public class RandomizableFilteredClassifier extends FilteredClassifier
-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).
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT
BayesNet, Newick, NOT_DRAWABLE, TREE
Constructor and Description |
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RandomizableFilteredClassifier()
Default constructor.
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Modifier and Type | Method and Description |
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void |
buildClassifier(Instances data)
Build the classifier on the filtered data.
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java.lang.String |
getRevision()
Returns the revision string.
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java.lang.String |
globalInfo()
Returns a string describing this classifier
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void |
initializeClassifier(Instances data)
Initializes an iterative classifier.
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static void |
main(java.lang.String[] argv)
Main method for testing this class.
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java.lang.String |
toString()
Output a representation of this classifier
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batchSizeTipText, distributionForInstance, distributionsForInstances, done, doNotCheckForModifiedClassAttributeTipText, filterTipText, generatePartition, getBatchSize, getCapabilities, getDoNotCheckForModifiedClassAttribute, getFilter, getMembershipValues, getOptions, getResume, graph, graphType, implementsMoreEfficientBatchPrediction, listOptions, next, numElements, resumeTipText, setBatchSize, setDoNotCheckForModifiedClassAttribute, setFilter, setOptions, setResume
getSeed, seedTipText, setSeed
classifierTipText, getClassifier, postExecution, preExecution, setClassifier
classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
classifyInstance
makeCopy
public RandomizableFilteredClassifier()
public java.lang.String globalInfo()
globalInfo
in class FilteredClassifier
public void initializeClassifier(Instances data) throws java.lang.Exception
initializeClassifier
in interface IterativeClassifier
initializeClassifier
in class FilteredClassifier
data
- the instances to be used in inductionjava.lang.Exception
- if the model cannot be initializedpublic void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in interface Classifier
buildClassifier
in class FilteredClassifier
data
- the training datajava.lang.Exception
- if the classifier could not be built successfullypublic java.lang.String toString()
toString
in class FilteredClassifier
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class FilteredClassifier
public static void main(java.lang.String[] argv)
argv
- should contain the following arguments:
-t training file [-T test file] [-c class index]