All Packages  Class Hierarchy  This Package  Previous  Next  Index  WEKA's home

Class weka.classifiers.LinearRegression

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

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)

Version:
$Revision: 1.12 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz)
Author:
Len Trigg (trigg@cs.waikato.ac.nz)

Variable Index

 o TAGS_SELECTION
 

Constructor Index

 o LinearRegression()
 

Method Index

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

Field Detail

 o TAGS_SELECTION
public static final Tag[] TAGS_SELECTION

Constructor Detail

 o LinearRegression
public LinearRegression()

Method Detail

 o buildClassifier
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
 o classifyInstance
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
 o toString
public java.lang.String toString()
          Outputs the linear regression model as a string.
Overrides:
toString in class java.lang.Object
 o listOptions
public java.util.Enumeration listOptions()
          Returns an enumeration describing the available options
Returns:
an enumeration of all the available options
 o 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)

Parameters:
options - the list of options as an array of strings
Throws:
java.lang.Exception - if an option is not supported
 o getOptions
public java.lang.String[] getOptions()
          Gets the current settings of the classifier.
Returns:
an array of strings suitable for passing to setOptions
 o numParameters
public int numParameters()
          Get the number of coefficients used in the model
Returns:
the number of coefficients
 o 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.
 o getAttributeSelectionMethod
public SelectedTag getAttributeSelectionMethod()
          Gets the method used to select attributes for use in the linear regression.
Returns:
the method to use.
 o setDebug
public void setDebug(boolean debug)
          Controls whether debugging output will be printed
Parameters:
debug - true if debugging output should be printed
 o getDebug
public boolean getDebug()
          Controls whether debugging output will be printed
Parameters:
debug - true if debugging output should be printed
 o main
public static void main(java.lang.String argv[])
          Generates a linear regression function predictor.
Parameters:
String - the options

All Packages  Class Hierarchy  This Package  Previous  Next  Index  WEKA's home