/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* UnivariateLinearRegression.java
* Copyright (C) 2002 Eibe Frank
*
*/
package weka.classifiers.functions;
import weka.core.*;
import weka.classifiers.*;
/**
* Class for learning a univariate linear regression model.
* Picks the attribute that results in the lowest squared error.
* Missing values are not allowed. Can only deal with numeric attributes.
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class UnivariateLinearRegression extends Classifier implements WeightedInstancesHandler {
/** The chosen attribute */
private Attribute m_attribute;
/** The slope */
private double m_slope;
/** The intercept */
private double m_intercept;
public double classifyInstance(Instance inst) throws Exception {
if (m_attribute == null) {
return m_intercept;
} else {
if (inst.isMissing(m_attribute.index())) {
throw new Exception("UnivariateLinearRegression: No missing values!");
}
return m_intercept + m_slope * inst.value(m_attribute.index());
}
}
public void buildClassifier(Instances insts) throws Exception {
// Compute mean of target value
double yMean = insts.meanOrMode(insts.classIndex());
// Choose best attribute
double minMsq = Double.MAX_VALUE;
m_attribute = null;
int chosen = -1;
double chosenSlope = Double.NaN;
double chosenIntercept = Double.NaN;
for (int i = 0; i < insts.numAttributes(); i++) {
if (i != insts.classIndex()) {
if (!insts.attribute(i).isNumeric()) {
throw new Exception("UnivariateLinearRegression: Only numeric attributes!");
}
m_attribute = insts.attribute(i);
// Compute slope and intercept
double xMean = insts.meanOrMode(i);
double sumWeightedXDiffSquared = 0;
double sumWeightedYDiffSquared = 0;
m_slope = 0;
for (int j = 0; j < insts.numInstances(); j++) {
Instance inst = insts.instance(j);
if (!inst.isMissing(i) && !inst.classIsMissing()) {
double xDiff = inst.value(i) - xMean;
double yDiff = inst.classValue() - yMean;
double weightedXDiff = inst.weight() * xDiff;
double weightedYDiff = inst.weight() * yDiff;
m_slope += weightedXDiff * yDiff;
sumWeightedXDiffSquared += weightedXDiff * xDiff;
sumWeightedYDiffSquared += weightedYDiff * yDiff;
}
}
// Skip attribute if not useful
if (sumWeightedXDiffSquared == 0) {
continue;
}
double numerator = m_slope;
m_slope /= sumWeightedXDiffSquared;
m_intercept = yMean - m_slope * xMean;
// Compute sum of squared errors
double msq = sumWeightedYDiffSquared - m_slope * numerator;
// Check whether this is the best attribute
if (msq < minMsq) {
minMsq = msq;
chosen = i;
chosenSlope = m_slope;
chosenIntercept = m_intercept;
}
}
}
// Set parameters
if (chosen == -1) {
System.err.println("----- no useful attribute found");
m_attribute = null;
m_slope = 0;
m_intercept = yMean;
} else {
m_attribute = insts.attribute(chosen);
m_slope = chosenSlope;
m_intercept = chosenIntercept;
}
}
public String toString() {
if (m_attribute == null) {
return "No model built yet.";
}
StringBuffer text = new StringBuffer();
if (m_attribute == null) {
text.append("Predicting constant " + m_intercept);
} else {
text.append("Linear regression on " + m_attribute.name() + "\n\n");
text.append(Utils.doubleToString(m_slope,2) + " * " +
m_attribute.name());
if (m_intercept > 0) {
text.append(" + " + Utils.doubleToString(m_intercept, 2));
} else {
text.append(" - " + Utils.doubleToString((-m_intercept), 2));
}
}
text.append("\n");
return text.toString();
}
/**
* Main method for testing this class
*
* @param argv options
*/
public static void main(String [] argv){
try{
System.out.println(Evaluation.evaluateModel(new UnivariateLinearRegression(), argv));
} catch (Exception e) {
System.out.println(e.getMessage());
e.printStackTrace();
}
}
}