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Posted to dev@commons.apache.org by to...@apache.org on 2003/05/15 08:33:20 UTC
cvs commit: jakarta-commons-sandbox/math/src/test/org/apache/commons/math ListUnivariateImplTest.java
tobrien 2003/05/14 23:33:19
Modified: math/src/java/org/apache/commons/math
ExpandableDoubleArray.java StoreUnivariate.java
StoreUnivariateImpl.java UnivariateImpl.java
Added: math/src/java/org/apache/commons/math
AbstractStoreUnivariate.java
ListUnivariateImpl.java
math/src/test/org/apache/commons/math
ListUnivariateImplTest.java
Log:
Another change to the stored Univariates. The calculations are now abstracted
into an AbstractStoreUnivariate class which take responsibility for
all statistical calculations. AbstractStoreUnivariate is implemented by
two classes:
* StoreUnivariateImpl - This class uses a ExpandableDoubleArray for
internal storage. This class is a more efficient class in terms
of storage and cycles for users who are interested in gathering statistics
not available in the UnivariateImpl implementation.
* ListUnivariateImpl - This class is for a situation where a user might
wish to maintain a List of numeric objects outside of a StoreUnivariate
instance. We still need to add serious error checking in the absence of
1.5's generics, but this implementation will work with any list that
contains Number objects - (BigDecimal, BigInteger, Byte, Double, Float,
Integer, Long, Short). This implementation ultimately transforms all
numeric objects into double primitives via Number.doubleValue().
Becuase AbstractStoreUnivariate does not hold on to any state, a user
can add values through the Univariate.addValue() function OR one can
directly manipulate the contents of the List directly.
Revision Changes Path
1.2 +2 -1 jakarta-commons-sandbox/math/src/java/org/apache/commons/math/ExpandableDoubleArray.java
Index: ExpandableDoubleArray.java
===================================================================
RCS file: /home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/ExpandableDoubleArray.java,v
retrieving revision 1.1
retrieving revision 1.2
diff -u -r1.1 -r1.2
--- ExpandableDoubleArray.java 15 May 2003 05:19:56 -0000 1.1
+++ ExpandableDoubleArray.java 15 May 2003 06:33:19 -0000 1.2
@@ -53,6 +53,7 @@
*/
package org.apache.commons.math;
+import java.io.Serializable;
import java.util.NoSuchElementException;
/**
@@ -60,7 +61,7 @@
*
* @author <a href="mailto:tobrien@apache.org">Tim O'Brien</a>
*/
-public class ExpandableDoubleArray {
+public class ExpandableDoubleArray implements Serializable {
// This is the internal storage array.
private double[] internalArray;
1.2 +8 -0 jakarta-commons-sandbox/math/src/java/org/apache/commons/math/StoreUnivariate.java
Index: StoreUnivariate.java
===================================================================
RCS file: /home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/StoreUnivariate.java,v
retrieving revision 1.1
retrieving revision 1.2
diff -u -r1.1 -r1.2
--- StoreUnivariate.java 15 May 2003 05:39:00 -0000 1.1
+++ StoreUnivariate.java 15 May 2003 06:33:19 -0000 1.2
@@ -114,5 +114,13 @@
*
* @return returns the current set of numbers in the order in which they were added to this set
*/
+ public abstract double[] getValues();
+ /**
+ * Returns the element at the specified index
+ *
+ * @return return the element at the specified index
+ */
+ public abstract double getElement(int index);
+
}
1.2 +14 -187 jakarta-commons-sandbox/math/src/java/org/apache/commons/math/StoreUnivariateImpl.java
Index: StoreUnivariateImpl.java
===================================================================
RCS file: /home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/StoreUnivariateImpl.java,v
retrieving revision 1.1
retrieving revision 1.2
diff -u -r1.1 -r1.2
--- StoreUnivariateImpl.java 15 May 2003 05:39:00 -0000 1.1
+++ StoreUnivariateImpl.java 15 May 2003 06:33:19 -0000 1.2
@@ -54,11 +54,9 @@
package org.apache.commons.math;
/**
- * Provides univariate measures for an array of doubles.
