public class ARAMNetworkSparseH extends ARAMNetworkClass
For more information on Naive Bayes classifiers, see
George H. John and Pat Langley (1995). Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. pp. 338-345. Morgan Kaufmann, San Mateo.
Valid options are:
-K
Use kernel estimation for modelling numeric attributes rather than
a single normal distribution.
-D
Use supervised discretization to process numeric attributes.
activity_report, neuronsactivated, neuronsactivity, numClasses, numFeatures, threshold
Constructor and Description |
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ARAMNetworkSparseH() |
ARAMNetworkSparseH(int fnumFeatures,
int fnumClasses,
double fro,
double fthreshold) |
Modifier and Type | Method and Description |
---|---|
double[] |
ARAMm_Ranking2Class(double[] rankings) |
void |
buildClassifier(weka.core.Instances D)
Generates the classifier.
|
double |
classifyInstance(weka.core.Instance instance)
Classifies the given test instance.
|
double[] |
distributionForInstance(weka.core.Instance instance)
Calculates the class membership probabilities for the given test
instance.
|
double[][] |
distributionForInstanceM(weka.core.Instances i) |
String |
getModel()
Returns a string representation of the model.
|
String[] |
getOptions()
Gets the current settings of the classifier.
|
String |
globalInfo()
Returns a string describing this classifier
|
boolean |
isThreaded() |
Enumeration |
listOptions()
Returns an enumeration describing the available options.
|
static void |
main(String[] argv) |
void |
setOptions(String[] options)
Parses a given list of options.
|
void |
setThreaded(boolean setv) |
String |
toString()
Returns a description of the classifier.
|
void |
updateClassifier(weka.core.Instance instance)
Updates the classifier with the given instance.
|
testCapabilities
classifierTipText, defaultClassifierOptions, defaultClassifierString, getCapabilities, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier
batchSizeTipText, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getRevision, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
debugTipText, getDebug, setDebug
public ARAMNetworkSparseH(int fnumFeatures, int fnumClasses, double fro, double fthreshold)
public ARAMNetworkSparseH()
public String globalInfo()
public void buildClassifier(weka.core.Instances D) throws Exception
instances
- set of instances serving as training dataException
- if the classifier has not been generated
successfullypublic void updateClassifier(weka.core.Instance instance) throws Exception
instance
- the new training instance to include in the modelException
- if the instance could not be incorporated in
the model.public double[] distributionForInstance(weka.core.Instance instance) throws Exception
distributionForInstance
in interface weka.classifiers.Classifier
distributionForInstance
in class weka.classifiers.AbstractClassifier
instance
- the instance to be classifiedException
- if there is a problem generating the predictionpublic double[] ARAMm_Ranking2Class(double[] rankings)
public double classifyInstance(weka.core.Instance instance) throws Exception
classifyInstance
in interface weka.classifiers.Classifier
classifyInstance
in class weka.classifiers.AbstractClassifier
instance
- the instance to be classifiedException
- if an error occurred during the predictionpublic Enumeration listOptions()
listOptions
in interface weka.core.OptionHandler
listOptions
in class weka.classifiers.SingleClassifierEnhancer
public void setOptions(String[] options) throws Exception
-K
Use kernel estimation for modelling numeric attributes rather than
a single normal distribution.
-D
Use supervised discretization to process numeric attributes.
setOptions
in interface weka.core.OptionHandler
setOptions
in class weka.classifiers.SingleClassifierEnhancer
options
- the list of options as an array of stringsException
- if an option is not supportedpublic String[] getOptions()
getOptions
in interface weka.core.OptionHandler
getOptions
in class weka.classifiers.SingleClassifierEnhancer
public String toString()
public static void main(String[] argv)
public boolean isThreaded()
public void setThreaded(boolean setv)
public double[][] distributionForInstanceM(weka.core.Instances i) throws Exception
Exception
public String getModel()
MultiLabelClassifier
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