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Date: Tue, 17 Jan 2012 08:16:38 +0100
Message-ID: 
 <CAPw+RifMacnCKSC8KcjucX5VaAn19QztV5gWqh=_vFC5GBFmGg@mail.gmail.com>
Subject: [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/
From: Mikkel Meyer Andersen <mikl@mikl.dk>
To: dev@commons.apache.org
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--20cf30563c21ae6b9704b6b41c85
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Hi,

You write for x >= 0, but isn't only x > 0?

Cheers, Mikkel.
Den 17/01/2012 08.12 skrev <celestin@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;
> +
> +    /** &radic;(2 &pi;) */
> +    private static final double SQRT2PI = FastMath.sqrt(2 * FastMath.PI);
> +
> +    /** &radic;(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() {
>
>
>

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