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Deprecated abstract factory methods and made DescriptiveStatistics a concrete 

class. Pushed implementations up from DescriptiveStatisticsImpl and deprecated
this class. Made implementations of statistics configurable via setters.


git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@598133 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz 2007-11-26 05:27:05 +00:00
parent 5103a53887
commit 2d4fe56383
7 changed files with 380 additions and 128 deletions
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325
src/java/org/apache/commons/math/stat/descriptive/DescriptiveStatistics.java
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@ -30,27 +30,80 @@ import org.apache.commons.math.stat.descriptive.rank.Min;
import org.apache.commons.math.stat.descriptive.rank.Percentile;
import org.apache.commons.math.stat.descriptive.summary.Sum;
import org.apache.commons.math.stat.descriptive.summary.SumOfSquares;
import org.apache.commons.math.util.ResizableDoubleArray;
/**
* Abstract factory class for univariate statistical summaries.
* Maintains a dataset of values of a single variable and computes descriptive
* statistics based on stored data. The {@link #getWindowSize() windowSize}
* property sets a limit on the number of values that can be stored in the
* dataset. The default value, INFINITE_WINDOW, puts no limit on the size of
* the dataset. This value should be used with caution, as the backing store
* will grow without bound in this case. For very large datasets,
* {@link SummaryStatistics}, which does not store the dataset, should be used
* instead of this class. If <code>windowSize</code> is not INFINITE_WINDOW and
* more values are added than can be stored in the dataset, new values are
* added in a "rolling" manner, with new values replacing the "oldest" values
* in the dataset.
*
* Note: this class is not threadsafe. Use
* {@link SynchronizedDescriptiveStatistics} if concurrent access from multiple
* threads is required.
*
* @version $Revision$ $Date$
*/
public abstract class DescriptiveStatistics implements StatisticalSummary, Serializable {
public class DescriptiveStatistics implements StatisticalSummary, Serializable {
/** Serialization UID */
private static final long serialVersionUID = 5188298269533339922L;
/** hold the window size **/
protected int windowSize = INFINITE_WINDOW;
/**
* Stored data values
*/
protected ResizableDoubleArray eDA = new ResizableDoubleArray();
// Cached implementation instances
// Can be reset by setters
private UnivariateStatistic meanImpl = null;
private UnivariateStatistic geometricMeanImpl = null;
private UnivariateStatistic kurtosisImpl = null;
private UnivariateStatistic maxImpl = null;
private UnivariateStatistic minImpl = null;
private UnivariateStatistic percentileImpl = null;
private UnivariateStatistic skewnessImpl = null;
private UnivariateStatistic varianceImpl = null;
private UnivariateStatistic sumsqImpl = null;
private UnivariateStatistic sumImpl = null;
/**
* Construct a DescriptiveStatistics instance with an infinite window
*/
public DescriptiveStatistics() {
}
/**
* Construct a DescriptiveStatistics instance with the specified window
*
* @param window the window size.
*/
public DescriptiveStatistics(int window) {
super();
setWindowSize(window);
}
/**
* Create an instance of a <code>DescriptiveStatistics</code>
* @param cls the type of <code>DescriptiveStatistics</code> object to
* create.
* @return a new factory.
* @return a new instance.
* @throws InstantiationException is thrown if the object can not be
* created.
* @throws IllegalAccessException is thrown if the type's default
* constructor is not accessible.
* @deprecated to be removed in commons-math 2.0
*/
public static DescriptiveStatistics newInstance(Class cls) throws InstantiationException, IllegalAccessException {
return (DescriptiveStatistics)cls.newInstance();
@ -58,7 +111,8 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
/**
* Create an instance of a <code>DescriptiveStatistics</code>
* @return a new factory.
* @return a new DescriptiveStatistics instance.
* @deprecated to be removed in commons-math 2.0
*/
public static DescriptiveStatistics newInstance() {
DescriptiveStatistics factory = null;
@ -82,10 +136,24 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
public static final int INFINITE_WINDOW = -1;
/**
* Adds the value to the set of numbers
* Adds the value to the dataset. If the dataset is at the maximum size
* (i.e., the number of stored elements equals the currently configured
* windowSize), the first (oldest) element in the dataset is discarded
* to make room for the new value.
*
* @param v the value to be added
*/
public abstract void addValue(double v);
public void addValue(double v) {
if (windowSize != INFINITE_WINDOW) {
if (getN() == windowSize) {
eDA.addElementRolling(v);
} else if (getN() < windowSize) {
eDA.addElement(v);
}
} else {
eDA.addElement(v);
}
}
/**
* Returns the <a href="http://www.xycoon.com/arithmetic_mean.htm">
@ -93,7 +161,7 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* @return The mean or Double.NaN if no values have been added.
*/
public double getMean() {
return apply(new Mean());
return apply(getMeanImpl());
}
/**
@ -103,7 +171,7 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* or if the productof the available values is less than or equal to 0.
*/
public double getGeometricMean() {
return apply(new GeometricMean());
return apply(getGeometricMeanImpl());
}
/**
@ -112,7 +180,7 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* or 0.0 for a single value set.
*/
public double getVariance() {
return apply(new Variance());
return apply(getVarianceImpl());
}
/**
@ -134,12 +202,12 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
/**
* Returns the skewness of the available values. Skewness is a
* measure of the assymetry of a given distribution.
* measure of the asymmetry of a given distribution.
* @return The skewness, Double.NaN if no values have been added
* or 0.0 for a value set &lt;=2.
*/
public double getSkewness() {
return apply(new Skewness());
return apply(getSkewnessImpl());
}
/**
@ -149,7 +217,7 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* for a value set &lt;=3.
*/
public double getKurtosis() {
return apply(new Kurtosis());
return apply(getKurtosisImpl());
}
/**
@ -157,7 +225,7 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* @return The max or Double.NaN if no values have been added.
*/
public double getMax() {
return apply(new Max());
return apply(getMaxImpl());
}
/**
@ -165,21 +233,23 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* @return The min or Double.NaN if no values have been added.
*/
public double getMin() {
return apply(new Min());
return apply(getMinImpl());
}
/**
* Returns the number of available values
* @return The number of available values
*/
public abstract long getN();
public long getN() {
return eDA.getNumElements();
}
/**
* Returns the sum of the values that have been added to Univariate.
* @return The sum or Double.NaN if no values have been added
*/
public double getSum() {
return apply(new Sum());
return apply(getSumImpl());
}
/**
@ -188,32 +258,52 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* values have been added.
*/
public double getSumsq() {
return apply(new SumOfSquares());
return apply(getSumsqImpl());
}
/**
* Resets all statistics and storage
*/
public abstract void clear();
public void clear() {
eDA.clear();
}
/**
* Univariate has the ability to return only measures for the
* last N elements added to the set of values.
* Returns the maximum number of values that can be stored in the
* dataset, or INFINITE_WINDOW (-1) if there is no limit.
*
* @return The current window size or -1 if its Infinite.
*/
public abstract int getWindowSize();
public int getWindowSize() {
return windowSize;
}
/**
* WindowSize controls the number of values which contribute
* to the values returned by Univariate. For example, if
* to the reported statistics. For example, if
* windowSize is set to 3 and the values {1,2,3,4,5}
* have been added <strong> in that order</strong>
* then the <i>available values</i> are {3,4,5} and all
* reported statistics will be based on these values
* @param windowSize sets the size of the window.
*/
public abstract void setWindowSize(int windowSize);
public void setWindowSize(int windowSize) {
if (windowSize < 1) {
if (windowSize != INFINITE_WINDOW) {
throw new IllegalArgumentException("window size must be positive.");
}
}
this.windowSize = windowSize;
// We need to check to see if we need to discard elements
// from the front of the array. If the windowSize is less than
// the current number of elements.
if (windowSize != INFINITE_WINDOW && windowSize < eDA.getNumElements()) {
eDA.discardFrontElements(eDA.getNumElements() - windowSize);
}
}
/**
* Returns the current set of values in an array of double primitives.
