient. Note - * that due to the floating point precision and under/overflow issues, this method will for some - * distributions be more precise and faster than computing the logarithm of - * {@link #density(double)}. The default implementation simply computes the logarithm of - * {@code density(x)}. - * - * @param x the point at which the PDF is evaluated - * @return the logarithm of the value of the probability density function at point {@code x} - */ - public double logDensity(double x) { - return FastMath.log(density(x)); - } -} - http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/distribution/BetaDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/distribution/BetaDistribution.java b/src/main/java/org/apache/commons/math3/distribution/BetaDistribution.java deleted file mode 100644 index 3f62f64..0000000 --- a/src/main/java/org/apache/commons/math3/distribution/BetaDistribution.java +++ /dev/null @@ -1,269 +0,0 @@ -/* - * 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.math3.distribution; - -import org.apache.commons.math3.exception.NumberIsTooSmallException; -import org.apache.commons.math3.exception.util.LocalizedFormats; -import org.apache.commons.math3.random.RandomGenerator; -import org.apache.commons.math3.random.Well19937c; -import org.apache.commons.math3.special.Beta; -import org.apache.commons.math3.special.Gamma; -import org.apache.commons.math3.util.FastMath; - -/** - * Implements the Beta distribution. - * - * @see Beta distribution - * @since 2.0 (changed to concrete class in 3.0) - */ -public class BetaDistribution extends AbstractRealDistribution { - /** - * 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 = -1221965979403477668L; - /** First shape parameter. */ - private final double alpha; - /** Second shape parameter. */ - private final double beta; - /** Normalizing factor used in density computations. - * updated whenever alpha or beta are changed. - */ - private double z; - /** Inverse cumulative probability accuracy. */ - private final double solverAbsoluteAccuracy; - - /** - * Build a new instance. - *

- * Note: this constructor will implicitly create an instance of - * {@link Well19937c} as random generator to be used for sampling only (see - * {@link #sample()} and {@link #sample(int)}). In case no sampling is - * needed for the created distribution, it is advised to pass {@code null} - * as random generator via the appropriate constructors to avoid the - * additional initialisation overhead. - * - * @param alpha First shape parameter (must be positive). - * @param beta Second shape parameter (must be positive). - */ - public BetaDistribution(double alpha, double beta) { - this(alpha, beta, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); - } - - /** - * Build a new instance. - *

- * Note: this constructor will implicitly create an instance of - * {@link Well19937c} as random generator to be used for sampling only (see - * {@link #sample()} and {@link #sample(int)}). In case no sampling is - * needed for the created distribution, it is advised to pass {@code null} - * as random generator via the appropriate constructors to avoid the - * additional initialisation overhead. - * - * @param alpha First shape parameter (must be positive). - * @param beta Second shape parameter (must be positive). - * @param inverseCumAccuracy Maximum absolute error in inverse - * cumulative probability estimates (defaults to - * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}). - * @since 2.1 - */ - public BetaDistribution(double alpha, double beta, double inverseCumAccuracy) { - this(new Well19937c(), alpha, beta, inverseCumAccuracy); - } - - /** - * Creates a β distribution. - * - * @param rng Random number generator. - * @param alpha First shape parameter (must be positive). - * @param beta Second shape parameter (must be positive). - * @since 3.3 - */ - public BetaDistribution(RandomGenerator rng, double alpha, double beta) { - this(rng, alpha, beta, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); - } - - /** - * Creates a β distribution. - * - * @param rng Random number generator. - * @param alpha First shape parameter (must be positive). - * @param beta Second shape parameter (must be positive). - * @param inverseCumAccuracy Maximum absolute error in inverse - * cumulative probability estimates (defaults to - * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}). - * @since 3.1 - */ - public BetaDistribution(RandomGenerator rng, - double alpha, - double beta, - double inverseCumAccuracy) { - super(rng); - - this.alpha = alpha; - this.beta = beta; - z = Double.NaN; - solverAbsoluteAccuracy = inverseCumAccuracy; - } - - /** - * Access the first shape parameter, {@code alpha}. - * - * @return the first shape parameter. - */ - public double getAlpha() { - return alpha; - } - - /** - * Access the second shape parameter, {@code beta}. - * - * @return the second shape parameter. - */ - public double getBeta() { - return beta; - } - - /** Recompute the normalization factor. */ - private void recomputeZ() { - if (Double.isNaN(z)) { - z = Gamma.logGamma(alpha) + Gamma.logGamma(beta) - Gamma.logGamma(alpha + beta); - } - } - - /** {@inheritDoc} */ - public double density(double x) { - final double logDensity = logDensity(x); - return logDensity == Double.NEGATIVE_INFINITY ? 0 : FastMath.exp(logDensity); - } - - /** {@inheritDoc} **/ - @Override - public double logDensity(double x) { - recomputeZ(); - if (x < 0 || x > 1) { - return Double.NEGATIVE_INFINITY; - } else if (x == 0) { - if (alpha < 1) { - throw new NumberIsTooSmallException(LocalizedFormats.CANNOT_COMPUTE_BETA_DENSITY_AT_0_FOR_SOME_ALPHA, alpha, 1, false); - } - return Double.NEGATIVE_INFINITY; - } else if (x == 1) { - if (beta < 1) { - throw new NumberIsTooSmallException(LocalizedFormats.CANNOT_COMPUTE_BETA_DENSITY_AT_1_FOR_SOME_BETA, beta, 1, false); - } - return Double.NEGATIVE_INFINITY; - } else { - double logX = FastMath.log(x); - double log1mX = FastMath.log1p(-x); - return (alpha - 1) * logX + (beta - 1) * log1mX - z; - } - } - - /** {@inheritDoc} */ - public double cumulativeProbability(double x) { - if (x <= 0) { - return 0; - } else if (x >= 1) { - return 1; - } else { - return Beta.regularizedBeta(x, alpha, beta); - } - } - - /** - * 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; - } - - /** - * {@inheritDoc} - * - * For first shape parameter {@code alpha} and second shape parameter - * {@code beta}, the mean is {@code alpha / (alpha + beta)}. - */ - public double getNumericalMean() { - final double a = getAlpha(); - return a / (a + getBeta()); - } - - /** - * {@inheritDoc} - * - * For first shape parameter {@code alpha} and second shape parameter - * {@code beta}, the variance is - * {@code (alpha * beta) / [(alpha + beta)^2 * (alpha + beta + 1)]}. - */ - public double getNumericalVariance() { - final double a = getAlpha(); - final double b = getBeta(); - final double alphabetasum = a + b; - return (a * b) / ((alphabetasum * alphabetasum) * (alphabetasum + 1)); - } - - /** - * {@inheritDoc} - * - * The lower bound of the support is always 0 no matter the parameters. - * - * @return lower bound of the support (always 0) - */ - public double getSupportLowerBound() { - return 0; - } - - /** - * {@inheritDoc} - * - * The upper bound of the support is always 1 no matter the parameters. - * - * @return upper bound of the support (always 1) - */ - public double getSupportUpperBound() { - return 1; - } - - /** {@inheritDoc} */ - public boolean isSupportLowerBoundInclusive() { - return false; - } - - /** {@inheritDoc} */ - public boolean isSupportUpperBoundInclusive() { - return false; - } - - /** - * {@inheritDoc} - * - * The support of this distribution is connected. - * - * @return {@code true} - */ - public boolean isSupportConnected() { - return true; - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/distribution/BinomialDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/distribution/BinomialDistribution.java b/src/main/java/org/apache/commons/math3/distribution/BinomialDistribution.java deleted file mode 100644 index f8cea26..0000000 --- a/src/main/java/org/apache/commons/math3/distribution/BinomialDistribution.java +++ /dev/null @@ -1,198 +0,0 @@ -/* - * 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.math3.distribution; - -import org.apache.commons.math3.exception.NotPositiveException; -import org.apache.commons.math3.exception.OutOfRangeException; -import org.apache.commons.math3.exception.util.LocalizedFormats; -import org.apache.commons.math3.random.RandomGenerator; -import org.apache.commons.math3.random.Well19937c; -import org.apache.commons.math3.special.Beta; -import org.apache.commons.math3.util.FastMath; - -/** - * Implementation of the binomial distribution. - * - * @see Binomial distribution (Wikipedia) - * @see Binomial Distribution (MathWorld) - */ -public class BinomialDistribution extends AbstractIntegerDistribution { - /** Serializable version identifier. */ - private static final long serialVersionUID = 6751309484392813623L; - /** The number of trials. */ - private final int numberOfTrials; - /** The probability of success. */ - private final double probabilityOfSuccess; - - /** - * Create a binomial distribution with the given number of trials and - * probability of success. - *

