Added SemiVariance. JIRA: MATH-323. Reported and patched by Larry Diamond.
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@910264 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz
parent
993dcad4b1
commit
f822b3285a
3
pom.xml
3
pom.xml
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@ -126,6 +126,9 @@
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<contributor>
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<name>Benjamin Croizet</name>
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</contributor>
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<contributor>
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<name>Larry Diamond</name>
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</contributor>
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<contributor>
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<name>Rodrigo di Lorenzo Lopes</name>
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</contributor>
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@ -649,6 +649,7 @@ public class MessagesResources_fr
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"valeur de quantile {0} hors bornes, doit \u00eatre dans l''intervalle ]0, 100]" },
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// org.apache.commons.math.stat.descriptive.moment.Variance
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// org.apache.commons.math.stat.descriptive.moment.SemiVariance
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// org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic
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// org.apache.commons.math.stat.descriptive.AbstractUnivariateStatistic
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{ "input values array is null",
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@ -0,0 +1,377 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math.stat.descriptive.moment;
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import java.io.Serializable;
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import org.apache.commons.math.MathRuntimeException;
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import org.apache.commons.math.stat.descriptive.AbstractUnivariateStatistic;
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/**
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* <p>Computes the semivariance of a set of values with respect to a given cutoff value.
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* We define the <i>downside semivariance</i> of a set of values <code>x</code>
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* against the <i>cutoff value</i> <code>cutoff</code> to be <br/>
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* <code>Σ (x[i] - target)<sup>2</sup> / df</code> <br/>
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* where the sum is taken over all <code>i</code> such that <code>x[i] < cutoff</code>
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* and <code>df</code> is the length of <code>x</code> (non-bias-corrected) or
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* one less than this number (bias corrected). The <i>upside semivariance</i>
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* is defined similarly, with the sum taken over values of <code>x</code> that
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* exceed the cutoff value.</p>
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*
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* <p>The cutoff value defaults to the mean, bias correction defaults to <code>true</code>
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* and the "variance direction" (upside or downside) defaults to downside. The variance direction
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* and bias correction may be set using property setters or their values can provided as
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* parameters to {@link #evaluate(double[], double, Direction, boolean, int, int)}.</p>
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*
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* <p>If the input array is null, <code>evaluate</code> methods throw
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* <code>IllegalArgumentException.</code> If the array has length 1, <code>0</code>
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* is returned, regardless of the value of the <code>cutoff.</code>
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*
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* <p><strong>Note that this class is not intended to be threadsafe.</strong> If
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* multiple threads access an instance of this class concurrently, and one or
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* more of these threads invoke property setters, external synchronization must
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* be provided to ensure correct results.</p>
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*
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* @version $Revision$ $Date$
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* @since 2.1
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*/
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public class SemiVariance extends AbstractUnivariateStatistic implements Serializable {
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/** Serializable version identifier */
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private static final long serialVersionUID = -2653430366886024994L;
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/**
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* Determines whether or not bias correction is applied when computing the
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* value of the statisic. True means that bias is corrected.
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*/
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private boolean biasCorrected = true;
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/**
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* Determines whether to calculate downside or upside SemiVariance.
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*/
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private Direction varianceDirection = Direction.DOWNSIDE;
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/**
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* The UPSIDE Direction is used to specify that the observations above the
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* cutoff point will be used to calculate SemiVariance.
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*/
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public static final Direction UPSIDE_VARIANCE = Direction.UPSIDE;
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/**
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* The DOWNSIDE Direction is used to specify that the observations below
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* the cutoff point will be used to calculate SemiVariance
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*/
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public static final Direction DOWNSIDE_VARIANCE = Direction.DOWNSIDE;
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/**
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* Constructs a SemiVariance with default (true) <code>biasCorrected</code>
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* property and default (Downside) <code>varianceDirection</code> property.
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*/
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public SemiVariance() {
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}
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/**
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* Constructs a SemiVariance with the specified <code>biasCorrected</code>
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* property and default (Downside) <code>varianceDirection</code> property.
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*
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* @param biasCorrected setting for bias correction - true means
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* bias will be corrected and is equivalent to using the argumentless
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* constructor
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*/
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public SemiVariance(final boolean biasCorrected) {
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this.biasCorrected = biasCorrected;
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}
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/**
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* Constructs a SemiVariance with the specified <code>Direction</code> property
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* and default (true) <code>biasCorrected</code> property
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*
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* @param direction setting for the direction of the SemiVariance
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* to calculate
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*/
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public SemiVariance(final Direction direction) {
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this.varianceDirection = direction;
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}
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/**
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* Constructs a SemiVariance with the specified <code>isBiasCorrected</code>
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* property and the specified <code>Direction</code> property.
