mdiggory 2003/07/09 14:45:24
Modified: math/src/java/org/apache/commons/math/stat Univariate.java
StoreUnivariate.java BeanListUnivariateImpl.java
UnivariateImpl.java TestStatisticImpl.java
AbstractStoreUnivariate.java
ListUnivariateImpl.java Frequency.java
StatUtils.java TestStatistic.java
StoreUnivariateImpl.java
math/src/test/org/apache/commons/math/stat
StatUtilsTest.java UnivariateImplTest.java
Added: math/src/java/org/apache/commons/math/stat
AbstractUnivariate.java
math/src/test/org/apache/commons/math/stat
MixedListUnivariateImplTest.java
Log:
Changes the Univariate implementations to use the UnivariateStatistic package. Slims
down StatUtils by removing some of the higher moments. Reimplmenets ListUnivariate to
work with NumberTransformers, turning into a Mixed Object List Univariate.
Revision Changes Path
1.8 +26 -6
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/Univariate.java
Index: Univariate.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/Univariate.java,v
retrieving revision 1.7
retrieving revision 1.8
diff -u -r1.7 -r1.8
--- Univariate.java 7 Jul 2003 23:25:13 -0000 1.7
+++ Univariate.java 9 Jul 2003 21:45:23 -0000 1.8
@@ -69,12 +69,21 @@
* reported statistics will be based on these values<p>
* The default windowSize is "infinite" -- i.e., all values added are included
* in all computations.
- *
- * @author <a href="mailto:[EMAIL PROTECTED]">Tim O'Brien</a>
* @version $Revision$ $Date$
- *
*/
public interface Univariate {
+ /**
+ * A LEPTOKURTIC set has a positive kurtosis (a high peak)
+ */
+ public static int LEPTOKURTIC = 1;
+ /**
+ * A MESOKURTIC set has a kurtosis of 0 - it is a normal distribution
+ */
+ public static int MESOKURTIC = 0;
+ /**
+ * A PLATYKURTIC set has a negative kurtosis (a flat "peak")
+ */
+ public static int PLATYKURTIC = -1;
/**
* Adds the value to the set of numbers
@@ -83,14 +92,14 @@
void addValue(double v);
/**
- * Returns the <a href=http://www.xycoon.com/arithmetic_mean.htm>
+ * Returns the <a href="http://www.xycoon.com/arithmetic_mean.htm";>
* arithmetic mean </a> of the available values
* @return The mean or Double.NaN if no values have been added.
*/
double getMean();
/**
- * Returns the <a href=http://www.xycoon.com/geometric_mean.htm>
+ * Returns the <a href="http://www.xycoon.com/geometric_mean.htm";>
* geometric mean </a> of the available values
* @return The geometricMean, Double.NaN if no values have been added,
* or if the productof the available values is less than or equal to 0.
@@ -127,6 +136,17 @@
*/
double getKurtosis();
+ /**
+ * Returns the Kurtosis "classification" a distribution can be
+ * leptokurtic (high peak), platykurtic (flat peak),
+ * or mesokurtic (zero kurtosis).
+ *
+ * @return A static constant defined in this interface,
+ * StoredDeviation.LEPTOKURITC, StoredDeviation.PLATYKURTIC, or
+ * StoredDeviation.MESOKURTIC
+ */
+ int getKurtosisClass();
+
/**
* Returns the maximum of the available values
* @return The max or Double.NaN if no values have been added.
1.7 +1 -43
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/StoreUnivariate.java
Index: StoreUnivariate.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/StoreUnivariate.java,v
retrieving revision 1.6
retrieving revision 1.7
diff -u -r1.6 -r1.7
--- StoreUnivariate.java 7 Jul 2003 23:25:13 -0000 1.6
+++ StoreUnivariate.java 9 Jul 2003 21:45:23 -0000 1.7
@@ -60,51 +60,9 @@
* Univariate provides additional percentile functionality
* such as. This additional functionality comes with
* a price of increased storage costs.
- *
- * @author <a href="mailto:[EMAIL PROTECTED]">Tim O'Brien</a>
+ * @version $Revision$ $Date$
*/
public interface StoreUnivariate extends Univariate {
-
- /**
- * A LEPTOKURTIC set has a positive kurtosis (a high peak)
- */
- public static int LEPTOKURTIC = 1;
-
- /**
- * A MESOKURTIC set has a kurtosis of 0 - it is a normal distribution
- */
- public static int MESOKURTIC = 0;
-
- /**
- * A PLATYKURTIC set has a negative kurtosis (a flat "peak")
- */
- public static int PLATYKURTIC = -1;
-
- /**
- * Returns the skewness of a given distribution. Skewness is a
- * measure of the assymetry of a given distribution.
- *
- * @return The skewness of this distribution
- */
- double getSkewness();
-
- /**
- * Kurtosis is a measure of the "peakedness" of a distribution
- *
- * @return the mode
- */
- double getKurtosis();
-
- /**
- * Returns the Kurtosis "classification" a distribution can be
- * leptokurtic (high peak), platykurtic (flat peak),
- * or mesokurtic (zero kurtosis).
- *
- * @return A static constant defined in this interface,
- * StoredDeviation.LEPTOKURITC, StoredDeviation.PLATYKURTIC, or
- * StoredDeviation.MESOKURTIC
- */
- int getKurtosisClass();
/**
* Returns the current set of values in an array of double primitives.
1.3 +37 -41
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/BeanListUnivariateImpl.java
Index: BeanListUnivariateImpl.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/BeanListUnivariateImpl.java,v
retrieving revision 1.2
retrieving revision 1.3
diff -u -r1.2 -r1.3
--- BeanListUnivariateImpl.java 22 Jun 2003 03:57:55 -0000 1.2
+++ BeanListUnivariateImpl.java 9 Jul 2003 21:45:23 -0000 1.3
@@ -54,49 +54,71 @@
package org.apache.commons.math.stat;
import java.util.List;
-import org.apache.commons.beanutils.PropertyUtils;
+
+import org.apache.commons.math.util.BeanTransformer;
/**
* This implementation of StoreUnivariate uses commons-beanutils to gather
* univariate statistics for a List of Java Beans by property. This
* implementation uses beanutils' PropertyUtils to get a simple, nested,
* indexed, mapped, or combined property from an element of a List.
- *
- * @author <a href="mailto:[EMAIL PROTECTED]">Tim O'Brien</a>
+ * @version $Revision$ $Date$
*/
public class BeanListUnivariateImpl extends ListUnivariateImpl {
+ /**
+ * propertyName of the property to get from the bean
+ */
private String propertyName;
+ /**
+ * Construct a BeanListUnivariate with specified
+ * backing list
+ * @param list Backing List
+ */
public BeanListUnivariateImpl(List list) {
- super( list );
+ super(list);
}
+ /**
+ * Construct a BeanListUnivariate with specified
+ * backing list and propertyName
+ * @param list Backing List
+ * @param propertyName Bean propertyName
+ */
public BeanListUnivariateImpl(List list, String propertyName) {
- super( list );
- setPropertyName( propertyName );
+ super(list);
+ setPropertyName(propertyName);
+ this.transformer = new BeanTransformer(propertyName);
}
+ /**
+ * @return propertyName
+ */
public String getPropertyName() {
return propertyName;
}
+ /**
+ * @param propertyName Name of Property
+ */
public void setPropertyName(String propertyName) {
- System.out.println( "Set prop name; " + propertyName );
+ System.out.println("Set prop name; " + propertyName);
this.propertyName = propertyName;
+ this.transformer = new BeanTransformer(propertyName);
}
-
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.Univariate#addValue(double)
*/
public void addValue(double v) {
- String msg = "The BeanListUnivariateImpl does not accept values " +
- "through the addValue method. Because elements of this list " +
- "are JavaBeans, one must be sure to set the 'propertyName' " +
- "property and add new Beans to the underlying list via the " +
- "addBean(Object bean) method";
- throw new UnsupportedOperationException( msg );
+ String msg =
+ "The BeanListUnivariateImpl does not accept values "
+ + "through the addValue method. Because elements of this list "
+ + "are JavaBeans, one must be sure to set the 'propertyName' "
+ + "property and add new Beans to the underlying list via the "
+ + "addBean(Object bean) method";
+ throw new UnsupportedOperationException(msg);
}
/**
@@ -106,32 +128,6 @@
*/
public void addObject(Object bean) {
list.add(bean);
- }
-
- /**
- * Reads the property of an element in the list.