- * Note: this constructor will implicitly create an instance of - * {@link Well19937c} as random generator to be used for sampling only (see - * {@link #sample()} and {@link #sample(int)}). In case no sampling is - * needed for the created distribution, it is advised to pass {@code null} - * as random generator via the appropriate constructors to avoid the - * additional initialisation overhead. - * - * @param trials Number of trials. - * @param p Probability of success. - * @throws NotPositiveException if {@code trials < 0}. - * @throws OutOfRangeException if {@code p < 0} or {@code p > 1}. - */ - public BinomialDistribution(int trials, double p) { - this(new Well19937c(), trials, p); - } - - /** - * Creates a binomial distribution. - * - * @param rng Random number generator. - * @param trials Number of trials. - * @param p Probability of success. - * @throws NotPositiveException if {@code trials < 0}. - * @throws OutOfRangeException if {@code p < 0} or {@code p > 1}. - * @since 3.1 - */ - public BinomialDistribution(RandomGenerator rng, - int trials, - double p) { - super(rng); - - if (trials < 0) { - throw new NotPositiveException(LocalizedFormats.NUMBER_OF_TRIALS, - trials); - } - if (p < 0 || p > 1) { - throw new OutOfRangeException(p, 0, 1); - } - - probabilityOfSuccess = p; - numberOfTrials = trials; - } - - /** - * 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; - } - - /** {@inheritDoc} */ - public double probability(int x) { - final double logProbability = logProbability(x); - return logProbability == Double.NEGATIVE_INFINITY ? 0 : FastMath.exp(logProbability); - } - - /** {@inheritDoc} **/ - @Override - public double logProbability(int x) { - if (numberOfTrials == 0) { - return (x == 0) ? 0. : Double.NEGATIVE_INFINITY; - } - double ret; - if (x < 0 || x > numberOfTrials) { - ret = Double.NEGATIVE_INFINITY; - } else { - ret = SaddlePointExpansion.logBinomialProbability(x, - numberOfTrials, probabilityOfSuccess, - 1.0 - probabilityOfSuccess); - } - return ret; - } - - /** {@inheritDoc} */ - public double cumulativeProbability(int x) { - double ret; - if (x < 0) { - ret = 0.0; - } else if (x >= numberOfTrials) { - ret = 1.0; - } else { - ret = 1.0 - Beta.regularizedBeta(probabilityOfSuccess, - x + 1.0, numberOfTrials - x); - } - return ret; - } - - /** - * {@inheritDoc} - * - * For {@code n} trials and probability parameter {@code p}, the mean is - * {@code n * p}. - */ - public double getNumericalMean() { - return numberOfTrials * probabilityOfSuccess; - } - - /** - * {@inheritDoc} - * - * For {@code n} trials and probability parameter {@code p}, the variance is - * {@code n * p * (1 - p)}. - */ - public double getNumericalVariance() { - final double p = probabilityOfSuccess; - return numberOfTrials * p * (1 - p); - } - - /** - * {@inheritDoc} - * - * The lower bound of the support is always 0 except for the probability - * parameter {@code p = 1}. - * - * @return lower bound of the support (0 or the number of trials) - */ - public int getSupportLowerBound() { - return probabilityOfSuccess < 1.0 ? 0 : numberOfTrials; - } - - /** - * {@inheritDoc} - * - * The upper bound of the support is the number of trials except for the - * probability parameter {@code p = 0}. - * - * @return upper bound of the support (number of trials or 0) - */ - public int getSupportUpperBound() { - return probabilityOfSuccess > 0.0 ? numberOfTrials : 0; - } - - /** - * {@inheritDoc} - * - * The support of this distribution is connected. - * - * @return {@code true} - */ - public boolean isSupportConnected() { - return true; - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/distribution/CauchyDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/distribution/CauchyDistribution.java b/src/main/java/org/apache/commons/math3/distribution/CauchyDistribution.java deleted file mode 100644 index af3d33a..0000000 --- a/src/main/java/org/apache/commons/math3/distribution/CauchyDistribution.java +++ /dev/null @@ -1,256 +0,0 @@ -/* - * 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.math3.distribution; - -import org.apache.commons.math3.exception.NotStrictlyPositiveException; -import org.apache.commons.math3.exception.OutOfRangeException; -import org.apache.commons.math3.exception.util.LocalizedFormats; -import org.apache.commons.math3.random.RandomGenerator; -import org.apache.commons.math3.random.Well19937c; -import org.apache.commons.math3.util.FastMath; - -/** - * Implementation of the Cauchy distribution. - * - * @see Cauchy distribution (Wikipedia) - * @see Cauchy Distribution (MathWorld) - * @since 1.1 (changed to concrete class in 3.0) - */ -public class CauchyDistribution extends AbstractRealDistribution { - /** - * 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 = 8589540077390120676L; - /** The median of this distribution. */ - private final double median; - /** The scale of this distribution. */ - private final double scale; - /** Inverse cumulative probability accuracy */ - private final double solverAbsoluteAccuracy; - - /** - * Creates a Cauchy distribution with the median equal to zero and scale - * equal to one. - */ - public CauchyDistribution() { - this(0, 1); - } - - /** - * Creates a Cauchy distribution using the given median and scale. - *

