Class GeneralRegression

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, PMMLModel, RevisionHandler

    public class GeneralRegression
    extends PMMLClassifier
    implements java.io.Serializable
    Class implementing import of PMML General Regression model. Can be used as a Weka classifier for prediction (buildClassifier() raises an Exception).
    Version:
    $Revision: 5562 $
    Author:
    Mark Hall (mhall{[at]}pentaho{[dot]}com)
    See Also:
    Serialized Form
    • Constructor Detail

      • GeneralRegression

        public GeneralRegression​(org.w3c.dom.Element model,
                                 Instances dataDictionary,
                                 MiningSchema miningSchema)
                          throws java.lang.Exception
        Constructs a GeneralRegression classifier.
        Parameters:
        model - the Element that holds the model definition
        dataDictionary - the data dictionary as a set of Instances
        miningSchema - the mining schema
        Throws:
        java.lang.Exception - if there is a problem constructing the general regression object from the PMML.
    • Method Detail

      • toString

        public java.lang.String toString()
        Return a textual description of this general regression.
        Overrides:
        toString in class java.lang.Object
        Returns:
        a description of this general regression
      • distributionForInstance

        public double[] distributionForInstance​(Instance inst)
                                         throws java.lang.Exception
        Classifies the given test instance. The instance has to belong to a dataset when it's being classified.
        Overrides:
        distributionForInstance in class Classifier
        Parameters:
        inst - the instance to be classified
        Returns:
        the predicted most likely class for the instance or Instance.missingValue() if no prediction is made
        Throws:
        java.lang.Exception - if an error occurred during the prediction