/* ===========================================================
* JFreeChart : a free chart library for the Java(tm) platform
* ===========================================================
*
* (C) Copyright 2000-2005, by Object Refinery Limited and Contributors.
*
* Project Info: http://www.jfree.org/jfreechart/index.html
*
* This library is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation; either version 2.1 of the License, or
* (at your option) any later version.
*
* This library is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
* or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public
* License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301,
* USA.
*
* [Java is a trademark or registered trademark of Sun Microsystems, Inc.
* in the United States and other countries.]
*
* ---------------
* Statistics.java
* ---------------
* (C) Copyright 2000-2005, by Matthew Wright and Contributors.
*
* Original Author: Matthew Wright;
* Contributor(s): David Gilbert (for Object Refinery Limited);
*
* $Id: Statistics.java,v 1.5.2.1 2005/10/25 21:34:46 mungady Exp $
*
* Changes (from 08-Nov-2001)
* --------------------------
* 08-Nov-2001 : Added standard header and tidied Javadoc comments (DG);
* Moved from JFreeChart to package com.jrefinery.data.* in
* JCommon class library (DG);
* 24-Jun-2002 : Removed unnecessary local variable (DG);
* 07-Oct-2002 : Fixed errors reported by Checkstyle (DG);
* 26-May-2004 : Moved calculateMean() method from BoxAndWhiskerCalculator (DG);
* 02-Jun-2004 : Fixed bug in calculateMedian() method (DG);
* 11-Jan-2005 : Removed deprecated code in preparation for the 1.0.0
* release (DG);
*
*/
package org.jfree.data.statistics;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;
/**
* A utility class that provides some simple statistical functions.
*/
public abstract class Statistics {
/**
* Returns the mean of an array of numbers.
*
* @param values the values (null
permitted, returns
* Double.NaN
).
*
* @return The mean.
*/
public static double calculateMean(Number[] values) {
double result = Double.NaN;
if (values != null && values.length > 0) {
double sum = 0.0;
int counter = 0;
for (; counter < values.length; counter++) {
sum = sum + values[counter].doubleValue();
}
result = (sum / counter);
}
return result;
}
/**
* Returns the mean of a collection of Number
objects.
*
* @param values the values (null
permitted, returns
* Double.NaN
).
*
* @return The mean.
*/
public static double calculateMean(Collection values) {
double result = Double.NaN;
int count = 0;
double total = 0.0;
Iterator iterator = values.iterator();
while (iterator.hasNext()) {
Object object = iterator.next();
if (object != null && object instanceof Number) {
Number number = (Number) object;
total = total + number.doubleValue();
count = count + 1;
}
}
if (count > 0) {
result = total / count;
}
return result;
}
/**
* Calculates the median for a list of values (Number
objects).
* The list of values will be sorted first.
*
* @param values the values.
*
* @return The median.
*/
public static double calculateMedian(List values) {
return calculateMedian(values, true);
}
/**
* Calculates the median for a list of values (Number
objects)
* that are assumed to be in ascending order.
*
* @param values the values.
* @param copyAndSort a flag that controls whether the list of values is
* copied and sorted.
*
* @return The median.
*/
public static double calculateMedian(List values, boolean copyAndSort) {
double result = Double.NaN;
if (values != null) {
if (copyAndSort) {
int itemCount = values.size();
List copy = new ArrayList(itemCount);
for (int i = 0; i < itemCount; i++) {
copy.add(i, values.get(i));
}
Collections.sort(copy);
values = copy;
}
int count = values.size();
if (count > 0) {
if (count % 2 == 1) {
if (count > 1) {
Number value = (Number) values.get((count - 1) / 2);
result = value.doubleValue();
}
else {
Number value = (Number) values.get(0);
result = value.doubleValue();
}
}
else {
Number value1 = (Number) values.get(count / 2 - 1);
Number value2 = (Number) values.get(count / 2);
result = (value1.doubleValue() + value2.doubleValue())
/ 2.0;
}
}
}
return result;
}
/**
* Calculates the median for a sublist within a list of values
* (Number
objects).
*
* @param values the values (in any order).
* @param start the start index.
* @param end the end index.
*
* @return The median.
*/
public static double calculateMedian(List values, int start, int end) {
return calculateMedian(values, start, end, true);
}
/**
* Calculates the median for a sublist within a list of values
* (Number
objects). The entire list will be sorted if the
* ascending
false.
*
* @param values the values.
* @param start the start index.
* @param end the end index.
* @param copyAndSort a flag that that controls whether the list of values
* is copied and sorted.
*
* @return The median.