- * Note: this constructor will implicitly create an instance of - * {@link Well19937c} as random generator to be used for sampling only (see - * {@link #sample()} and {@link #sample(int)}). In case no sampling is - * needed for the created distribution, it is advised to pass {@code null} - * as random generator via the appropriate constructors to avoid the - * additional initialisation overhead. - * - * @param median Median for this distribution. - * @param scale Scale parameter for this distribution. - */ - public CauchyDistribution(double median, double scale) { - this(median, scale, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); - } - - /** - * Creates a Cauchy distribution using the given median and scale. - *

- * Note: this constructor will implicitly create an instance of - * {@link Well19937c} as random generator to be used for sampling only (see - * {@link #sample()} and {@link #sample(int)}). In case no sampling is - * needed for the created distribution, it is advised to pass {@code null} - * as random generator via the appropriate constructors to avoid the - * additional initialisation overhead. - * - * @param median Median for this distribution. - * @param scale Scale parameter for this distribution. - * @param inverseCumAccuracy Maximum absolute error in inverse - * cumulative probability estimates - * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}). - * @throws NotStrictlyPositiveException if {@code scale <= 0}. - * @since 2.1 - */ - public CauchyDistribution(double median, double scale, - double inverseCumAccuracy) { - this(new Well19937c(), median, scale, inverseCumAccuracy); - } - - /** - * Creates a Cauchy distribution. - * - * @param rng Random number generator. - * @param median Median for this distribution. - * @param scale Scale parameter for this distribution. - * @throws NotStrictlyPositiveException if {@code scale <= 0}. - * @since 3.3 - */ - public CauchyDistribution(RandomGenerator rng, double median, double scale) { - this(rng, median, scale, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); - } - - /** - * Creates a Cauchy distribution. - * - * @param rng Random number generator. - * @param median Median for this distribution. - * @param scale Scale parameter for this distribution. - * @param inverseCumAccuracy Maximum absolute error in inverse - * cumulative probability estimates - * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}). - * @throws NotStrictlyPositiveException if {@code scale <= 0}. - * @since 3.1 - */ - public CauchyDistribution(RandomGenerator rng, - double median, - double scale, - double inverseCumAccuracy) { - super(rng); - if (scale <= 0) { - throw new NotStrictlyPositiveException(LocalizedFormats.SCALE, scale); - } - this.scale = scale; - this.median = median; - solverAbsoluteAccuracy = inverseCumAccuracy; - } - - /** {@inheritDoc} */ - public double cumulativeProbability(double x) { - return 0.5 + (FastMath.atan((x - median) / scale) / FastMath.PI); - } - - /** - * Access the median. - * - * @return the median for this distribution. - */ - public double getMedian() { - return median; - } - - /** - * Access the scale parameter. - * - * @return the scale parameter for this distribution. - */ - public double getScale() { - return scale; - } - - /** {@inheritDoc} */ - public double density(double x) { - final double dev = x - median; - return (1 / FastMath.PI) * (scale / (dev * dev + scale * scale)); - } - - /** - * {@inheritDoc} - * - * Returns {@code Double.NEGATIVE_INFINITY} when {@code p == 0} - * and {@code Double.POSITIVE_INFINITY} when {@code p == 1}. - */ - @Override - public double inverseCumulativeProbability(double p) throws OutOfRangeException { - double ret; - if (p < 0 || p > 1) { - throw new OutOfRangeException(p, 0, 1); - } else if (p == 0) { - ret = Double.NEGATIVE_INFINITY; - } else if (p == 1) { - ret = Double.POSITIVE_INFINITY; - } else { - ret = median + scale * FastMath.tan(FastMath.PI * (p - .5)); - } - return ret; - } - - /** {@inheritDoc} */ - @Override - protected double getSolverAbsoluteAccuracy() { - return solverAbsoluteAccuracy; - } - - /** - * {@inheritDoc} - * - * The mean is always undefined no matter the parameters. - * - * @return mean (always Double.NaN) - */ - public double getNumericalMean() { - return Double.NaN; - } - - /** - * {@inheritDoc} - * - * The variance is always undefined no matter the parameters. - * - * @return variance (always Double.NaN) - */ - public double getNumericalVariance() { - return Double.NaN; - } - - /** - * {@inheritDoc} - * - * The lower bound of the support is always negative infinity no matter - * the parameters. - * - * @return lower bound of the support (always Double.NEGATIVE_INFINITY) - */ - public double getSupportLowerBound() { - return Double.NEGATIVE_INFINITY; - } - - /** - * {@inheritDoc} - * - * The upper bound of the support is always positive infinity no matter - * the parameters. - * - * @return upper bound of the support (always Double.POSITIVE_INFINITY) - */ - public double getSupportUpperBound() { - return Double.POSITIVE_INFINITY; - } - - /** {@inheritDoc} */ - public boolean isSupportLowerBoundInclusive() { - return false; - } - - /** {@inheritDoc} */ - public boolean isSupportUpperBoundInclusive() { - return false; - } - - /** - * {@inheritDoc} - * - * The support of this distribution is connected. - * - * @return {@code true} - */ - public boolean isSupportConnected() { - return true; - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/distribution/ChiSquaredDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/distribution/ChiSquaredDistribution.java b/src/main/java/org/apache/commons/math3/distribution/ChiSquaredDistribution.java deleted file mode 100644 index 393d352..0000000 --- a/src/main/java/org/apache/commons/math3/distribution/ChiSquaredDistribution.java +++ /dev/null @@ -1,196 +0,0 @@ -/* - * 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.math3.distribution; - -import org.apache.commons.math3.random.RandomGenerator; -import org.apache.commons.math3.random.Well19937c; - -/** - * Implementation of the chi-squared distribution. - * - * @see Chi-squared distribution (Wikipedia) - * @see Chi-squared Distribution (MathWorld) - */ -public class ChiSquaredDistribution extends AbstractRealDistribution { - /** - * 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 final GammaDistribution gamma; - /** Inverse cumulative probability accuracy */ - private final double solverAbsoluteAccuracy; - - /** - * Create a Chi-Squared distribution with the given degrees of freedom. - * - * @param degreesOfFreedom Degrees of freedom. - */ - public ChiSquaredDistribution(double degreesOfFreedom) { - this(degreesOfFreedom, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); - } - - /** - * Create a Chi-Squared distribution with the given degrees of freedom and - * inverse cumulative probability accuracy. - *

