////  OpenForecast - open source, general-purpose forecasting package.//  Copyright (C) 2002-2003  Steven R. Gould////  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., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA//package net.sourceforge.openforecast.models;import java.util.Iterator;import net.sourceforge.openforecast.ForecastingModel;import net.sourceforge.openforecast.DataPoint;import net.sourceforge.openforecast.DataSet;/** * Implements a single variable linear regression model using the variable * named in the constructor as the independent variable. The cofficients of * the regression - the intercept and the slope - as well as the accuracy * indicators are determined from the data set passed to init. * * <p>Once initialized, this model can be applied to another data set using * the forecast method to forecast values of the dependent variable based on * values of the dependent variable (the one named in the constructor). * * <p>A single variable linear regression model essentially attempts to put a * straight line through the data points. For the more mathematically inclined, * this line is defined by its gradient or slope, and the point at which it * intercepts the x-axis (i.e. where the independent variable has, perhaps only * theoretically, a value of zero). Mathematically, assuming the independent * variable is x and the dependent variable is y, then this line can be * represented as: <pre>y = intercept + slope * x</pre> * @author Steven R. Gould */public class RegressionModel extends AbstractForecastingModel{	 /**	  * The name of the independent variable used in this regression model.	  */	 private String independentVariable;	 /**	  * The intercept for the linear regression model. Initialized following a	  * call to init.	  */	 private double intercept = 0.0;	 /**	  * The slope for the linear regression model. Initialized following a call	  * to init.	  */	 private double slope = 0.0;	 /**	  * Constructs a new linear regression model, using the given name as the	  * independent variable. For a valid model to be constructed, you should	  * call init and pass in a data set containing a series of data points	  * involving the given independent variable.	  * @param independentVariable the name of the independent variable to use	  * in this model.	  */	 public RegressionModel( String independentVariable )	 {		  this.independentVariable = independentVariable;	 }	 /**	  * Initializes the coefficients to use for this regression model. The	  * intercept and slope are derived so as to give the best fit line for the	  * given data set.	  *	  * <p>Additionally, the accuracy indicators are calculated based on this	  * data set.	  * @param dataSet the set of observations to use to derive the regression	  * coefficients for this model.	  */	 public void init( DataSet dataSet )	 {		  int n = dataSet.size();		  double sumX = 0.0;		  double sumY = 0.0;		  double sumXX = 0.0;		  double sumXY = 0.0;		  		  Iterator it = dataSet.iterator();		  while ( it.hasNext() )				{					 DataPoint dp = (DataPoint)it.next();					 					 double x = dp.getIndependentValue( independentVariable );					 double y = dp.getDependentValue();					 					 sumX += x;					 sumY += y;					 sumXX += x*x;					 sumXY += x*y;				}		  		  double xMean = sumX / n;		  double yMean = sumY / n;		  		  slope = (n*sumXY - sumX*sumY) / (n*sumXX - sumX*sumX);		  intercept = yMean - slope*xMean;		  		  // Calculate the accuracy of this model		  calculateAccuracyIndicators( dataSet );	 }	 /**	  * Using the current model parameters (initialized in init), apply the	  * forecast model to the given data point. The data point must have valid	  * values for the independent variables. Upon return, the value of the	  * dependent variable will be updated with the forecast value computed for	  * that data point.	  * @param dataPoint the data point for which a forecast value (for the	  *        dependent variable) is required.	  * @return the same data point passed in but with the dependent value	  *         updated to contain the new forecast value.	  * @throws ModelNotInitializedException if forecast is called before the	  *         model has been initialized with a call to init.	  */	 public double forecast( DataPoint dataPoint )		  throws ModelNotInitializedException	 {		  if ( !initialized )				throw new ModelNotInitializedException();		  		  double x = dataPoint.getIndependentValue( independentVariable );		  double forecastValue = intercept + slope*x;		  		  dataPoint.setDependentValue( forecastValue );		  		  return forecastValue;	 }	 	 /**	  * Returns a short name for this type of forecasting model. A more detailed	  * explanation is provided by the toString method.	  * @return a short string describing this type of forecasting model.	  */	 public String getForecastType()	 {		  return "Single variable regression";	 }	 	 /**	  * Returns a detailed description of this forcasting model, including the	  * intercept and slope. A shortened version of this is provided by the	  * getForecastType method.	  * @return a description of this forecasting model.	  */	 public String toString()	 {		  return "Single variable regression model with a slope of " + slope				+ " and an intercept of " + intercept				+ ". That is, y=" + intercept + (slope>0.0 ? "+" : "") + slope				+ "*" + independentVariable + ".";	 }}