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Class weka.classifiers.functions.LinearRegression

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

public class LinearRegression

extends Classifier

implements OptionHandler, WeightedInstancesHandler

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: 1.18 $

Author:

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

Author:

Len Trigg (trigg@cs.waikato.ac.nz)

SELECTION_GREEDY

 

SELECTION_M5

 

SELECTION_NONE

 

TAGS_SELECTION

 

LinearRegression()

 

buildClassifier(Instances)

Builds a regression model for the given data.

classifyInstance(Instance)

Classifies the given instance using the linear regression function.

coefficients()

Returns the coefficients for this linear model.

getAttributeSelectionMethod()

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

getDebug()

Controls whether debugging output will be printed

getEliminateColinearAttributes()

Get the value of EliminateColinearAttributes.

getOptions()

Gets the current settings of the classifier.

getRidge()

Get the value of Ridge.

listOptions()

Returns an enumeration describing the available options.

main(String[])

Generates a linear regression function predictor.

numParameters()

Get the number of coefficients used in the model

setAttributeSelectionMethod(SelectedTag)

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

setDebug(boolean)

Controls whether debugging output will be printed

setEliminateColinearAttributes(boolean)

Set the value of EliminateColinearAttributes.

setOptions(String[])

Parses a given list of options.

setRidge(double)

Set the value of Ridge.

toString()

Outputs the linear regression model as a string.

turnChecksOff()

Turns off checks for missing values, etc.

turnChecksOn()

Turns on checks for missing values, etc.

public static final int SELECTION_M5

public static final int SELECTION_NONE

public static final int SELECTION_GREEDY

public static final Tag[] TAGS_SELECTION

public LinearRegression()

public void turnChecksOff()

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

public void turnChecksOn()

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

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

          Builds a regression model for the given data.

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

Overrides:

buildClassifier in class Classifier

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

          Classifies the given instance using the linear regression function.

Parameters:

instance - the test instance

Returns:

the classification

Throws:

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

Overrides:

classifyInstance in class Classifier

public java.lang.String toString()

          Outputs the linear regression model as a string.

Overrides:

toString in class java.lang.Object

public java.util.Enumeration listOptions()

          Returns an enumeration describing the available options.

Returns:

an enumeration of all the available options.

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)

Parameters:

options - the list of options as an array of strings

Throws:

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

public double[] coefficients()

          Returns the coefficients for this linear model.

public java.lang.String[] getOptions()

          Gets the current settings of the classifier.

Returns:

an array of strings suitable for passing to setOptions

public double getRidge()

          Get the value of Ridge.

Returns:

Value of Ridge.

public void setRidge(double newRidge)

          Set the value of Ridge.

Parameters:

newRidge - Value to assign to Ridge.

public boolean getEliminateColinearAttributes()

          Get the value of EliminateColinearAttributes.

Returns:

Value of EliminateColinearAttributes.

public void setEliminateColinearAttributes(boolean newEliminateColinearAttributes)

          Set the value of EliminateColinearAttributes.

Parameters:

newEliminateColinearAttributes - Value to assign to EliminateColinearAttributes.

public int numParameters()

          Get the number of coefficients used in the model

Returns:

the number of coefficients

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.

public SelectedTag getAttributeSelectionMethod()

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

Returns:

the method to use.

public void setDebug(boolean debug)

          Controls whether debugging output will be printed

Parameters:

debug - true if debugging output should be printed

public boolean getDebug()

          Controls whether debugging output will be printed

Parameters:

debug - true if debugging output should be printed

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

          Generates a linear regression function predictor.

Parameters:

String - the options

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