Apache
/
commons-math
mirror of https://github.com/apache/commons-math.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Cleaned up some errors / omissions from r598133 

* Regenerated serialization version IDs
* Eliminated unecessary import
* Eliminated lazy initialization
* Changed getPercentile() to use configured implementation
* Added tests for percentile impl injection 


git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@600196 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz 2007-12-01 21:19:13 +00:00
parent 2d4fe56383
commit 8140c9da58
3 changed files with 154 additions and 84 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

160
src/java/org/apache/commons/math/stat/descriptive/DescriptiveStatistics.java
View File

@ -55,7 +55,7 @@ import org.apache.commons.math.util.ResizableDoubleArray;
public class DescriptiveStatistics implements StatisticalSummary, Serializable {
/** Serialization UID */
private static final long serialVersionUID = 5188298269533339922L;
private static final long serialVersionUID = -2734185686570407433L;
/** hold the window size **/
protected int windowSize = INFINITE_WINDOW;
@ -64,19 +64,18 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* 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;
// UnivariateStatistic stats implementations - can be reset by setters
private UnivariateStatistic meanImpl = new Mean();
private UnivariateStatistic geometricMeanImpl = new GeometricMean();
private UnivariateStatistic kurtosisImpl = new Kurtosis();
private UnivariateStatistic maxImpl = new Max();
private UnivariateStatistic minImpl = new Min();
private UnivariateStatistic percentileImpl = new Percentile();
private UnivariateStatistic skewnessImpl = new Skewness();
private UnivariateStatistic varianceImpl = new Variance();
private UnivariateStatistic sumsqImpl = new SumOfSquares();
private UnivariateStatistic sumImpl = new Sum();
/**
* Construct a DescriptiveStatistics instance with an infinite window
@ -128,10 +127,9 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
}
/**
* This constant signals that a Univariate implementation
* takes into account the contributions of an infinite number of
* elements. In other words, if getWindow returns this
* constant, there is, in effect, no "window".
* Represents an infinite window size. When the {@link #getWindowSize()}
* returns this value, there is no limit to the number of data values
* that can be stored in the dataset.
*/
public static final int INFINITE_WINDOW = -1;
@ -161,7 +159,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* @return The mean or Double.NaN if no values have been added.
*/
public double getMean() {
return apply(getMeanImpl());
return apply(meanImpl);
}
/**
@ -171,7 +169,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* or if the productof the available values is less than or equal to 0.
*/
public double getGeometricMean() {
return apply(getGeometricMeanImpl());
return apply(geometricMeanImpl);
}
/**
@ -180,7 +178,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* or 0.0 for a single value set.
*/
public double getVariance() {
return apply(getVarianceImpl());
return apply(varianceImpl);
}
/**
@ -207,7 +205,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* or 0.0 for a value set <=2.
*/
public double getSkewness() {
return apply(getSkewnessImpl());
return apply(skewnessImpl);
}
/**
@ -217,7 +215,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* for a value set <=3.
*/
public double getKurtosis() {
return apply(getKurtosisImpl());
return apply(kurtosisImpl);
}
/**
@ -225,7 +223,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* @return The max or Double.NaN if no values have been added.
*/
public double getMax() {
return apply(getMaxImpl());
return apply(maxImpl);
}
/**
@ -233,7 +231,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* @return The min or Double.NaN if no values have been added.
*/
public double getMin() {
return apply(getMinImpl());
return apply(minImpl);
}
/**
@ -249,7 +247,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* @return The sum or Double.NaN if no values have been added
*/
public double getSum() {
return apply(getSumImpl());
return apply(sumImpl);
}
/**
@ -258,7 +256,7 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
* values have been added.
*/
public double getSumsq() {
return apply(getSumsqImpl());
return apply(sumsqImpl);
}
/**
@ -359,10 +357,24 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
*
* @param p the requested percentile (scaled from 0 - 100)
* @return An estimate for the pth percentile of the stored data
* @throws IllegalStateException if percentile implementation has been
* overridden and the supplied implementation does not support setQuantile
* values
*/
public double getPercentile(double p) {
return apply(new Percentile(p));
if (percentileImpl instanceof Percentile) {
((Percentile) percentileImpl).setQuantile(p);
} else {
try {
percentileImpl.getClass().getMethod("setQuantile",
new Class[] {Double.TYPE}).invoke(percentileImpl,
new Object[] {new Double(p)});
} catch (Exception ex) { // Should never happen, guarded by setter
throw new IllegalStateException(
