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Posted to dev@commons.apache.org by Mikkel Meyer Andersen <mi...@mikl.dk> on 2012/01/17 08:16:38 UTC
[MATH] Re: svn commit: r1232324 - in /commons/proper/math/trunk/src:
main/java/org/apache/commons/math/distribution/ test/R/ test/java/org/apache/commons/math/distribution/
Hi,
You write for x >= 0, but isn't only x > 0?
Cheers, Mikkel.
Den 17/01/2012 08.12 skrev <ce...@apache.org>:
> Author: celestin
> Date: Tue Jan 17 07:12:02 2012
> New Revision: 1232324
>
> URL: http://svn.apache.org/viewvc?rev=1232324&view=rev
> Log:
> Implementation of log-normal distributions (MATH-733). Patch contributed
> by Dennis Hendriks.
>
> Added:
>
> commons/proper/math/trunk/src/main/java/org/apache/commons/math/distribution/LogNormalDistribution.java
> (with props)
> commons/proper/math/trunk/src/test/R/logNormalTestCases (with props)
>
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/LogNormalDistributionTest.java
> (with props)
> Modified:
>
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/NormalDistributionTest.java
>
> Added:
> commons/proper/math/trunk/src/main/java/org/apache/commons/math/distribution/LogNormalDistribution.java
> URL:
> http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/distribution/LogNormalDistribution.java?rev=1232324&view=auto
>
> ==============================================================================
> ---
> commons/proper/math/trunk/src/main/java/org/apache/commons/math/distribution/LogNormalDistribution.java
> (added)
> +++
> commons/proper/math/trunk/src/main/java/org/apache/commons/math/distribution/LogNormalDistribution.java
> Tue Jan 17 07:12:02 2012
> @@ -0,0 +1,295 @@
> +/*
> + * 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 org.apache.commons.math.exception.NotStrictlyPositiveException;
> +import org.apache.commons.math.exception.NumberIsTooLargeException;
> +import org.apache.commons.math.exception.util.LocalizedFormats;
> +import org.apache.commons.math.special.Erf;
> +import org.apache.commons.math.util.FastMath;
> +
> +/**
> + * Implementation of the log-normal (gaussian) distribution.
> + *
> + * <p>
> + * <a id="parameters"><strong>Parameters:</strong></a>
> + * {@code X} is log-normally distributed if its natural logarithm {@code
> log(X)}
> + * is normally distributed. The probability distribution function of
> {@code X}
> + * is given by (for {@code x >= 0})
> + * </p>
> + * <p>
> + * {@code exp(-0.5 * ((ln(x) - m) / s)^2) / (s * sqrt(2 * pi) * x)}
> + * </p>
> + * <ul>
> + * <li>{@code m} is the <em>scale</em> parameter: this is the mean of the
> + * normally distributed natural logarithm of this distribution,</li>
> + * <li>{@code s} is the <em>shape</em> parameter: this is the standard
> + * deviation of the normally distributed natural logarithm of this
> + * distribution.
> + * </ul>
> + *
> + * @see <a href="http://en.wikipedia.org/wiki/Log-normal_distribution">
> + * Log-normal distribution (Wikipedia)</a>
> + * @see <a href="http://mathworld.wolfram.com/LogNormalDistribution.html
> ">
> + * Log Normal distribution (MathWorld)</a>
> + *
> + * @version $Id$
> + * @since 3.0
> + */
> +public class LogNormalDistribution extends AbstractRealDistribution {
> + /** Default inverse cumulative probability accuracy. */
> + public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
> +
> + /** Serializable version identifier. */
> + private static final long serialVersionUID = 20120112;
> +
> + /** √(2 π) */
> + private static final double SQRT2PI = FastMath.sqrt(2 * FastMath.PI);
> +
> + /** √(2) */
> + private static final double SQRT2 = FastMath.sqrt(2.0);
> +
> + /** The <a href="#parameters">scale</a> parameter of this
> distribution. */
> + private final double scale;
> +
> + /** The <a href="#parameters">shape</a> parameter of this
> distribution. */
> + private final double shape;
> +
> + /** Inverse cumulative probability accuracy. */
> + private final double solverAbsoluteAccuracy;
> +
> + /**
> + * Create a log-normal distribution using the specified
> + * <a href="#parameters">scale</a> and
> + * <a href="#parameters">shape</a>.