- *
* @author <a href="mailto:tobrien@apache.org">Tim O'Brien</a>
*/
-public class StoreUnivariateImpl implements StoreUnivariate {
+public class StoreUnivariateImpl extends AbstractStoreUnivariate {
ExpandableDoubleArray eDA;
@@ -66,207 +64,36 @@
eDA = new ExpandableDoubleArray();
}
- /* (non-Javadoc)
- * @see org.apache.commons.math.StoreUnivariate#getMode()
- */
- public double getMode() {
- // Mode depends on a refactor Freq class
- throw new UnsupportedOperationException("getMode() is not yet implemented");
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.StoreUnivariate#getSkewness()
- */
- public double getSkewness() {
- // Initialize the skewness
- double skewness = Double.NaN;
-
- // Get the mean and the standard deviation
- double mean = getMean();
- double stdDev = getStandardDeviation();
-
- // Sum the cubes of the distance from the mean divided by the standard deviation
- double accum = 0.0;
- for( int i = 0; i < eDA.getNumElements(); i++ ) {
- accum += Math.pow( (eDA.getElement(i) - mean) / stdDev, 3.0);
- }
-
- // Get N
- double n = getN();
-
- // Calculate skewness
- skewness = ( n / ( (n-1) * (n-2) ) ) * accum;
-
- return skewness;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.StoreUnivariate#getKurtosis()
- */
- public double getKurtosis() {
- // Initialize the kurtosis
- double kurtosis = Double.NaN;
-
- // Get the mean and the standard deviation
- double mean = getMean();
- double stdDev = getStandardDeviation();
-
- // Sum the ^4 of the distance from the mean divided by the standard deviation
- double accum = 0.0;
- for( int i = 0; i < eDA.getNumElements(); i++ ) {
- accum += Math.pow( (eDA.getElement(i) - mean) / stdDev, 4.0);
- }
-
- // Get N
- double n = getN();
-
- double coefficientOne = ( n * (n+1)) / ( (n-1) * (n-2) * (n-3) );
- double termTwo = ( ( 3 * Math.pow( n - 1, 2.0)) / ( (n-2) * (n-3) ) );
- // Calculate kurtosis
- kurtosis = ( coefficientOne * accum ) - termTwo;
-
- return kurtosis;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.StoreUnivariate#getKurtosisClass()
- */
- public int getKurtosisClass() {
-
- int kClass = StoreUnivariate.MESOKURTIC;
-
- double kurtosis = getKurtosis();
- if( kurtosis > 0 ) {
- kClass = StoreUnivariate.LEPTOKURTIC;
- } else if( kurtosis < 0 ) {
- kClass = StoreUnivariate.PLATYKURTIC;
- }
-
- return( kClass );
-
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#addValue(double)
- */
- public void addValue(double v) {
- eDA.addElement( v );
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getMean()
- */
- public double getMean() {
- double arithMean = getSum() / getN();
- return arithMean;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getVariance()
- */
- public double getVariance() {
- // Initialize variance
- double variance = Double.NaN;
-
- if( getN() == 1 ) {
- // If this is a single value
- variance = 0;
- } else if( getN() > 1 ) {
- // Get the mean
- double mean = getMean();
-
- // Calculate the sum of the squares of the distance between each value and the mean
- double accum = 0.0;
- for( int i = 0; i < eDA.getNumElements(); i++ ){
- accum += Math.pow( (eDA.getElement(i) - mean), 2.0 );
- }
-
- // Divide the accumulator by N - Hmmm... unbiased or biased?