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*
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* @param corrected setting for bias correction - true means
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* bias will be corrected and is equivalent to using the argumentless
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* constructor
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*
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* @param direction setting for the direction of the SemiVariance
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* to calculate
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*/
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public SemiVariance(final boolean corrected, final Direction direction) {
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this.biasCorrected = corrected;
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this.varianceDirection = direction;
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}
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/**
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* Copy constructor, creates a new {@code SemiVariance} identical
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* to the {@code original}
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*
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* @param original the {@code SemiVariance} instance to copy
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*/
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public SemiVariance(final SemiVariance original) {
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copy(original, this);
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}
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/**
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* {@inheritDoc}
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*/
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@Override
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public SemiVariance copy() {
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SemiVariance result = new SemiVariance();
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copy(this, result);
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return result;
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}
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/**
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* Copies source to dest.
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* <p>Neither source nor dest can be null.</p>
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*
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* @param source SemiVariance to copy
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* @param dest SemiVariance to copy to
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* @throws NullPointerException if either source or dest is null
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*/
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public static void copy(final SemiVariance source, SemiVariance dest) {
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dest.biasCorrected = source.biasCorrected;
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dest.varianceDirection = source.varianceDirection;
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}
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/**
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* This method calculates {@link SemiVariance} for the entire array against the mean, using
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* instance properties varianceDirection and biasCorrection.
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*
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* @param values the input array
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* @return the SemiVariance
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* @throws IllegalArgumentException if values is null
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*
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*/
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@Override
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public double evaluate(final double[] values) {
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if (values == null) {
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throw MathRuntimeException.createIllegalArgumentException("input values array is null");
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}
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return evaluate(values, 0, values.length);
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}
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/**
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* <p>Returns the {@link SemiVariance} of the designated values against the mean, using
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* instance properties varianceDirection and biasCorrection.</p>
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*
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* <p>Returns <code>NaN</code> if the array is empty and throws
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* <code>IllegalArgumentException</code> if the array is null.</p>
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*
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* @param values the input array
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* @param start index of the first array element to include
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* @param length the number of elements to include
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* @return the SemiVariance
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* @throws IllegalArgumentException if the parameters are not valid
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*
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*/
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@Override
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public double evaluate(final double[] values, final int start, final int length) {
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double m = (new Mean()).evaluate(values, start, length);
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return evaluate(values, m, varianceDirection, biasCorrected, 0, values.length);
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}
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/**
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* This method calculates {@link SemiVariance} for the entire array against the mean, using
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* the current value of the biasCorrection instance property.
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*
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* @param values the input array
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* @param direction the {@link Direction} of the semivariance
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* @return the SemiVariance
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* @throws IllegalArgumentException if values is null
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*
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*/
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public double evaluate(final double[] values, Direction direction) {
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double m = (new Mean()).evaluate(values);
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return evaluate (values, m, direction, biasCorrected, 0, values.length);
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}
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/**
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* <p>Returns the {@link SemiVariance} of the designated values against the cutoff, using
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* instance properties variancDirection and biasCorrection.</p>
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*
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* <p>Returns <code>NaN</code> if the array is empty and throws
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* <code>IllegalArgumentException</code> if the array is null.</p>
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*
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* @param values the input array
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* @param cutoff the reference point
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* @return the SemiVariance
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* @throws IllegalArgumentException if values is null
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*/
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public double evaluate(final double[] values, final double cutoff) {
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return evaluate(values, cutoff, varianceDirection, biasCorrected, 0, values.length);
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}
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/**
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* <p>Returns the {@link SemiVariance} of the designated values against the cutoff in the
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* given direction, using the current value of the biasCorrection instance property.</p>
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*
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* <p>Returns <code>NaN</code> if the array is empty and throws
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* <code>IllegalArgumentException</code> if the array is null.</p>
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*
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* @param values the input array
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* @param cutoff the reference point
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* @param direction the {@link Direction} of the semivariance
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* @return the SemiVariance
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* @throws IllegalArgumentException if values is null
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*/
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public double evaluate(final double[] values, final double cutoff, final Direction direction) {
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return evaluate(values, cutoff, direction, biasCorrected, 0, values.length);
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}
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/**
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* <p>Returns the {@link SemiVariance} of the designated values against the cutoff
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* in the given direction with the provided bias correction.</p>
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*
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* <p>Returns <code>NaN</code> if the array is empty and throws
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* <code>IllegalArgumentException</code> if the array is null.</p>
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*
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* @param values the input array
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* @param cutoff the reference point
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* @param direction the {@link Direction} of the semivariance
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* @param corrected the BiasCorrection flag
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* @param start index of the first array element to include
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* @param length the number of elements to include
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* @return the SemiVariance
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* @throws IllegalArgumentException if the parameters are not valid
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*
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*/
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public double evaluate (final double[] values, final double cutoff, final Direction direction,
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final boolean corrected, final int start, final int length) {
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test(values, start, length);
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if (values.length == 0) {
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return Double.NaN;
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} else {
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if (values.length == 1) {
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return 0.0;
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} else {
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final boolean booleanDirection = direction.getDirection();
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double dev = 0.0;
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double sumsq = 0.0;
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for (int i = start; i < length; i++) {
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if ((values[i] > cutoff) == booleanDirection) {
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dev = values[i] - cutoff;
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sumsq += dev * dev;
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}
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}
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if (corrected) {
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return sumsq / (length - 1.0);
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} else {
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return sumsq / length;
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}
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}
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}
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}
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/**
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* Returns true iff biasCorrected property is set to true.
