Package weka.classifiers.functions

Class SimpleLinearRegression

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.functions.SimpleLinearRegression

All Implemented Interfaces:

Serializable, Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class SimpleLinearRegression
extends AbstractClassifier
implements WeightedInstancesHandler

Learns a simple linear regression model. Picks the attribute that results in the lowest squared error. Can only deal with numeric attributes.

Valid options are:

 -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:

$Revision$

Author:

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

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor	Description
SimpleLinearRegression()

Method Summary

Modifier and Type	Method	Description
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.
Capabilities	getCapabilities()	Returns default capabilities of the classifier.
double	getIntercept()	Returns the intercept of the function.
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	getSlope()	Returns the slope of the function.
String	globalInfo()	Returns a string describing this classifier
Enumeration<Option>	listOptions()	Returns an enumeration describing the available options.
static void	main(String[] argv)	Main method for testing this class
String	outputAdditionalStatsTipText()	Returns the tip text for this property.
void	setOptions(String[] options)	Parses a given list of options.
void	setOutputAdditionalStats(boolean additional)	Set whether to output additional statistics (such as std.
void	setSuppressErrorMessage(boolean s)	Turn off the error message that is reported when no useful attribute is found.
String	toString()	Returns a description of this classifier as a string

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Constructor Detail

SimpleLinearRegression

public SimpleLinearRegression()

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

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:

 -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

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

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

classifyInstance

public double classifyInstance(Instance inst)
                        throws Exception

Generate a prediction for the supplied instance.

Specified by:

classifyInstance in interface Classifier

Overrides:

classifyInstance in class AbstractClassifier

Parameters:

inst - the instance to predict.

Returns:

the prediction

Throws:

Exception - if an error occurs

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface CapabilitiesHandler

Specified by:

getCapabilities in interface Classifier

Overrides:

getCapabilities in class AbstractClassifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances insts)
                     throws Exception

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

Specified by:

buildClassifier in interface Classifier

Parameters:

insts - the training data.

Throws:

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 String toString()

Returns a description of this classifier as a string

Overrides:

toString in class Object

Returns:

a description of the classifier.

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)

Main method for testing this class

Parameters:

argv - options