001 /* =========================================================== 002 * JFreeChart : a free chart library for the Java(tm) platform 003 * =========================================================== 004 * 005 * (C) Copyright 2000-2005, by Object Refinery Limited and Contributors. 006 * 007 * Project Info: http://www.jfree.org/jfreechart/index.html 008 * 009 * This library is free software; you can redistribute it and/or modify it 010 * under the terms of the GNU Lesser General Public License as published by 011 * the Free Software Foundation; either version 2.1 of the License, or 012 * (at your option) any later version. 013 * 014 * This library is distributed in the hope that it will be useful, but 015 * WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY 016 * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public 017 * License for more details. 018 * 019 * You should have received a copy of the GNU Lesser General Public 020 * License along with this library; if not, write to the Free Software 021 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, 022 * USA. 023 * 024 * [Java is a trademark or registered trademark of Sun Microsystems, Inc. 025 * in the United States and other countries.] 026 * 027 * --------------- 028 * Regression.java 029 * --------------- 030 * (C) Copyright 2002-2005, by Object Refinery Limited. 031 * 032 * Original Author: David Gilbert (for Object Refinery Limited); 033 * Contributor(s): -; 034 * 035 * $Id: Regression.java,v 1.3.2.1 2005/10/25 21:34:46 mungady Exp $ 036 * 037 * Changes 038 * ------- 039 * 30-Sep-2002 : Version 1 (DG); 040 * 18-Aug-2003 : Added 'abstract' (DG); 041 * 15-Jul-2004 : Switched getX() with getXValue() and getY() with 042 * getYValue() (DG); 043 * 044 */ 045 046 package org.jfree.data.statistics; 047 048 import org.jfree.data.xy.XYDataset; 049 050 /** 051 * A utility class for fitting regression curves to data. 052 */ 053 public abstract class Regression { 054 055 /** 056 * Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to 057 * the data using ordinary least squares regression. The result is 058 * returned as a double[], where result[0] --> a, and result[1] --> b. 059 * 060 * @param data the data. 061 * 062 * @return The parameters. 063 */ 064 public static double[] getOLSRegression(double[][] data) { 065 066 int n = data.length; 067 if (n < 2) { 068 throw new IllegalArgumentException("Not enough data."); 069 } 070 071 double sumX = 0; 072 double sumY = 0; 073 double sumXX = 0; 074 double sumXY = 0; 075 for (int i = 0; i < n; i++) { 076 double x = data[i][0]; 077 double y = data[i][1]; 078 sumX += x; 079 sumY += y; 080 double xx = x * x; 081 sumXX += xx; 082 double xy = x * y; 083 sumXY += xy; 084 } 085 double sxx = sumXX - (sumX * sumX) / n; 086 double sxy = sumXY - (sumX * sumY) / n; 087 double xbar = sumX / n; 088 double ybar = sumY / n; 089 090 double[] result = new double[2]; 091 result[1] = sxy / sxx; 092 result[0] = ybar - result[1] * xbar; 093 094 return result; 095 096 } 097 098 /** 099 * Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to 100 * the data using ordinary least squares regression. The result is returned 101 * as a double[], where result[0] --> a, and result[1] --> b. 102 * 103 * @param data the data. 104 * @param series the series (zero-based index). 105 * 106 * @return The parameters. 107 */ 108 public static double[] getOLSRegression(XYDataset data, int series) { 109 110 int n = data.getItemCount(series); 111 if (n < 2) { 112 throw new IllegalArgumentException("Not enough data."); 113 } 114 115 double sumX = 0; 116 double sumY = 0; 117 double sumXX = 0; 118 double sumXY = 0; 119 for (int i = 0; i < n; i++) { 120 double x = data.getXValue(series, i); 121 double y = data.getYValue(series, i); 122 sumX += x; 123 sumY += y; 124 double xx = x * x; 125 sumXX += xx; 126 double xy = x * y; 127 sumXY += xy; 128 } 129 double sxx = sumXX - (sumX * sumX) / n; 130 double sxy = sumXY - (sumX * sumY) / n; 131 double xbar = sumX / n; 132 double ybar = sumY / n; 133 134 double[] result = new double[2]; 135 result[1] = sxy / sxx; 136 result[0] = ybar - result[1] * xbar; 137 138 return result; 139 140 } 141 142 /** 143 * Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to 144 * the data using a power regression equation. The result is returned as 145 * an array, where double[0] --> a, and double[1] --> b. 146 * 147 * @param data the data. 148 * 149 * @return The parameters. 150 */ 151 public static double[] getPowerRegression(double[][] data) { 152 153 int n = data.length; 154 if (n < 2) { 155 throw new IllegalArgumentException("Not enough data."); 156 } 157 158 double sumX = 0; 159 double sumY = 0; 160 double sumXX = 0; 161 double sumXY = 0; 162 for (int i = 0; i < n; i++) { 163 double x = Math.log(data[i][0]); 164 double y = Math.log(data[i][1]); 165 sumX += x; 166 sumY += y; 167 double xx = x * x; 168 sumXX += xx; 169 double xy = x * y; 170 sumXY += xy; 171 } 172 double sxx = sumXX - (sumX * sumX) / n; 173 double sxy = sumXY - (sumX * sumY) / n; 174 double xbar = sumX / n; 175 double ybar = sumY / n; 176 177 double[] result = new double[2]; 178 result[1] = sxy / sxx; 179 result[0] = Math.pow(Math.exp(1.0), ybar - result[1] * xbar); 180 181 return result; 182 183 } 184 185 /** 186 * Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to 187 * the data using a power regression equation. The result is returned as 188 * an array, where double[0] --> a, and double[1] --> b. 189 * 190 * @param data the data. 191 * @param series the series to fit the regression line against. 192 * 193 * @return The parameters. 194 */ 195 public static double[] getPowerRegression(XYDataset data, int series) { 196 197 int n = data.getItemCount(series); 198 if (n < 2) { 199 throw new IllegalArgumentException("Not enough data."); 200 } 201 202 double sumX = 0; 203 double sumY = 0; 204 double sumXX = 0; 205 double sumXY = 0; 206 for (int i = 0; i < n; i++) { 207 double x = Math.log(data.getXValue(series, i)); 208 double y = Math.log(data.getYValue(series, i)); 209 sumX += x; 210 sumY += y; 211 double xx = x * x; 212 sumXX += xx; 213 double xy = x * y; 214 sumXY += xy; 215 } 216 double sxx = sumXX - (sumX * sumX) / n; 217 double sxy = sumXY - (sumX * sumY) / n; 218 double xbar = sumX / n; 219 double ybar = sumY / n; 220 221 double[] result = new double[2]; 222 result[1] = sxy / sxx; 223 result[0] = Math.pow(Math.exp(1.0), ybar - result[1] * xbar); 224 225 return result; 226 227 } 228 229 }