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This is an Implementation of StatUtils that uses the new UnivariateStatistic Framework and passes all JUnit StatUtils tests. 

git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@140963 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Mark R. Diggory 2003-07-05 18:29:35 +00:00
parent 88d6952806
commit 77aa09dab9
1 changed files with 105 additions and 173 deletions
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274
src/java/org/apache/commons/math/stat/StatUtils.java
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@ -53,6 +53,21 @@
*/
package org.apache.commons.math.stat;
import org.apache.commons.math.stat.univariate.UnivariateStatistic;
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.Median;
import org.apache.commons.math.stat.univariate.rank.Min;
import org.apache.commons.math.stat.univariate.rank.Percentile;
import org.apache.commons.math.stat.univariate.summary.Product;
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;
/**
* StatUtils provides easy static implementations of common double[] based
* statistical methods. These return a single result value or in some cases, as
@ -62,13 +77,52 @@ package org.apache.commons.math.stat;
*/
public class StatUtils {
/** Sum Of Logs */
private static UnivariateStatistic sumLog = new SumOfLogs();
/** Product */
private static UnivariateStatistic product = new Product();
/** Geometric Mean */
private static UnivariateStatistic geoMean = new GeometricMean();
/** Mean */
private static UnivariateStatistic mean = new Mean();
/** Variance */
private static UnivariateStatistic var = new Variance();
/** Skewness */
private static UnivariateStatistic skew = new Skewness();
/** Kurtosis */
private static UnivariateStatistic kurt = new Kurtosis();
/** Min Of Logs */
private static UnivariateStatistic min = new Min();
/** Max */
private static UnivariateStatistic max = new Max();
/** Median */
private static UnivariateStatistic median = new Median();
/** Sum */
private static UnivariateStatistic sum = new Sum();
/** Sum Of Squares */
private static UnivariateStatistic sumSq = new SumOfSquares();
/** Percentile */
private static Percentile percentile = new Percentile();
/**
* The sum of the values that have been added to Univariate.
* @param values Is a double[] containing the values
* @return the sum of the values or Double.NaN if the array is empty
*/
public static double sum(double[] values) {
return sum(values, 0, values.length);
return sum.evaluate(values, 0, values.length);
}
/**
@ -79,12 +133,7 @@ public class StatUtils {
* @return the sum of the values or Double.NaN if the array is empty
*/
public static double sum(double[] values, int begin, int length) {
testInput(values, begin, length);
double accum = 0.0;
for (int i = begin; i < begin + length; i++) {
accum += values[i];
}
return accum;
return sum.evaluate(values, begin, length);
}
/**
@ -93,7 +142,7 @@ public class StatUtils {
* @return the sum of the squared values or Double.NaN if the array is empty
*/
public static double sumSq(double[] values) {
return sumSq(values, 0, values.length);
return sumSq.evaluate(values);
}
/**
@ -104,12 +153,7 @@ public class StatUtils {
* @return the sum of the squared values or Double.NaN if the array is empty
*/
public static double sumSq(double[] values, int begin, int length) {
testInput(values, begin, length);
double accum = 0.0;
for (int i = begin; i < begin + length; i++) {
accum += Math.pow(values[i], 2.0);
}
return accum;
return sumSq.evaluate(values, begin, length);
}
/**
@ -118,7 +162,7 @@ public class StatUtils {
* @return the product values or Double.NaN if the array is empty
*/
public static double product(double[] values) {
return product(values, 0, values.length);
return product.evaluate(values);
}
/**
@ -129,12 +173,7 @@ public class StatUtils {
* @return the product values or Double.NaN if the array is empty
*/
public static double product(double[] values, int begin, int length) {
testInput(values, begin, length);
double product = 1.0;
for (int i = begin; i < begin + length; i++) {
product *= values[i];
}
return product;
return product.evaluate(values, begin, length);
}
/**
@ -143,7 +182,7 @@ public class StatUtils {
* @return the sumLog value or Double.NaN if the array is empty
*/
public static double sumLog(double[] values) {
return sumLog(values, 0, values.length);
return sumLog.evaluate(values);
}
/**
@ -154,12 +193,7 @@ public class StatUtils {
* @return the sumLog value or Double.NaN if the array is empty
*/
public static double sumLog(double[] values, int begin, int length) {
testInput(values, begin, length);
double sumLog = 0.0;
for (int i = begin; i < begin + length; i++) {
sumLog += Math.log(values[i]);
}
return sumLog;
return sumLog.evaluate(values, begin, length);
}
/**
@ -169,7 +203,7 @@ public class StatUtils {
* any of the values are &lt;= 0.
