LinearRegression (interruptible-weka 0.1.2 API)

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

All Implemented Interfaces:: java.io.Serializable, java.lang.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).

See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected int`	`m_AttributeSelection` The current attribute selection method
`protected boolean`	`m_checksTurnedOff` Turn off all checks and conversions?
`protected int`	`m_ClassIndex` The index of the class attribute
`protected double`	`m_ClassMean` The mean of the class attribute
`protected double`	`m_ClassStdDev` The standard deviations of the class attribute
`protected double[]`	`m_Coefficients` Array for storing coefficients of linear regression.
`protected boolean`	`m_EliminateColinearAttributes` Try to eliminate correlated attributes?
`protected boolean`	`m_isZeroR` True if the model is a zero R one
`protected double[]`	`m_Means` The attributes means
`protected boolean`	`m_Minimal` Conserve memory?
`protected ReplaceMissingValues`	`m_MissingFilter` The filter for removing missing values.
`protected boolean`	`m_ModelBuilt` Model already built?
`protected boolean`	`m_outputAdditionalStats` Whether to output additional statistics such as std. dev. of coefficients and t-stats
`protected double`	`m_Ridge` The ridge parameter
`protected boolean[]`	`m_SelectedAttributes` Which attributes are relevant?
`protected double[]`	`m_StdDevs` The attribute standard deviations
`protected Instances`	`m_TransformedData` Variable for storing transformed training data.
`protected NominalToBinary`	`m_TransformFilter` The filter storing the transformation from nominal to binary attributes.
`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, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

LinearRegression()

Constructors
Constructor and Description
`LinearRegression()`

Method Summary

All Methods Static Methods Instance Methods Concrete 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.
`protected double`	`calculateSE(boolean[] selectedAttributes, double[] coefficients)` Calculate the squared error of a regression model on the training data
`double`	`classifyInstance(Instance instance)` Classifies the given instance using the linear regression function.
`double[]`	`coefficients()` Returns the coefficients for this linear model.
`protected boolean`	`deselectColinearAttributes(boolean[] selectedAttributes, double[] coefficients)` Removes the attribute with the highest standardised coefficient greater than 1.5 from the selected attributes.
`protected double[]`	`doRegression(boolean[] selectedAttributes)` Calculate a linear regression using the selected attributes
`java.lang.String`	`eliminateColinearAttributesTipText()` Returns the tip text for this property
`protected void`	`findBestModel()` Performs a greedy search for the best regression model using Akaike's criterion.
`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.
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`boolean`	`getOutputAdditionalStats()` Get whether to output additional statistics (such as std. deviation of coefficients and t-statistics
`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<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Generates a linear regression function predictor.
`java.lang.String`	`minimalTipText()` Returns the tip text for this property.
`int`	`numParameters()` Get the number of coefficients used in the model
`java.lang.String`	`outputAdditionalStatsTipText()` Returns the tip text for this property.
`protected double`	`regressionPrediction(Instance transformedInstance, boolean[] selectedAttributes, double[] coefficients)` Calculate the dependent value for a given instance for a given regression 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`	`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(java.lang.String[] options)` Parses a given list of options.
`void`	`setOutputAdditionalStats(boolean additional)` Set whether to output additional statistics (such as std. deviation of coefficients and t-statistics
`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 java.lang.Object
clone, equals, finalize, 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
  - m_Coefficients
```
protected double[] m_Coefficients
```
    Array for storing coefficients of linear regression.
  - m_SelectedAttributes
```
protected boolean[] m_SelectedAttributes
```
    Which attributes are relevant?
  - m_TransformedData
```
protected Instances m_TransformedData
```
    Variable for storing transformed training data.
  - m_MissingFilter
```
protected ReplaceMissingValues m_MissingFilter
```
    The filter for removing missing values.
  - m_TransformFilter
```
protected NominalToBinary m_TransformFilter
```
    The filter storing the transformation from nominal to binary attributes.
  - m_ClassStdDev
```
protected double m_ClassStdDev
```
    The standard deviations of the class attribute
  - m_ClassMean
```
protected double m_ClassMean
```
    The mean of the class attribute
  - m_ClassIndex
```
protected int m_ClassIndex
```
    The index of the class attribute
  - m_Means
```
protected double[] m_Means
```
    The attributes means
  - m_StdDevs
```
protected double[] m_StdDevs
```
    The attribute standard deviations
  - m_outputAdditionalStats
```
protected boolean m_outputAdditionalStats
```
    Whether to output additional statistics such as std. dev. of coefficients and t-stats
  - m_AttributeSelection
```
protected int m_AttributeSelection
```
    The current attribute selection method
  - m_EliminateColinearAttributes
```
protected boolean m_EliminateColinearAttributes
```
    Try to eliminate correlated attributes?
  - m_checksTurnedOff
```
protected boolean m_checksTurnedOff
```
    Turn off all checks and conversions?
  - m_Ridge
```
protected double m_Ridge
```
    The ridge parameter
  - m_Minimal
```
protected boolean m_Minimal
```
    Conserve memory?
  - m_ModelBuilt
```
protected boolean m_ModelBuilt
```
    Model already built?
  - m_isZeroR
```
protected boolean m_isZeroR
```
    True if the model is a zero R one
- Constructor Detail
  - LinearRegression
```
public LinearRegression()
```
- Method Detail
  - main
```
public static void main(java.lang.String[] argv)
```
    Generates a linear regression function predictor.
    
