weka.classifiers
Class LinearRegression

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.LinearRegression

All Implemented Interfaces:

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

public class LinearRegression

extends Classifier

implements OptionHandler, WeightedInstancesHandler

Class for using linear regression for prediction. Uses the Akaike criterion for model selection, and is able to deal with weighted instances.

Valid options are:

-D
Produce debugging output.

-S num
Set the attriute selection method to use. 1 = None, 2 = Greedy (default 0 = M5' method)

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

static Tag[]

TAGS_SELECTION

Constructor Summary

LinearRegression()

Method Summary

void	buildClassifier(Instances data) Builds a regression model for the given data.
double	classifyInstance(Instance instance) Classifies the given instance using the linear regression function.
SelectedTag	getAttributeSelectionMethod() Gets the method used to select attributes for use in the linear regression.
boolean	getDebug() Controls whether debugging output will be printed
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Generates a linear regression function predictor.
int	numParameters() Get the number of coefficients used in the model
void	setAttributeSelectionMethod(SelectedTag method) Sets the method used to select attributes for use in the linear regression.
void	setDebug(boolean debug) Controls whether debugging output will be printed
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Outputs the linear regression model as a string.

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

TAGS_SELECTION

public static final Tag[] TAGS_SELECTION

Constructor Detail

LinearRegression

public LinearRegression()

Method Detail

buildClassifier

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

Builds a regression model for the given data.

Overrides:

buildClassifier in class Classifier

Parameters:

data - the training data to be used for generating the linear regression function

Throws:

java.lang.Exception - if the classifier could not be built successfully

classifyInstance

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

Classifies the given instance using the linear regression function.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the test instance

Returns:

the classification

Throws:

java.lang.Exception - if classification can't be done successfully

toString

public java.lang.String toString()

Outputs the linear regression model as a string.

Overrides:

toString in class java.lang.Object

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options. Valid options are:

-D
Produce debugging output.

-S num
Set the attriute selection method to use. 1 = None, 2 = Greedy (default 0 = M5' method)

Specified by:

setOptions in interface OptionHandler

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions

numParameters

public int numParameters()

Get the number of coefficients used in the model

Returns:

the number of coefficients

setAttributeSelectionMethod

public void setAttributeSelectionMethod(SelectedTag method)

Sets the method used to select attributes for use in the linear regression.

Parameters:

method - the attribute selection method to use.

getAttributeSelectionMethod

public SelectedTag getAttributeSelectionMethod()

Gets the method used to select attributes for use in the linear regression.

Returns:

the method to use.

setDebug

public void setDebug(boolean debug)

Controls whether debugging output will be printed

Parameters:

debug - true if debugging output should be printed

getDebug

public boolean getDebug()

Controls whether debugging output will be printed

Parameters:

debug - true if debugging output should be printed

main

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

Generates a linear regression function predictor.

Parameters:

String - the options