> + *
> + * @param scale the scale parameter of this distribution
> + * @param shape the shape parameter of this distribution
> + * @throws NotStrictlyPositiveException if {@code shape <= 0}.
> + */
> + public LogNormalDistribution(double scale, double shape)
> + throws NotStrictlyPositiveException {
> + this(scale, shape, DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
> + }
> +
> + /**
> + * Create a log-normal distribution using the specified
> + * <a href="#parameters">scale</a>, <a href="#parameters">shape</a>
> and
> + * inverse cumulative distribution accuracy.
> + *
> + * @param scale the scale parameter of this distribution
> + * @param shape the shape parameter of this distribution
> + * @param inverseCumAccuracy Inverse cumulative probability accuracy.
> + * @throws NotStrictlyPositiveException if {@code shape <= 0}.
> + */
> + public LogNormalDistribution(double scale, double shape,
> + double inverseCumAccuracy) throws NotStrictlyPositiveException {
> + if (shape <= 0) {
> + throw new
> NotStrictlyPositiveException(LocalizedFormats.STANDARD_DEVIATION, shape);
> + }
> +
> + this.scale = scale;
> + this.shape = shape;
> + this.solverAbsoluteAccuracy = inverseCumAccuracy;
> + }
> +
> + /**
> + * Create a log-normal distribution, where the mean and standard
> deviation
> + * of the {@link NormalDistribution normally distributed} natural
> + * logarithm of the log-normal distribution are equal to zero and one
> + * respectively. In other words, the scale of the returned
> distribution is
> + * {@code 0}, while its shape is {@code 1}.
> + */
> + public LogNormalDistribution() {
> + this(0, 1);
> + }
> +
> + /**
> + * Returns the <a href="#parameters">scale</a> parameter of this
> distribution.
> + *
> + * @return the scale parameter
> + */
> + public double getScale() {
> + return scale;
> + }
> +
> + /**
> + * Returns the <a href="#parameters">shape</a> parameter of this
> + * distribution.
> + *
> + * @return the shape parameter
> + */
> + public double getShape() {
> + return shape;
> + }
> +
> + /**
> + * {@inheritDoc}
> + *
> + * For this distribution {@code P(X = x)} always evaluates to 0.
> + *
> + * @return 0
> + */
> + public double probability(double x) {
> + return 0.0;
> + }
> +
> + /**
> + * {@inheritDoc}
> + *
> + * For scale {@code m}, and shape {@code s} of this distribution, the
> PDF
> + * is given by
> + * <ul>
> + * <li>{@code 0} if {@code x <= 0},</li>
> + * <li>{@code exp(-0.5 * ((ln(x) - m) / s)^2) / (s * sqrt(2 * pi) *
> x)}
> + * otherwise.</li>
> + * </ul>
> + */
> + public double density(double x) {
> + if (x <= 0) {
> + return 0;
> + }
> + final double x0 = FastMath.log(x) - scale;
> + final double x1 = x0 / shape;
> + return FastMath.exp(-0.5 * x1 * x1) / (shape * SQRT2PI * x);
> + }
> +
> + /**
> + * {@inheritDoc}
> + *
> + * For scale {@code m}, and shape {@code s} of this distribution, the
> CDF
> + * is given by
> + * <ul>
> + * <li>{@code 0} if {@code x <= 0},</li>
> + * <li>{@code 0} if {@code ln(x) - m < 0} and {@code m - ln(x) > 40 *
> s}, as
> + * in these cases the actual value is within {@code Double.MIN_VALUE}
> of 0,
> + * <li>{@code 1} if {@code ln(x) - m >= 0} and {@code ln(x) - m > 40