- * Note: this constructor will implicitly create an instance of - * {@link Well19937c} as random generator to be used for sampling only (see - * {@link #sample()} and {@link #sample(int)}). In case no sampling is - * needed for the created distribution, it is advised to pass {@code null} - * as random generator via the appropriate constructors to avoid the - * additional initialisation overhead. - * - * @param degreesOfFreedom 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 ChiSquaredDistribution(double degreesOfFreedom, - double inverseCumAccuracy) { - this(new Well19937c(), degreesOfFreedom, inverseCumAccuracy); - } - - /** - * Create a Chi-Squared distribution with the given degrees of freedom. - * - * @param rng Random number generator. - * @param degreesOfFreedom Degrees of freedom. - * @since 3.3 - */ - public ChiSquaredDistribution(RandomGenerator rng, double degreesOfFreedom) { - this(rng, degreesOfFreedom, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); - } - - /** - * Create a Chi-Squared distribution with the given degrees of freedom and - * inverse cumulative probability accuracy. - * - * @param rng Random number generator. - * @param degreesOfFreedom Degrees of freedom. - * @param inverseCumAccuracy the maximum absolute error in inverse - * cumulative probability estimates (defaults to - * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}). - * @since 3.1 - */ - public ChiSquaredDistribution(RandomGenerator rng, - double degreesOfFreedom, - double inverseCumAccuracy) { - super(rng); - - gamma = new GammaDistribution(degreesOfFreedom / 2, 2); - solverAbsoluteAccuracy = inverseCumAccuracy; - } - - /** - * Access the number of degrees of freedom. - * - * @return the degrees of freedom. - */ - public double getDegreesOfFreedom() { - return gamma.getShape() * 2.0; - } - - /** {@inheritDoc} */ - public double density(double x) { - return gamma.density(x); - } - - /** {@inheritDoc} **/ - @Override - public double logDensity(double x) { - return gamma.logDensity(x); - } - - /** {@inheritDoc} */ - public double cumulativeProbability(double x) { - return gamma.cumulativeProbability(x); - } - - /** {@inheritDoc} */ - @Override - protected double getSolverAbsoluteAccuracy() { - return solverAbsoluteAccuracy; - } - - /** - * {@inheritDoc} - * - * For {@code k} degrees of freedom, the mean is {@code k}. - */ - public double getNumericalMean() { - return getDegreesOfFreedom(); - } - - /** - * {@inheritDoc} - * - * @return {@code 2 * k}, where {@code k} is the number of degrees of freedom. - */ - public double getNumericalVariance() { - return 2 * getDegreesOfFreedom(); - } - - /** - * {@inheritDoc} - * - * The lower bound of the support is always 0 no matter the - * degrees of freedom. - * - * @return zero. - */ - public double getSupportLowerBound() { - return 0; - } - - /** - * {@inheritDoc} - * - * The upper bound of the support is always positive infinity no matter the - * degrees of freedom. - * - * @return {@code Double.POSITIVE_INFINITY}. - */ - public double getSupportUpperBound() { - return Double.POSITIVE_INFINITY; - } - - /** {@inheritDoc} */ - public boolean isSupportLowerBoundInclusive() { - return true; - } - - /** {@inheritDoc} */ - public boolean isSupportUpperBoundInclusive() { - return false; - } - - /** - * {@inheritDoc} - * - * The support of this distribution is connected. - * - * @return {@code true} - */ - public boolean isSupportConnected() { - return true; - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/distribution/ConstantRealDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/distribution/ConstantRealDistribution.java b/src/main/java/org/apache/commons/math3/distribution/ConstantRealDistribution.java deleted file mode 100644 index 0dbb6aa..0000000 --- a/src/main/java/org/apache/commons/math3/distribution/ConstantRealDistribution.java +++ /dev/null @@ -1,121 +0,0 @@ -/* - * 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.math3.distribution; - -import org.apache.commons.math3.exception.OutOfRangeException; - -/** - * Implementation of the constant real distribution. - * - * @since 3.4 - */ -public class ConstantRealDistribution extends AbstractRealDistribution { - - /** Serialization ID */ - private static final long serialVersionUID = -4157745166772046273L; - - /** Constant value of the distribution */ - private final double value; - - /** - * Create a constant real distribution with the given value. - * - * @param value the constant value of this distribution - */ - public ConstantRealDistribution(double value) { - super(null); // Avoid creating RandomGenerator - this.value = value; - } - - /** {@inheritDoc} */ - public double density(double x) { - return x == value ? 1 : 0; - } - - /** {@inheritDoc} */ - public double cumulativeProbability(double x) { - return x < value ? 0 : 1; - } - - @Override - public double inverseCumulativeProbability(final double p) - throws OutOfRangeException { - if (p < 0.0 || p > 1.0) { - throw new OutOfRangeException(p, 0, 1); - } - return value; - } - - /** - * {@inheritDoc} - */ - public double getNumericalMean() { - return value; - } - - /** - * {@inheritDoc} - */ - public double getNumericalVariance() { - return 0; - } - - /** - * {@inheritDoc} - */ - public double getSupportLowerBound() { - return value; - } - - /** - * {@inheritDoc} - */ - public double getSupportUpperBound() { - return value; - } - - /** {@inheritDoc} */ - public boolean isSupportLowerBoundInclusive() { - return true; - } - - /** {@inheritDoc} */ - public boolean isSupportUpperBoundInclusive() { - return true; - } - - /** - * {@inheritDoc} - */ - public boolean isSupportConnected() { - return true; - } - - /** {@inheritDoc} */ - @Override - public double sample() { - return value; - } - - /** - * Override with no-op (there is no generator). - * @param seed (ignored) - */ - @Override - public void reseedRandomGenerator(long seed) {} -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/distribution/EnumeratedDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/distribution/EnumeratedDistribution.java b/src/main/java/org/apache/commons/math3/distribution/EnumeratedDistribution.java deleted file mode 100644 index 4cb23c1..0000000 --- a/src/main/java/org/apache/commons/math3/distribution/EnumeratedDistribution.java +++ /dev/null @@ -1,291 +0,0 @@ -/* - * 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.math3.distribution; - -import java.io.Serializable; -import java.lang.reflect.Array; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.List; - -import org.apache.commons.math3.exception.MathArithmeticException; -import org.apache.commons.math3.exception.NotANumberException; -import org.apache.commons.math3.exception.NotFiniteNumberException; -import org.apache.commons.math3.exception.NotPositiveException; -import org.apache.commons.math3.exception.NotStrictlyPositiveException; -import org.apache.commons.math3.exception.NullArgumentException; -import org.apache.commons.math3.exception.util.LocalizedFormats; -import org.apache.commons.math3.random.RandomGenerator; -import org.apache.commons.math3.random.Well19937c; -import org.apache.commons.math3.util.MathArrays; -import org.apache.commons.math3.util.Pair; - -/** - *

