LinearRegression

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weka.classifiers.functions

Class LinearRegression

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

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, 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.
 (default no debugging output)
```
```
 -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).
 
```
Version:

$Revision: 9770 $

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 ()

Constructors
Constructor and Description
`LinearRegression ()`

Method Summary

All Methods
Modifier and Type	Method and Description
`java.lang.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.
`java.lang.String`	`debugTipText ()` Returns the tip text for this property
`java.lang.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`	`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.
`java.lang.String`	`getRevision ()` Returns the revision string.
`double`	`getRidge ()` Get the value of Ridge.
`java.lang.String`	`globalInfo ()` Returns a string describing this classifier
`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
`java.lang.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`	`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
distributionForInstance, forName, makeCopies, makeCopy

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
  - 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.
  - globalInfo
```
public java.lang.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 CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - 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.
    
    Overrides:
    
    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.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    the model as string
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class Classifier
    
    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.
 (default no debugging output)
```
```
 -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).
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    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.
    
    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
    
    Overrides:
    
    getOptions in class Classifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - ridgeTipText
```
public java.lang.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 java.lang.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 java.lang.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.
  - debugTipText
```
public java.lang.String debugTipText()
```
    Returns the tip text for this property
    
    Overrides:
    
    debugTipText in class Classifier
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDebug
```
public void setDebug(boolean debug)
```
    Controls whether debugging output will be printed
    
    Overrides:
    
    setDebug in class Classifier
    
    Parameters:
    
    debug - true if debugging output should be printed
  - getDebug
```
public boolean getDebug()
```
    Controls whether debugging output will be printed
    
    Overrides:
    
    getDebug in class Classifier
    
    Returns:
    
    true if debugging output is printed
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Generates a linear regression function predictor.
    
    Parameters:
    
    argv - the options

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Summary:
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Field |
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Detail:
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Constr |
Method

Class LinearRegression

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

SELECTION_M5

SELECTION_NONE

SELECTION_GREEDY

TAGS_SELECTION

Constructor Detail

LinearRegression

Method Detail

turnChecksOff

turnChecksOn

globalInfo

getCapabilities

buildClassifier

classifyInstance

toString

listOptions

setOptions

coefficients

getOptions

ridgeTipText

getRidge

setRidge

eliminateColinearAttributesTipText

getEliminateColinearAttributes

setEliminateColinearAttributes

numParameters

attributeSelectionMethodTipText

setAttributeSelectionMethod

getAttributeSelectionMethod

debugTipText

setDebug

getDebug

getRevision

main