- *
- * @param index The location of the value in the internal List
- * @return A Number object representing the value at a given
- * index
- */
- protected Number getInternalIndex(int index) {
-
- try {
- Number n = (Number) PropertyUtils.getProperty( list.get( index ),
- propertyName );
-
- return n;
- } catch( Exception e ) {
- // TODO: We could use a better strategy for error handling
- // here.
-
- // This is a somewhat foolish design decision, but until
- // we figure out what needs to be done, let's return NaN
- return new Double(Double.NaN);
- }
-
-
}
}
1.18 +54 -264
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/UnivariateImpl.java
Index: UnivariateImpl.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/UnivariateImpl.java,v
retrieving revision 1.17
retrieving revision 1.18
diff -u -r1.17 -r1.18
--- UnivariateImpl.java 7 Jul 2003 23:25:13 -0000 1.17
+++ UnivariateImpl.java 9 Jul 2003 21:45:23 -0000 1.18
@@ -54,8 +54,6 @@
package org.apache.commons.math.stat;
import java.io.Serializable;
-
-import org.apache.commons.math.util.DoubleArray;
import org.apache.commons.math.util.FixedDoubleArray;
/**
@@ -66,203 +64,32 @@
* Integers, floats and longs can be added, but they will be converted
* to doubles by addValue().
*
- * @author <a href="mailto:[EMAIL PROTECTED]">Tim O'Brien</a>
- * @author Brent Worden
* @version $Revision$ $Date$
- *
*/
-public class UnivariateImpl implements Univariate, Serializable {
-
- /** hold the window size **/
- private int windowSize = Univariate.INFINITE_WINDOW;
-
- /** Just in case the windowSize is not infinite, we need to
- * keep an array to remember values 0 to N
- */
- private DoubleArray doubleArray;
-
- /** count of values that have been added */
- private int n = 0;
-
- /** sum of values that have been added */
- private double sum = Double.NaN;
-
- /** sum of the square of each value that has been added */
- private double sumsq = Double.NaN;
-
- /** min of values that have been added */
- private double min = Double.NaN;
-
- /** max of values that have been added */
- private double max = Double.NaN;
-
- /** sumLog of values that have been added */
- private double sumLog = Double.NaN;
+public class UnivariateImpl
+ extends AbstractUnivariate
+ implements Univariate, Serializable {
- /** mean of values that have been added */
- private double mean = Double.NaN;
-
- /** second moment of values that have been added */
- private double m2 = Double.NaN;
-
- /** third moment of values that have been added */
- private double m3 = Double.NaN;
-
- /** fourth moment of values that have been added */
- private double m4 = Double.NaN;
-
- /** variance of values that have been added */
- private double variance = Double.NaN;
-
- /** skewness of values that have been added */
- private double skewness = Double.NaN;
-
- /** kurtosis of values that have been added */
- private double kurtosis = Double.NaN;
+ /** fixed storage */
+ private FixedDoubleArray storage = null;
/** Creates new univariate with an infinite window */
public UnivariateImpl() {
+ super();
}
- /** Creates a new univariate with a fixed window **/
- public UnivariateImpl(int window) {
- setWindowSize(window);
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.stat.Univariate#getN()
+ /**
+ * Creates a new univariate with a fixed window
+ * @param window Window Size
*/
- public int getN() {
- return n;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.stat.Univariate#getSum()
- */
- public double getSum() {
- if (windowSize != Univariate.INFINITE_WINDOW) {
- return StatUtils.sum(doubleArray.getElements());
- }
-
- return sum;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.stat.Univariate#getSumsq()
- */
- public double getSumsq() {
- if (windowSize != Univariate.INFINITE_WINDOW) {
- return StatUtils.sumSq(doubleArray.getElements());
- }
-
- return sumsq;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.stat.Univariate#getMean()
- */
- public double getMean() {
- if (windowSize != Univariate.INFINITE_WINDOW) {
- return StatUtils.mean(doubleArray.getElements());
- }
-
- return mean;
- }
-
- /**
- * Returns the standard deviation for this collection of values
- * @see org.apache.commons.math.stat.Univariate#getStandardDeviation()
- */
- public double getStandardDeviation() {
- double stdDev = Double.NaN;
- if (getN() != 0) {
- stdDev = Math.sqrt(getVariance());
- }
- return (stdDev);
- }
-
- /**
- * Returns the variance of the values that have been added via West's
- * algorithm as described by
- * <a href="http://doi.acm.org/10.1145/359146.359152";>Chan, T. F. and
- * J. G. Lewis 1979, <i>Communications of the ACM</i>,
- * vol. 22 no. 9, pp. 526-531.</a>.
- *
- * @return The variance of a set of values. Double.NaN is returned for
- * an empty set of values and 0.0 is returned for a <= 1 value set.
- */
- public double getVariance() {
- if (windowSize != Univariate.INFINITE_WINDOW) {
- variance = StatUtils.variance(doubleArray.getElements());
- }
- return variance;
- }
-
- /**
- * Returns the skewness of the values that have been added as described by
- * <a href="http://mathworld.wolfram.com/k-Statistic.html";>Equation (6) for
k-Statistics</a>.
- *
- * @return The skew of a set of values. Double.NaN is returned for
- * an empty set of values and 0.0 is returned for a <= 2 value set.
- */
- public double getSkewness() {
- if (windowSize != Univariate.INFINITE_WINDOW) {
- return StatUtils.skewness(doubleArray.getElements());
- }
- return skewness;
+ public UnivariateImpl(int window) {
+ super(window);
+ storage = new FixedDoubleArray(window);
}
/**
- * Returns the kurtosis of the values that have been added as described by
- * <a href="http://mathworld.wolfram.com/k-Statistic.html";>Equation (7) for
k-Statistics</a>.
- *
- * @return The kurtosis of a set of values. Double.NaN is returned for
- * an empty set of values and 0.0 is returned for a <= 3 value set.