> * s},
> + * as in these cases the actual value is within {@code
> Double.MIN_VALUE} of
> + * 1,</li>
> + * <li>{@code 0.5 + 0.5 * erf((ln(x) - m) / (s * sqrt(2))}
> otherwise.</li>
> + * </ul>
> + */
> + public double cumulativeProbability(double x) {
> + if (x <= 0) {
> + return 0;
> + }
> + final double dev = FastMath.log(x) - scale;
> + if (FastMath.abs(dev) > 40 * shape) {
> + return dev < 0 ? 0.0d : 1.0d;
> + }
> + return 0.5 + 0.5 * Erf.erf(dev / (shape * SQRT2));
> + }
> +
> + /** {@inheritDoc} */
> + @Override
> + public double cumulativeProbability(double x0, double x1)
> + throws NumberIsTooLargeException {
> + if (x0 > x1) {
> + throw new
> NumberIsTooLargeException(LocalizedFormats.LOWER_ENDPOINT_ABOVE_UPPER_ENDPOINT,
> + x0, x1, true);
> + }
> + if (x0 <= 0 || x1 <= 0) {
> + return super.cumulativeProbability(x0, x1);
> + }
> + final double denom = shape * SQRT2;
> + final double v0 = (FastMath.log(x0) - scale) / denom;
> + final double v1 = (FastMath.log(x1) - scale) / denom;
> + return 0.5 * Erf.erf(v0, v1);
> + }
> +
> + /** {@inheritDoc} */
> + @Override
> + protected double getSolverAbsoluteAccuracy() {
> + return solverAbsoluteAccuracy;
> + }
> +
> + /**
> + * {@inheritDoc}
> + *
> + * For scale {@code m} and shape {@code s}, the mean is
> + * {@code exp(m + s^2 / 2)}.
> + */
> + public double getNumericalMean() {
> + double s = shape;
> + return FastMath.exp(scale + (s * s / 2));
> + }
> +
> + /**
> + * {@inheritDoc}
> + *
> + * For scale {@code m} and shape {@code s}, the variance is
> + * {@code (exp(s^2) - 1) * exp(2 * m + s^2)}.
> + */
> + public double getNumericalVariance() {
> + final double s = shape;
> + final double ss = s * s;
> + return (FastMath.exp(ss) - 1) * FastMath.exp(2 * scale + ss);
> + }
> +
> + /**
> + * {@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 positive infinity
> + * no matter the parameters.
> + *
> + * @return upper bound of the support (always
> + * {@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;
> + }
> +
> + /** {@inheritDoc} */
> + @Override
> + public double sample() {
> + double n = randomData.nextGaussian(0, 1);
> + return FastMath.exp(scale + shape * n);
> + }
> +}
>
> Propchange:
> commons/proper/math/trunk/src/main/java/org/apache/commons/math/distribution/LogNormalDistribution.java
>
> ------------------------------------------------------------------------------
> svn:eol-style = native
>
> Propchange:
> commons/proper/math/trunk/src/main/java/org/apache/commons/math/distribution/LogNormalDistribution.java
>
> ------------------------------------------------------------------------------
> svn:keywords = Author Date Id Revision
>
> Added: commons/proper/math/trunk/src/test/R/logNormalTestCases
> URL:
> http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/R/logNormalTestCases?rev=1232324&view=auto
>
> ==============================================================================
> --- commons/proper/math/trunk/src/test/R/logNormalTestCases (added)
> +++ commons/proper/math/trunk/src/test/R/logNormalTestCases Tue Jan 17
> 07:12:02 2012
> @@ -0,0 +1,107 @@
> +# 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.
