java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.StoppableClassifier
  - - weka.classifiers.functions.LinearRegressionJ

All Implemented Interfaces:: Stoppable, StoppableWithFeedback, Serializable, Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.WeightedInstancesHandler

public class LinearRegressionJ
extends StoppableClassifier
implements weka.core.OptionHandler, weka.core.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.

 -disable-preprocessing
  Disable preprocessing.

 -disable-input-scaling
  Disable input scaling.

 -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).

 -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 4).

 -batch-size
  The desired batch size for batch prediction  (default 100).

LinearRegression version based on r12246 before switch to MTJ.

Author:: Eibe Frank ([email protected]), Len Trigg ([email protected])
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field	Description
`protected int`	`m_AttributeSelection`	The current attribute selection method
`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 int`	`m_DF`	The degrees of freedom of the regression model
`protected boolean`	`m_EliminateColinearAttributes`	Try to eliminate correlated attributes?
`protected boolean`	`m_EnableInputScaling`	whether to scale the input.
`protected boolean`	`m_EnablePreprocessing`	whether to perform preprocessing.
`protected double`	`m_FStat`	The F-statistic of the regression model
`protected double[]`	`m_Means`	The attributes means
`protected boolean`	`m_Minimal`	Conserve memory?
`protected weka.filters.unsupervised.attribute.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 double`	`m_RSquared`	The R-squared value of the regression model
`protected double`	`m_RSquaredAdj`	The adjusted R-squared value of the regression model
`protected boolean[]`	`m_SelectedAttributes`	Which attributes are relevant?
`protected double[]`	`m_StdDevs`	The attribute standard deviations
`protected double[]`	`m_StdErrorOfCoef`	Array for storing the standard error of each coefficient
`protected weka.core.Instances`	`m_TransformedData`	Variable for storing transformed training data.
`protected weka.filters.supervised.attribute.NominalToBinary`	`m_TransformFilter`	The filter storing the transformation from nominal to binary attributes.
`protected double[]`	`m_TStats`	Array for storing the t-statistic of each coefficient
`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 weka.core.Tag[]`	`TAGS_SELECTION`	Attribute selection methods

Fields inherited from class weka.classifiers.StoppableClassifier
m_Stopped

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 Description

LinearRegressionJ()
Default constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method	Description
`String`	`attributeSelectionMethodTipText()`	Returns the tip text for this property
`void`	`buildClassifier(weka.core.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(weka.core.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
`String`	`eliminateColinearAttributesTipText()`	Returns the tip text for this property
`String`	`enableInputScalingTipText()`	Returns the tip text for this property.
`String`	`enablePreprocessingTipText()`	Returns the tip text for this property.
`protected void`	`findBestModel()`	Performs a greedy search for the best regression model using Akaike's criterion.
`weka.core.SelectedTag`	`getAttributeSelectionMethod()`	Gets the method used to select attributes for use in the linear regression.
`weka.core.Capabilities`	`getCapabilities()`	Returns default capabilities of the classifier.
`boolean`	`getEliminateColinearAttributes()`	Get the value of EliminateColinearAttributes.
`boolean`	`getEnableInputScaling()`	Get whether to scale the input
`boolean`	`getEnablePreprocessing()`	Get whether to enable preprocessing.
`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. deviation of coefficients and t-statistics
`String`	`getRevision()`	Returns the revision string.
`double`	`getRidge()`	Get the value of Ridge.
`String`	`globalInfo()`	Returns a string describing this classifier
`Enumeration<weka.core.Option>`	`listOptions()`	Returns an enumeration describing the available options.
`static void`	`main(String[] args)`	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.
`protected double`	`regressionPrediction(weka.core.Instance transformedInstance, boolean[] selectedAttributes, double[] coefficients)`	Calculate the dependent value for a given instance for a given regression model.
`String`	`ridgeTipText()`	Returns the tip text for this property
`void`	`setAttributeSelectionMethod(weka.core.SelectedTag value)`	Sets the method used to select attributes for use in the linear regression.
`void`	`setEliminateColinearAttributes(boolean value)`	Set the value of EliminateColinearAttributes.
`void`	`setEnableInputScaling(boolean value)`	Set whether to scale the input.
`void`	`setEnablePreprocessing(boolean value)`	Set whether to enable preprocessing.
`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 value)`	Set whether to output additional statistics (such as std. deviation of coefficients and t-statistics
`void`	`setRidge(double value)`	Set the value of Ridge.
`String`	`toString()`	Outputs the linear regression model as a string.
`void`	`turnChecksOff()`	Deprecated.
`void`	`turnChecksOn()`	Deprecated.

Methods inherited from class weka.classifiers.StoppableClassifier
isStopped, stopExecution

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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 weka.core.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 weka.core.Instances m_TransformedData
```
    Variable for storing transformed training data.
  - m_MissingFilter
```
protected weka.filters.unsupervised.attribute.ReplaceMissingValues m_MissingFilter
```
    The filter for removing missing values.
  - m_TransformFilter
```
protected weka.filters.supervised.attribute.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_EnablePreprocessing
```
protected boolean m_EnablePreprocessing
```
    whether to perform preprocessing.
  - m_EnableInputScaling
```
protected boolean m_EnableInputScaling
```
    whether to scale the input.
  - 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_DF
```
protected int m_DF
```
    The degrees of freedom of the regression model
  - m_RSquared
```
protected double m_RSquared
```
    The R-squared value of the regression model
  - m_RSquaredAdj
```
protected double m_RSquaredAdj
```
    The adjusted R-squared value of the regression model
  - m_FStat
```
protected double m_FStat
```
    The F-statistic of the regression model
  - m_StdErrorOfCoef
```
protected double[] m_StdErrorOfCoef
```
    Array for storing the standard error of each coefficient
  - m_TStats
```
protected double[] m_TStats
```
    Array for storing the t-statistic of each coefficient
- Constructor Detail
  - LinearRegressionJ
```
public LinearRegressionJ()
```
    Default constructor.
- Method Detail
  - 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 weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances data)
                     throws Exception
```
    Builds a regression model for the given data.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.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(weka.core.Instance instance)
                        throws Exception
```
    Classifies the given instance using the linear regression function.
    