- */
- public double getKurtosis() {
- if (windowSize != Univariate.INFINITE_WINDOW) {
- return StatUtils.kurtosis(doubleArray.getElements());
- }
- return kurtosis;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.stat.Univariate#getMax()
- */
- public double getMax() {
- if (windowSize != Univariate.INFINITE_WINDOW) {
- return StatUtils.max(doubleArray.getElements());
- }
- return max;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.stat.Univariate#getMin()
- */
- public double getMin() {
- if (windowSize != Univariate.INFINITE_WINDOW) {
- return StatUtils.min(doubleArray.getElements());
- }
- return min;
- }
-
- /* (non-Javadoc)
- * @see org.apache.commons.math.stat.Univariate#getGeometricMean()
- */
- public double getGeometricMean() {
-
- if (windowSize != Univariate.INFINITE_WINDOW) {
- return StatUtils.geometricMean(doubleArray.getElements());
- }
-
- if (n == 0) {
- return Double.NaN;
- } else {
- return Math.exp(sumLog / (double) n);
- }
- }
-
- /* If windowSize is set to Infinite, moments are calculated using the following
+ * If windowSize is set to Infinite, moments
+ * are calculated using the following
* <a href="http://www.spss.com/tech/stat/Algorithms/11.5/descriptives.pdf";>
* recursive strategy
* </a>.
@@ -271,15 +98,15 @@
*/
public void addValue(double value) {
- if (windowSize != Univariate.INFINITE_WINDOW) {
+ if (storage != null) {
/* then all getters deligate to StatUtils
* and this clause simply adds/rolls a value in the storage array
*/
- if (windowSize == n) {
- doubleArray.addElementRolling(value);
+ if (getWindowSize() == n) {
+ storage.addElementRolling(value);
} else {
n++;
- doubleArray.addElement(value);
+ storage.addElement(value);
}
} else {
@@ -287,57 +114,18 @@
* is no need to discard the influence of any single item.
*/
n++;
-
- if (n <= 1) {
- /* if n <= 1, initialize the sumLog, min, max, mean, variance and
pre-variance */
- sumLog = 0.0;
- sum = min = max = mean = value;
- sumsq = value * value;
- variance = m2 = 0.0;
- skewness = kurtosis = 0.0;
- m2 = m3 = m4 = 0.0;
- } else {
- /* otherwise calc these values */
- sumLog += Math.log(value);
- sum += value;
- sumsq += value * value;
- min = Math.min(min, value);
- max = Math.max(max, value);
-
- double dev = value - mean;
- double v = dev / ((double) n);
- double v2 = v * v;
-
- double n0 = (double) n;
- double n1 = (double) (n - 1);
- double n2 = (double) (n - 2);
- double n3 = (double) (n - 3);
-
- m4 =
- m4
- - (4.0 * v * m3)
- + (6.0 * v2 * m2)
- + ((n0 * n0) - 3 * n1) * (v2 * v2 * n1 * n0);
-
- m3 = m3 - (3.0 * v * m2) + (n0 * n1 * n2 * v2 * v);
-
- m2 += n1 * dev * v;
-
- mean += v;
-
- variance = (n <= 1) ? 0.0 : m2 / n1;
-
- skewness =
- (n <= 2 || variance < 10E-20)
- ? 0.0
- : (n0 * m3) / (n1 * n2 * Math.sqrt(variance) * variance);
-
- kurtosis =
- (n <= 3 || variance < 10E-20)
- ? 0.0
- : (n0 * (n0 + 1) * m4 - 3 * m2 * m2 * n1)
- / (n1 * n2 * n3 * variance * variance);
- }
+ min.increment(value);
+ max.increment(value);
+ sum.increment(value);
+ sumsq.increment(value);
+ sumLog.increment(value);
+ geoMean.increment(value);
+
+ moment.increment(value);
+ //mean.increment(value);
+ //variance.increment(value);
+ //skewness.increment(value);
+ //kurtosis.increment(value);
}
}
@@ -350,9 +138,9 @@
public String toString() {
StringBuffer outBuffer = new StringBuffer();
outBuffer.append("UnivariateImpl:\n");
- outBuffer.append("n: " + n + "\n");
- outBuffer.append("min: " + min + "\n");
- outBuffer.append("max: " + max + "\n");
+ outBuffer.append("n: " + getN() + "\n");
+ outBuffer.append("min: " + getMin() + "\n");
+ outBuffer.append("max: " + getMax() + "\n");
outBuffer.append("mean: " + getMean() + "\n");
outBuffer.append("std dev: " + getStandardDeviation() + "\n");
outBuffer.append("skewness: " + getSkewness() + "\n");
@@ -360,33 +148,35 @@
return outBuffer.toString();
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.Univariate#clear()
*/
public void clear() {
- this.n = 0;
- this.min = this.max = Double.NaN;
- this.sumLog = this.mean = Double.NaN;
- this.variance = this.skewness = this.kurtosis = Double.NaN;
- this.m2 = this.m3 = this.m4 = Double.NaN;
- if (doubleArray != null)
- doubleArray = new FixedDoubleArray(windowSize);
+ super.clear();
+ if (getWindowSize() != INFINITE_WINDOW) {
+ storage = new FixedDoubleArray(getWindowSize());
+ }
+ }
+
+ /**
+ * @see org.apache.commons.math.stat.AbstractUnivariate#internalValues()
+ */
+ protected double[] internalValues() {
+ return storage == null ? null : storage.getValues();
}
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getWindowSize()
+ /**
+ * @see org.apache.commons.math.stat.AbstractUnivariate#start()
*/
- public int getWindowSize() {
- return windowSize;
+ protected int start() {
+ return storage.start();
}
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#setWindowSize(int)
+ /**
+ * @see org.apache.commons.math.stat.AbstractUnivariate#size()
*/
- public void setWindowSize(int windowSize) {
- clear();
- this.windowSize = windowSize;
- doubleArray = new FixedDoubleArray(windowSize);
+ protected int size() {
+ return storage.getNumElements();
}
}
1.4 +1 -2
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/TestStatisticImpl.java
Index: TestStatisticImpl.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/TestStatisticImpl.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -u -r1.3 -r1.4
--- TestStatisticImpl.java 7 Jul 2003 23:19:20 -0000 1.3
+++ TestStatisticImpl.java 9 Jul 2003 21:45:23 -0000 1.4
@@ -62,7 +62,6 @@
* Implements test statistics defined in the TestStatistic interface.
*
* @version $Revision$ $Date$
- *
*/
public class TestStatisticImpl implements TestStatistic {
1.9 +35 -232
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/AbstractStoreUnivariate.java
Index: AbstractStoreUnivariate.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/AbstractStoreUnivariate.java,v
retrieving revision 1.8
retrieving revision 1.9
diff -u -r1.8 -r1.9
--- AbstractStoreUnivariate.java 7 Jul 2003 23:25:13 -0000 1.8
+++ AbstractStoreUnivariate.java 9 Jul 2003 21:45:23 -0000 1.9
@@ -52,267 +52,70 @@
* <http://www.apache.org/>.
*/
package org.apache.commons.math.stat;
+
import java.util.Arrays;
+import org.apache.commons.math.stat.univariate.rank.Percentile;
+
/**
- * Provides univariate measures for an array of doubles.
- *
- * @author <a href="mailto:[EMAIL PROTECTED]">Tim O'Brien</a>
+ * Provides univariate measures for an array of doubles.