> +#
>
> +#------------------------------------------------------------------------------
> +# R source file to validate LogNormal distribution tests in
> +# org.apache.commons.math.distribution.LogNormalDistributionTest
> +#
> +# To run the test, install R, put this file and testFunctions
> +# into the same directory, launch R from this directory and then enter
> +# source("<name-of-this-file>")
> +#
> +# R functions used
> +# plnorm(q, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE) <--
> distribution
>
> +#-----------------------------------------------------------------------------
> +tol <- 1E-9
> +
> +# Function definitions
> +
> +source("testFunctions") # utility test functions
> +
> +# function to verify distribution computations
> +
> +verifyDistribution <- function(points, expected, mu, sigma, tol) {
> + rDistValues <- rep(0, length(points))
> + i <- 0
> + for (point in points) {
> + i <- i + 1
> + rDistValues[i] <- plnorm(point, mu, sigma, log = FALSE)
> + }
> + output <- c("Distribution test mu = ",mu,", sigma = ", sigma)
> + if (assertEquals(expected, rDistValues, tol, "Distribution Values")) {
> + displayPadded(output, SUCCEEDED, WIDTH)
> + } else {
> + displayPadded(output, FAILED, WIDTH)
> + }
> +}
> +
> +# function to verify density computations
> +
> +verifyDensity <- function(points, expected, mu, sigma, tol) {
> + rDensityValues <- rep(0, length(points))
> + i <- 0
> + for (point in points) {
> + i <- i + 1
> + rDensityValues[i] <- dlnorm(point, mu, sigma, log = FALSE)
> + }
> + output <- c("Density test mu = ",mu,", sigma = ", sigma)
> + if (assertEquals(expected, rDensityValues, tol, "Density Values")) {
> + displayPadded(output, SUCCEEDED, WIDTH)
> + } else {
> + displayPadded(output, FAILED, WIDTH)
> + }
> +}
> +
>
> +#--------------------------------------------------------------------------
> +cat("LogNormal test cases\n")
> +
> +mu <- 2.1
> +sigma <- 1.4
> +distributionValues <- c(0, 0, 0, 0, 0.00948199951485, 0.432056525076,
> 0.381648158697, 0.354555726206, 0.329513316888, 0.298422824228)
> +densityValues <- c(0, 0, 0, 0, 0.0594218160072, 0.0436977691036,
> 0.0508364857798, 0.054873528325, 0.0587182664085, 0.0636229042785)
> +distributionPoints <- c(-2.226325228634938, -1.156887023657177,
> -0.643949578356075, -0.2027950777320613, 0.305827808237559,
> + 6.42632522863494, 5.35688702365718, 4.843949578356074,
> 4.40279507773206, 3.89417219176244)
> +verifyDistribution(distributionPoints, distributionValues, mu, sigma, tol)
> +verifyDensity(distributionPoints, densityValues, mu, sigma, tol)
> +
> +distributionValues <- c(0, 0.0396495152787, 0.16601209243,
> 0.272533253269, 0.357618409638, 0.426488363093, 0.483255136841,
> 0.530823013877)
> +densityValues <- c(0, 0.0873055825147, 0.0847676303432, 0.0677935186237,
> 0.0544105523058, 0.0444614628804, 0.0369750288945, 0.0312206409653)
> +distributionPoints <- c(mu - 2 *sigma, mu - sigma, mu, mu + sigma,
> + mu + 2 * sigma, mu + 3 * sigma, mu + 4 * sigma,
> + mu + 5 * sigma)
> +verifyDistribution(distributionPoints, distributionValues, mu, sigma, tol)
> +verifyDensity(distributionPoints, densityValues, mu, sigma, tol)
> +
> +mu <- 0
> +sigma <- 1
> +distributionPoints <- c(mu - 2 *sigma, mu - sigma, mu, mu + sigma,
> + mu + 2 * sigma, mu + 3 * sigma, mu + 4 * sigma,
> + mu + 5 * sigma)
> +distributionValues <- c(0, 0, 0, 0.5, 0.755891404214, 0.864031392359,
> 0.917171480998, 0.946239689548)
> +densityValues <- c(0, 0, 0, 0.398942280401, 0.156874019279,
> 0.07272825614, 0.0381534565119, 0.0218507148303)
> +verifyDistribution(distributionPoints, distributionValues, mu, sigma, tol)
> +verifyDensity(distributionPoints, densityValues, mu, sigma, tol)
> +
> +mu <- 0
> +sigma <- 0.1
> +distributionPoints <- c(mu - 2 *sigma, mu - sigma, mu, mu + sigma,
