weka.classifiers.functions
Class LinearRegression

java.lang.Object
  extended byweka.classifiers.Classifier
      extended byweka.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)

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
 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.
 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.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
 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
forName, makeCopies
 
Methods inherited from class java.lang.Object
clone, equals, finalize, 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.


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.

Specified by:
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.


listOptions

public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.

Specified by:
listOptions in interface OptionHandler
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
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
Returns:
an array of strings suitable for passing to setOptions

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.

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

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.

setDebug

public void setDebug(boolean debug)
Controls whether debugging output will be printed

Parameters:
debug - true if debugging output should be printed

getDebug

public boolean getDebug()
Controls whether debugging output will be printed


main

public static void main(java.lang.String[] argv)
Generates a linear regression function predictor.