LinearRegression (weka-stable 3.8.0 API)

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

All Implemented Interfaces:: Serializable, Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class LinearRegression
extends AbstractClassifier
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:

 -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).

 -minimal
  Conserve memory, don't keep dataset header and means/stdevs.
  Model cannot be printed out if this option is enabled. (default: keep data)

 -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: 12643 $
Author:: Eibe Frank ([email protected]), Len Trigg ([email protected])
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

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

LinearRegression()

Constructors
Constructor and Description
`LinearRegression()`

Method Summary

Methods
Modifier and Type	Method and Description
`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.
`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`	`getEliminateColinearAttributes()` Get the value of EliminateColinearAttributes.
`boolean`	`getMinimal()` Returns whether to be more memory conservative or being able to output the model as string.
`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`	`getRidge()` Get the value of Ridge.
`String`	`globalInfo()` Returns a string describing this classifier
`Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Generates a linear regression function predictor.
`String`	`minimalTipText()` Returns the tip text for this property.
`int`	`numParameters()` Get the number of coefficients used in the model
`String`	`outputAdditionalStatsTipText()` Returns the tip text for this property.
`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`	`setEliminateColinearAttributes(boolean newEliminateColinearAttributes)` Set the value of EliminateColinearAttributes.
`void`	`setMinimal(boolean value)` Sets whether to be more memory conservative or being able to output the model as string.
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setOutputAdditionalStats(boolean additional)` Set whether to output additional statistics (such as std.
`void`	`setRidge(double newRidge)` Set the value of Ridge.
`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 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
  - main
```
public static void main(String[] argv)
```
    Generates a linear regression function predictor.
    
    Parameters:
    argv - the options
  - globalInfo
```
public 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 Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    the capabilities of this classifier
    See Also:
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws Exception
```
    Builds a regression model for the given data.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    data - the training data to be used for generating the linear regression function
    
    Throws:
    
    Exception - if the classifier could not be built successfully
  - classifyInstance
```
public double classifyInstance(Instance instance)
                        throws Exception
```
    Classifies the given instance using the linear regression function.
    
    Specified by:
    
    classifyInstance in interface Classifier
    
    Overrides:
    
    classifyInstance in class AbstractClassifier
    
    Parameters:
    instance - the test instance
    
    Returns:
    the classification
    
    Throws:
    
    Exception - if classification can't be done successfully
  - toString
```
public String toString()
```
    Outputs the linear regression model as a string.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    the model as string
  - 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.
  - coefficients
```
public double[] coefficients()
```
    Returns the coefficients for this linear model.
    
    Returns:
    the coefficients for this linear model
  - 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
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -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).
 
```
```
 -minimal
  Conserve memory, don't keep dataset header and means/stdevs.
  Model cannot be printed out if this option is enabled. (default: keep data)
 
```
```
 -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
  - ridgeTipText
```
public 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 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 String attributeSelectionMethodTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getAttributeSelectionMethod
```
public SelectedTag getAttributeSelectionMethod()
```
    Gets the method used to select attributes for use in the linear regression.
    
    Returns:
    the method to use.
  - 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.
  - minimalTipText
```
public String minimalTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMinimal
```
public boolean getMinimal()
```
    Returns whether to be more memory conservative or being able to output the model as string.
    
    Returns:
    true if memory conservation is preferred over outputting model description
  - setMinimal
```
public void setMinimal(boolean value)
```
    Sets whether to be more memory conservative or being able to output the model as string.
    
    Parameters:
    value - if true memory will be conserved
  - 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
  - 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
  - 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
  - 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.
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    the revision

Class LinearRegression

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

SELECTION_M5

SELECTION_NONE

SELECTION_GREEDY

TAGS_SELECTION

Constructor Detail

LinearRegression

Method Detail

main

globalInfo

getCapabilities

buildClassifier

classifyInstance

toString

listOptions

coefficients

getOptions

setOptions

ridgeTipText

getRidge

setRidge

eliminateColinearAttributesTipText

getEliminateColinearAttributes

setEliminateColinearAttributes

numParameters

attributeSelectionMethodTipText

getAttributeSelectionMethod

setAttributeSelectionMethod

minimalTipText

getMinimal

setMinimal

outputAdditionalStatsTipText

getOutputAdditionalStats

setOutputAdditionalStats

turnChecksOff

turnChecksOn

getRevision