> + mu + 2 * sigma, mu + 3 * sigma, mu + 4 * sigma,
> + mu + 5 * sigma)
> +distributionValues <- c(0, 0, 0, 1.28417563064e-117, 1.39679883412e-58,
> 1.09839325447e-33, 2.52587961726e-20, 2.0824223487e-12)
> +densityValues <- c(0, 0, 0, 2.96247992535e-114, 1.1283370232e-55,
> 4.43812313223e-31, 5.85346445002e-18, 2.9446618076e-10)
> +verifyDistribution(distributionPoints, distributionValues, mu, sigma, tol)
> +verifyDensity(distributionPoints, densityValues, mu, sigma, tol)
> +
> +displayDashes(WIDTH)
>
> Propchange: commons/proper/math/trunk/src/test/R/logNormalTestCases
>
> ------------------------------------------------------------------------------
> svn:eol-style = native
>
> Added:
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/LogNormalDistributionTest.java
> URL:
> http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/LogNormalDistributionTest.java?rev=1232324&view=auto
>
> ==============================================================================
> ---
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/LogNormalDistributionTest.java
> (added)
> +++
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/LogNormalDistributionTest.java
> Tue Jan 17 07:12:02 2012
> @@ -0,0 +1,245 @@
> +/*
> + * 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 org.apache.commons.math.exception.NotStrictlyPositiveException;
> +import org.junit.Assert;
> +import org.junit.Test;
> +
> +/**
> + * Test cases for {@link LogNormalDistribution}. Extends
> + * {@link RealDistributionAbstractTest}. See class javadoc of that class
> + * for details.
> + *
> + * @version $Id$
> + * @since 3.0
> + */
> +public class LogNormalDistributionTest extends
> RealDistributionAbstractTest {
> +
> + //-------------- Implementations for abstract methods
> -----------------------
> +
> + /** Creates the default real distribution instance to use in tests. */
> + @Override
> + public LogNormalDistribution makeDistribution() {
> + return new LogNormalDistribution(2.1, 1.4);
> + }
> +
> + /** Creates the default cumulative probability distribution test
> input values */
> + @Override
> + public double[] makeCumulativeTestPoints() {
> + // quantiles computed using R
> + return new double[] { -2.226325228634938, -1.156887023657177,
> + -0.643949578356075, -0.2027950777320613,
> + 0.305827808237559, 6.42632522863494,
> + 5.35688702365718, 4.843949578356074,
> + 4.40279507773206, 3.89417219176244 };
> + }
> +
> + /** Creates the default cumulative probability density test expected
> values */
> + @Override
> + public double[] makeCumulativeTestValues() {
> + return new double[] { 0, 0, 0, 0, 0.00948199951485,
> 0.432056525076,
> + 0.381648158697, 0.354555726206,
> 0.329513316888,
> + 0.298422824228 };
> + }
> +
> + /** Creates the default probability density test expected values */
> + @Override
> + public double[] makeDensityTestValues() {
> + return new double[] { 0, 0, 0, 0, 0.0594218160072,
> 0.0436977691036,
> + 0.0508364857798, 0.054873528325,
> 0.0587182664085,
> + 0.0636229042785 };
> + }
> +
> + /**
> + * Creates the default inverse cumulative probability distribution
> test
> + * input values.
> + */
> + @Override
> + public double[] makeInverseCumulativeTestPoints() {
> + // Exclude the test points less than zero, as they have cumulative
> + // probability of zero, meaning the inverse returns zero, and not
> the
> + // points less than zero.
> + double[] points = makeCumulativeTestValues();
> + double[] points2 = new double[points.length - 4];
> + System.arraycopy(points, 4, points2, 0, points2.length - 4);
> + return points2;
> + //return Arrays.copyOfRange(points, 4, points.length - 4);
> + }
> +
> + /**
> + * Creates the default inverse cumulative probability test expected
> + * values.