A generic implementation of a - * - * discrete probability distribution (Wikipedia) over a finite sample space, - * based on an enumerated list of <value, probability> pairs. Input probabilities must all be non-negative, - * but zero values are allowed and their sum does not have to equal one. Constructors will normalize input - * probabilities to make them sum to one.

- * - *

The list of pairs does not, strictly speaking, have to be a function and it can - * contain null values. The pmf created by the constructor will combine probabilities of equal values and - * will treat null values as equal. For example, if the list of pairs <"dog", 0.2>, <null, 0.1>, - * <"pig", 0.2>, <"dog", 0.1>, <null, 0.4> is provided to the constructor, the resulting - * pmf will assign mass of 0.5 to null, 0.3 to "dog" and 0.2 to null.

- * - * @param type of the elements in the sample space. - * @since 3.2 - */ -public class EnumeratedDistribution implements Serializable { - - /** Serializable UID. */ - private static final long serialVersionUID = 20123308L; - - /** - * RNG instance used to generate samples from the distribution. - */ - protected final RandomGenerator random; - - /** - * List of random variable values. - */ - private final List singletons; - - /** - * Probabilities of respective random variable values. For i = 0, ..., singletons.size() - 1, - * probability[i] is the probability that a random variable following this distribution takes - * the value singletons[i]. - */ - private final double[] probabilities; - - /** - * Cumulative probabilities, cached to speed up sampling. - */ - private final double[] cumulativeProbabilities; - - /** - * Create an enumerated distribution using the given probability mass function - * enumeration. - *

- * Note: this constructor will implicitly create an instance of - * {@link Well19937c} as random generator to be used for sampling only (see - * {@link #sample()} and {@link #sample(int)}). In case no sampling is - * needed for the created distribution, it is advised to pass {@code null} - * as random generator via the appropriate constructors to avoid the - * additional initialisation overhead. - * - * @param pmf probability mass function enumerated as a list of - * pairs. - * @throws NotPositiveException if any of the probabilities are negative. - * @throws NotFiniteNumberException if any of the probabilities are infinite. - * @throws NotANumberException if any of the probabilities are NaN. - * @throws MathArithmeticException all of the probabilities are 0. - */ - public EnumeratedDistribution(final List> pmf) - throws NotPositiveException, MathArithmeticException, NotFiniteNumberException, NotANumberException { - this(new Well19937c(), pmf); - } - - /** - * Create an enumerated distribution using the given random number generator - * and probability mass function enumeration. - * - * @param rng random number generator. - * @param pmf probability mass function enumerated as a list of - * pairs. - * @throws NotPositiveException if any of the probabilities are negative. - * @throws NotFiniteNumberException if any of the probabilities are infinite. - * @throws NotANumberException if any of the probabilities are NaN. - * @throws MathArithmeticException all of the probabilities are 0. - */ - public EnumeratedDistribution(final RandomGenerator rng, final List> pmf) - throws NotPositiveException, MathArithmeticException, NotFiniteNumberException, NotANumberException { - random = rng; - - singletons = new ArrayList(pmf.size()); - final double[] probs = new double[pmf.size()]; - - for (int i = 0; i < pmf.size(); i++) { - final Pair sample = pmf.get(i); - singletons.add(sample.getKey()); - final double p = sample.getValue(); - if (p < 0) { - throw new NotPositiveException(sample.getValue()); - } - if (Double.isInfinite(p)) { - throw new NotFiniteNumberException(p); - } - if (Double.isNaN(p)) { - throw new NotANumberException(); - } - probs[i] = p; - } - - probabilities = MathArrays.normalizeArray(probs, 1.0); - - cumulativeProbabilities = new double[probabilities.length]; - double sum = 0; - for (int i = 0; i < probabilities.length; i++) { - sum += probabilities[i]; - cumulativeProbabilities[i] = sum; - } - } - - /** - * Reseed the random generator used to generate samples. - * - * @param seed the new seed - */ - public void reseedRandomGenerator(long seed) { - random.setSeed(seed); - } - - /** - *