+ * @version $Revision$ $Date$
*/
-public abstract class AbstractStoreUnivariate implements StoreUnivariate {
+public abstract class AbstractStoreUnivariate
+ extends AbstractUnivariate
+ implements StoreUnivariate {
+ /** Percentile */
+ protected Percentile percentile = new Percentile(50);
+
/**
- * Returns the skewness of this collection of values
- * @see org.apache.commons.math.stat.StoreUnivariate#getSkewness()
+ * Create an AbstractStoreUnivariate
*/
- 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;
+ public AbstractStoreUnivariate() {
+ super();
}
/**
- * Returns the kurtosis for this collection of values
- * @see org.apache.commons.math.stat.StoreUnivariate#getKurtosis()
+ * Create an AbstractStoreUnivariate with a specific Window
+ * @param window WindowSIze for stat calculation
*/
- 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;
+ public AbstractStoreUnivariate(int window) {
+ super(window);
}
/**
- * Returns the type or class of kurtosis that this collection of
- * values exhibits
- * @see org.apache.commons.math.stat.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);
-
- }
-
- /**
- * Returns the mean for this collection of values
- * @see org.apache.commons.math.stat.Univariate#getMean()
- */
- public double getMean() {
- double arithMean = getSum() / getN();
- return arithMean;
- }
-
- /**
- * Returns the geometric mean for this collection of values
- * @see org.apache.commons.math.stat.Univariate#getGeometricMean()
- */
- public double getGeometricMean() {
- double gMean = Double.NaN;
-
- if (getN() > 0) {
- double sumLog = 0.0;
- for (int i = 0; i < getN(); i++) {
- sumLog += Math.log(getElement(i));
- }
- gMean = Math.exp(sumLog / (double)getN() );
- }
-
- return gMean;
- }
-
- /**
- * Returns the variance for this collection of values
- * @see org.apache.commons.math.stat.Univariate#getVariance()
+ * @see org.apache.commons.math.stat.StoreUnivariate#getPercentile(double)
*/
- 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;
+ public double getPercentile(double p) {
+ percentile.setPercentile(p);
+ return percentile.evaluate(this.getValues(), this.start(), this.size());
}
-
+
/**
- * Returns the standard deviation for this collection of values
- * @see org.apache.commons.math.stat.Univariate#getStandardDeviation()
+ * @see org.apache.commons.math.stat2.AbstractStoreUnivariate#getSortedValues()
*/
- public double getStandardDeviation() {
- double stdDev = Double.NaN;
- if (getN() != 0) {
- stdDev = Math.sqrt(getVariance());
- }
- return (stdDev);
+ public double[] getSortedValues() {
+ double[] sort = getValues();
+ Arrays.sort(sort);
+ return sort;
}
-
+
/**
- * Returns the maximum value contained herein.
- * @see org.apache.commons.math.stat.Univariate#getMax()
+ * @see org.apache.commons.math.stat.Univariate#addValue(double)
*/
- 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;
- }
+ public abstract void addValue(double value);
/**
- * Returns the minimum value contained herein
- * @see org.apache.commons.math.stat.Univariate#getMin()
+ * @see org.apache.commons.math.stat.StoreUnivariate#getValues()
*/
- 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);
- }
- }
- }
+ public abstract double[] getValues();
- return min;
- }
-
- /**
- * Returns the sum of all values contained herein
- * @see org.apache.commons.math.stat.Univariate#getSum()
- */
- public double getSum() {
- double accum = 0.0;
- for (int i = 0; i < getN(); i++) {
- accum += getElement(i);
- }
- return accum;
- }
/**
- * Returns the sun of the squares of all values contained herein
- * @see org.apache.commons.math.stat.Univariate#getSumsq()
+ * @see org.apache.commons.math.stat.StoreUnivariate#getElement(int)
*/
- public double getSumsq() {
- double accum = 0.0;
- for (int i = 0; i < getN(); i++) {
- accum += Math.pow(getElement(i), 2.0);
- }
- return accum;
- }
+ public abstract double getElement(int index);
- /**
- * @see org.apache.commons.math.stat.StoreUnivariate#getSortedValues()
- *
- */
- public double[] getSortedValues() {
- double[] values = getValues();
- Arrays.sort(values);
- return values;
- }
- /**
- * Returns an estimate for the pth percentile of the stored values
- * @see org.apache.commons.math.stat.StoreUnivariate#getPercentile(double)
- */
- public double getPercentile(double p) {
- if ((p > 100) || (p <= 0)) {
- throw new IllegalArgumentException("invalid percentile value");
- }
- double n = (double) getN();
- if (n == 0) {
- return Double.NaN;
- }
- if (n == 1) {
- return getElement(0); // always return single value for n = 1
- }
- double pos = p * (n + 1) / 100;
- double fpos = Math.floor(pos);
- int intPos = (int) fpos;
- double d = pos - fpos;
- double[] sorted = getSortedValues();
- if (pos < 1) {
- return sorted[0];
- }
- if (pos > n) {
- return sorted[getN() - 1];
- }
- double lower = sorted[intPos - 1];
- double upper = sorted[intPos];
- return lower + d * (upper - lower);
- }
}
1.3 +93 -58
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/ListUnivariateImpl.java
Index: ListUnivariateImpl.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/ListUnivariateImpl.java,v
retrieving revision 1.2
retrieving revision 1.3
diff -u -r1.2 -r1.3
--- ListUnivariateImpl.java 22 Jun 2003 03:57:55 -0000 1.2
+++ ListUnivariateImpl.java 9 Jul 2003 21:45:23 -0000 1.3
@@ -54,132 +54,167 @@
package org.apache.commons.math.stat;
import java.util.List;
+import org.apache.commons.math.util.DefaultTransformer;
+import org.apache.commons.math.util.NumberTransformer;
/**
- * @author <a href="mailto:[EMAIL PROTECTED]">Tim O'Brien</a>
+ * @version $Revision$ $Date$
*/
-public class ListUnivariateImpl extends AbstractStoreUnivariate {
+public class ListUnivariateImpl
+ extends AbstractStoreUnivariate
+ implements StoreUnivariate {
- // Holds the value of the windowSize, initial windowSize is the constant
- // Univariate.INFINITE_WINDOW
- private int windowSize = Univariate.INFINITE_WINDOW;
-
- // Holds a reference to a list - GENERICs are going to make
- // out lives easier here as we could only accept List<Number>
- List list;
+ /**
+ * Holds a reference to a list - GENERICs are going to make
+ * out lives easier here as we could only accept List<Number>
+ */
+ protected List list;
+
+ /** Number Transformer maps Objects to Number for us. */
+ protected NumberTransformer transformer;
+ /**
+ * Construct a ListUnivariate with a specific List.
+ * @param list The list that will back this Univariate
+ */
public ListUnivariateImpl(List list) {
+ super();
+ this.list = list;
+ transformer = new DefaultTransformer();
+ }
+
+ /**
+ * Construct a ListUnivariate with a specific List.