> + */
> + @Override
> + public double[] makeInverseCumulativeTestValues() {
> + // Exclude the test points less than zero, as they have cumulative
> + // probability of zero, meaning the inverse returns zero, and not
> the
> + // points less than zero.
> + double[] points = makeCumulativeTestPoints();
> + double[] points2 = new double[points.length - 4];
> + System.arraycopy(points, 4, points2, 0, points2.length - 4);
> + return points2;
> + //return Arrays.copyOfRange(points, 1, points.length - 4);
> + }
> +
> + // --------------------- Override tolerance --------------
> + @Override
> + public void setUp() throws Exception {
> + super.setUp();
> +
> setTolerance(LogNormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
> + }
> +
> + //---------------------------- Additional test cases
> -------------------------
> +
> + private void verifyQuantiles() throws Exception {
> + LogNormalDistribution distribution =
> (LogNormalDistribution)getDistribution();
> + double mu = distribution.getScale();
> + double sigma = distribution.getShape();
> + setCumulativeTestPoints( new double[] { mu - 2 *sigma, mu - sigma,
> + mu, mu + sigma, mu + 2 *
> sigma,
> + mu + 3 * sigma,mu + 4 *
> sigma,
> + mu + 5 * sigma });
> + verifyCumulativeProbabilities();
> + }
> +
> + @Test
> + public void testQuantiles() throws Exception {
> + setCumulativeTestValues(new double[] {0, 0.0396495152787,
> + 0.16601209243,
> 0.272533253269,
> + 0.357618409638,
> 0.426488363093,
> + 0.483255136841,
> 0.530823013877});
> + setDensityTestValues(new double[] {0, 0.0873055825147,
> 0.0847676303432,
> + 0.0677935186237,
> 0.0544105523058,
> + 0.0444614628804,
> 0.0369750288945,
> + 0.0312206409653});
> + verifyQuantiles();
> + verifyDensities();
> +
> + setDistribution(new LogNormalDistribution(0, 1));
> + setCumulativeTestValues(new double[] {0, 0, 0, 0.5,
> 0.755891404214,
> + 0.864031392359,
> 0.917171480998,
> + 0.946239689548});
> + setDensityTestValues(new double[] {0, 0, 0, 0.398942280401,
> + 0.156874019279, 0.07272825614,
> + 0.0381534565119,
> 0.0218507148303});
> + verifyQuantiles();
> + verifyDensities();
> +
> + setDistribution(new LogNormalDistribution(0, 0.1));
> + setCumulativeTestValues(new double[] {0, 0, 0, 1.28417563064e-117,
> + 1.39679883412e-58,
> + 1.09839325447e-33,
> + 2.52587961726e-20,
> + 2.0824223487e-12});
> + setDensityTestValues(new double[] {0, 0, 0, 2.96247992535e-114,
> + 1.1283370232e-55,
> 4.43812313223e-31,
> + 5.85346445002e-18,
> + 2.9446618076e-10});
> + verifyQuantiles();
> + verifyDensities();
> + }
> +
> + @Test
> + public void testInverseCumulativeProbabilityExtremes() throws
> Exception {
> + setInverseCumulativeTestPoints(new double[] {0, 1});
> + setInverseCumulativeTestValues(
> + new double[] {0, Double.POSITIVE_INFINITY});
> + verifyInverseCumulativeProbabilities();
> + }
> +
> + @Test
> + public void testGetMean() {
> + LogNormalDistribution distribution =
> (LogNormalDistribution)getDistribution();
> + Assert.assertEquals(2.1, distribution.getScale(), 0);
> + }
> +
> + @Test
> + public void testGetStandardDeviation() {
> + LogNormalDistribution distribution =
> (LogNormalDistribution)getDistribution();
> + Assert.assertEquals(1.4, distribution.getShape(), 0);
> + }
> +
> + @Test(expected=NotStrictlyPositiveException.class)
> + public void testPreconditions() {
> + new LogNormalDistribution(1, 0);
> + }
> +
> + @Test
> + public void testDensity() {
