Package weka.classifiers.functions
Class LinearRegressionJ
- java.lang.Object
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- weka.classifiers.AbstractClassifier
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- weka.classifiers.functions.LinearRegressionJ
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- All Implemented Interfaces:
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 weka.classifiers.AbstractClassifier 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.
-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).
LinearRegression version based on r12246 before switch to MTJ.- Version:
- $Revision: 12246 $
- Author:
- Eibe Frank ([email protected]), Len Trigg ([email protected])
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description protected int
m_AttributeSelection
The current attribute selection methodprotected boolean
m_checksTurnedOff
Turn off all checks and conversions?protected int
m_ClassIndex
The index of the class attributeprotected double
m_ClassMean
The mean of the class attributeprotected double
m_ClassStdDev
The standard deviations of the class attributeprotected double[]
m_Coefficients
Array for storing coefficients of linear regression.protected boolean
m_EliminateColinearAttributes
Try to eliminate correlated attributes?protected double[]
m_Means
The attributes meansprotected 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.protected double
m_Ridge
The ridge parameterprotected boolean[]
m_SelectedAttributes
Which attributes are relevant?protected double[]
m_StdDevs
The attribute standard deviationsprotected 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.static int
SELECTION_GREEDY
Attribute selection method: Greedy methodstatic int
SELECTION_M5
Attribute selection method: M5 methodstatic int
SELECTION_NONE
Attribute selection method: No attribute selectionstatic weka.core.Tag[]
TAGS_SELECTION
Attribute selection methods
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Constructor Summary
Constructors Constructor Description LinearRegressionJ()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description String
attributeSelectionMethodTipText()
Returns the tip text for this propertyvoid
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 datadouble
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 attributesString
eliminateColinearAttributesTipText()
Returns the tip text for this propertyprotected 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
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 classifierEnumeration<weka.core.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 modelString
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 propertyvoid
setAttributeSelectionMethod(weka.core.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 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
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Field Detail
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SELECTION_M5
public static final int SELECTION_M5
Attribute selection method: M5 method- See Also:
- Constant Field Values
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SELECTION_NONE
public static final int SELECTION_NONE
Attribute selection method: No attribute selection- See Also:
- Constant Field Values
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SELECTION_GREEDY
public static final int SELECTION_GREEDY
Attribute selection method: Greedy method- See Also:
- Constant Field Values
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TAGS_SELECTION
public static final weka.core.Tag[] TAGS_SELECTION
Attribute selection methods
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m_Coefficients
protected double[] m_Coefficients
Array for storing coefficients of linear regression.
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m_SelectedAttributes
protected boolean[] m_SelectedAttributes
Which attributes are relevant?
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m_TransformedData
protected weka.core.Instances m_TransformedData
Variable for storing transformed training data.
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m_MissingFilter
protected weka.filters.unsupervised.attribute.ReplaceMissingValues m_MissingFilter
The filter for removing missing values.
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m_TransformFilter
protected weka.filters.supervised.attribute.NominalToBinary m_TransformFilter
The filter storing the transformation from nominal to binary attributes.
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m_ClassStdDev
protected double m_ClassStdDev
The standard deviations of the class attribute
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m_ClassMean
protected double m_ClassMean
The mean of the class attribute
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m_ClassIndex
protected int m_ClassIndex
The index of the class attribute
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m_Means
protected double[] m_Means
The attributes means
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m_StdDevs
protected double[] m_StdDevs
The attribute standard deviations
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m_outputAdditionalStats
protected boolean m_outputAdditionalStats
Whether to output additional statistics such as std. dev. of coefficients and t-stats
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m_AttributeSelection
protected int m_AttributeSelection
The current attribute selection method
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m_EliminateColinearAttributes
protected boolean m_EliminateColinearAttributes
Try to eliminate correlated attributes?
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m_checksTurnedOff
protected boolean m_checksTurnedOff
Turn off all checks and conversions?