+ * @param list The list that will back this Univariate
+ */
+ public ListUnivariateImpl(List list, NumberTransformer transformer) {
+ super();
this.list = list;
+ this.transformer = transformer;
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.StoreUnivariate#getValues()
*/
public double[] getValues() {
- int startIndex = 0;
- int endIndex = list.size() - 1;
-
+ int length = list.size();
// If the window size is not INFINITE_WINDOW AND
// the current list is larger that the window size, we need to
// take into account only the last n elements of the list
// as definied by windowSize
- if (windowSize != Univariate.INFINITE_WINDOW &&
- windowSize < list.size()) {
- startIndex = (list.size() - 1) - windowSize;
+
+ if (windowSize != Univariate.INFINITE_WINDOW
+ && windowSize < list.size()) {
+ length = list.size() - Math.max(0, list.size() - windowSize);
}
// Create an array to hold all values
- double[] copiedArray = new double[list.size() - startIndex];
+ double[] copiedArray = new double[length];
- for( int i = startIndex; i <= endIndex; i++ ) {
- Number n = (Number) getInternalIndex( i );
- copiedArray[i] = n.doubleValue();
- i++;
+ for (int i = 0; i < copiedArray.length; i++) {
+ copiedArray[i] = getElement(i);
}
-
return copiedArray;
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.StoreUnivariate#getElement(int)
*/
public double getElement(int index) {
double value = Double.NaN;
- if (windowSize != Univariate.INFINITE_WINDOW &&
- windowSize < list.size()) {
- int calcIndex = (list.size() - windowSize) + index;
+ int calcIndex = index;
- Number n = (Number) getInternalIndex(calcIndex);
- value = n.doubleValue();
- } else {
- Number n = (Number) getInternalIndex(index);
- value = n.doubleValue();
+ if (windowSize != Univariate.INFINITE_WINDOW
+ && windowSize < list.size()) {
+ calcIndex = (list.size() - windowSize) + index;
}
+
+ try {
+ value = transformer.transform(list.get(calcIndex));
+ } catch (Exception e) {
+ e.printStackTrace();
+ }
+
return value;
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.Univariate#getN()
*/
public int getN() {
- int N = 0;
+ int n = 0;
if (windowSize != Univariate.INFINITE_WINDOW) {
if (list.size() > windowSize) {
- N = windowSize;
+ n = windowSize;
} else {
- N = list.size();
+ n = list.size();
}
} else {
- N = list.size();
+ n = list.size();
}
- return N;
+ return n;
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.Univariate#addValue(double)
*/
public void addValue(double v) {
list.add(new Double(v));
}
+
+ /**
+ * Adds an object to this list.
+ * @param o Object to add to the list
+ */
+ public void addObject(Object o) {
+ list.add(o);
+ }
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.Univariate#clear()
*/
public void clear() {
+ super.clear();
list.clear();
}
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getWindowSize()
+ /**
+ * @see org.apache.commons.math.stat.AbstractUnivariate#internalValues()
*/
- public int getWindowSize() {
- return windowSize;
+ protected double[] internalValues() {
+ return getValues();
}
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#setWindowSize(int)
+ /**
+ * @see org.apache.commons.math.stat.AbstractUnivariate#start()
*/
- public void setWindowSize(int windowSize) {
- this.windowSize = windowSize;
+ protected int start() {
+ return 0;
}
/**
- * This function exists to support the function of classes which
- * extend the ListUnivariateImpl.
- *
- * @param index The location of the value in the internal List
- * @return A Number object representing the value at a given
- * index
+ * @see org.apache.commons.math.stat.AbstractUnivariate#size()
*/
- protected Number getInternalIndex(int index) {
-
- Number n = (Number) list.get( index );
- return n;
+ protected int size() {
+ return getN();
+ }
+ /**
+ * @return
+ */
+ public NumberTransformer getTransformer() {
+ return transformer;
+ }
+ /**
+ * @param transformer
+ */
+ public void setTransformer(NumberTransformer transformer) {
+ this.transformer = transformer;
}
-}
+
+}
\ No newline at end of file
1.4 +2 -1
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/Frequency.java
Index: Frequency.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/Frequency.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -u -r1.3 -r1.4
--- Frequency.java 7 Jul 2003 23:19:20 -0000 1.3
+++ Frequency.java 9 Jul 2003 21:45:23 -0000 1.4
@@ -62,7 +62,8 @@
* Maintains a frequency distribution. <br>
* Accepts int, long or string values, converting
* all to Strings and maintaining frequency counts.
- *
+ *
+ * @version $Revision$ $Date$
*/
public class Frequency {
/** name for this frequency distribution. */
1.14 +1 -169
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/StatUtils.java
Index: StatUtils.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/StatUtils.java,v
retrieving revision 1.13
retrieving revision 1.14
diff -u -r1.13 -r1.14
--- StatUtils.java 7 Jul 2003 23:25:13 -0000 1.13
+++ StatUtils.java 9 Jul 2003 21:45:23 -0000 1.14
@@ -57,6 +57,7 @@
* StatUtils provides easy static implementations of common double[] based
* statistical methods. These return a single result value or in some cases, as
* identified in the javadoc for each method, Double.NaN.
+ * @version $Revision$ $Date$
*/
public class StatUtils {
@@ -161,29 +162,6 @@
}
/**
- * Returns the geometric mean for this collection of values
- * @param values Is a double[] containing the values
- * @return the geometric mean or Double.NaN if the array is empty or
- * any of the values are <= 0.
- */
- public static double geometricMean(double[] values) {
- return geometricMean(values, 0, values.length);
- }
-
- /**
- * Returns the geometric mean for this collection of values
- * @param values Is a double[] containing the values
- * @param begin processing at this point in the array
- * @param length processing at this point in the array
- * @return the geometric mean or Double.NaN if the array is empty or
- * any of the values are <= 0.
- */
- public static double geometricMean(double[] values, int begin, int length) {
- testInput(values, begin, length);
- return Math.exp(sumLog(values, begin, length) / (double) length );
- }
-
- /**
* Returns the <a href=http://www.xycoon.com/arithmetic_mean.htm>
* arithmetic mean </a> of the available values
* @param values Is a double[] containing the values
@@ -207,36 +185,6 @@
}
/**
- *
- * @param values Is a double[] containing the values
- * @return the result, Double.NaN if no values for an empty array
- * or 0.0 for a single value set.
- */
- public static double standardDeviation(double[] values) {
- return standardDeviation(values, 0, values.length);
- }
-
- /**
- *
- * @param values Is a double[] containing the values
- * @param begin processing at this point in the array
- * @param length processing at this point in the array
- * @return the result, Double.NaN if no values for an empty array
- * or 0.0 for a single value set.
- */
- public static double standardDeviation(
- double[] values,
- int begin,
- int length) {
- testInput(values, begin, length);
- double stdDev = Double.NaN;
- if (values.length != 0) {
- stdDev = Math.sqrt(variance(values, begin, length));
- }
- return (stdDev);
- }
-
- /**
* Returns the variance of the available values. This uses a corrected
* two pass algorithm of the following
* <a href="http://lib-www.lanl.gov/numerical/bookcpdf/c14-1.pdf";>
@@ -289,122 +237,6 @@
return variance;
}
- /**
- * Returns the skewness of a collection of values. Skewness is a
- * measure of the assymetry of a given distribution.
- * @param values Is a double[] containing the values
- * @return the skewness of the values or Double.NaN if the array is empty
- */
- public static double skewness(double[] values) {
- return skewness(values, 0, values.length);
- }
- /**
- * Returns the skewness of a collection of values. Skewness is a
- * measure of the assymetry of a given distribution.
- * @param values Is a double[] containing the values
- * @param begin processing at this point in the array
- * @param length processing at this point in the array
- * @return the skewness of the values or Double.NaN if the array is empty
- */
- public static double skewness(double[] values, int begin, int length) {
-
- testInput(values, begin, length);
-
- // Initialize the skewness
- double skewness = Double.NaN;
-
- // Get the mean and the standard deviation
- double mean = mean(values, begin, length);
-
- // Calc the std, this is implemented here instead of using the
- // standardDeviation method eliminate a duplicate pass to get the mean
- double accum = 0.0;
- double accum2 = 0.0;
- for (int i = begin; i < begin + length; i++) {
- accum += Math.pow((values[i] - mean), 2.0);
- accum2 += (values[i] - mean);
- }
- double stdDev =
- Math.sqrt(
- (accum - (Math.pow(accum2, 2) / ((double) length)))
- / (double) (length - 1));
-
- // Calculate the skew as the sum the cubes of the distance
- // from the mean divided by the standard deviation.
