/*
* 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 3 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, see <http://www.gnu.org/licenses/>.
*/
/*
* SimpleLinearRegression.java
* Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.functions;
import weka.classifiers.AbstractClassifier;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
/**
<!-- globalinfo-start -->
* Learns a simple linear regression model. Picks the attribute that results in the lowest squared error. Missing values are not allowed. Can only deal with numeric attributes.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public class SimpleLinearRegression extends AbstractClassifier
implements WeightedInstancesHandler {
/** for serialization */
static final long serialVersionUID = 1679336022895414137L;
/** The chosen attribute */
private Attribute m_attribute;
/** The index of the chosen attribute */
private int m_attributeIndex;
/** The slope */
private double m_slope;
/** The intercept */
private double m_intercept;
/** If true, suppress error message if no useful attribute was found*/
private boolean m_suppressErrorMessage = false;
/**
* Returns a string describing this classifier
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Learns a simple linear regression model. "
+"Picks the attribute that results in the lowest squared error. "
+"Missing values are not allowed. Can only deal with numeric attributes.";
}
/**
* Generate a prediction for the supplied instance.
*
* @param inst the instance to predict.
* @return the prediction
* @throws Exception if an error occurs
*/
public double classifyInstance(Instance inst) throws Exception {
if (m_attribute == null) {
return m_intercept;
} else {
if (inst.isMissing(m_attribute.index())) {
throw new Exception("SimpleLinearRegression: No missing values!");
}
return m_intercept + m_slope * inst.value(m_attribute.index());
}
}
/**
* Returns default capabilities of the classifier.
*
* @return the capabilities of this classifier
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.DATE_ATTRIBUTES);
// class
result.enable(Capability.NUMERIC_CLASS);
result.enable(Capability.DATE_CLASS);
result.enable(Capability.MISSING_CLASS_VALUES);
return result;
}
/**
* Builds a simple linear regression model given the supplied training data.
*
* @param insts the training data.
* @throws Exception if an error occurs
*/
public void buildClassifier(Instances insts) throws Exception {
// can classifier handle the data?
getCapabilities().testWithFail(insts);
// remove instances with missing class
insts = new Instances(insts);
insts.deleteWithMissingClass();
// 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()) {
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) {
if (!m_suppressErrorMessage) System.err.println("----- no useful attribute found");
m_attribute = null;
m_attributeIndex = 0;
m_slope = 0;
m_intercept = yMean;
} else {
m_attribute = insts.attribute(chosen);
m_attributeIndex = chosen;
m_slope = chosenSlope;
m_intercept = chosenIntercept;
}
}
/**
* Returns true if a usable attribute was found.
*
* @return true if a usable attribute was found.
*/
public boolean foundUsefulAttribute(){
return (m_attribute != null);
}
/**
* Returns the index of the attribute used in the regression.
*
* @return the index of the attribute.
*/
public int getAttributeIndex(){
return m_attributeIndex;
}
/**
* Returns the slope of the function.
*
* @return the slope.
*/
public double getSlope(){
return m_slope;
}
/**
* Returns the intercept of the function.
*
* @return the intercept.
*/
public double getIntercept(){
return m_intercept;
}
/**
* Turn off the error message that is reported when no useful attribute is found.
*
* @param s if set to true turns off the error message
*/
public void setSuppressErrorMessage(boolean s){
m_suppressErrorMessage = s;
}
/**
* Returns a description of this classifier as a string
*
* @return a description of the classifier.
*/
public String toString() {
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();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Main method for testing this class
*
* @param argv options
*/
public static void main(String [] argv){
runClassifier(new SimpleLinearRegression(), argv);
}
}