"Percentile implementation does not support setQuantile");
}
}
return apply(percentileImpl);
}
/**
@ -400,171 +412,159 @@ public class DescriptiveStatistics implements StatisticalSummary, Serializable {
/**
* @return the meanImpl
*/
public UnivariateStatistic getMeanImpl() {
if (meanImpl == null) {
meanImpl = new Mean();
}
public synchronized UnivariateStatistic getMeanImpl() {
return meanImpl;
}
/**
* @param meanImpl the meanImpl to set
*/
public void setMeanImpl(UnivariateStatistic meanImpl) {
public synchronized void setMeanImpl(UnivariateStatistic meanImpl) {
this.meanImpl = meanImpl;
}
/**
* @return the geometricMeanImpl
*/
public UnivariateStatistic getGeometricMeanImpl() {
if (geometricMeanImpl == null) {
geometricMeanImpl = new GeometricMean();
}
public synchronized UnivariateStatistic getGeometricMeanImpl() {
return geometricMeanImpl;
}
/**
* @param geometricMeanImpl the geometricMeanImpl to set
*/
public void setGeometricMeanImpl(UnivariateStatistic geometricMeanImpl) {
public synchronized void setGeometricMeanImpl(
UnivariateStatistic geometricMeanImpl) {
this.geometricMeanImpl = geometricMeanImpl;
}
/**
* @return the kurtosisImpl
*/
public UnivariateStatistic getKurtosisImpl() {
if (kurtosisImpl == null) {
kurtosisImpl = new Kurtosis();
}
public synchronized UnivariateStatistic getKurtosisImpl() {
return kurtosisImpl;
}
/**
* @param kurtosisImpl the kurtosisImpl to set
*/
public void setKurtosisImpl(UnivariateStatistic kurtosisImpl) {
public synchronized void setKurtosisImpl(UnivariateStatistic kurtosisImpl) {
this.kurtosisImpl = kurtosisImpl;
}
/**
* @return the maxImpl
*/
public UnivariateStatistic getMaxImpl() {
if (maxImpl == null) {
maxImpl = new Max();
}
public synchronized UnivariateStatistic getMaxImpl() {
return maxImpl;
}
/**
* @param maxImpl the maxImpl to set
*/
public void setMaxImpl(UnivariateStatistic maxImpl) {
public synchronized void setMaxImpl(UnivariateStatistic maxImpl) {
this.maxImpl = maxImpl;
}
/**
* @return the minImpl
*/
public UnivariateStatistic getMinImpl() {
if (minImpl == null) {
minImpl = new Min();
}
public synchronized UnivariateStatistic getMinImpl() {
return minImpl;
}
/**
* @param minImpl the minImpl to set
*/
public void setMinImpl(UnivariateStatistic minImpl) {
public synchronized void setMinImpl(UnivariateStatistic minImpl) {
this.minImpl = minImpl;
}
/**
* @return the percentileImpl
*/
public UnivariateStatistic getPercentileImpl() {
if (percentileImpl == null) {
percentileImpl = new Percentile();
}
public synchronized UnivariateStatistic getPercentileImpl() {
return percentileImpl;
}
/**
* Sets the implementation to be used by {@link #getPercentile(double)}.
* The supplied <code>UnivariateStatistic</code> must provide a
* <code>setQuantile(double)</code> method; otherwise
* <code>IllegalArgumentException</code> is thrown.
*
* @param percentileImpl the percentileImpl to set
* @throws IllegalArgumentException if the supplied implementation does not
* provide a <code>setQuantile</code> method
*/
public void setPercentileImpl(UnivariateStatistic percentileImpl) {
public synchronized void setPercentileImpl(
UnivariateStatistic percentileImpl) {
try {
percentileImpl.getClass().getMethod("setQuantile",
new Class[] {Double.TYPE}).invoke(percentileImpl,
new Object[] {new Double(50.0d)});
} catch (Exception ex) {
throw new IllegalArgumentException(
"Percentile implementation does not support setQuantile");
}
this.percentileImpl = percentileImpl;
}
/**
* @return the skewnessImpl
*/
public UnivariateStatistic getSkewnessImpl() {
if (skewnessImpl == null) {
skewnessImpl = new Skewness();
}
public synchronized UnivariateStatistic getSkewnessImpl() {
return skewnessImpl;
}
/**
* @param skewnessImpl the skewnessImpl to set
*/
public void setSkewnessImpl(UnivariateStatistic skewnessImpl) {
public synchronized void setSkewnessImpl(
UnivariateStatistic skewnessImpl) {
this.skewnessImpl = skewnessImpl;
}
/**
* @return the varianceImpl
*/
public UnivariateStatistic getVarianceImpl() {
if (varianceImpl == null) {
varianceImpl = new Variance();
}
public synchronized UnivariateStatistic getVarianceImpl() {
return varianceImpl;
}
/**
* @param varianceImpl the varianceImpl to set
*/
public void setVarianceImpl(UnivariateStatistic varianceImpl) {
public synchronized void setVarianceImpl(
UnivariateStatistic varianceImpl) {
this.varianceImpl = varianceImpl;
}
/**
* @return the sumsqImpl
*/
public UnivariateStatistic getSumsqImpl() {
if (sumsqImpl == null) {
sumsqImpl = new SumOfSquares();
}
public synchronized UnivariateStatistic getSumsqImpl() {
return sumsqImpl;
}
/**
* @param sumsqImpl the sumsqImpl to set
*/
public void setSumsqImpl(UnivariateStatistic sumsqImpl) {
public synchronized void setSumsqImpl(UnivariateStatistic sumsqImpl) {
this.sumsqImpl = sumsqImpl;
}
/**
* @return the sumImpl
*/
public UnivariateStatistic getSumImpl() {
if (sumImpl == null) {
sumImpl = new Sum();
}
public synchronized UnivariateStatistic getSumImpl() {
return sumImpl;
}
/**
* @param sumImpl the sumImpl to set
*/
public void setSumImpl(UnivariateStatistic sumImpl) {
public synchronized void setSumImpl(UnivariateStatistic sumImpl) {
this.sumImpl = sumImpl;
}
}
}

