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Improved Variance calculation, test for negative variance and added some more javadoc. 

git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140887 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Mark R. Diggory 2003-06-04 04:05:39 +00:00
parent b3dd72f6e0
commit acde85f2bf
1 changed files with 66 additions and 43 deletions
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103
src/java/org/apache/commons/math/stat/UnivariateImpl.java
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@ -68,9 +68,9 @@ import org.apache.commons.math.FixedDoubleArray;
*
* @author Phil Steitz
* @author <a href="mailto:tobrien@apache.org">Tim O'Brien</a>
* @author Mark Diggory
* @author <a href="mailto:mdiggory@apache.org">Mark Diggory</a>
* @author Brent Worden
* @version $Revision: 1.1 $ $Date: 2003/05/29 20:35:45 $
* @version $Revision: 1.2 $ $Date: 2003/06/04 04:05:39 $
*
*/
public class UnivariateImpl implements Univariate, Serializable {
@ -79,7 +79,7 @@ public class UnivariateImpl implements Univariate, Serializable {
private int windowSize = Univariate.INFINITE_WINDOW;
/** Just in case, the windowSize is not inifinite, we need to
* keep an array to remember values 0 to N
* keep an array to remember values 0 to N
*/
private DoubleArray doubleArray;
@ -107,25 +107,30 @@ public class UnivariateImpl implements Univariate, Serializable {
/** product of values that have been added */
private double product = Double.NaN;
/** Creates new univariate */
/** Creates new univariate with an inifinite window */
public UnivariateImpl() {
clear();
}
/** Create a new univariate with a fixed window **/
/** Creates a new univariate with a fixed window **/
public UnivariateImpl(int window) {
windowSize = window;
doubleArray = new FixedDoubleArray( window );
}
public void addValue(double v) {
/**
* @see org.apache.commons.math.stat.Univariate#addValue(double)
*/
public void addValue(double v) {
insertValue(v);
}
public double getMean() {
/**
* @see org.apache.commons.math.stat.Univariate#getMean()
*/
public double getMean() {
if (n == 0) {
return Double.NaN;
} else {
@ -134,7 +139,10 @@ public class UnivariateImpl implements Univariate, Serializable {
}
public double getGeometricMean() {
/**
* @see org.apache.commons.math.stat.Univariate#getGeometricMean()
*/
public double getGeometricMean() {
if ((product <= 0.0) || (n == 0)) {
return Double.NaN;
} else {
@ -142,35 +150,43 @@ public class UnivariateImpl implements Univariate, Serializable {
}
}
public double getProduct() {
/**
* @see org.apache.commons.math.stat.Univariate#getProduct()
*/
public double getProduct() {
return product;
}
/**
* @see org.apache.commons.math.stat.Univariate#getStandardDeviation()
*/
public double getStandardDeviation() {
double variance = getVariance();
if ((variance == 0.0) || (variance == Double.NaN)) {
return variance;
} else {
return Math.sqrt(variance);
}
}
public double getVariance() {
double variance = Double.NaN;
/**
* Returns the variance of the values that have been added as described by
* <a href=http://mathworld.wolfram.com/k-Statistic.html>Equation (5) for k-Statistics</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 &lt;= 1 value set.
*/
public double getVariance() {
double variance = Double.NaN;
if( n == 1 ) {
variance = 0.0;
} else if( n > 1 ) {
double xbar = getMean();
variance = (sumsq - xbar*xbar*((double) n))/(((double) n)-1);
}
return variance;
}
public double getStandardDeviation() {
double variance = getVariance();
if ((variance == 0.0) || (variance == Double.NaN)) {
return variance;
} else {
return Math.sqrt(variance);
}
}
if( n == 1 ) {
variance = 0.0;
} else if( n > 1 ) {
variance = (((double)n)*sumsq - (sum * sum)) / (double) (n * (n - 1));
}
return variance < 0 ? 0.0 : variance;
}
/**
* Returns the skewness of the values that have been added as described by
@ -184,8 +200,8 @@ public class UnivariateImpl implements Univariate, Serializable {
if( n < 1) return Double.NaN;
if( n <= 2 ) return 0.0;
return ( 2*Math.pow(sum,3) - 3*sum*sumsq + ((double)n)*((double)n)*sumCube ) /
( ((double)n)*(((double)n)-1)*(((double)n)-2));
return ( 2*Math.pow(sum,3) - 3*sum*sumsq + ((double)(n*n))*sumCube ) /
( (double)(n*(n-1)*(n-2)) ) ;
}
/**
@ -202,14 +218,19 @@ public class UnivariateImpl implements Univariate, Serializable {
double x1 = -6*Math.pow(sum,4);
double x2 = 12*((double)n)*Math.pow(sum,2)*sumsq;
double x3 = -3*((double)n)*(((double)n)-1)*Math.pow(sumsq,2);
double x4 = -4*((double)n)*(((double)n)+1)*sum*sumCube;
double x5 = Math.pow(((double)n),2)*(((double)n)+1)*sumQuad;
double x3 = -3*((double)(n*(n-1)))*Math.pow(sumsq,2);
double x4 = -4*((double)(n*(n+1)))*sum*sumCube;
double x5 = Math.pow(((double)n),2)*((double)(n+1))*sumQuad;
return (x1 + x2 + x3 + x4 + x5) /
(((double)n)*(((double)n)-1)*(((double)n)-2)*(((double)n)-3));
( (double)(n*(n-1)*(n-2)*(n-3)) );
}
private void insertValue(double v) {
/**
* Called in "addValue" to insert a new value into the statistic.
* @param v The value to be added.
*/
private void insertValue(double v) {
// The default value of product is NaN, if you
// try to retrieve the product for a univariate with
@ -351,7 +372,9 @@ public class UnivariateImpl implements Univariate, Serializable {
return outBuffer.toString();
}
/** Resets all sums to 0, resets min and max */
/**
* Resets all sums to 0, resets min and max
*/
public void clear() {
this.sum = this.sumsq = this.sumCube = this.sumQuad = 0.0;
this.n = 0;