////  OpenForecast - open source, general-purpose forecasting package.//  Copyright (C) 2002-2004  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 polynomial regression model using the variable * named in the constructor as the independent variable. The cofficients of * the regression 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 polynomial regression model essentially attempts to * put a polynomial line - a curve if you prefer - through the data points. * Mathematically, assuming the independent variable is x and the dependent * variable is y, then this line can be represented as: * * <pre>y = a<sub>0</sub> + a<sub>1</sub>*x + a<sub>2</sub>*x<sup>2</sup> + a<sub>3</sub>*x<sup>3</sup> + ... + a<sub>m</sub>*x<sup>m</sup></pre> * * You can specify the order of the polynomial fit (the value of * <code>m</code> in the above equation) in the constructor. * @author Steven R. Gould */public class PolynomialRegressionModel extends AbstractForecastingModel{	/**	 * The name of the independent variable used in this regression model.	 */	private String independentVariable;	/**	 * The order of the polynomial to fit in this regression model.	 */	private int order = 0;	/**	 * An array of coefficients for this polynomial regression model. These are	 * initialized following a call to init.	 */	private double coefficient[];	/**	 * Constructs a new polynomial 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.	 *	 * <p>Using this constructor the order of the polynomial fit is not	 * specified. The effect is that the model will try to determine an	 * appropriate order for the given data. It will do this by calculating	 * up to 10 coefficients and once the coefficients become numerically	 * insignificant they will be excluded from the model.	 * @param independentVariable the name of the independent variable to use	 * in this model.	 */	public PolynomialRegressionModel( String independentVariable )	{		this( independentVariable, 10 );	}	/**	 * 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.	 * @param order the required order of the polynomial to fit.	 */	public PolynomialRegressionModel( String independentVariable, int order )	{		this.independentVariable = independentVariable;		this.order = order;	}	/**	 * 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 )	{		double a[][] = new double[order][order+1];		int n = dataSet.size();		for ( int i=0; i<order; i++ )			{				for ( int j=0; j<order; j++ )					{						int k = i + j;						Iterator it = dataSet.iterator();						while ( it.hasNext() )							{							DataPoint dp = (DataPoint)it.next();														double x = dp.getIndependentValue( independentVariable );							double y = dp.getDependentValue();														a[i][j] = a[i][j] + Math.pow(x,k);							}					}				Iterator it = dataSet.iterator();				while ( it.hasNext() )					{						DataPoint dp = (DataPoint)it.next();												double x = dp.getIndependentValue( independentVariable );						double y = dp.getDependentValue();												a[i][order] += y*Math.pow(x,i);					}			}		coefficient = Utils.GaussElimination( order, a );		// Calculate the accuracy indicators		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 )	{		if ( !initialized )			throw new ModelNotInitializedException();		double x = dataPoint.getIndependentValue( independentVariable );		double forecastValue = 0.0;		for ( int i=0; i<order; i++ )			forecastValue += coefficient[i] * Math.pow(x,i);		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 polynomial 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()	{		String description = "Single variable polynomial regression model";		if ( !initialized )			return description + " (uninitialized)";		description += " with an equation of: y = "+coefficient[0];		for ( int i=1; i<coefficient.length; i++ )			 if ( Math.abs(coefficient[i]) > 0.001 )				  description += (coefficient[i]<0 ? "" : "+")						+ coefficient[i] + "*"						+ independentVariable + (i>1 ? "^"+i : "" );		return description;	}}