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*
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* @return the value of biasCorrected.
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*/
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public boolean isBiasCorrected() {
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return biasCorrected;
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}
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/**
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* Sets the biasCorrected property.
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*
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* @param biasCorrected new biasCorrected property value
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*/
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public void setBiasCorrected(boolean biasCorrected) {
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this.biasCorrected = biasCorrected;
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}
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/**
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* Returns the varianceDirection property.
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*
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* @return the varianceDirection
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*/
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public Direction getVarianceDirection () {
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return varianceDirection;
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}
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/**
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* Sets the variance direction
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*
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* @param varianceDirection the direction of the semivariance
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*/
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public void setVarianceDirection(Direction varianceDirection) {
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this.varianceDirection = varianceDirection;
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}
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/**
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* The direction of the semivariance - either upside or downside. The direction
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* is represented by boolean, with true corresponding to UPSIDE semivariance.
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*/
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public enum Direction {
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/**
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* The UPSIDE Direction is used to specify that the observations above the
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* cutoff point will be used to calculate SemiVariance
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*/
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UPSIDE (true),
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/**
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* The DOWNSIDE Direction is used to specify that the observations below
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* the cutoff point will be used to calculate SemiVariance
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*/
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DOWNSIDE (false);
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/**
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* boolean value UPSIDE <-> true
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*/
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private boolean direction;
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/**
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* Create a Direction with the given value.
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*
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* @param b boolean value representing the Direction. True corresponds to UPSIDE.
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*/
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Direction (boolean b) {
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direction = b;
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}
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/**
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* Returns the value of this Direction. True corresponds to UPSIDE.
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*
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* @return true if direction is UPSIDE; false otherwise
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*/
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boolean getDirection () {
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return direction;
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}
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}
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}
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@ -39,6 +39,9 @@ The <action> type attribute can be add,update,fix,remove.
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</properties>
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<body>
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<release version="2.1" date="TBD" description="TBD">
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<action dev="psteitz" type="add" issue="MATH-323" due-to="Larry Diamond">
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Added SemiVariance statistic.
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</action>
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<action dev="luc" type="add" issue="MATH-341" >
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Added a warning in the getCoefficients method documentation for
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PolynomialFunctionLagrangeForm. Computation may be ill-conditioned
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@ -0,0 +1,142 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
|
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* (the "License"); you may not use this file except in compliance with
|
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* the License. You may obtain a copy of the License at
|
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
|
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
|
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* limitations under the License.
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*/
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package org.apache.commons.math.stat.descriptive.moment;
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import org.apache.commons.math.TestUtils;
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import org.apache.commons.math.stat.StatUtils;
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import junit.framework.TestCase;
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public class SemiVarianceTest extends TestCase {
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public void testInsufficientData() {
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double[] nothing = null;
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SemiVariance sv = new SemiVariance();
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try {
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sv.evaluate(nothing);
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fail("null is not a valid data array.");
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} catch (IllegalArgumentException iae) {
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}
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try {
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sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
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sv.evaluate(nothing);
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fail("null is not a valid data array.");
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} catch (IllegalArgumentException iae) {
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}
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nothing = new double[] {};
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assertTrue(Double.isNaN(sv.evaluate(nothing)));
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}
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public void testSingleDown() {
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SemiVariance sv = new SemiVariance();
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double[] values = { 50.0d };
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double singletest = sv.evaluate(values);
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assertEquals(0.0d, singletest, 0);
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}
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public void testSingleUp() {
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SemiVariance sv = new SemiVariance(SemiVariance.UPSIDE_VARIANCE);
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double[] values = { 50.0d };
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double singletest = sv.evaluate(values);
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assertEquals(0.0d, singletest, 0);
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}
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public void testSample() {
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final double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
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final int length = values.length;
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final double mean = StatUtils.mean(values); // 6.333...