@ -224,7 +314,12 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* @return returns the current set of numbers in the order in which they
* were added to this set
*/
public abstract double[] getValues();
public double[] getValues() {
double[] copiedArray = new double[eDA.getNumElements()];
System.arraycopy(eDA.getElements(), 0, copiedArray,
0, eDA.getNumElements());
return copiedArray;
}
/**
* Returns the current set of values in an array of double primitives,
@ -245,7 +340,9 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* @param index The Index of the element
* @return return the element at the specified index
*/
public abstract double getElement(int index);
public double getElement(int index) {
return eDA.getElement(index);
}
/**
* Returns an estimate for the pth percentile of the stored values.
@ -294,6 +391,180 @@ public abstract class DescriptiveStatistics implements StatisticalSummary, Seria
* @param stat the statistic to apply
* @return the computed value of the statistic.
*/
public abstract double apply(UnivariateStatistic stat);
public double apply(UnivariateStatistic stat) {
return stat.evaluate(eDA.getValues(), eDA.start(), eDA.getNumElements());
}
// Implementation getters and setter
/**
* @return the meanImpl
*/
public UnivariateStatistic getMeanImpl() {
if (meanImpl == null) {
meanImpl = new Mean();
}
return meanImpl;
}
/**
* @param meanImpl the meanImpl to set
*/
public void setMeanImpl(UnivariateStatistic meanImpl) {
this.meanImpl = meanImpl;
}
/**
* @return the geometricMeanImpl
*/
public UnivariateStatistic getGeometricMeanImpl() {
if (geometricMeanImpl == null) {
geometricMeanImpl = new GeometricMean();
}
return geometricMeanImpl;
}
/**
* @param geometricMeanImpl the geometricMeanImpl to set
*/
public void setGeometricMeanImpl(UnivariateStatistic geometricMeanImpl) {
this.geometricMeanImpl = geometricMeanImpl;
}
/**
* @return the kurtosisImpl
*/
public UnivariateStatistic getKurtosisImpl() {
if (kurtosisImpl == null) {
kurtosisImpl = new Kurtosis();
}
return kurtosisImpl;
}
/**
* @param kurtosisImpl the kurtosisImpl to set
*/
public void setKurtosisImpl(UnivariateStatistic kurtosisImpl) {
this.kurtosisImpl = kurtosisImpl;
}
/**
* @return the maxImpl
*/
public UnivariateStatistic getMaxImpl() {
if (maxImpl == null) {
maxImpl = new Max();
}
return maxImpl;
}
/**
* @param maxImpl the maxImpl to set
*/
public void setMaxImpl(UnivariateStatistic maxImpl) {
this.maxImpl = maxImpl;
}
/**
* @return the minImpl
*/
public UnivariateStatistic getMinImpl() {
if (minImpl == null) {
minImpl = new Min();
}
return minImpl;
}
/**
* @param minImpl the minImpl to set
*/
public void setMinImpl(UnivariateStatistic minImpl) {
this.minImpl = minImpl;
}
/**
* @return the percentileImpl
*/
public UnivariateStatistic getPercentileImpl() {
if (percentileImpl == null) {
percentileImpl = new Percentile();
}
return percentileImpl;
}
/**
* @param percentileImpl the percentileImpl to set
*/
public void setPercentileImpl(UnivariateStatistic percentileImpl) {
this.percentileImpl = percentileImpl;
}
/**
* @return the skewnessImpl
*/
public UnivariateStatistic getSkewnessImpl() {
if (skewnessImpl == null) {
skewnessImpl = new Skewness();
}
return skewnessImpl;
}
/**
* @param skewnessImpl the skewnessImpl to set
*/
public void setSkewnessImpl(UnivariateStatistic skewnessImpl) {
this.skewnessImpl = skewnessImpl;
}
/**
* @return the varianceImpl
*/
public UnivariateStatistic getVarianceImpl() {
if (varianceImpl == null) {
varianceImpl = new Variance();
}
return varianceImpl;
}
/**
* @param varianceImpl the varianceImpl to set
*/
public void setVarianceImpl(UnivariateStatistic varianceImpl) {
this.varianceImpl = varianceImpl;
}
/**
* @return the sumsqImpl
*/
public UnivariateStatistic getSumsqImpl() {
if (sumsqImpl == null) {
sumsqImpl = new SumOfSquares();
}
return sumsqImpl;
}
/**
* @param sumsqImpl the sumsqImpl to set
*/
public void setSumsqImpl(UnivariateStatistic sumsqImpl) {
this.sumsqImpl = sumsqImpl;
}
/**
* @return the sumImpl
*/
public UnivariateStatistic getSumImpl() {
if (sumImpl == null) {
sumImpl = new Sum();
}
return sumImpl;
}
/**
* @param sumImpl the sumImpl to set
*/
public void setSumImpl(UnivariateStatistic sumImpl) {
this.sumImpl = sumImpl;
}
}

101
src/java/org/apache/commons/math/stat/descriptive/DescriptiveStatisticsImpl.java
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@ -30,20 +30,12 @@ public class DescriptiveStatisticsImpl extends DescriptiveStatistics implements
/** Serializable version identifier */
private static final long serialVersionUID = -1868088725461221010L;
/** hold the window size **/
protected int windowSize;
/**
* Stored data values
*/
protected ResizableDoubleArray eDA;
/**
* Construct a DescriptiveStatisticsImpl with infinite window
*/
public DescriptiveStatisticsImpl() {
this(INFINITE_WINDOW);
super();
}
/**
@ -51,96 +43,13 @@ public class DescriptiveStatisticsImpl extends DescriptiveStatistics implements
* @param window the finite window size.
*/
public DescriptiveStatisticsImpl(int window) {
super();
eDA = new ResizableDoubleArray();
setWindowSize(window);
}
/**
* Access the window size.
* @return the current window size.
*/
public int getWindowSize() {
return windowSize;
super(window);
}
/**
* @see org.apache.commons.math.stat.descriptive.DescriptiveStatistics#getValues()
*/
public double[] getValues() {
double[] copiedArray = new double[eDA.getNumElements()];
System.arraycopy(
eDA.getElements(),
0,
copiedArray,
0,
eDA.getNumElements());
return copiedArray;
}
/**
* @see org.apache.commons.math.stat.descriptive.DescriptiveStatistics#getElement(int)
*/
public double getElement(int index) {
return eDA.getElement(index);
}
/**
* @see org.apache.commons.math.stat.descriptive.DescriptiveStatistics#getN()
*/
public long getN() {
return eDA.getNumElements();
}
/**
* @see org.apache.commons.math.stat.descriptive.DescriptiveStatistics#addValue(double)
*/
public void addValue(double v) {
if (windowSize != INFINITE_WINDOW) {
if (getN() == windowSize) {
eDA.addElementRolling(v);
} else if (getN() < windowSize) {
eDA.addElement(v);
}
} else {
eDA.addElement(v);
}
}
/**
* @see org.apache.commons.math.stat.descriptive.DescriptiveStatistics#clear()
/**
* Resets all statistics and storage
*/
public void clear() {
eDA.clear();
}
/**
* @see org.apache.commons.math.stat.descriptive.DescriptiveStatistics#setWindowSize(int)
*/
public void setWindowSize(int windowSize) {
if (windowSize < 1) {
if (windowSize != INFINITE_WINDOW) {
throw new IllegalArgumentException("window size must be positive.");
}
}
this.windowSize = windowSize;
// We need to check to see if we need to discard elements
// from the front of the array. If the windowSize is less than
// the current number of elements.
if (windowSize != INFINITE_WINDOW && windowSize < eDA.getNumElements()) {
eDA.discardFrontElements(eDA.getNumElements() - windowSize);
}
}
/**
* Apply the given statistic to this univariate collection.
* @param stat the statistic to apply
* @return the computed value of the statistic.