- variance = accum / (getN() - 1);
- }
-
- return variance;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getStandardDeviation()
- */
- public double getStandardDeviation() {
- double stdDev = Double.NaN;
- if( getN() != 0 ) {
- stdDev = Math.sqrt( getVariance() );
- }
- return( stdDev );
- }
/* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getMax()
+ * @see org.apache.commons.math.StoreUnivariate#getValues()
*/
- public double getMax() {
-
- // Initialize maximum to NaN
- double max = Double.NaN;
-
- for( int i = 0; i < eDA.getNumElements(); i++) {
- if( i == 0 ) {
- max = eDA.getElement(i);
- } else {
- if( eDA.getElement(i) > max ) {
- max = eDA.getElement(i);
- }
- }
- }
+ public double[] getValues() {
- return max;
+ double[] copiedArray = new double[ eDA.getNumElements() ];
+ System.arraycopy( eDA.getValues(), 0, copiedArray, 0, eDA.getNumElements());
+ return copiedArray;
}
/* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getMin()
+ * @see org.apache.commons.math.StoreUnivariate#getElement(int)
*/
- public double getMin() {
- // Initialize minimum to NaN
- double min = Double.NaN;
-
- for( int i = 0; i < eDA.getNumElements(); i++) {
- if( i == 0 ) {
- min = eDA.getElement(i);
- } else {
- if( eDA.getElement(i) < min ) {
- min = eDA.getElement(i);
- }
- }
- }
-
- return min;
+ public double getElement(int index) {
+ return eDA.getElement(index);
}
-
+
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#getN()
*/
public double getN() {
return eDA.getNumElements();
}
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getSum()
- */
- public double getSum() {
- double accum = 0.0;
- for( int i = 0; i < eDA.getNumElements(); i++) {
- accum += eDA.getElement(i);
- }
- return accum;
- }
-
+
/* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getSumsq()
+ * @see org.apache.commons.math.Univariate#addValue(double)
*/
- public double getSumsq() {
- double accum = 0.0;
- for( int i = 0; i < eDA.getNumElements(); i++) {
- accum += Math.pow(eDA.getElement(i), 2.0);
- }
- return accum;
+ public void addValue(double v) {
+ eDA.addElement( v );
}
/* (non-Javadoc)
1.2 +4 -2 jakarta-commons-sandbox/math/src/java/org/apache/commons/math/UnivariateImpl.java
Index: UnivariateImpl.java
===================================================================
RCS file: /home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/UnivariateImpl.java,v
retrieving revision 1.1
retrieving revision 1.2
diff -u -r1.1 -r1.2
--- UnivariateImpl.java 15 May 2003 05:39:00 -0000 1.1
+++ UnivariateImpl.java 15 May 2003 06:33:19 -0000 1.2
@@ -53,6 +53,8 @@
*/
package org.apache.commons.math;
+import java.io.Serializable;
+
/**
*
* Accumulates univariate statistics for values fed in
@@ -65,7 +67,7 @@
* @version $Revision$ $Date$
*
*/
-public class UnivariateImpl implements Univariate {
+public class UnivariateImpl implements Univariate, Serializable {
/** running sum of values that have been added */
private double sum = 0.0;
1.1 jakarta-commons-sandbox/math/src/java/org/apache/commons/math/AbstractStoreUnivariate.java
Index: AbstractStoreUnivariate.java
===================================================================
/* ====================================================================
* The Apache Software License, Version 1.1
*
* Copyright (c) 2003 The Apache Software Foundation. All rights
* reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in
* the documentation and/or other materials provided with the
* distribution.
*
* 3. The end-user documentation included with the redistribution, if
* any, must include the following acknowlegement:
* "This product includes software developed by the
* Apache Software Foundation (http://www.apache.org/)."
* Alternately, this acknowlegement may appear in the software itself,
* if and wherever such third-party acknowlegements normally appear.
*
* 4. The names "The Jakarta Project", "Commons", and "Apache Software
* Foundation" must not be used to endorse or promote products derived
* from this software without prior written permission. For written
* permission, please contact apache@apache.org.
*
* 5. Products derived from this software may not be called "Apache"
* nor may "Apache" appear in their names without prior written
* permission of the Apache Software Foundation.
*
* THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE APACHE SOFTWARE FOUNDATION OR
* ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
* USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
* OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
* SUCH DAMAGE.
* ====================================================================
*
* This software consists of voluntary contributions made by many
* individuals on behalf of the Apache Software Foundation. For more
* information on the Apache Software Foundation, please see
* <http://www.apache.org/>.
