/* ===========================================================
* JFreeChart : a free chart library for the Java(tm) platform
* ===========================================================
*
* (C) Copyright 2000-2007, 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.]
*
* ---------------
* Regression.java
* ---------------
* (C) Copyright 2002-2007, by Object Refinery Limited.
*
* Original Author: David Gilbert (for Object Refinery Limited);
* Contributor(s): -;
*
* Changes
* -------
* 30-Sep-2002 : Version 1 (DG);
* 18-Aug-2003 : Added 'abstract' (DG);
* 15-Jul-2004 : Switched getX() with getXValue() and getY() with
* getYValue() (DG);
*
*/
package org.jfree.data.statistics;
import org.jfree.data.xy.XYDataset;
/**
* A utility class for fitting regression curves to data.
*/
public abstract class Regression {
/**
* Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to
* the data using ordinary least squares regression. The result is
* returned as a double[], where result[0] --> a, and result[1] --> b.
*
* @param data the data.
*
* @return The parameters.
*/
public static double[] getOLSRegression(double[][] data) {
int n = data.length;
if (n < 2) {
throw new IllegalArgumentException("Not enough data.");
}
double sumX = 0;
double sumY = 0;
double sumXX = 0;
double sumXY = 0;
for (int i = 0; i < n; i++) {
double x = data[i][0];
double y = data[i][1];
sumX += x;
sumY += y;
double xx = x * x;
sumXX += xx;
double xy = x * y;
sumXY += xy;
}
double sxx = sumXX - (sumX * sumX) / n;
double sxy = sumXY - (sumX * sumY) / n;
double xbar = sumX / n;
double ybar = sumY / n;
double[] result = new double[2];
result[1] = sxy / sxx;
result[0] = ybar - result[1] * xbar;
return result;
}
/**
* Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to
* the data using ordinary least squares regression. The result is returned
* as a double[], where result[0] --> a, and result[1] --> b.
*
* @param data the data.
* @param series the series (zero-based index).
*
* @return The parameters.
*/
public static double[] getOLSRegression(XYDataset data, int series) {
int n = data.getItemCount(series);
if (n < 2) {
throw new IllegalArgumentException("Not enough data.");
}
double sumX = 0;
double sumY = 0;
double sumXX = 0;
double sumXY = 0;
for (int i = 0; i < n; i++) {
double x = data.getXValue(series, i);
double y = data.getYValue(series, i);
sumX += x;
sumY += y;
double xx = x * x;
sumXX += xx;
double xy = x * y;
sumXY += xy;
}
double sxx = sumXX - (sumX * sumX) / n;
double sxy = sumXY - (sumX * sumY) / n;
double xbar = sumX / n;
double ybar = sumY / n;
double[] result = new double[2];
result[1] = sxy / sxx;
result[0] = ybar - result[1] * xbar;
return result;
}
/**
* Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to
* the data using a power regression equation. The result is returned as
* an array, where double[0] --> a, and double[1] --> b.
*
* @param data the data.
*
* @return The parameters.
*/
public static double[] getPowerRegression(double[][] data) {
int n = data.length;
if (n < 2) {
throw new IllegalArgumentException("Not enough data.");
}
double sumX = 0;
double sumY = 0;
double sumXX = 0;
double sumXY = 0;
for (int i = 0; i < n; i++) {
double x = Math.log(data[i][0]);
double y = Math.log(data[i][1]);
sumX += x;
sumY += y;
double xx = x * x;
sumXX += xx;
double xy = x * y;
sumXY += xy;
}
double sxx = sumXX - (sumX * sumX) / n;
double sxy = sumXY - (sumX * sumY) / n;
double xbar = sumX / n;
double ybar = sumY / n;
double[] result = new double[2];
result[1] = sxy / sxx;
result[0] = Math.pow(Math.exp(1.0), ybar - result[1] * xbar);
return result;
}
/**
* Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to
* the data using a power regression equation. The result is returned as
* an array, where double[0] --> a, and double[1] --> b.
*
* @param data the data.
* @param series the series to fit the regression line against.
*
* @return The parameters.
*/
public static double[] getPowerRegression(XYDataset data, int series) {
int n = data.getItemCount(series);
if (n < 2) {
throw new IllegalArgumentException("Not enough data.");
}
double sumX = 0;
double sumY = 0;
double sumXX = 0;
double sumXY = 0;
for (int i = 0; i < n; i++) {
double x = Math.log(data.getXValue(series, i));
double y = Math.log(data.getYValue(series, i));
sumX += x;
sumY += y;
double xx = x * x;
sumXX += xx;
double xy = x * y;
sumXY += xy;
}
double sxx = sumXX - (sumX * sumX) / n;
double sxy = sumXY - (sumX * sumY) / n;
double xbar = sumX / n;
double ybar = sumY / n;
double[] result = new double[2];
result[1] = sxy / sxx;
result[0] = Math.pow(Math.exp(1.0), ybar - result[1] * xbar);
return result;
}
}
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