Package weka.classifiers

Interface IterativeClassifier

All Superinterfaces:
Classifier
All Known Implementing Classes:
AdaBoostM1, AdditiveRegression, FilteredClassifier, LogitBoost, MultilayerPerceptron, RandomizableFilteredClassifier
public interface IterativeClassifier extends Classifier
Interface for classifiers that can induce models of growing complexity one step at a time.
Version:
$Revision: 15021 $
Author:
Gabi Schmidberger (gabi@cs.waikato.ac.nz), Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)

  • Method Summary

    Modifier and Type
    Method
    Description
    void
    done()
    Signal end of iterating, for either the time being or permanently if setFinalized(true) has been called.
    boolean
    getResume()
    Returns true if the classifier will be able to be trained further (with more iterations) at a later date.
    void
    initializeClassifier(Instances instances)
    Initializes an iterative classifier.
    boolean
    next()
    Performs one iteration.
    void
    setResume(boolean resume)
    If called with argument true then the classifier will be able to be trained further (with more iterations) at a later date.

    Methods inherited from interface weka.classifiers.Classifier

    buildClassifier, classifyInstance, distributionForInstance, getCapabilities

  • Method Details

    • initializeClassifier

      void initializeClassifier(Instances instances) throws Exception
      Initializes an iterative classifier.
      Parameters:
      instances - the instances to be used in induction
      Throws:
      Exception - if the model cannot be initialized

    • setResume

      void setResume(boolean resume) throws Exception
      If called with argument true then the classifier will be able to be trained further (with more iterations) at a later date. Note that this can potentially make serialized model structures larger, because certain data and data structures might need to be retained to facilitate continued training. When set to false, and done() is called, then the classifier will be cleaned up, resulting in (potentially) a more compact serialized model.
      Parameters:
      resume - true to enable training to continue at a later point after the initial model is built.
      Throws:
      Exception - if finalization cannot be set

    • getResume

      boolean getResume()
      Returns true if the classifier will be able to be trained further (with more iterations) at a later date. Note that this can potentially make serialized model structures larger, because certain data and data structures might need to be retained to facilitate continued training. When set to false, and done() is called, then the classifier will be cleaned up, resulting in (potentially) a more compact serialized model.
      Returns:
      true to enable training to continue at a later point after the initial model is built.
      Throws:
      Exception - if finalization cannot be set

    • next

      boolean next() throws Exception
      Performs one iteration.
      Returns:
      false if no further iterations could be performed, true otherwise
      Throws:
      Exception - if this iteration fails for unexpected reasons

    • done

      void done() throws Exception
      Signal end of iterating, for either the time being or permanently if setFinalized(true) has been called. Useful for any house-keeping/cleanup.
      Throws:
      Exception - if cleanup fails