*/
package org.apache.commons.math;
/**
* Provides univariate measures for an array of doubles.
*
* @author <a href="mailto:tobrien@apache.org">Tim O'Brien</a>
*/
public abstract class AbstractStoreUnivariate implements StoreUnivariate {
/* (non-Javadoc)
* @see org.apache.commons.math.StoreUnivariate#getMode()
*/
public double getMode() {
// Mode depends on a refactor Freq class
throw new UnsupportedOperationException("getMode() is not yet implemented");
}
/* (non-Javadoc)
* @see org.apache.commons.math.StoreUnivariate#getSkewness()
*/
public double getSkewness() {
// Initialize the skewness
double skewness = Double.NaN;
// Get the mean and the standard deviation
double mean = getMean();
double stdDev = getStandardDeviation();
// Sum the cubes of the distance from the mean divided by the standard deviation
double accum = 0.0;
for( int i = 0; i < getN(); i++ ) {
accum += Math.pow( (getElement(i) - mean) / stdDev, 3.0);
}
// Get N
double n = getN();
// Calculate skewness
skewness = ( n / ( (n-1) * (n-2) ) ) * accum;
return skewness;
}
/* (non-Javadoc)
* @see org.apache.commons.math.StoreUnivariate#getKurtosis()
*/
public double getKurtosis() {
// Initialize the kurtosis
double kurtosis = Double.NaN;
// Get the mean and the standard deviation
double mean = getMean();
double stdDev = getStandardDeviation();
// Sum the ^4 of the distance from the mean divided by the standard deviation
double accum = 0.0;
for( int i = 0; i < getN(); i++ ) {
accum += Math.pow( (getElement(i) - mean) / stdDev, 4.0);
}
// Get N
double n = getN();
double coefficientOne = ( n * (n+1)) / ( (n-1) * (n-2) * (n-3) );
double termTwo = ( ( 3 * Math.pow( n - 1, 2.0)) / ( (n-2) * (n-3) ) );
// Calculate kurtosis
kurtosis = ( coefficientOne * accum ) - termTwo;
return kurtosis;
}
/* (non-Javadoc)
* @see org.apache.commons.math.StoreUnivariate#getKurtosisClass()
*/
public int getKurtosisClass() {
int kClass = StoreUnivariate.MESOKURTIC;
double kurtosis = getKurtosis();
if( kurtosis > 0 ) {
kClass = StoreUnivariate.LEPTOKURTIC;
} else if( kurtosis < 0 ) {
kClass = StoreUnivariate.PLATYKURTIC;
}
return( kClass );
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#getMean()
*/
public double getMean() {
double arithMean = getSum() / getN();
return arithMean;
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#getVariance()
*/
public double getVariance() {
// Initialize variance
double variance = Double.NaN;
if( getN() == 1 ) {
// If this is a single value
variance = 0;
} else if( getN() > 1 ) {
// Get the mean
double mean = getMean();
// Calculate the sum of the squares of the distance between each value and the mean
double accum = 0.0;
for( int i = 0; i < getN(); i++ ){
accum += Math.pow( (getElement(i) - mean), 2.0 );
}
// Divide the accumulator by N - Hmmm... unbiased or biased?