*/
public double apply(UnivariateStatistic stat) {
return stat.evaluate(eDA.getValues(), eDA.start(), eDA.getNumElements());
super.clear();
}
}

4
src/test/org/apache/commons/math/stat/descriptive/DescriptiveStatisticsAbstractTest.java
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@ -25,9 +25,11 @@ import org.apache.commons.math.random.RandomData;
import org.apache.commons.math.random.RandomDataImpl;
/**
* Test cases for the {@link Univariate} class.
* Test cases for the DescriptiveStatistics implementations.
*
* @version $Revision$ $Date$
* @deprecated should be moved down into DescriptiveStatisticsTest
* when DescriptiveStatisticsImpl is removed in 2.0
*/
public abstract class DescriptiveStatisticsAbstractTest extends TestCase {
private double var = 0.666666666666666666667;

3
src/test/org/apache/commons/math/stat/descriptive/DescriptiveStatisticsImplTest.java
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@ -17,7 +17,8 @@ import junit.framework.Test;
import junit.framework.TestSuite;
/**
* Test cases for the {@link Univariate} class.
* Test cases for the DescriptiveStatisticsImpl class.
* @deprecated - to be removed in 2.0 with DescriptiveStatisticsImpl
* @version $Revision$ $Date: 2007-08-16 15:36:33 -0500 (Thu, 16 Aug
* 2007) $
*/

66
src/test/org/apache/commons/math/stat/descriptive/DescriptiveStatisticsTest.java Normal file
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@ -0,0 +1,66 @@
/*
* 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.stat.descriptive;
import junit.framework.Test;
import junit.framework.TestSuite;
/**
* Test cases for the DescriptiveStatistics class.
* When DescriptiveStatisticsImpl is removed, this class should replace
* DescriptiveStatisticsAbstractTest
*
* @version $Revision: 592121 $ $Date: 2007-08-16 15:36:33 -0500 (Thu, 16 Aug
* 2007) $
*/
public final class DescriptiveStatisticsTest extends DescriptiveStatisticsAbstractTest {
public DescriptiveStatisticsTest(String name) {
super(name);
}
public static Test suite() {
TestSuite suite = new TestSuite(DescriptiveStatisticsTest.class);
suite.setName("DescriptiveStatistics Tests");
return suite;
}
protected DescriptiveStatistics createDescriptiveStatistics() {
return new DescriptiveStatistics();
}
public void testSetterInjection() throws Exception {
DescriptiveStatistics stats = new DescriptiveStatistics();
stats.addValue(1);
stats.addValue(3);
assertEquals(2, stats.getMean(), 1E-10);
// Now lets try some new math
stats.setMeanImpl(new deepMean());
assertEquals(42, stats.getMean(), 1E-10);
}
/**
* A new way to compute the mean
*/
class deepMean implements UnivariateStatistic {
public double evaluate(double[] values, int begin, int length) {
return 42;
}
public double evaluate(double[] values) {
return 42;
}
}
}

3
src/test/org/apache/commons/math/stat/descriptive/ListUnivariateImpl.java
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@ -42,9 +42,6 @@ public class ListUnivariateImpl extends DescriptiveStatistics implements Seriali
/** Number Transformer maps Objects to Number for us. */
protected NumberTransformer transformer;
/** hold the window size **/
protected int windowSize = DescriptiveStatistics.INFINITE_WINDOW;
/**
* No argument Constructor

6
xdocs/changes.xml
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@ -106,6 +106,12 @@ Commons Math Release Notes</title>
Merged most functions from ComplexUtils into Complex class, added
static factory method to Complex.
</action>
<action dev="psteitz" type="update">
Deprecated abstract factory methods and made DescriptiveStatistics a
concrete class. Pushed implementations up from DescriptiveStatisticsImpl
and deprecated this class. Made implementations of statistics
configurable via setters.
</action>
</release>
<release version="1.1" date="2005-12-17"
description="This is a maintenance release containing bug fixes and enhancements.