Java tutorial
/* =========================================================== * JFreeChart : a free chart library for the Java(tm) platform * =========================================================== * * (C) Copyright 2000-2014, 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. * * [Oracle and Java are registered trademarks of Oracle and/or its affiliates. * Other names may be trademarks of their respective owners.] * * --------------- * Statistics.java * --------------- * (C) Copyright 2000-2014, by Matthew Wright and Contributors. * * Original Author: Matthew Wright; * Contributor(s): David Gilbert (for Object Refinery Limited); * * 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); * 02-Jul-2013 : Use ParamChecks (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; import org.jfree.chart.util.ParamChecks; /** * A utility class that provides some common statistical functions. */ public abstract class Statistics { /** * Returns the mean of an array of numbers. This is equivalent to calling * {@code calculateMean(values, true)}. * * @param values the values ({@code null} not permitted). * * @return The mean. */ public static double calculateMean(Number[] values) { return calculateMean(values, true); } /** * Returns the mean of an array of numbers. * * @param values the values ({@code null} not permitted). * @param includeNullAndNaN a flag that controls whether or not * {@code null} and {@code Double.NaN} values are included * in the calculation (if either is present in the array, the result is * {@link Double#NaN}). * * @return The mean. * * @since 1.0.3 */ public static double calculateMean(Number[] values, boolean includeNullAndNaN) { ParamChecks.nullNotPermitted(values, "values"); double sum = 0.0; double current; int counter = 0; for (int i = 0; i < values.length; i++) { // treat nulls the same as NaNs if (values[i] != null) { current = values[i].doubleValue(); } else { current = Double.NaN; } // calculate the sum and count if (includeNullAndNaN || !Double.isNaN(current)) { sum = sum + current; counter++; } } double result = (sum / counter); return result; } /** * Returns the mean of a collection of {@code Number} objects. * * @param values the values ({@code null} not permitted). * * @return The mean. */ public static double calculateMean(Collection values) { return calculateMean(values, true); } /** * Returns the mean of a collection of {@code Number} objects. * * @param values the values ({@code null} not permitted). * @param includeNullAndNaN a flag that controls whether or not * {@code null} and {@code Double.NaN} values are included * in the calculation (if either is present in the array, the result is * {@link Double#NaN}). * * @return The mean. * * @since 1.0.3 */ public static double calculateMean(Collection values, boolean includeNullAndNaN) { ParamChecks.nullNotPermitted(values, "values"); int count = 0; double total = 0.0; Iterator iterator = values.iterator(); while (iterator.hasNext()) { Object object = iterator.next(); if (object == null) { if (includeNullAndNaN) { return Double.NaN; } } else { if (object instanceof Number) { Number number = (Number) object; double value = number.doubleValue(); if (Double.isNaN(value)) { if (includeNullAndNaN) { return Double.NaN; } } else { total = total + number.doubleValue(); count = count + 1; } } } } return total / count; } /** * Calculates the median for a list of values ({@code Number} objects). * The list of values will be copied, and the copy sorted, before * calculating the median. To avoid this step (if your list of values * is already sorted), use the {@link #calculateMedian(List, boolean)} * method. * * @param values the values ({@code null} permitted). * * @return The median. */ public static double calculateMedian(List values) { return calculateMedian(values, true); } /** * Calculates the median for a list of values ({@code Number} objects). * If {@code copyAndSort} is {@code false}, the list is assumed * to be presorted in ascending order by value. * * @param values the values ({@code null} permitted). * @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 * ({@code Number} objects). * * @param values the values, in any order ({@code null} not permitted). * @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 * ({@code Number} objects). The entire list will be sorted if the * {@code ascending} argument is {@code false}. * * @param values the values ({@code null} not permitted). * @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 ({@code null} or zero length array not * permitted). * * @return The standard deviation of a set of numbers. */ public static double getStdDev(Number[] data) { ParamChecks.nullNotPermitted(data, "data"); if (data.length == 0) { throw new IllegalArgumentException("Zero length 'data' array."); } 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 ({@code null} not permitted). * @param yData the y-data ({@code null} not permitted). * * @return A double array with the intercept in [0] and the slope in [1]. */ public static double[] getLinearFit(Number[] xData, Number[] yData) { ParamChecks.nullNotPermitted(xData, "xData"); ParamChecks.nullNotPermitted(yData, "yData"); 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 the x-values ({@code null} not permitted). * @param yData the y-values ({@code null} not permitted). * * @return The slope. */ public static double getSlope(Number[] xData, Number[] yData) { ParamChecks.nullNotPermitted(xData, "xData"); ParamChecks.nullNotPermitted(yData, "yData"); 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. * <P> * 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) { ParamChecks.nullNotPermitted(data1, "data1"); ParamChecks.nullNotPermitted(data2, "data2"); 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; } }