    Parameters:
    
    argv - the options
  - 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 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 java.lang.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:
    
    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 interface Classifier
    
    Overrides:
    
    classifyInstance in class AbstractClassifier
    
    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<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 java.lang.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(java.lang.String[] options)
                throws java.lang.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:
    
    java.lang.Exception - if an option is not supported
  - 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
  - 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 java.lang.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 java.lang.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.
  - deselectColinearAttributes
```
protected boolean deselectColinearAttributes(boolean[] selectedAttributes,
                                             double[] coefficients)
```
    Removes the attribute with the highest standardised coefficient greater than 1.5 from the selected attributes.
    
    Parameters:
    
    selectedAttributes - an array of flags indicating which attributes are included in the regression model
    
    coefficients - an array of coefficients for the regression model
    
    Returns:
    
    true if an attribute was removed
  - findBestModel
```
protected void findBestModel()
                      throws java.lang.Exception
```
    Performs a greedy search for the best regression model using Akaike's criterion.
    
    Throws:
    
    java.lang.Exception - if regression can't be done
  - calculateSE
```
protected double calculateSE(boolean[] selectedAttributes,
                             double[] coefficients)
                      throws java.lang.Exception
```
    Calculate the squared error of a regression model on the training data
    
    Parameters:
    
    selectedAttributes - an array of flags indicating which attributes are included in the regression model
    
    coefficients - an array of coefficients for the regression model
    
    Returns:
    
    the mean squared error on the training data
    
    Throws:
    
    java.lang.Exception - if there is a missing class value in the training data
  - regressionPrediction
```
protected double regressionPrediction(Instance transformedInstance,
                                      boolean[] selectedAttributes,
                                      double[] coefficients)
                               throws java.lang.Exception
```
    Calculate the dependent value for a given instance for a given regression model.
    
    Parameters:
    
    transformedInstance - the input instance
    
    selectedAttributes - an array of flags indicating which attributes are included in the regression model
    
    coefficients - an array of coefficients for the regression model
    
    Returns:
    
    the regression value for the instance.
    
    Throws:
    
    java.lang.Exception - if the class attribute of the input instance is not assigned
  - doRegression
```
protected double[] doRegression(boolean[] selectedAttributes)
                         throws java.lang.Exception
```
    Calculate a linear regression using the selected attributes
    
    Parameters:
    
    selectedAttributes - an array of booleans where each element is true if the corresponding attribute should be included in the regression.
    
    Returns:
    
    an array of coefficients for the linear regression model.
    
    Throws:
    
    java.lang.Exception - if an error occurred during the regression.
  - getRevision
```
public java.lang.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

m_Coefficients

m_SelectedAttributes

m_TransformedData

m_MissingFilter

m_TransformFilter

m_ClassStdDev

m_ClassMean

m_ClassIndex

m_Means

m_StdDevs

m_outputAdditionalStats

m_AttributeSelection

m_EliminateColinearAttributes

m_checksTurnedOff

m_Ridge

m_Minimal

m_ModelBuilt

m_isZeroR

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

deselectColinearAttributes

findBestModel

calculateSE

regressionPrediction

doRegression

getRevision