- double accum3 = 0.0;
- for (int i = begin; i < begin + length; i++) {
- accum3 += Math.pow((values[i] - mean) / stdDev, 3.0);
- }
-
- // Get N
- double n = length;
-
- // Calculate skewness
- skewness = (n / ((n - 1) * (n - 2))) * accum3;
-
- return skewness;
- }
-
- /**
- * Returns the kurtosis for this collection of values. Kurtosis is a
- * measure of the "peakedness" of a distribution.
- * @param values Is a double[] containing the values
- * @return the kurtosis of the values or Double.NaN if the array is empty
- */
- public static double kurtosis(double[] values) {
- return kurtosis(values, 0, values.length);
- }
-
- /**
- * Returns the kurtosis for this collection of values. Kurtosis is a
- * measure of the "peakedness" of a distribution.
- * @param values Is a double[] containing the values
- * @param begin processing at this point in the array
- * @param length processing at this point in the array
- * @return the kurtosis of the values or Double.NaN if the array is empty
- */
- public static double kurtosis(double[] values, int begin, int length) {
- testInput(values, begin, length);
-
- // Initialize the kurtosis
- double kurtosis = Double.NaN;
-
- // Get the mean and the standard deviation
- double mean = mean(values, begin, length);
-
- // Calc the std, this is implemented here instead of using the
- // standardDeviation method eliminate a duplicate pass to get the mean
- double accum = 0.0;
- double accum2 = 0.0;
- for (int i = begin; i < begin + length; i++) {
- accum += Math.pow((values[i] - mean), 2.0);
- accum2 += (values[i] - mean);
- }
-
- double stdDev =
- Math.sqrt(
- (accum - (Math.pow(accum2, 2) / ((double) length)))
- / (double) (length - 1));
-
- // Sum the ^4 of the distance from the mean divided by the
- // standard deviation
- double accum3 = 0.0;
- for (int i = begin; i < begin + length; i++) {
- accum3 += Math.pow((values[i] - mean) / stdDev, 4.0);
- }
-
- // Get N
- double n = length;
-
- 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 * accum3) - termTwo;
-
- return kurtosis;
- }
-
/**
* Returns the maximum of the available values
* @param values Is a double[] containing the values
1.4 +1 -2
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/TestStatistic.java
Index: TestStatistic.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/TestStatistic.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -u -r1.3 -r1.4
--- TestStatistic.java 7 Jul 2003 23:19:20 -0000 1.3
+++ TestStatistic.java 9 Jul 2003 21:45:23 -0000 1.4
@@ -55,8 +55,7 @@
/**
* A collection of commonly used test statistics and statistical tests.
*
- * @version $Revision$ $Date$
- *
+ * @version $Revision$ $Date$
*/
public interface TestStatistic {
1.4 +54 -34
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/StoreUnivariateImpl.java
Index: StoreUnivariateImpl.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/StoreUnivariateImpl.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -u -r1.3 -r1.4
--- StoreUnivariateImpl.java 27 Jun 2003 20:58:28 -0000 1.3
+++ StoreUnivariateImpl.java 9 Jul 2003 21:45:23 -0000 1.4
@@ -56,81 +56,80 @@
import org.apache.commons.math.util.ContractableDoubleArray;
/**
- * @author <a href="mailto:[EMAIL PROTECTED]">Tim O'Brien</a>
+ * @version $Revision$ $Date$
*/
public class StoreUnivariateImpl extends AbstractStoreUnivariate {
- // Use an internal double array
- ContractableDoubleArray eDA;
-
- // Store the windowSize
- private int windowSize = Univariate.INFINITE_WINDOW;
+ /** A contractable double array is used. memory is reclaimed when
+ * the storage of the array becomes too empty.
+ */
+ protected ContractableDoubleArray eDA;
+ /**
+ * Construct a StoreUnivariateImpl
+ */
public StoreUnivariateImpl() {
- // A contractable double array is used. memory is reclaimed when
- // the storage of the array becomes too empty.
eDA = new ContractableDoubleArray();
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.StoreUnivariate#getValues()
*/
public double[] getValues() {
- double[] copiedArray = new double[ eDA.getNumElements() ];
- System.arraycopy( eDA.getElements(), 0,
- copiedArray, 0, eDA.getNumElements());
+ double[] copiedArray = new double[eDA.getNumElements()];
+ System.arraycopy(
+ eDA.getElements(),
+ 0,
+ copiedArray,
+ 0,
+ eDA.getNumElements());
return copiedArray;
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.StoreUnivariate#getElement(int)
*/
public double getElement(int index) {
return eDA.getElement(index);
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.Univariate#getN()
*/
public int getN() {
return eDA.getNumElements();
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.Univariate#addValue(double)
*/
public synchronized void addValue(double v) {
- if( windowSize != Univariate.INFINITE_WINDOW ) {
- if( getN() == windowSize ) {
- eDA.addElementRolling( v );
- } else if( getN() < windowSize ) {
+ if (windowSize != Univariate.INFINITE_WINDOW) {
+ if (getN() == windowSize) {
+ eDA.addElementRolling(v);
+ } else if (getN() < windowSize) {
eDA.addElement(v);
} else {
- String msg = "A window Univariate had more element than " +
- "the windowSize. This is an inconsistent
state.";
- throw new RuntimeException( msg );
+ String msg =
+ "A window Univariate had more element than "
+ + "the windowSize. This is an inconsistent state.";
+ throw new RuntimeException(msg);
}
} else {
eDA.addElement(v);
}
}
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.Univariate#clear()
*/
public synchronized void clear() {
+ super.clear();
eDA.clear();
}
- /* (non-Javadoc)
- * @see org.apache.commons.math.Univariate#getWindowSize()
- */
- public int getWindowSize() {
- return windowSize;
- }
-
- /* (non-Javadoc)
+ /**
* @see org.apache.commons.math.Univariate#setWindowSize(int)
*/
public synchronized void setWindowSize(int windowSize) {
@@ -139,8 +138,29 @@
// 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 < eDA.getNumElements() ) {
- eDA.discardFrontElements( eDA.getNumElements() - windowSize);
+ if (windowSize < eDA.getNumElements()) {
+ eDA.discardFrontElements(eDA.getNumElements() - windowSize);
}
}
-}
+
+ /**
+ * @see org.apache.commons.math.stat.AbstractUnivariate#internalValues()
+ */
+ protected double[] internalValues() {
+ return eDA.getValues();
+ }
+
+ /**
+ * @see org.apache.commons.math.stat.AbstractUnivariate#start()
+ */
+ protected int start() {
+ return eDA.start();
+ }
+
+ /**
+ * @see org.apache.commons.math.stat.AbstractUnivariate#size()
+ */
+ protected int size() {
+ return eDA.getNumElements();
+ }
+}
\ No newline at end of file
1.1
jakarta-commons-sandbox/math/src/java/org/apache/commons/math/stat/AbstractUnivariate.java
Index: AbstractUnivariate.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 [EMAIL PROTECTED]
*
* 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.stat;
import org.apache.commons.math.stat.univariate.moment.FourthMoment;
import org.apache.commons.math.stat.univariate.moment.GeometricMean;
import org.apache.commons.math.stat.univariate.moment.Kurtosis;
import org.apache.commons.math.stat.univariate.moment.Mean;
import org.apache.commons.math.stat.univariate.moment.Skewness;
import org.apache.commons.math.stat.univariate.moment.Variance;
import org.apache.commons.math.stat.univariate.rank.Max;
import org.apache.commons.math.stat.univariate.rank.Min;
import org.apache.commons.math.stat.univariate.summary.Sum;
import org.apache.commons.math.stat.univariate.summary.SumOfLogs;
import org.apache.commons.math.stat.univariate.summary.SumOfSquares;
/**
* Provides univariate measures for an array of doubles.
