Package weka.classifiers.functions

Class 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).
 
     -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
    Version:
    $Revision: 14873 $
    Author:
    Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • 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​(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

      • 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

      • 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).
 
         -use-qr
  If set, QR decomposition will be used to find coefficients.
        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

      • useQRDecompositionTipText

        public java.lang.String useQRDecompositionTipText()
        Returns the tip text for this property.
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • getUseQRDecomposition

        public boolean getUseQRDecomposition()
        Get whether to use QR decomposition.
        Returns:
        true if QR decomposition is to be used

      • setUseQRDecomposition

        public void setUseQRDecomposition​(boolean useQR)
        Set whether to use QR decomposition.
        Parameters:
        useQR - true if QR decomposition is to be used

      • 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.