7
src/java/org/apache/commons/math/stat/descriptive/DescriptiveStatisticsImpl.java
View File

@ -18,18 +18,19 @@ package org.apache.commons.math.stat.descriptive;
import java.io.Serializable;
import org.apache.commons.math.util.ResizableDoubleArray;
/**
* Default implementation of
* {@link org.apache.commons.math.stat.descriptive.DescriptiveStatistics}.
*
* @deprectated to be removed in commons-math 2.0.
* Use {@link DescriptiveStatisitics}
*
* @version $Revision$ $Date$
*/
public class DescriptiveStatisticsImpl extends DescriptiveStatistics implements Serializable {
/** Serializable version identifier */
private static final long serialVersionUID = -1868088725461221010L;
private static final long serialVersionUID = -6467796944112488424L;
/**
* Construct a DescriptiveStatisticsImpl with infinite window

71
src/test/org/apache/commons/math/stat/descriptive/DescriptiveStatisticsTest.java
View File

@ -16,6 +16,8 @@ package org.apache.commons.math.stat.descriptive;
import junit.framework.Test;
import junit.framework.TestSuite;
import org.apache.commons.math.stat.descriptive.rank.Percentile;
/**
* Test cases for the DescriptiveStatistics class.
* When DescriptiveStatisticsImpl is removed, this class should replace
@ -50,6 +52,32 @@ public final class DescriptiveStatisticsTest extends DescriptiveStatisticsAbstra
assertEquals(42, stats.getMean(), 1E-10);
}
public void testPercentileSetter() throws Exception {
DescriptiveStatistics stats = new DescriptiveStatistics();
stats.addValue(1);
stats.addValue(2);
stats.addValue(3);
assertEquals(2, stats.getPercentile(50.0), 1E-10);
// Inject wrapped Percentile impl
stats.setPercentileImpl(new goodPercentile());
assertEquals(2, stats.getPercentile(50.0), 1E-10);
// Try "new math" impl
stats.setPercentileImpl(new subPercentile());
assertEquals(10.0, stats.getPercentile(10.0), 1E-10);
// Try to set bad impl
try {
stats.setPercentileImpl(new badPercentile());
fail("Expecting IllegalArgumentException");
} catch (IllegalArgumentException ex) {
// expected
}
}
// Test UnivariateStatistics impls for setter injection tests
/**
* A new way to compute the mean
*/
@ -60,7 +88,48 @@ public final class DescriptiveStatisticsTest extends DescriptiveStatisticsAbstra
public double evaluate(double[] values) {
return 42;
}
}
/**
* Test percentile implementation - wraps a Percentile
*/
class goodPercentile implements UnivariateStatistic {
Percentile percentile = new Percentile();
public void setQuantile(double quantile) {
percentile.setQuantile(quantile);
}
public double evaluate(double[] values, int begin, int length) {
return percentile.evaluate(values, begin, length);
}
public double evaluate(double[] values) {
return evaluate(values);
}
}
/**
* Test percentile subclass - another "new math" impl
* Always returns currently set quantile
*/
class subPercentile extends Percentile {
public double evaluate(double[] values, int begin, int length) {
return getQuantile();
}
public double evaluate(double[] values) {
return getQuantile();
}
}
/**
* "Bad" test percentile implementation - no setQuantile
*/
class badPercentile implements UnivariateStatistic {
Percentile percentile = new Percentile();
public double evaluate(double[] values, int begin, int length) {
return percentile.evaluate(values, begin, length);
}
public double evaluate(double[] values) {
return evaluate(values);
}
}
}