* @version $Revision: 1.1 $ $Date: 2003/07/09 21:45:23 $
*/
public abstract class AbstractUnivariate implements Univariate {
/** hold the window size **/
protected int windowSize = Univariate.INFINITE_WINDOW;
/** count of values that have been added */
protected int n = 0;
/** FourthMoment is used in calculating mean, variance,skew and kurtosis */
protected FourthMoment moment = null;
/** sum of values that have been added */
protected Sum sum = null;
/** sum of the square of each value that has been added */
protected SumOfSquares sumsq = null;
/** min of values that have been added */
protected Min min = null;
/** max of values that have been added */
protected Max max = null;
/** sumLog of values that have been added */
protected SumOfLogs sumLog = null;
/** geoMean of values that have been added */
protected GeometricMean geoMean = null;
/** mean of values that have been added */
protected Mean mean = null;
/** variance of values that have been added */
protected Variance variance = null;
/** skewness of values that have been added */
protected Skewness skewness = null;
/** kurtosis of values that have been added */
protected Kurtosis kurtosis = null;
/**
* Construct an AbstractUnivariate
*/
public AbstractUnivariate() {
super();
sum = new Sum();
sumsq = new SumOfSquares();
min = new Min();
max = new Max();
sumLog = new SumOfLogs();
geoMean = new GeometricMean();
moment = new FourthMoment();
mean = new Mean(moment);
variance = new Variance(moment);
skewness = new Skewness(moment);
kurtosis = new Kurtosis(moment);
}
/**
* Construct an AbstractUnivariate with a window
* @param window The Window Size
*/
public AbstractUnivariate(int window) {
this();
setWindowSize(window);
}
/**
* Returns the internalValues array.
* @return the array
*/
protected abstract double[] internalValues();
/**
* Returns the start index of the array
* @return start index
*/
protected abstract int start();
/**
* Returns the size of the array appropriate for doing calculations.
* @return Usually this is just numElements.
*/
protected abstract int size();
/**
* If windowSize is set to Infinite,
* statistics are calculated using the following
* <a href="http://www.spss.com/tech/stat/Algorithms/11.5/descriptives.pdf";>
* recursive strategy
* </a>.
* @see org.apache.commons.math.stat.Univariate#addValue(double)
*/
public abstract void addValue(double value);
/**
* @see org.apache.commons.math.stat.Univariate#getN()
*/
public int getN() {
return n;
}
/**
* @see org.apache.commons.math.stat.Univariate#getSum()
*/
public double getSum() {
double[] v = internalValues();
if (v != null) {
return sum.evaluate(v, this.start(), this.size());
}
return sum.getResult();
}
/**
* @see org.apache.commons.math.stat.Univariate#getSumsq()
*/
public double getSumsq() {
double[] v = internalValues();
if (v != null) {
return sumsq.evaluate(v, this.start(), this.size());
}
return sumsq.getResult();
}
/**
* @see org.apache.commons.math.stat.Univariate#getMean()
*/
public double getMean() {
double[] v = internalValues();
if (v != null) {
return mean.evaluate(v, this.start(), this.size());
}
return mean.getResult();
}
/**
* Returns the standard deviation for this collection of values
* @see org.apache.commons.math.stat.Univariate#getStandardDeviation()
*/
public double getStandardDeviation() {
double stdDev = Double.NaN;
if (getN() > 0) {
if (getN() > 1) {
stdDev = Math.sqrt(getVariance());
} else {
stdDev = 0.0;
}
}
return (stdDev);
}
/**
* Returns the variance of the values that have been added via West's
* algorithm as described by
* <a href="http://doi.acm.org/10.1145/359146.359152";>Chan, T. F. and
* J. G. Lewis 1979, <i>Communications of the ACM</i>,
* vol. 22 no. 9, pp. 526-531.</a>.
*
* @return The variance of a set of values.
* Double.NaN is returned for an empty
* set of values and 0.0 is returned for
* a <= 1 value set.
*/
public double getVariance() {
double[] v = internalValues();
if (v != null) {
return variance.evaluate(v, this.start(), this.size());
}
return variance.getResult();
}
/**
* Returns the skewness of the values that have been added as described by
* <a href="http://mathworld.wolfram.com/k-Statistic.html";>
* Equation (6) for k-Statistics</a>.
* @return The skew of a set of values. Double.NaN is returned for
* an empty set of values and 0.0 is returned for a
* <= 2 value set.
*/
public double getSkewness() {
double[] v = internalValues();
if (v != null) {
return skewness.evaluate(v, this.start(), this.size());
}
return skewness.getResult();
}
/**
* Returns the kurtosis of the values that have been added as described by
* <a href="http://mathworld.wolfram.com/k-Statistic.html";>
* Equation (7) for k-Statistics</a>.
*
* @return The kurtosis of a set of values. Double.NaN is returned for
* an empty set of values and 0.0 is returned for a <= 3
* value set.
*/
public double getKurtosis() {
double[] v = internalValues();
if (v != null) {
return kurtosis.evaluate(v, this.start(), this.size());
}
return kurtosis.getResult();
}
/**
* @see org.apache.commons.math.stat.StoreUnivariate#getKurtosisClass()
*/
public int getKurtosisClass() {
int kClass = Univariate.MESOKURTIC;
double kurtosis = getKurtosis();
if (kurtosis > 0) {
kClass = Univariate.LEPTOKURTIC;
} else if (kurtosis < 0) {
kClass = Univariate.PLATYKURTIC;
}
return (kClass);
}
/**
* @see org.apache.commons.math.stat.Univariate#getMax()
*/
public double getMax() {
double[] v = internalValues();
if (v != null) {
return max.evaluate(v, this.start(), this.size());
}
return max.getResult();
}
/**
* @see org.apache.commons.math.stat.Univariate#getMin()
*/
public double getMin() {
double[] v = internalValues();
if (v != null) {
return min.evaluate(v, this.start(), this.size());
}
return min.getResult();
}
/**
* @see org.apache.commons.math.stat.Univariate#getGeometricMean()
*/
public double getGeometricMean() {
double[] v = internalValues();
if (v != null) {
return geoMean.evaluate(v, this.start(), this.size());
}
return geoMean.getResult();
}
/**
* Generates a text report displaying
* univariate statistics from values that
* have been added.
