weka.classifiers.functions
Class LinearRegression

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

-C
Do not try to eliminate colinear attributes

-R num
The ridge parameter (default 1.0e-8)

Version:

$Revision: 1.19 $

Author:

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

See Also:

Serialized Form

Field Summary

static int	SELECTION_GREEDY
static int	SELECTION_M5
static int	SELECTION_NONE
static Tag[]	TAGS_SELECTION

Constructor Summary

LinearRegression()

Method Summary

java.lang.String	attributeSelectionMethodTipText() Returns the tip text for this property
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.
double[]	coefficients() Returns the coefficients for this linear model.
java.lang.String	debugTipText() Returns the tip text for this property
java.lang.String	eliminateColinearAttributesTipText() Returns the tip text for this property
SelectedTag	getAttributeSelectionMethod() Gets the method used to select attributes for use in the linear regression.
boolean	getDebug() Controls whether debugging output will be printed
boolean	getEliminateColinearAttributes() Get the value of EliminateColinearAttributes.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
double	getRidge() Get the value of Ridge.
java.lang.String	globalInfo() Returns a string describing this 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
java.lang.String	ridgeTipText() Returns the tip text for this property
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	setEliminateColinearAttributes(boolean newEliminateColinearAttributes) Set the value of EliminateColinearAttributes.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setRidge(double newRidge) Set the value of Ridge.
java.lang.String	toString() Outputs the linear regression model as a string.
void	turnChecksOff() Turns off checks for missing values, etc.
void	turnChecksOn() Turns on checks for missing values, etc.

Methods inherited from class weka.classifiers.Classifier

distributionForInstance, forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

SELECTION_M5

public static final int SELECTION_M5

See Also:

Constant Field Values

SELECTION_NONE

public static final int SELECTION_NONE

See Also:

Constant Field Values

SELECTION_GREEDY

public static final int SELECTION_GREEDY

See Also:

Constant Field Values

TAGS_SELECTION

public static final Tag[] TAGS_SELECTION

Constructor Detail

LinearRegression

public LinearRegression()

Method Detail

turnChecksOff

public void turnChecksOff()

Turns off checks for missing values, etc. Use with caution. Also turns off scaling.

turnChecksOn

public void turnChecksOn()

Turns on checks for missing values, etc. Also turns on scaling.

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

buildClassifier

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

Builds a regression model for the given data.

Specified by:

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

Overrides:

listOptions in class Classifier

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)

-C
Do not try to eliminate colinear attributes

-R num
The ridge parameter (default 1.0e-8)

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Classifier

Parameters:

options - the list of options as an array of strings

Throws:

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

coefficients

public double[] coefficients()

Returns the coefficients for this linear model.

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Classifier

Returns:

an array of strings suitable for passing to setOptions

ridgeTipText

public java.lang.String ridgeTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getRidge

public double getRidge()

Get the value of Ridge.

Returns:

Value of Ridge.

setRidge

public void setRidge(double newRidge)

Set the value of Ridge.

Parameters:

newRidge - Value to assign to Ridge.

eliminateColinearAttributesTipText

public java.lang.String eliminateColinearAttributesTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getEliminateColinearAttributes

public boolean getEliminateColinearAttributes()

Get the value of EliminateColinearAttributes.

Returns:

Value of EliminateColinearAttributes.

setEliminateColinearAttributes

public void setEliminateColinearAttributes(boolean newEliminateColinearAttributes)

Set the value of EliminateColinearAttributes.

Parameters:

newEliminateColinearAttributes - Value to assign to EliminateColinearAttributes.

numParameters

public int numParameters()

Get the number of coefficients used in the model

Returns:

the number of coefficients

attributeSelectionMethodTipText

public java.lang.String attributeSelectionMethodTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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.

debugTipText

public java.lang.String debugTipText()

Returns the tip text for this property

Overrides:

debugTipText in class Classifier

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setDebug

public void setDebug(boolean debug)

Controls whether debugging output will be printed

Overrides:

setDebug in class Classifier

Parameters:

debug - true if debugging output should be printed

getDebug

public boolean getDebug()

Controls whether debugging output will be printed

Overrides:

getDebug in class Classifier

Returns:

true if debugging output is on

main

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

Generates a linear regression function predictor.