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final SemiVariance sv = new SemiVariance(); // Default bias correction is true
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final double downsideSemiVariance = sv.evaluate(values); // Downside is the default
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assertEquals(TestUtils.sumSquareDev(new double[] {-2d, 2d, 4d, -2d, 3d, 5d}, mean) / (length - 1),
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downsideSemiVariance, 1E-14);
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sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
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final double upsideSemiVariance = sv.evaluate(values);
|
||||
assertEquals(TestUtils.sumSquareDev(new double[] {22d, 11d, 14d}, mean) / (length - 1),
|
||||
upsideSemiVariance, 1E-14);
|
||||
|
||||
// Verify that upper + lower semivariance against the mean sum to variance
|
||||
assertEquals(StatUtils.variance(values), downsideSemiVariance + upsideSemiVariance, 10e-12);
|
||||
}
|
||||
|
||||
public void testPopulation() {
|
||||
double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
|
||||
SemiVariance sv = new SemiVariance(false);
|
||||
|
||||
double singletest = sv.evaluate(values);
|
||||
assertEquals(19.556d, singletest, 0.01d);
|
||||
|
||||
sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
|
||||
singletest = sv.evaluate(values);
|
||||
assertEquals(36.222d, singletest, 0.01d);
|
||||
}
|
||||
|
||||
public void testNonMeanCutoffs() {
|
||||
double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
|
||||
SemiVariance sv = new SemiVariance(false); // Turn off bias correction - use df = length
|
||||
|
||||
double singletest = sv.evaluate(values, 1.0d, SemiVariance.DOWNSIDE_VARIANCE, false, 0, values.length);
|
||||
assertEquals(TestUtils.sumSquareDev(new double[] { -2d, -2d }, 1.0d) / values.length,
|
||||
singletest, 0.01d);
|
||||
|
||||
singletest = sv.evaluate(values, 3.0d, SemiVariance.UPSIDE_VARIANCE, false, 0, values.length);
|
||||
assertEquals(TestUtils.sumSquareDev(new double[] { 4d, 22d, 11d, 14d, 5d }, 3.0d) / values.length, singletest,
|
||||
0.01d);
|
||||
}
|
||||
|
||||
/**
|
||||
* Check that the lower + upper semivariance against the mean sum to the
|
||||
* variance.
|
||||
*/
|
||||
public void testVarianceDecompMeanCutoff() {
|
||||
double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
|
||||
double variance = StatUtils.variance(values);
|
||||
SemiVariance sv = new SemiVariance(true); // Bias corrected
|
||||
sv.setVarianceDirection(SemiVariance.DOWNSIDE_VARIANCE);
|
||||
final double lower = sv.evaluate(values);
|
||||
sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
|
||||
final double upper = sv.evaluate(values);
|
||||
assertEquals(variance, lower + upper, 10e-12);
|
||||
}
|
||||
|
||||
/**
|
||||
* Check that upper and lower semivariances against a cutoff sum to the sum
|
||||
* of squared deviations of the full set of values against the cutoff
|
||||
* divided by df = length - 1 (assuming bias-corrected).
|
||||
*/
|
||||
public void testVarianceDecompNonMeanCutoff() {
|
||||
double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
|
||||
double target = 0;
|
||||
double totalSumOfSquares = TestUtils.sumSquareDev(values, target);
|
||||
SemiVariance sv = new SemiVariance(true); // Bias corrected
|
||||
sv.setVarianceDirection(SemiVariance.DOWNSIDE_VARIANCE);
|
||||
double lower = sv.evaluate(values, target);
|
||||
sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
|
||||
double upper = sv.evaluate(values, target);
|
||||
assertEquals(totalSumOfSquares / (values.length - 1), lower + upper, 10e-12);
|
||||
}
|
||||
|
||||
public void testNoVariance() {
|
||||
final double[] values = {100d, 100d, 100d, 100d};
|
||||
SemiVariance sv = new SemiVariance();
|
||||
assertEquals(0, sv.evaluate(values), 10E-12);
|
||||
assertEquals(0, sv.evaluate(values, 100d), 10E-12);
|
||||
assertEquals(0, sv.evaluate(values, 100d, SemiVariance.UPSIDE_VARIANCE, false, 0, values.length), 10E-12);
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue