content: /* ===========================================================
   * 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;
  
      }
  
  }
  