weka.classifiers.meta

Class RandomizableFilteredClassifier

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.RandomizableSingleClassifierEnhancer

weka.classifiers.meta.FilteredClassifier

weka.classifiers.meta.RandomizableFilteredClassifier

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, WeightedAttributesHandler, WeightedInstancesHandler

public class RandomizableFilteredClassifier
extends FilteredClassifier

Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.

Valid options are:

 -F <filter specification>
  Full class name of filter to use, followed
  by filter options.
  default: "weka.filters.unsupervised.attribute.RandomProjection -N 10 -D Sparse1"

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.lazy.IBk)

 -S num
 Set the random number seed (default 1). 

 
 Options specific to classifier weka.classifiers.lazy.IBk:
 

 -I
  Weight neighbours by the inverse of their distance
  (use when k > 1)

 -F
  Weight neighbours by 1 - their distance
  (use when k > 1)

 -K <number of neighbors>
  Number of nearest neighbours (k) used in classification.
  (Default = 1)

 -E
  Minimise mean squared error rather than mean absolute
  error when using -X option with numeric prediction.

 -W <window size>
  Maximum number of training instances maintained.
  Training instances are dropped FIFO. (Default = no window)

 -X
  Select the number of nearest neighbours between 1
  and the k value specified using hold-one-out evaluation
  on the training data (use when k > 1)

 -A
  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
 

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.meta.FilteredClassifier

m_DoNotCheckForModifiedClassAttribute, m_Filter, m_FilteredInstances, m_ReorderFiltered, m_ReorderOriginal

Fields inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

m_Seed

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

m_Classifier

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.core.Drawable

BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors

Constructor and Description
RandomizableFilteredClassifier() Default constructor.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Build the classifier on the filtered data.
protected java.lang.String	defaultClassifierString() String describing default classifier.
protected java.lang.String	defaultFilterString() String describing default filter.
java.lang.String	getRevision() Returns the revision string.
java.lang.String	globalInfo() Returns a string describing this classifier
void	initializeClassifier(Instances data) Initializes an iterative classifier.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	toString() Output a representation of this classifier

Methods inherited from class weka.classifiers.meta.FilteredClassifier

batchSizeTipText, distributionForInstance, distributionsForInstances, done, doNotCheckForModifiedClassAttributeTipText, filterInstance, filterTipText, generatePartition, getBatchSize, getCapabilities, getDoNotCheckForModifiedClassAttribute, getFilter, getFilterSpec, getMembershipValues, getOptions, graph, graphType, implementsMoreEfficientBatchPrediction, listOptions, next, numElements, resampleAttributes, setBatchSize, setDoNotCheckForModifiedClassAttribute, setFilter, setOptions, setUp

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.classifiers.Classifier

classifyInstance

Constructor Detail

RandomizableFilteredClassifier

public RandomizableFilteredClassifier()

Default constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Overrides:

globalInfo in class FilteredClassifier

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

defaultClassifierString

protected java.lang.String defaultClassifierString()

String describing default classifier.

Overrides:

defaultClassifierString in class FilteredClassifier

Returns:

the default classifier classname

defaultFilterString

protected java.lang.String defaultFilterString()

String describing default filter.

Overrides:

defaultFilterString in class FilteredClassifier

initializeClassifier

public void initializeClassifier(Instances data)
                          throws java.lang.Exception

Initializes an iterative classifier. (If the base classifier supports this.)

Specified by:

initializeClassifier in interface IterativeClassifier

Overrides:

initializeClassifier in class FilteredClassifier

Parameters:

data - the instances to be used in induction

Throws:

java.lang.Exception - if the model cannot be initialized

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Build the classifier on the filtered data.

Specified by:

buildClassifier in interface Classifier

Overrides:

buildClassifier in class FilteredClassifier

Parameters:

data - the training data

Throws:

java.lang.Exception - if the classifier could not be built successfully

toString

public java.lang.String toString()

Output a representation of this classifier

Overrides:

toString in class FilteredClassifier

Returns:

a representation of this classifier

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class FilteredClassifier

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain the following arguments: -t training file [-T test file] [-c class index]