For a random variable {@code X} whose values are distributed according to - * this distribution, this method returns {@code P(X = x)}. In other words, - * this method represents the probability mass function (PMF) for the - * distribution.

- * - *

Note that if {@code x1} and {@code x2} satisfy {@code x1.equals(x2)}, - * or both are null, then {@code probability(x1) = probability(x2)}.

- * - * @param x the point at which the PMF is evaluated - * @return the value of the probability mass function at {@code x} - */ - double probability(final T x) { - double probability = 0; - - for (int i = 0; i < probabilities.length; i++) { - if ((x == null && singletons.get(i) == null) || - (x != null && x.equals(singletons.get(i)))) { - probability += probabilities[i]; - } - } - - return probability; - } - - /** - *

Return the probability mass function as a list of pairs.

- * - *

Note that if duplicate and / or null values were provided to the constructor - * when creating this EnumeratedDistribution, the returned list will contain these - * values. If duplicates values exist, what is returned will not represent - * a pmf (i.e., it is up to the caller to consolidate duplicate mass points).

- * - * @return the probability mass function. - */ - public List> getPmf() { - final List> samples = new ArrayList>(probabilities.length); - - for (int i = 0; i < probabilities.length; i++) { - samples.add(new Pair(singletons.get(i), probabilities[i])); - } - - return samples; - } - - /** - * Generate a random value sampled from this distribution. - * - * @return a random value. - */ - public T sample() { - final double randomValue = random.nextDouble(); - - int index = Arrays.binarySearch(cumulativeProbabilities, randomValue); - if (index < 0) { - index = -index-1; - } - - if (index >= 0 && index < probabilities.length) { - if (randomValue < cumulativeProbabilities[index]) { - return singletons.get(index); - } - } - - /* This should never happen, but it ensures we will return a correct - * object in case there is some floating point inequality problem - * wrt the cumulative probabilities. */ - return singletons.get(singletons.size() - 1); - } - - /** - * Generate a random sample from the distribution. - * - * @param sampleSize the number of random values to generate. - * @return an array representing the random sample. - * @throws NotStrictlyPositiveException if {@code sampleSize} is not - * positive. - */ - public Object[] sample(int sampleSize) throws NotStrictlyPositiveException { - if (sampleSize <= 0) { - throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, - sampleSize); - } - - final Object[] out = new Object[sampleSize]; - - for (int i = 0; i < sampleSize; i++) { - out[i] = sample(); - } - - return out; - - } - - /** - * Generate a random sample from the distribution. - *

- * If the requested samples fit in the specified array, it is returned - * therein. Otherwise, a new array is allocated with the runtime type of - * the specified array and the size of this collection. - * - * @param sampleSize the number of random values to generate. - * @param array the array to populate. - * @return an array representing the random sample. - * @throws NotStrictlyPositiveException if {@code sampleSize} is not positive. - * @throws NullArgumentException if {@code array} is null - */ - public T[] sample(int sampleSize, final T[] array) throws NotStrictlyPositiveException { - if (sampleSize <= 0) { - throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, sampleSize); - } - - if (array == null) { - throw new NullArgumentException(LocalizedFormats.INPUT_ARRAY); - } - - T[] out; - if (array.length < sampleSize) { - @SuppressWarnings("unchecked") // safe as both are of type T - final T[] unchecked = (T[]) Array.newInstance(array.getClass().getComponentType(), sampleSize); - out = unchecked; - } else { - out = array; - } - - for (int i = 0; i < sampleSize; i++) { - out[i] = sample(); - } - - return out; - - } - -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/distribution/EnumeratedIntegerDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/distribution/EnumeratedIntegerDistribution.java b/src/main/java/org/apache/commons/math3/distribution/EnumeratedIntegerDistribution.java deleted file mode 100644 index 9a07787..0000000 --- a/src/main/java/org/apache/commons/math3/distribution/EnumeratedIntegerDistribution.java +++ /dev/null @@ -1,218 +0,0 @@ -/* - * 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.math3.distribution; - -import java.util.ArrayList; -import java.util.List; - -import org.apache.commons.math3.exception.DimensionMismatchException; -import org.apache.commons.math3.exception.MathArithmeticException; -import org.apache.commons.math3.exception.NotANumberException; -import org.apache.commons.math3.exception.NotFiniteNumberException; -import org.apache.commons.math3.exception.NotPositiveException; -import org.apache.commons.math3.random.RandomGenerator; -import org.apache.commons.math3.random.Well19937c; -import org.apache.commons.math3.util.Pair; - -/** - *

Implementation of an integer-valued {@link EnumeratedDistribution}.

- * - *

Values with zero-probability are allowed but they do not extend the - * support.
- * Duplicate values are allowed. Probabilities of duplicate values are combined - * when computing cumulative probabilities and statistics.