    Specified by:
    
    classifyInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    classifyInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - the test instance
    
    Returns:
    
    the classification
    
    Throws:
    
    Exception - if classification can't be done successfully
  - listOptions
```
public Enumeration<weka.core.Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.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 weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.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.
    
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.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 value)
```
    Set the value of Ridge.
    
    Parameters:
    
    value - 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 value)
```
    Set the value of EliminateColinearAttributes.
    
    Parameters:
    
    value - 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 weka.core.SelectedTag getAttributeSelectionMethod()
```
    Gets the method used to select attributes for use in the linear regression.
    
    Returns:
    
    the method to use.
  - setAttributeSelectionMethod
```
public void setAttributeSelectionMethod(weka.core.SelectedTag value)
```
    Sets the method used to select attributes for use in the linear regression.
    
    Parameters:
    
    value - 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 value)
```
    Set whether to output additional statistics (such as std. deviation of coefficients and t-statistics
    
    Parameters:
    
    value - true if additional stats are to be output
  - enablePreprocessingTipText
```
public String enablePreprocessingTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getEnablePreprocessing
```
public boolean getEnablePreprocessing()
```
    Get whether to enable preprocessing.
    
    Returns:
    
    true if enabled
    
    See Also:
    
    m_TransformFilter, m_MissingFilter
  - setEnablePreprocessing
```
public void setEnablePreprocessing(boolean value)
```
    Set whether to enable preprocessing.
    
    Parameters:
    
    value - true if to enable
    
    See Also:
    
    m_TransformFilter, m_MissingFilter
  - enableInputScalingTipText
```
public String enableInputScalingTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getEnableInputScaling
```
public boolean getEnableInputScaling()
```
    Get whether to scale the input
    
    Returns:
    
    true if to scale
  - setEnableInputScaling
```
public void setEnableInputScaling(boolean value)
```
    Set whether to scale the input.
    
    Parameters:
    
    value - true if to scale the input
  - turnChecksOff
```
@Deprecated
public void turnChecksOff()
```
    Deprecated.
    
    Turns off preprocessing (NominalToBinary, ReplaceMissingValues). Use with caution. Also turns off scaling.
    
    See Also:
    
    setEnablePreprocessing(boolean), setEnableInputScaling(boolean)
  - turnChecksOn
```
@Deprecated
public void turnChecksOn()
```
    Deprecated.
    
    Turns on preprocessing (NominalToBinary, ReplaceMissingValues). Also turns on scaling.
    
    See Also:
    
    setEnablePreprocessing(boolean), setEnableInputScaling(boolean)
  - 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 Exception
```
    Performs a greedy search for the best regression model using Akaike's criterion.
    
    Throws:
    
    Exception - if regression can't be done
  - calculateSE
```
protected double calculateSE(boolean[] selectedAttributes,
                             double[] coefficients)
                      throws 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:
    
    Exception - if there is a missing class value in the training data
  - regressionPrediction
```
protected double regressionPrediction(weka.core.Instance transformedInstance,
                                      boolean[] selectedAttributes,
                                      double[] coefficients)
                               throws 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:
    
    Exception - if the class attribute of the input instance is not assigned
  - doRegression
```
protected double[] doRegression(boolean[] selectedAttributes)
                         throws 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:
    
    Exception - if an error occurred during the regression.
  - toString
```
public String toString()
```
    Outputs the linear regression model as a string.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    the model as string
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(String[] args)
```
    Generates a linear regression function predictor.
    
    Parameters:
    
    args - the options

Class LinearRegressionJ

Field Summary

Fields inherited from class weka.classifiers.StoppableClassifier

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.StoppableClassifier

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_EnablePreprocessing

m_EnableInputScaling

m_Ridge

m_Minimal

m_ModelBuilt

m_DF

m_RSquared

m_RSquaredAdj

m_FStat

m_StdErrorOfCoef

m_TStats

Constructor Detail

LinearRegressionJ

Method Detail

globalInfo

getCapabilities

buildClassifier

classifyInstance

listOptions

coefficients

getOptions

setOptions

ridgeTipText

getRidge

setRidge

eliminateColinearAttributesTipText

getEliminateColinearAttributes

setEliminateColinearAttributes

numParameters

attributeSelectionMethodTipText

getAttributeSelectionMethod

setAttributeSelectionMethod

minimalTipText

getMinimal

setMinimal

outputAdditionalStatsTipText

getOutputAdditionalStats

setOutputAdditionalStats

enablePreprocessingTipText

getEnablePreprocessing

setEnablePreprocessing

enableInputScalingTipText

getEnableInputScaling

setEnableInputScaling

turnChecksOff

turnChecksOn

deselectColinearAttributes

findBestModel

calculateSE

regressionPrediction

doRegression

toString

getRevision