weka.classifiers.functions
Class SimpleLinearRegression

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.functions.SimpleLinearRegression

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class SimpleLinearRegression

extends Classifier

implements WeightedInstancesHandler

Class for learning 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.

Version:

$Revision: 1.5 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

SimpleLinearRegression()

Method Summary

void	buildClassifier(Instances insts) Builds a simple linear regression model given the supplied training data.
double	classifyInstance(Instance inst) Generate a prediction for the supplied instance.
boolean	foundUsefulAttribute() Returns true if a usable attribute was found.
int	getAttributeIndex() Returns the index of the attribute used in the regression.
double	getIntercept() Returns the intercept of the function.
double	getSlope() Returns the slope of the function.
java.lang.String	globalInfo() Returns a string describing this classifier
static void	main(java.lang.String[] argv) Main method for testing this class
void	setSuppressErrorMessage(boolean s) Turn off the error message that is reported when no useful attribute is found.
java.lang.String	toString() Returns a description of this classifier as a string

Methods inherited from class weka.classifiers.Classifier

debugTipText, distributionForInstance, forName, getDebug, getOptions, listOptions, makeCopies, setDebug, setOptions

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

SimpleLinearRegression

public SimpleLinearRegression()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

classifyInstance

public double classifyInstance(Instance inst)
                        throws java.lang.Exception

Generate a prediction for the supplied instance.

Overrides:

classifyInstance in class Classifier

Parameters:

inst - the instance to predict.

Returns:

the prediction

Throws:

java.lang.Exception - if an error occurs

buildClassifier

public void buildClassifier(Instances insts)
                     throws java.lang.Exception

Builds a simple linear regression model given the supplied training data.

Specified by:

buildClassifier in class Classifier

Parameters:

insts - the training data.

Throws:

java.lang.Exception - if an error occurs

foundUsefulAttribute

public boolean foundUsefulAttribute()

Returns true if a usable attribute was found.

Returns:

true if a usable attribute was found.

getAttributeIndex

public int getAttributeIndex()

Returns the index of the attribute used in the regression.

Returns:

the index of the attribute.

getSlope

public double getSlope()

Returns the slope of the function.

Returns:

the slope.

getIntercept

public double getIntercept()

Returns the intercept of the function.

Returns:

the intercept.

setSuppressErrorMessage

public void setSuppressErrorMessage(boolean s)

Turn off the error message that is reported when no useful attribute is found.

Parameters:

s - if set to true turns off the error message

toString

public java.lang.String toString()

Returns a description of this classifier as a string

Overrides:

toString in class java.lang.Object

Returns:

a description of the classifier.

main

public static void main(java.lang.String[] argv)

Main method for testing this class

Parameters:

argv - options