home | career | drupal | java | mac | mysql | perl | scala | uml | unix  

jfreechart example source code file (Regression.java)

This example jfreechart source code file (Regression.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Java - jfreechart tags/keywords

illegalargumentexception, illegalargumentexception, not, not, regression, regression

The jfreechart Regression.java source code

/* ===========================================================
 * JFreeChart : a free chart library for the Java(tm) platform
 * ===========================================================
 *
 * (C) Copyright 2000-2008, 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 2002-2008, 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;

    }

}

Other jfreechart examples (source code examples)

Here is a short list of links related to this jfreechart Regression.java source code file:



my book on functional programming

 

new blog posts

 

Copyright 1998-2019 Alvin Alexander, alvinalexander.com
All Rights Reserved.

A percentage of advertising revenue from
pages under the /java/jwarehouse URI on this website is
paid back to open source projects.