- * - * @since 3.2 - */ -public class EnumeratedIntegerDistribution extends AbstractIntegerDistribution { - - /** Serializable UID. */ - private static final long serialVersionUID = 20130308L; - - /** - * {@link EnumeratedDistribution} instance (using the {@link Integer} wrapper) - * used to generate the pmf. - */ - protected final EnumeratedDistribution innerDistribution; - - /** - * Create a discrete distribution using the given probability mass function - * definition. - *

- * Note: this constructor will implicitly create an instance of - * {@link Well19937c} as random generator to be used for sampling only (see - * {@link #sample()} and {@link #sample(int)}). In case no sampling is - * needed for the created distribution, it is advised to pass {@code null} - * as random generator via the appropriate constructors to avoid the - * additional initialisation overhead. - * - * @param singletons array of random variable values. - * @param probabilities array of probabilities. - * @throws DimensionMismatchException if - * {@code singletons.length != probabilities.length} - * @throws NotPositiveException if any of the probabilities are negative. - * @throws NotFiniteNumberException if any of the probabilities are infinite. - * @throws NotANumberException if any of the probabilities are NaN. - * @throws MathArithmeticException all of the probabilities are 0. - */ - public EnumeratedIntegerDistribution(final int[] singletons, final double[] probabilities) - throws DimensionMismatchException, NotPositiveException, MathArithmeticException, - NotFiniteNumberException, NotANumberException{ - this(new Well19937c(), singletons, probabilities); - } - - /** - * Create a discrete distribution using the given random number generator - * and probability mass function definition. - * - * @param rng random number generator. - * @param singletons array of random variable values. - * @param probabilities array of probabilities. - * @throws DimensionMismatchException if - * {@code singletons.length != probabilities.length} - * @throws NotPositiveException if any of the probabilities are negative. - * @throws NotFiniteNumberException if any of the probabilities are infinite. - * @throws NotANumberException if any of the probabilities are NaN. - * @throws MathArithmeticException all of the probabilities are 0. - */ - public EnumeratedIntegerDistribution(final RandomGenerator rng, - final int[] singletons, final double[] probabilities) - throws DimensionMismatchException, NotPositiveException, MathArithmeticException, - NotFiniteNumberException, NotANumberException { - super(rng); - if (singletons.length != probabilities.length) { - throw new DimensionMismatchException(probabilities.length, singletons.length); - } - - final List> samples = new ArrayList>(singletons.length); - - for (int i = 0; i < singletons.length; i++) { - samples.add(new Pair(singletons[i], probabilities[i])); - } - - innerDistribution = new EnumeratedDistribution(rng, samples); - } - - /** - * {@inheritDoc} - */ - public double probability(final int x) { - return innerDistribution.probability(x); - } - - /** - * {@inheritDoc} - */ - public double cumulativeProbability(final int x) { - double probability = 0; - - for (final Pair sample : innerDistribution.getPmf()) { - if (sample.getKey() <= x) { - probability += sample.getValue(); - } - } - - return probability; - } - - /** - * {@inheritDoc} - * - * @return {@code sum(singletons[i] * probabilities[i])} - */ - public double getNumericalMean() { - double mean = 0; - - for (final Pair sample : innerDistribution.getPmf()) { - mean += sample.getValue() * sample.getKey(); - } - - return mean; - } - - /** - * {@inheritDoc} - * - * @return {@code sum((singletons[i] - mean) ^ 2 * probabilities[i])} - */ - public double getNumericalVariance() { - double mean = 0; - double meanOfSquares = 0; - - for (final Pair sample : innerDistribution.getPmf()) { - mean += sample.getValue() * sample.getKey(); - meanOfSquares += sample.getValue() * sample.getKey() * sample.getKey(); - } - - return meanOfSquares - mean * mean; - } - - /** - * {@inheritDoc} - * - * Returns the lowest value with non-zero probability. - * - * @return the lowest value with non-zero probability. - */ - public int getSupportLowerBound() { - int min = Integer.MAX_VALUE; - for (final Pair sample : innerDistribution.getPmf()) { - if (sample.getKey() < min && sample.getValue() > 0) { - min = sample.getKey(); - } - } - - return min; - } - - /** - * {@inheritDoc} - * - * Returns the highest value with non-zero probability. - * - * @return the highest value with non-zero probability. - */ - public int getSupportUpperBound() { - int max = Integer.MIN_VALUE; - for (final Pair sample : innerDistribution.getPmf()) { - if (sample.getKey() > max && sample.getValue() > 0) { - max = sample.getKey(); - } - } - - return max; - } - - /** - * {@inheritDoc} - * - * The support of this distribution is connected. - * - * @return {@code true} - */ - public boolean isSupportConnected() { - return true; - } - - /** - * {@inheritDoc} - */ - @Override - public int sample() { - return innerDistribution.sample(); - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/a7b4803f/src/main/java/org/apache/commons/math3/distribution/EnumeratedRealDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math3/distribution/EnumeratedRealDistribution.java b/src/main/java/org/apache/commons/math3/distribution/EnumeratedRealDistribution.java deleted file mode 100644 index 07b96bc..0000000 --- a/src/main/java/org/apache/commons/math3/distribution/EnumeratedRealDistribution.java +++ /dev/null @@ -1,282 +0,0 @@ -/* - * 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.math3.distribution; - -import java.util.ArrayList; -import java.util.List; - -import org.apache.commons.math3.exception.DimensionMismatchException; -import org.apache.commons.math3.exception.MathArithmeticException; -import org.apache.commons.math3.exception.NotANumberException; -import org.apache.commons.math3.exception.NotFiniteNumberException; -import org.apache.commons.math3.exception.NotPositiveException; -import org.apache.commons.math3.exception.OutOfRangeException; -import org.apache.commons.math3.random.RandomGenerator; -import org.apache.commons.math3.random.Well19937c; -import org.apache.commons.math3.util.Pair; - -/** - *

Implementation of a real-valued {@link EnumeratedDistribution}. - * - *

Values with zero-probability are allowed but they do not extend the - * support.
- * Duplicate values are allowed. Probabilities of duplicate values are combined - * when computing cumulative probabilities and statistics.