> + double [] x = new double[]{-2, -1, 0, 1, 2};
> + // R 2.13: print(dlnorm(c(-2,-1,0,1,2)), digits=10)
> + checkDensity(0, 1, x, new double[] { 0.0000000000, 0.0000000000,
> + 0.0000000000, 0.3989422804,
> + 0.1568740193 });
> + // R 2.13: print(dlnorm(c(-2,-1,0,1,2), mean=1.1), digits=10)
> + checkDensity(1.1, 1, x, new double[] { 0.0000000000, 0.0000000000,
> + 0.0000000000, 0.2178521770,
> + 0.1836267118});
> + }
> +
> + private void checkDensity(double mean, double sd, double[] x,
> double[] expected) {
> + LogNormalDistribution d = new LogNormalDistribution(mean, sd);
> + for (int i = 0; i < x.length; i++) {
> + Assert.assertEquals(expected[i], d.density(x[i]), 1e-9);
> + }
> + }
> +
> + /**
> + * Check to make sure top-coding of extreme values works correctly.
> + * Verifies fixes for JIRA MATH-167, MATH-414
> + */
> + @Test
> + public void testExtremeValues() throws Exception {
> + LogNormalDistribution d = new LogNormalDistribution(0, 1);
> + for (int i = 0; i < 1e5; i++) { // make sure no convergence
> exception
> + double upperTail = d.cumulativeProbability(i);
> + if (i <= 72) { // make sure not top-coded
> + Assert.assertTrue(upperTail < 1.0d);
> + }
> + else { // make sure top coding not reversed
> + Assert.assertTrue(upperTail > 0.99999);
> + }
> + }
> +
> + Assert.assertEquals(d.cumulativeProbability(Double.MAX_VALUE), 1,
> 0);
> + Assert.assertEquals(d.cumulativeProbability(-Double.MAX_VALUE),
> 0, 0);
> +
> Assert.assertEquals(d.cumulativeProbability(Double.POSITIVE_INFINITY), 1,
> 0);
> +
> Assert.assertEquals(d.cumulativeProbability(Double.NEGATIVE_INFINITY), 0,
> 0);
> + }
> +
> + @Test
> + public void testMeanVariance() {
> + final double tol = 1e-9;
> + LogNormalDistribution dist;
> +
> + dist = new LogNormalDistribution(0, 1);
> + Assert.assertEquals(dist.getNumericalMean(), 1.6487212707001282,
> tol);
> + Assert.assertEquals(dist.getNumericalVariance(),
> + 4.670774270471604, tol);
> +
> + dist = new LogNormalDistribution(2.2, 1.4);
> + Assert.assertEquals(dist.getNumericalMean(), 24.046753552064498,
> tol);
> + Assert.assertEquals(dist.getNumericalVariance(),
> + 3526.913651880464, tol);
> +
> + dist = new LogNormalDistribution(-2000.9, 10.4);
> + Assert.assertEquals(dist.getNumericalMean(), 0.0, tol);
> + Assert.assertEquals(dist.getNumericalVariance(), 0.0, tol);
> + }
> +}
>
> Propchange:
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/LogNormalDistributionTest.java
>
> ------------------------------------------------------------------------------
> svn:eol-style = native
>
> Propchange:
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/LogNormalDistributionTest.java
>
> ------------------------------------------------------------------------------
> svn:keywords = Author Date Id Revision
>
> Modified:
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/NormalDistributionTest.java
> URL:
> http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/NormalDistributionTest.java?rev=1232324&r1=1232323&r2=1232324&view=diff
>
> ==============================================================================
> ---
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/NormalDistributionTest.java
> (original)
> +++
> commons/proper/math/trunk/src/test/java/org/apache/commons/math/distribution/NormalDistributionTest.java
> Tue Jan 17 07:12:02 2012
> @@ -22,9 +22,9 @@ import org.junit.Assert;
> import org.junit.Test;
>
> /**
> - * Test cases for NormalDistribution.