*/
public static double geometricMean(double[] values) {
return geometricMean(values, 0, values.length);
return geoMean.evaluate(values);
}
/**
@ -180,9 +214,11 @@ public class StatUtils {
* @return the geometric mean or Double.NaN if the array is empty or
* any of the values are &lt;= 0.
*/
public static double geometricMean(double[] values, int begin, int length) {
testInput(values, begin, length);
return Math.exp(sumLog(values, begin, length) / (double) length );
public static double geometricMean(
double[] values,
int begin,
int length) {
return geoMean.evaluate(values, begin, length);
}
/**
@ -192,7 +228,7 @@ public class StatUtils {
* @return the mean of the values or Double.NaN if the array is empty
*/
public static double mean(double[] values) {
return sum(values) / (double) values.length;
return mean.evaluate(values);
}
/**
@ -204,8 +240,7 @@ public class StatUtils {
* @return the mean of the values or Double.NaN if the array is empty
*/
public static double mean(double[] values, int begin, int length) {
testInput(values, begin, length);
return sum(values, begin, length) / ((double) length);
return mean.evaluate(values, begin, length);
}
/**
@ -230,7 +265,7 @@ public class StatUtils {
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));
@ -271,24 +306,7 @@ public class StatUtils {
* or 0.0 for a single value set.
*/
public static double variance(double[] values, int begin, int length) {
testInput(values, begin, length);
double variance = Double.NaN;
if (values.length == 1) {
variance = 0;
} else if (values.length > 1) {
double mean = mean(values, begin, length);
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);
}
variance =
(accum - (Math.pow(accum2, 2) / ((double)length)))
/ (double) (length - 1);
}
return variance;
return var.evaluate(values, begin, length);
}
/**
@ -300,51 +318,16 @@ public class StatUtils {
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
*/
/**
* 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;
return skew.evaluate(values, begin, length);
}
/**
@ -366,45 +349,7 @@ public class StatUtils {
* @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;
return kurt.evaluate(values, begin, length);
}
/**
@ -424,16 +369,7 @@ public class StatUtils {
* @return the maximum of the values or Double.NaN if the array is empty
*/
public static double max(double[] values, int begin, int length) {
testInput(values, begin, length);
double max = Double.NaN;
for (int i = begin; i < begin + length; i++) {
if (i == 0) {
max = values[i];
} else {
max = (max > values[i]) ? max : values[i];
}
}
return max;
return max.evaluate(values, begin, length);
}
/**
@ -453,36 +389,32 @@ public class StatUtils {
* @return the minimum of the values or Double.NaN if the array is empty
*/
public static double min(double[] values, int begin, int length) {
testInput(values, begin, length);
double min = Double.NaN;
for (int i = begin; i < begin + length; i++) {
if (i == 0) {
min = values[i];
} else {
min = (min < values[i]) ? min : values[i];
}
}
return min;
return min.evaluate(values, begin, length);
}
/**
* Private testInput method used by all methods to verify the content
* of the array and indicies are correct.
* Returns the p'th percentile for a double[]
* @param values Is a double[] containing the values
* @param p is 0 <= p <= 100
* @return the value at the p'th percentile
*/
public static double percentile(double[] values, double p) {
return percentile.evaluate(values, p);
}
/**
* Returns the p'th percentile for a double[]
* @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
* @param p is 0 <= p <= 100
* @return the value at the p'th percentile
*/
private static void testInput(double[] values, int begin, int length) {
if (length > values.length)
throw new IllegalArgumentException("length > values.length");
if (begin + length > values.length)
throw new IllegalArgumentException("begin + length > values.length");
if (values == null)
throw new IllegalArgumentException("input value array is null");
public static double percentile(
double[] values,
int begin,
int length,
double p) {
return percentile.evaluate(values, begin, length, p);
}
}