- * - * @since 3.2 - */ -public class EnumeratedRealDistribution extends AbstractRealDistribution { - - /** Serializable UID. */ - private static final long serialVersionUID = 20130308L; - - /** - * {@link EnumeratedDistribution} (using the {@link Double} wrapper) - * used to generate the pmf. - */ - protected final EnumeratedDistribution innerDistribution; - - /** - * Create a discrete distribution using the given probability mass function - * enumeration. - *

- * Note: this constructor will implicitly create an instance of - * {@link Well19937c} as random generator to be used for sampling only (see - * {@link #sample()} and {@link #sample(int)}). In case no sampling is - * needed for the created distribution, it is advised to pass {@code null} - * as random generator via the appropriate constructors to avoid the - * additional initialisation overhead. - * - * @param singletons array of random variable values. - * @param probabilities array of probabilities. - * @throws DimensionMismatchException if - * {@code singletons.length != probabilities.length} - * @throws NotPositiveException if any of the probabilities are negative. - * @throws NotFiniteNumberException if any of the probabilities are infinite. - * @throws NotANumberException if any of the probabilities are NaN. - * @throws MathArithmeticException all of the probabilities are 0. - */ - public EnumeratedRealDistribution(final double[] singletons, final double[] probabilities) - throws DimensionMismatchException, NotPositiveException, MathArithmeticException, - NotFiniteNumberException, NotANumberException { - this(new Well19937c(), singletons, probabilities); - } - - /** - * Create a discrete distribution using the given random number generator - * and probability mass function enumeration. - * - * @param rng random number generator. - * @param singletons array of random variable values. - * @param probabilities array of probabilities. - * @throws DimensionMismatchException if - * {@code singletons.length != probabilities.length} - * @throws NotPositiveException if any of the probabilities are negative. - * @throws NotFiniteNumberException if any of the probabilities are infinite. - * @throws NotANumberException if any of the probabilities are NaN. - * @throws MathArithmeticException all of the probabilities are 0. - */ - public EnumeratedRealDistribution(final RandomGenerator rng, - final double[] singletons, final double[] probabilities) - throws DimensionMismatchException, NotPositiveException, MathArithmeticException, - NotFiniteNumberException, NotANumberException { - super(rng); - if (singletons.length != probabilities.length) { - throw new DimensionMismatchException(probabilities.length, singletons.length); - } - - List> samples = new ArrayList>(singletons.length); - - for (int i = 0; i < singletons.length; i++) { - samples.add(new Pair(singletons[i], probabilities[i])); - } - - innerDistribution = new EnumeratedDistribution(rng, samples); - } - - /** - * {@inheritDoc} - */ - @Override - public double probability(final double x) { - return innerDistribution.probability(x); - } - - /** - * For a random variable {@code X} whose values are distributed according to - * this distribution, this method returns {@code P(X = x)}. In other words, - * this method represents the probability mass function (PMF) for the - * distribution. - * - * @param x the point at which the PMF is evaluated - * @return the value of the probability mass function at point {@code x} - */ - public double density(final double x) { - return probability(x); - } - - /** - * {@inheritDoc} - */ - public double cumulativeProbability(final double x) { - double probability = 0; - - for (final Pair sample : innerDistribution.getPmf()) { - if (sample.getKey() <= x) { - probability += sample.getValue(); - } - } - - return probability; - } - - /** - * {@inheritDoc} - */ - @Override - public double inverseCumulativeProbability(final double p) throws OutOfRangeException { - if (p < 0.0 || p > 1.0) { - throw new OutOfRangeException(p, 0, 1); - } - - double probability = 0; - double x = getSupportLowerBound(); - for (final Pair sample : innerDistribution.getPmf()) { - if (sample.getValue() == 0.0) { - continue; - } - - probability += sample.getValue(); - x = sample.getKey(); - - if (probability >= p) { - break; - } - } - - return x; - } - - /** - * {@inheritDoc} - * - * @return {@code sum(singletons[i] * probabilities[i])} - */ - public double getNumericalMean() { - double mean = 0; - - for (final Pair sample : innerDistribution.getPmf()) { - mean += sample.getValue() * sample.getKey(); - } - - return mean; - } - - /** - * {@inheritDoc} - * - * @return {@code sum((singletons[i] - mean) ^ 2 * probabilities[i])} - */ - public double getNumericalVariance() { - double mean = 0; - double meanOfSquares = 0; - - for (final Pair sample : innerDistribution.getPmf()) { - mean += sample.getValue() * sample.getKey(); - meanOfSquares += sample.getValue() * sample.getKey() * sample.getKey(); - } - - return meanOfSquares - mean * mean; - } - - /** - * {@inheritDoc} - * - * Returns the lowest value with non-zero probability. - * - * @return the lowest value with non-zero probability. - */ - public double getSupportLowerBound() { - double min = Double.POSITIVE_INFINITY; - for (final Pair sample : innerDistribution.getPmf()) { - if (sample.getKey() < min && sample.getValue() > 0) { - min = sample.getKey(); - } - } - - return min; - } - - /** - * {@inheritDoc} - * - * Returns the highest value with non-zero probability. - * - * @return the highest value with non-zero probability. - */ - public double getSupportUpperBound() { - double max = Double.NEGATIVE_INFINITY; - for (final Pair sample : innerDistribution.getPmf()) { - if (sample.getKey() > max && sample.getValue() > 0) { - max = sample.getKey(); - } - } - - return max; - } - - /** - * {@inheritDoc} - * - * The support of this distribution includes the lower bound. - * - * @return {@code true} - */ - public boolean isSupportLowerBoundInclusive() { - return true; - } - - /** - * {@inheritDoc} - * - * The support of this distribution includes the upper bound. - * - * @return {@code true} - */ - public boolean isSupportUpperBoundInclusive() { - return true; - } - - /** - * {@inheritDoc} - * - * The support of this distribution is connected. - * - * @return {@code true} - */ - public boolean isSupportConnected() { - return true; - } - - /** - * {@inheritDoc} - */ - @Override - public double sample() { - return innerDistribution.sample(); - } -}