* @return String with line feeds displaying statistics
*/
public String toString() {
StringBuffer outBuffer = new StringBuffer();
outBuffer.append("UnivariateImpl:\n");
outBuffer.append("n: " + n + "\n");
outBuffer.append("min: " + min + "\n");
outBuffer.append("max: " + max + "\n");
outBuffer.append("mean: " + getMean() + "\n");
outBuffer.append("std dev: " + getStandardDeviation() + "\n");
outBuffer.append("skewness: " + getSkewness() + "\n");
outBuffer.append("kurtosis: " + getKurtosis() + "\n");
return outBuffer.toString();
}
/**
* @see org.apache.commons.math.Univariate#clear()
*/
public void clear() {
this.n = 0;
min.clear();
max.clear();
sum.clear();
sumLog.clear();
sumsq.clear();
geoMean.clear();
moment.clear();
mean.clear();
variance.clear();
skewness.clear();
kurtosis.clear();
}
/**
* @see org.apache.commons.math.Univariate#getWindowSize()
*/
public int getWindowSize() {
return windowSize;
}
/**
* @see org.apache.commons.math.Univariate#setWindowSize(int)
*/
public void setWindowSize(int windowSize) {
clear();
this.windowSize = windowSize;
}
}
1.6 +1 -20
jakarta-commons-sandbox/math/src/test/org/apache/commons/math/stat/StatUtilsTest.java
Index: StatUtilsTest.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/test/org/apache/commons/math/stat/StatUtilsTest.java,v
retrieving revision 1.5
retrieving revision 1.6
diff -u -r1.5 -r1.6
--- StatUtilsTest.java 7 Jul 2003 23:25:14 -0000 1.5
+++ StatUtilsTest.java 9 Jul 2003 21:45:24 -0000 1.6
@@ -99,11 +99,6 @@
assertEquals("sum", sum, StatUtils.sum(values), tolerance);
assertEquals("sumsq", sumSq, StatUtils.sumSq(values), tolerance);
assertEquals("var", var, StatUtils.variance(values), tolerance);
- assertEquals(
- "std",
- std,
- StatUtils.standardDeviation(values),
- tolerance);
assertEquals("mean", mean, StatUtils.mean(values), tolerance);
assertEquals("min", min, StatUtils.min(values), tolerance);
assertEquals("max", max, StatUtils.max(values), tolerance);
@@ -116,9 +111,6 @@
"Mean of n = 0 set should be NaN",
Double.isNaN(StatUtils.mean(values)));
assertTrue(
- "Standard Deviation of n = 0 set should be NaN",
- Double.isNaN(StatUtils.standardDeviation(values)));
- assertTrue(
"Variance of n = 0 set should be NaN",
Double.isNaN(StatUtils.variance(values)));
@@ -128,10 +120,6 @@
"Mean of n = 1 set should be value of single item n1",
StatUtils.mean(values) == one);
assertTrue(
- "StdDev of n = 1 set should be zero, instead it is: "
- + StatUtils.standardDeviation(values),
- StatUtils.standardDeviation(values) == 0);
- assertTrue(
"Variance of n = 1 set should be zero",
StatUtils.variance(values) == 0);
}
@@ -165,8 +153,6 @@
assertEquals("mean", 12.40455, StatUtils.mean(values), 0.0001);
assertEquals("variance", 10.00236, StatUtils.variance(values), 0.0001);
- assertEquals("skewness", 1.437424, StatUtils.skewness(values), 0.0001);
- assertEquals("kurtosis", 2.37719, StatUtils.kurtosis(values), 0.0001);
}
public void testProductAndGeometricMean() throws Exception {
@@ -177,11 +163,6 @@
24.0,
StatUtils.product(values),
Double.MIN_VALUE);
- assertEquals(
- "Geometric mean not expected",
- 2.213364,
- StatUtils.geometricMean(values),
- 0.00001);
}
public void testArrayIndexConditions() throws Exception {
1.6 +2 -2
jakarta-commons-sandbox/math/src/test/org/apache/commons/math/stat/UnivariateImplTest.java
Index: UnivariateImplTest.java
===================================================================
RCS file:
/home/cvs/jakarta-commons-sandbox/math/src/test/org/apache/commons/math/stat/UnivariateImplTest.java,v
retrieving revision 1.5
retrieving revision 1.6
diff -u -r1.5 -r1.6
--- UnivariateImplTest.java 7 Jul 2003 23:19:19 -0000 1.5
+++ UnivariateImplTest.java 9 Jul 2003 21:45:24 -0000 1.6
@@ -130,7 +130,7 @@
u.addValue(one);
assertTrue("mean should be one (n = 1)",
u.getMean() == one);
- assertTrue("geometric should be one (n = 1)",
+ assertTrue("geometric should be one (n = 1) instead it is " +
u.getGeometricMean(),
u.getGeometricMean() == one);
assertTrue("Std should be zero (n = 1)",
u.getStandardDeviation() == 0.0);
1.1
jakarta-commons-sandbox/math/src/test/org/apache/commons/math/stat/MixedListUnivariateImplTest.java
Index: MixedListUnivariateImplTest.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 [EMAIL PROTECTED]
*
* 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.stat;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math.util.NumberTransformer;
import org.apache.commons.math.util.TransformerMap;
import junit.framework.Test;
import junit.framework.TestCase;
import junit.framework.TestSuite;
/**
* Test cases for the [EMAIL PROTECTED] Univariate} class.
*
* @version $Revision: 1.1 $ $Date: 2003/07/09 21:45:24 $
*/
public final class MixedListUnivariateImplTest 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;
private TransformerMap transformers = new TransformerMap();
public MixedListUnivariateImplTest(String name) {
super(name);
transformers = new TransformerMap();
transformers.putTransformer(Foo.class, new NumberTransformer() {
public double transform(Object o) {
return Double.parseDouble(((Foo) o).heresFoo());
}
});
transformers.putTransformer(Bar.class, new NumberTransformer() {
public double transform(Object o) {
return Double.parseDouble(((Bar) o).heresBar());
}
});
}
public void setUp() {
}
public static Test suite() {
TestSuite suite = new TestSuite(MixedListUnivariateImplTest.class);
suite.setName("Mixed List Tests");
return suite;
}
/** test stats */
public void testStats() {
List externalList = new ArrayList();
StoreUnivariate u = new ListUnivariateImpl(externalList,transformers);
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(new ArrayList(),transformers);
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()));
u.addValue(one);
assertTrue(
"Mean of n = 1 set should be value of single item n1, instead it is " +
u.getMean() ,
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() {
ListUnivariateImpl u =
new ListUnivariateImpl(new ArrayList(), transformers);
u.addObject("12.5");
u.addObject(new Integer(12));
u.addObject("11.8");
u.addObject("14.2");
u.addObject(new Foo());
u.addObject("14.5");
u.addObject(new Long(21));
u.addObject("8.2");
u.addObject("10.3");
u.addObject("11.3");
u.addObject(new Float(14.1));
u.addObject("9.9");
u.addObject("12.2");
u.addObject(new Bar());
u.addObject("12.1");
u.addObject("11");
u.addObject(new Double(19.8));
u.addObject("11");
u.addObject("10");
u.addObject("8.8");
u.addObject("9");
u.addObject("12.3");
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);
}
public void testProductAndGeometricMean() throws Exception {
ListUnivariateImpl u = new ListUnivariateImpl(new ArrayList(),transformers);
u.setWindowSize(10);
u.addValue(1.0);
u.addValue(2.0);
u.addValue(3.0);
u.addValue(4.0);
assertEquals(
"Geometric mean not expected",
2.213364,
u.getGeometricMean(),
0.00001);
// Now test rolling - UnivariateImpl should discount the contribution
// of a discarded element
for (int i = 0; i < 10; i++) {
u.addValue(i + 2);
}
// Values should be (2,3,4,5,6,7,8,9,10,11)
assertEquals(
"Geometric mean not expected",
5.755931,
u.getGeometricMean(),
0.00001);
}
public final class Foo {
public String heresFoo() {
return "14.9";
}
}
public final class Bar {
public String heresBar() {
return "12.0";
}
}
}
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