weka.classifiers.functions

Class LinearRegression

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.functions.LinearRegression

All Implemented Interfaces:

Serializable, Cloneable, Classifier, CapabilitiesHandler, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class LinearRegression
extends AbstractClassifier
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:

 -S <number of selection method>
  Set the attribute selection method to use. 1 = None, 2 = Greedy.
  (default 0 = M5' method)
 

 -C
  Do not try to eliminate colinear attributes.
 

 -R <double>
  Set ridge parameter (default 1.0e-8).
 

 -minimal
  Conserve memory, don't keep dataset header and means/stdevs.
  Model cannot be printed out if this option is enabled. (default: keep data)
 

 -additional-stats
  Output additional statistics.
 

 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 

Version:

$Revision: 10382 $

Author:

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

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	SELECTION_GREEDY Attribute selection method: Greedy method
static int	SELECTION_M5 Attribute selection method: M5 method
static int	SELECTION_NONE Attribute selection method: No attribute selection
static Tag[]	TAGS_SELECTION Attribute selection methods

Constructor Summary

Constructors

Constructor and Description
LinearRegression()

Method Summary

Methods

Modifier and Type	Method and Description
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.
String	eliminateColinearAttributesTipText() Returns the tip text for this property
SelectedTag	getAttributeSelectionMethod() Gets the method used to select attributes for use in the linear regression.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
boolean	getEliminateColinearAttributes() Get the value of EliminateColinearAttributes.
boolean	getMinimal() Returns whether to be more memory conservative or being able to output the model as string.
String[]	getOptions() Gets the current settings of the classifier.
boolean	getOutputAdditionalStats() Get whether to output additional statistics (such as std.
String	getRevision() Returns the revision string.
double	getRidge() Get the value of Ridge.
String	globalInfo() Returns a string describing this classifier
Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Generates a linear regression function predictor.
String	minimalTipText() Returns the tip text for this property.
int	numParameters() Get the number of coefficients used in the model
String	outputAdditionalStatsTipText() Returns the tip text for this property.
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	setEliminateColinearAttributes(boolean newEliminateColinearAttributes) Set the value of EliminateColinearAttributes.
void	setMinimal(boolean value) Sets whether to be more memory conservative or being able to output the model as string.
void	setOptions(String[] options) Parses a given list of options.
void	setOutputAdditionalStats(boolean additional) Set whether to output additional statistics (such as std.
void	setRidge(double newRidge) Set the value of Ridge.
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.AbstractClassifier

debugTipText, distributionForInstance, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, makeCopies, makeCopy, runClassifier, setDebug, setDoNotCheckCapabilities

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

Attribute selection method: M5 method

See Also:

Constant Field Values

SELECTION_NONE

public static final int SELECTION_NONE

Attribute selection method: No attribute selection

See Also:

Constant Field Values

SELECTION_GREEDY

public static final int SELECTION_GREEDY

Attribute selection method: Greedy method

See Also:

Constant Field Values

TAGS_SELECTION

public static final Tag[] TAGS_SELECTION

Attribute selection methods

Constructor Detail

LinearRegression

public LinearRegression()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this classifier

Returns:

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

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClassifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

Builds a regression model for the given data.

Specified by:

buildClassifier in interface Classifier

Parameters:

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

Throws:

Exception - if the classifier could not be built successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws Exception

Classifies the given instance using the linear regression function.

Specified by:

classifyInstance in interface Classifier

Overrides:

classifyInstance in class AbstractClassifier

Parameters:

instance - the test instance

Returns:

the classification

Throws:

Exception - if classification can't be done successfully

toString

public String toString()

Outputs the linear regression model as a string.

Overrides:

toString in class Object

Returns:

the model as string

listOptions

public Enumeration<Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class AbstractClassifier

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -S <number of selection method>
  Set the attribute selection method to use. 1 = None, 2 = Greedy.
  (default 0 = M5' method)
 

 -C
  Do not try to eliminate colinear attributes.
 

 -R <double>
  Set ridge parameter (default 1.0e-8).
 

 -minimal
  Conserve memory, don't keep dataset header and means/stdevs.
  Model cannot be printed out if this option is enabled. (default: keep data)
 

 -additional-stats
  Output additional statistics.
 

 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class AbstractClassifier

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

coefficients

public double[] coefficients()

Returns the coefficients for this linear model.

Returns:

the coefficients for this linear model

getOptions

public String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

ridgeTipText

public 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 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 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.

minimalTipText

public String minimalTipText()

Returns the tip text for this property.

Returns:

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

setMinimal

public void setMinimal(boolean value)

Sets whether to be more memory conservative or being able to output the model as string.

Parameters:

value - if true memory will be conserved

getMinimal

public boolean getMinimal()

Returns whether to be more memory conservative or being able to output the model as string.

Returns:

true if memory conservation is preferred over outputting model description

outputAdditionalStatsTipText

public String outputAdditionalStatsTipText()

Returns the tip text for this property.

Returns:

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

setOutputAdditionalStats

public void setOutputAdditionalStats(boolean additional)

Set whether to output additional statistics (such as std. deviation of coefficients and t-statistics

Parameters:

additional - true if additional stats are to be output

getOutputAdditionalStats

public boolean getOutputAdditionalStats()

Get whether to output additional statistics (such as std. deviation of coefficients and t-statistics

Returns:

true if additional stats are to be output

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.

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(String[] argv)

Generates a linear regression function predictor.

Parameters:

argv - the options