variance = accum / (getN() - 1);
}
return variance;
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#getStandardDeviation()
*/
public double getStandardDeviation() {
double stdDev = Double.NaN;
if( getN() != 0 ) {
stdDev = Math.sqrt( getVariance() );
}
return( stdDev );
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#getMax()
*/
public double getMax() {
// Initialize maximum to NaN
double max = Double.NaN;
for( int i = 0; i < getN(); i++) {
if( i == 0 ) {
max = getElement(i);
} else {
if( getElement(i) > max ) {
max = getElement(i);
}
}
}
return max;
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#getMin()
*/
public double getMin() {
// Initialize minimum to NaN
double min = Double.NaN;
for( int i = 0; i < getN(); i++) {
if( i == 0 ) {
min = getElement(i);
} else {
if( getElement(i) < min ) {
min = getElement(i);
}
}
}
return min;
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#getSum()
*/
public double getSum() {
double accum = 0.0;
for( int i = 0; i < getN(); i++) {
accum += getElement(i);
}
return accum;
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#getSumsq()
*/
public double getSumsq() {
double accum = 0.0;
for( int i = 0; i < getN(); i++) {
accum += Math.pow(getElement(i), 2.0);
}
return accum;
}
}
1.1 jakarta-commons-sandbox/math/src/java/org/apache/commons/math/ListUnivariateImpl.java
Index: ListUnivariateImpl.java
===================================================================
/* ====================================================================
* The Apache Software License, Version 1.1
*
* Copyright (c) 2003 The Apache Software Foundation. All rights
* reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in
* the documentation and/or other materials provided with the
* distribution.
*
* 3. The end-user documentation included with the redistribution, if
* any, must include the following acknowlegement:
* "This product includes software developed by the
* Apache Software Foundation (http://www.apache.org/)."
* Alternately, this acknowlegement may appear in the software itself,
* if and wherever such third-party acknowlegements normally appear.
*
* 4. The names "The Jakarta Project", "Commons", and "Apache Software
* Foundation" must not be used to endorse or promote products derived
* from this software without prior written permission. For written
* permission, please contact apache@apache.org.
*
* 5. Products derived from this software may not be called "Apache"
* nor may "Apache" appear in their names without prior written
* permission of the Apache Software Foundation.
*
* THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE APACHE SOFTWARE FOUNDATION OR
* ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
* USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
* OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
* SUCH DAMAGE.
* ====================================================================
*
* This software consists of voluntary contributions made by many
* individuals on behalf of the Apache Software Foundation. For more
* information on the Apache Software Foundation, please see
* <http://www.apache.org/>.
*/
package org.apache.commons.math;
import java.util.Iterator;
import java.util.List;
/**
* @author <a href="mailto:tobrien@apache.org">Tim O'Brien</a>
*/
public class ListUnivariateImpl extends AbstractStoreUnivariate {
// Holds a reference to a list - GENERICs are going to make
// out lives easier here as we could only accept List<Number>
List list;
public ListUnivariateImpl( List list ) {
this.list = list;
}
/* (non-Javadoc)
* @see org.apache.commons.math.StoreUnivariate#getValues()
*/
public double[] getValues() {
double[] copiedArray = new double[list.size()];
int i = 0;
Iterator it = list.iterator();
while( it.hasNext() ) {
Number n = (Number) it.next();
copiedArray[i] = n.doubleValue();
i++;
}
return copiedArray;
}
/* (non-Javadoc)
* @see org.apache.commons.math.StoreUnivariate#getElement(int)
*/
public double getElement(int index) {
Number n = (Number) list.get(index);
return n.doubleValue();
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#getN()
*/
public double getN() {
return list.size();
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#addValue(double)
*/
public void addValue(double v) {
list.add( new Double(v));
}
/* (non-Javadoc)
* @see org.apache.commons.math.Univariate#clear()
*/
public void clear() {
list.clear();
}
}
1.1 jakarta-commons-sandbox/math/src/test/org/apache/commons/math/ListUnivariateImplTest.java
Index: ListUnivariateImplTest.java
===================================================================
/* ====================================================================
* The Apache Software License, Version 1.1
*
* Copyright (c) 2003 The Apache Software Foundation. All rights
* reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in
* the documentation and/or other materials provided with the
* distribution.
*
* 3. The end-user documentation included with the redistribution, if
* any, must include the following acknowlegement:
* "This product includes software developed by the
* Apache Software Foundation (http://www.apache.org/)."
* Alternately, this acknowlegement may appear in the software itself,
* if and wherever such third-party acknowlegements normally appear.
*
* 4. The names "The Jakarta Project", "Commons", and "Apache Software
* Foundation" must not be used to endorse or promote products derived
* from this software without prior written permission. For written
* permission, please contact apache@apache.org.
