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Fixed CheckStyle warnings. 

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1372208 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Gilles Sadowski 2012-08-12 22:55:34 +00:00
parent 0f9adb1875
commit 1ec0b8d527
3 changed files with 64 additions and 31 deletions
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18
src/main/java/org/apache/commons/math3/distribution/AbstractMultivariateRealDistribution.java
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@ -21,17 +21,17 @@ import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.random.RandomGenerator;
/**
* Base class for probability distributions on the multivariate reals.
* Default implementations are provided for some of the methods that do
* not vary from distribution to distribution.
*
* Base class for multivariate probability distributions.
*
* @version $Id$
* @since 3.1
*/
public abstract class AbstractMultivariateRealDistribution
implements MultivariateRealDistribution {
/** The number of dimensions or columns in the multivariate distribution. */
private final int numDimensions;
/** RNG instance used to generate samples from the distribution. */
protected final RandomGenerator random;
/** The number of dimensions or columns in the multivariate distribution. */
private final int numDimensions;
/**
* @param rng Random number generator.
@ -49,8 +49,10 @@ public abstract class AbstractMultivariateRealDistribution
}
/**
*
* @return the number of dimensions in the multivariate distribution .
* Gets the number of dimensions (i.e. the number of random variables) of
* the distribution.
*
* @return the number of dimensions.
*/
public int getDimensions() {
return numDimensions;

47
src/main/java/org/apache/commons/math3/distribution/MultivariateNormalDistribution.java
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@ -1,3 +1,19 @@
/*
* 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.DimensionMismatchException;
@ -13,11 +29,14 @@ import org.apache.commons.math3.util.MathArrays;
/**
* Implementation of the multivariate normal (Gaussian) distribution.
*
*
* @see <a href="http://en.wikipedia.org/wiki/Multivariate_normal_distribution">
* Multivariate normal distribution (Wikipedia)</a>
* @see <a href="http://mathworld.wolfram.com/MultivariateNormalDistribution.html">
* Multivariate normal distribution (MathWorld)</a>
*
* @version $Id$
* @since 3.1
*/
public class MultivariateNormalDistribution
extends AbstractMultivariateRealDistribution {
@ -39,9 +58,15 @@ public class MultivariateNormalDistribution
* The number of dimensions is equal to the length of the mean vector
* and to the number of rows and columns of the covariance matrix.
* It is frequently written as "p" in formulae.
*
*
* @param means Vector of means.
* @param covariances Covariance matrix.
* @throws DimensionMismatchException if the arrays length are
* inconsistent.
* @throws SingularMatrixException if the eigenvalue decomposition cannot
* be performed on the provided covariance matrix.
* @throws NonPositiveDefiniteMatrixException if any of the eigenvalues is
* negative.
*/
public MultivariateNormalDistribution(final double[] means,
final double[][] covariances)
@ -58,10 +83,16 @@ public class MultivariateNormalDistribution
* The number of dimensions is equal to the length of the mean vector
* and to the number of rows and columns of the covariance matrix.
* It is frequently written as "p" in formulae.
*
*
* @param rng Random Number Generator.
* @param means Vector of means.
* @param covariances Covariance matrix.
* @throws DimensionMismatchException if the arrays length are
* inconsistent.
* @throws SingularMatrixException if the eigenvalue decomposition cannot
* be performed on the provided covariance matrix.
* @throws NonPositiveDefiniteMatrixException if any of the eigenvalues is
* negative.
*/
public MultivariateNormalDistribution(RandomGenerator rng,
final double[] means,
@ -126,7 +157,7 @@ public class MultivariateNormalDistribution
/**
* Gets the mean vector.
*
*
* @return the mean vector.
*/
public double[] getMeans() {
@ -135,13 +166,13 @@ public class MultivariateNormalDistribution
/**
* Gets the covariance matrix.
*
*
* @return the covariance matrix.
*/
public RealMatrix getCovariances() {
return covarianceMatrix.copy();
}
/** {@inheritDoc} */
public double density(final double[] vals) throws DimensionMismatchException {
final int dim = getDimensions();
@ -157,7 +188,7 @@ public class MultivariateNormalDistribution
/**
* Gets the square root of each element on the diagonal of the covariance
* matrix.
*
*
* @return the standard deviations.
*/
public double[] getStandardDeviations() {
@ -190,7 +221,7 @@ public class MultivariateNormalDistribution
/**
* Computes the term used in the exponent (see definition of the distribution).
*
*
* @param values Values at which to compute density.
* @return the multiplication factor of density calculations.
*/

30
src/main/java/org/apache/commons/math3/distribution/MultivariateRealDistribution.java
View File

@ -16,17 +16,17 @@
*/
package org.apache.commons.math3.distribution;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.OutOfRangeException;
/**
* Base interface for multivariate distributions on the reals.
*
*
* This is based largely on the RealDistribution interface, but cumulative
* distribution functions are not required because they are often quite
* difficult to compute for multivariate distributions.
*
* @version $Id$
* @since 3.1
*/
public interface MultivariateRealDistribution {
/**
@ -34,7 +34,7 @@ public interface MultivariateRealDistribution {
* 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 Point at which the PMF is evaluated.
* @return the value of the probability mass function at point {@code x}.
*/
@ -47,11 +47,11 @@ public interface MultivariateRealDistribution {
* 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.
*
*
* @param x Point at which the PDF is evaluated.
* @return the value of the probability density function at point {@code x}.
*/
double density(double[] x) throws DimensionMismatchException;
double density(double[] x);
/**
* Access the lower bound of the support.
@ -60,7 +60,7 @@ public interface MultivariateRealDistribution {
* <p>
* <code>inf {x in R | P(X <= x) > 0}</code>.
* </p>
*
*
* @return the lower bound of the support (might be
* {@code Double.NEGATIVE_INFINITY}).
*/
@ -73,7 +73,7 @@ public interface MultivariateRealDistribution {
* <p>
* <code>inf {x in R | P(X <= x) = 1}</code>.
* </p>
*
*
* @return the upper bound of the support (might be
* {@code Double.POSITIVE_INFINITY}).
*/
@ -82,7 +82,7 @@ public interface MultivariateRealDistribution {
/**
* Gets information about whether the lower bound of the support is
* inclusive or not.
*
*
* @return whether the lower bound of the support is inclusive or not.
*/
boolean isSupportLowerBoundInclusive();
@ -90,7 +90,7 @@ public interface MultivariateRealDistribution {
/**
* gets information about whether the upper bound of the support is
* inclusive or not.
*
*
* @return whether the upper bound of the support is inclusive or not.
*/
boolean isSupportUpperBoundInclusive();
@ -99,28 +99,28 @@ public interface MultivariateRealDistribution {
* Gets information about whether the support is connected (i.e. all
* values between the lower and upper bound of the support are included
* in the support).
*
*
* @return whether the support is connected or not.
*/
boolean isSupportConnected();
/**
* Reseeds the random generator used to generate samples.
*
*
* @param seed Seed with which to initialize the random number generator.
*/
void reseedRandomGenerator(long seed);
/**
* Generates a random value vector sampled from this distribution.
*
*
* @return a random value vector.
*/
double[] sample();
/**
* Generates a list of a random value vectors from the distribution.
*
*
* @param sampleSize the number of random vectors to generate.
* @return an array representing the random samples.
* @throws org.apache.commons.math3.exception.NotStrictlyPositiveException