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m_Ridge
protected double m_Ridge
The ridge parameter
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m_Minimal
protected boolean m_Minimal
Conserve memory?
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m_ModelBuilt
protected boolean m_ModelBuilt
Model already built?
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Method Detail
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main
public static void main(String[] argv)
Generates a linear regression function predictor.- Parameters:
argv
- the options
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globalInfo
public String globalInfo()
Returns a string describing this classifier- Returns:
- a description of the classifier suitable for displaying in the explorer/experimenter gui
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getCapabilities
public weka.core.Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceweka.core.CapabilitiesHandler
- Specified by:
getCapabilities
in interfaceweka.classifiers.Classifier
- Overrides:
getCapabilities
in classweka.classifiers.AbstractClassifier
- Returns:
- the capabilities of this classifier
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buildClassifier
public void buildClassifier(weka.core.Instances data) throws Exception
Builds a regression model for the given data.- Specified by:
buildClassifier
in interfaceweka.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
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classifyInstance
public double classifyInstance(weka.core.Instance instance) throws Exception
Classifies the given instance using the linear regression function.- Specified by:
classifyInstance
in interfaceweka.classifiers.Classifier
- Overrides:
classifyInstance
in classweka.classifiers.AbstractClassifier
- Parameters:
instance
- the test instance- Returns:
- the classification
- Throws:
Exception
- if classification can't be done successfully
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toString
public String toString()
Outputs the linear regression model as a string.
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listOptions
public Enumeration<weka.core.Option> listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceweka.core.OptionHandler
- Overrides:
listOptions
in classweka.classifiers.AbstractClassifier
- Returns:
- an enumeration of all the available options.
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coefficients
public double[] coefficients()
Returns the coefficients for this linear model.- Returns:
- the coefficients for this linear model
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getOptions
public String[] getOptions()
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceweka.core.OptionHandler
- Overrides:
getOptions
in classweka.classifiers.AbstractClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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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 interfaceweka.core.OptionHandler
- Overrides:
setOptions
in classweka.classifiers.AbstractClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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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
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getRidge
public double getRidge()
Get the value of Ridge.- Returns:
- Value of Ridge.
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setRidge
public void setRidge(double newRidge)
Set the value of Ridge.- Parameters:
newRidge
- Value to assign to Ridge.
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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
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getEliminateColinearAttributes
public boolean getEliminateColinearAttributes()
Get the value of EliminateColinearAttributes.- Returns:
- Value of EliminateColinearAttributes.
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setEliminateColinearAttributes
public void setEliminateColinearAttributes(boolean newEliminateColinearAttributes)
Set the value of EliminateColinearAttributes.- Parameters:
newEliminateColinearAttributes
- Value to assign to EliminateColinearAttributes.
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numParameters
public int numParameters()
Get the number of coefficients used in the model- Returns:
- the number of coefficients
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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
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getAttributeSelectionMethod
public weka.core.SelectedTag getAttributeSelectionMethod()
Gets the method used to select attributes for use in the linear regression.- Returns:
- the method to use.
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setAttributeSelectionMethod
public void setAttributeSelectionMethod(weka.core.SelectedTag method)
Sets the method used to select attributes for use in the linear regression.- Parameters:
method
- the attribute selection method to use.
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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
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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
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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
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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
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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
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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
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turnChecksOff
public void turnChecksOff()
Turns off checks for missing values, etc. Use with caution. Also turns off scaling.
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turnChecksOn
public void turnChecksOn()
Turns on checks for missing values, etc. Also turns on scaling.
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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 modelcoefficients
- an array of coefficients for the regression model- Returns:
- true if an attribute was removed
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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
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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 modelcoefficients
- 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
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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 instanceselectedAttributes
- an array of flags indicating which attributes are included in the regression modelcoefficients
- 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
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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.
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getRevision
public String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceweka.core.RevisionHandler
- Overrides:
getRevision
in classweka.classifiers.AbstractClassifier
- Returns:
- the revision
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