*
* 5. Products derived from this software may not be called "Apache"
* nor may "Apache" appear in their names without prior written
* permission of the Apache Software Foundation.
*
* THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE APACHE SOFTWARE FOUNDATION OR
* ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
* USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
* OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
* SUCH DAMAGE.
* ====================================================================
*
* This software consists of voluntary contributions made by many
* individuals on behalf of the Apache Software Foundation. For more
* information on the Apache Software Foundation, please see
* <http://www.apache.org/>.
*/
package org.apache.commons.math;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import junit.framework.Test;
import junit.framework.TestCase;
import junit.framework.TestSuite;
/**
* Test cases for the {@link Univariate} class.
*
* @author <a href="mailto:phil@steitz.com">Phil Steitz</a>
* @version $Revision: 1.1 $ $Date: 2003/05/15 06:33:19 $
*/
public final class ListUnivariateImplTest extends TestCase {
private double one = 1;
private float two = 2;
private int three = 3;
private double mean = 2;
private double sumSq = 18;
private double sum = 8;
private double var = 0.666666666666666666667;
private double std = Math.sqrt(var);
private double n = 4;
private double min = 1;
private double max = 3;
private double skewness = 0;
private double kurtosis = 0.5;
private int kClass = StoreUnivariate.LEPTOKURTIC;
private double tolerance = 10E-15;
public ListUnivariateImplTest(String name) {
super(name);
}
public void setUp() {
}
public static Test suite() {
TestSuite suite = new TestSuite(ListUnivariateImplTest.class);
suite.setName("Freq Tests");
return suite;
}
/** test stats */
public void testStats() {
List externalList = new ArrayList();
StoreUnivariate u = new ListUnivariateImpl( externalList );
assertEquals("total count",0,u.getN(),tolerance);
u.addValue(one);
u.addValue(two);
u.addValue(two);
u.addValue(three);
assertEquals("N",n,u.getN(),tolerance);
assertEquals("sum",sum,u.getSum(),tolerance);
assertEquals("sumsq",sumSq,u.getSumsq(),tolerance);
assertEquals("var",var,u.getVariance(),tolerance);
assertEquals("std",std,u.getStandardDeviation(),tolerance);
assertEquals("mean",mean,u.getMean(),tolerance);
assertEquals("min",min,u.getMin(),tolerance);
assertEquals("max",max,u.getMax(),tolerance);
u.clear();
assertEquals("total count",0,u.getN(),tolerance);
}
public void testN0andN1Conditions() throws Exception {
List list = new ArrayList();
StoreUnivariate u = new ListUnivariateImpl( list );
assertTrue("Mean of n = 0 set should be NaN", Double.isNaN( u.getMean() ) );
assertTrue("Standard Deviation of n = 0 set should be NaN", Double.isNaN( u.getStandardDeviation() ) );
assertTrue("Variance of n = 0 set should be NaN", Double.isNaN(u.getVariance() ) );
list.add( new Double(one));
assertTrue( "Mean of n = 1 set should be value of single item n1", u.getMean() == one);
assertTrue( "StdDev of n = 1 set should be zero, instead it is: " + u.getStandardDeviation(), u.getStandardDeviation() == 0);
assertTrue( "Variance of n = 1 set should be zero", u.getVariance() == 0);
}
public void testSkewAndKurtosis() {
StoreUnivariate u = new StoreUnivariateImpl();
double[] testArray = { 12.5, 12, 11.8, 14.2, 14.9, 14.5, 21, 8.2, 10.3, 11.3, 14.1,
9.9, 12.2, 12, 12.1, 11, 19.8, 11, 10, 8.8, 9, 12.3 };
for( int i = 0; i < testArray.length; i++) {
u.addValue( testArray[i]);
}
assertEquals("mean", 12.40455, u.getMean(), 0.0001);
assertEquals("variance", 10.00236, u.getVariance(), 0.0001);
assertEquals("skewness", 1.437424, u.getSkewness(), 0.0001);
assertEquals("kurtosis", 2.37719, u.getKurtosis(), 0.0001);
}
}
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