*/
public static double calculateMedian(List values, int start, int end,
boolean copyAndSort) {
double result = Double.NaN;
if (copyAndSort) {
List working = new ArrayList(end - start + 1);
for (int i = start; i <= end; i++) {
working.add(values.get(i));
}
Collections.sort(working);
result = calculateMedian(working, false);
}
else {
int count = end - start + 1;
if (count > 0) {
if (count % 2 == 1) {
if (count > 1) {
Number value
= (Number) values.get(start + (count - 1) / 2);
result = value.doubleValue();
}
else {
Number value = (Number) values.get(start);
result = value.doubleValue();
}
}
else {
Number value1 = (Number) values.get(start + count / 2 - 1);
Number value2 = (Number) values.get(start + count / 2);
result
= (value1.doubleValue() + value2.doubleValue()) / 2.0;
}
}
}
return result;
}
/**
* Returns the standard deviation of a set of numbers.
*
* @param data the data.
*
* @return The standard deviation of a set of numbers.
*/
public static double getStdDev(Number[] data) {
double avg = calculateMean(data);
double sum = 0.0;
for (int counter = 0; counter < data.length; counter++) {
double diff = data[counter].doubleValue() - avg;
sum = sum + diff * diff;
}
return Math.sqrt(sum / (data.length - 1));
}
/**
* Fits a straight line to a set of (x, y) data, returning the slope and
* intercept.
*
* @param xData the x-data.
* @param yData the y-data.
*
* @return A double array with the intercept in [0] and the slope in [1].
*/
public static double[] getLinearFit(Number[] xData, Number[] yData) {
// check arguments...
if (xData.length != yData.length) {
throw new IllegalArgumentException(
"Statistics.getLinearFit(): array lengths must be equal.");
}
double[] result = new double[2];
// slope
result[1] = getSlope(xData, yData);
// intercept
result[0] = calculateMean(yData) - result[1] * calculateMean(xData);
return result;
}
/**
* Finds the slope of a regression line using least squares.
*
* @param xData an array of Numbers (the x values).
* @param yData an array of Numbers (the y values).
*
* @return The slope.
*/
public static double getSlope(Number[] xData, Number[] yData) {
// check arguments...
if (xData.length != yData.length) {
throw new IllegalArgumentException("Array lengths must be equal.");
}
// ********* stat function for linear slope ********
// y = a + bx
// a = ybar - b * xbar
// sum(x * y) - (sum (x) * sum(y)) / n
// b = ------------------------------------
// sum (x^2) - (sum(x)^2 / n
// *************************************************
// sum of x, x^2, x * y, y
double sx = 0.0, sxx = 0.0, sxy = 0.0, sy = 0.0;
int counter;
for (counter = 0; counter < xData.length; counter++) {
sx = sx + xData[counter].doubleValue();
sxx = sxx + Math.pow(xData[counter].doubleValue(), 2);
sxy = sxy + yData[counter].doubleValue()
* xData[counter].doubleValue();
sy = sy + yData[counter].doubleValue();
}
return (sxy - (sx * sy) / counter) / (sxx - (sx * sx) / counter);
}
/**
* Calculates the correlation between two datasets. Both arrays should
* contain the same number of items. Null values are treated as zero.
*
* Information about the correlation calculation was obtained from: * * http://trochim.human.cornell.edu/kb/statcorr.htm * * @param data1 the first dataset. * @param data2 the second dataset. * * @return The correlation. */ public static double getCorrelation(Number[] data1, Number[] data2) { if (data1 == null) { throw new IllegalArgumentException("Null 'data1' argument."); } if (data2 == null) { throw new IllegalArgumentException("Null 'data2' argument."); } if (data1.length != data2.length) { throw new IllegalArgumentException( "'data1' and 'data2' arrays must have same length." ); } int n = data1.length; double sumX = 0.0; double sumY = 0.0; double sumX2 = 0.0; double sumY2 = 0.0; double sumXY = 0.0; for (int i = 0; i < n; i++) { double x = 0.0; if (data1[i] != null) { x = data1[i].doubleValue(); } double y = 0.0; if (data2[i] != null) { y = data2[i].doubleValue(); } sumX = sumX + x; sumY = sumY + y; sumXY = sumXY + (x * y); sumX2 = sumX2 + (x * x); sumY2 = sumY2 + (y * y); } return (n * sumXY - sumX * sumY) / Math.pow((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY), 0.5); } /** * Returns a data set for a moving average on the data set passed in. * * @param xData an array of the x data. * @param yData an array of the y data. * @param period the number of data points to average * * @return A double[][] the length of the data set in the first dimension, * with two doubles for x and y in the second dimension */ public static double[][] getMovingAverage(Number[] xData, Number[] yData, int period) { // check arguments... if (xData.length != yData.length) { throw new IllegalArgumentException("Array lengths must be equal."); } if (period > xData.length) { throw new IllegalArgumentException( "Period can't be longer than dataset." ); } double[][] result = new double[xData.length - period][2]; for (int i = 0; i < result.length; i++) { result[i][0] = xData[i + period].doubleValue(); // holds the moving average sum double sum = 0.0; for (int j = 0; j < period; j++) { sum += yData[i + j].doubleValue(); } sum = sum / period; result[i][1] = sum; } return result; } }