> - * Extends ContinuousDistributionAbstractTest. See class javadoc for
> - * ContinuousDistributionAbstractTest for details.
> + * Test cases for {@link NormalDistribution}. Extends
> + * {@link RealDistributionAbstractTest}. See class javadoc of that class
> + * for details.
> *
> * @version $Id$
> */
> @@ -32,7 +32,7 @@ public class NormalDistributionTest exte
>
> //-------------- Implementations for abstract methods
> -----------------------
>
> - /** Creates the default continuous distribution instance to use in
> tests. */
> + /** Creates the default real distribution instance to use in tests. */
> @Override
> public NormalDistribution makeDistribution() {
> return new NormalDistribution(2.1, 1.4);
> @@ -170,8 +170,7 @@ public class NormalDistributionTest exte
>
> Assert.assertEquals(distribution.cumulativeProbability(-Double.MAX_VALUE),
> 0, 0);
>
> Assert.assertEquals(distribution.cumulativeProbability(Double.POSITIVE_INFINITY),
> 1, 0);
>
> Assert.assertEquals(distribution.cumulativeProbability(Double.NEGATIVE_INFINITY),
> 0, 0);
> -
> - }
> + }
>
> @Test
> public void testMath280() {
>
>
>
Re: [MATH] Re: svn commit: r1232324 - in /commons/proper/math/trunk/src:
main/java/org/apache/commons/math/distribution/ test/R/ test/java/org/apache/commons/math/distribution/
Posted by Sébastien Brisard <se...@m4x.org>.
2012/1/17 Mikkel Meyer Andersen <mi...@mikl.dk>:
> 2012/1/17 Sébastien Brisard <se...@m4x.org>:
>> Hi Mikkel,
>>> Hi,
>>>
>>> You write for x >= 0, but isn't only x > 0?
>>>
>>> Cheers, Mikkel.
>>>
>> OK (although the formula does make sense, since the limit exists).
>> Thanks for pointing that out,
>> Sébastien
>>
> Hi,
>
> I agree, but normally the support is not including the extended real
> numbers, which I thought we should obey :-).
>
> Cheers, Mikkel.
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: dev-unsubscribe@commons.apache.org
> For additional commands, e-mail: dev-help@commons.apache.org
>
What I meant by that is
1. I don't like to be proved wrong :-D,
2. more seriously: the boundary case is correctly handled by Dennis'
code, while the human reader can still make sense (I believe) of the
Javadoc.
Anyhow, you probably have noticed that I've already changed that in r1232325.
Many thanks again for helping remove ambiguities from the Javadoc.
Sébastien
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Re: [MATH] Re: svn commit: r1232324 - in /commons/proper/math/trunk/src:
main/java/org/apache/commons/math/distribution/ test/R/ test/java/org/apache/commons/math/distribution/
Posted by Mikkel Meyer Andersen <mi...@mikl.dk>.
2012/1/17 Sébastien Brisard <se...@m4x.org>:
> Hi Mikkel,
>> Hi,
>>
>> You write for x >= 0, but isn't only x > 0?
>>
>> Cheers, Mikkel.
>>
> OK (although the formula does make sense, since the limit exists).
> Thanks for pointing that out,
> Sébastien
>
Hi,
I agree, but normally the support is not including the extended real
numbers, which I thought we should obey :-).
Cheers, Mikkel.
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Re: [MATH] Re: svn commit: r1232324 - in /commons/proper/math/trunk/src:
main/java/org/apache/commons/math/distribution/ test/R/ test/java/org/apache/commons/math/distribution/
Posted by Sébastien Brisard <se...@m4x.org>.
Hi Mikkel,
> Hi,
>
> You write for x >= 0, but isn't only x > 0?
>
> Cheers, Mikkel.
>
OK (although the formula does make sense, since the limit exists).
Thanks for pointing that out,
Sébastien
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