HARAMNetwork (meka 1.9.1 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - meka.classifiers.multilabel.neurofuzzy.ARAMNetworkClass
    - - meka.classifiers.multilabel.neurofuzzy.HARAMNetwork

All Implemented Interfaces:

Serializable, Cloneable, IncrementalMultiLabelClassifier, MultiLabelClassifier, MultiLabelClassifierThreaded, weka.classifiers.Classifier, weka.classifiers.UpdateableClassifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler
```
public class HARAMNetwork
extends ARAMNetworkClass
implements weka.core.OptionHandler, weka.core.WeightedInstancesHandler, weka.classifiers.UpdateableClassifier, weka.core.Randomizable, weka.core.TechnicalInformationHandler, MultiLabelClassifier
```
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION. Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) -- if you need the UpdateableClassifier functionality, use the NaiveBayesUpdateable classifier. The NaiveBayesUpdateable classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.
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.

Version:

$Revision: 1.16 $ Modified by Rushi for use as a CN710 template

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz), Rushi Bhatt (rushi@cns.bu.edu)

See Also:

Serialized Form

Field Summary
- Fields inherited from class meka.classifiers.multilabel.neurofuzzy.ARAMNetworkClass
  activity_report, neuronsactivated, neuronsactivity, numClasses, numFeatures, threshold
- 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

Constructor Summary

Constructors
Constructor and Description
`HARAMNetwork()`
`HARAMNetwork(int fnumFeatures, int fnumClasses, double fro, double fthreshold, double cvig)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
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)`
`double`	`getClusterVigilance()`
`String`	`getModel()` Returns a string representation of the model.
`String[]`	`getOptions()` Gets the current settings of the classifier.
`int`	`getSeed()`
`weka.core.TechnicalInformation`	`getTechnicalInformation()`
`double`	`getThreshold()`
`double`	`getVigilancy()`
`String`	`globalInfo()` Description to display in the GUI.
`boolean`	`isThreaded()`
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)`
`void`	`PrepareHClusters()`
`void`	`setClusterVigilance(double fclustervig)`
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setSeed(int seed)`
`void`	`setThreaded(boolean setv)`
`void`	`setThreshold(double fthreshold)`
`void`	`setVigilancy(double vigilancy)`
`String`	`toString()` Returns a description of the classifier.
`void`	`updateClassifier(weka.core.Instance instance)` Updates the classifier with the given instance.

Methods inherited from class meka.classifiers.multilabel.neurofuzzy.ARAMNetworkClass
testCapabilities

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, defaultClassifierOptions, defaultClassifierString, getCapabilities, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getRevision, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface meka.classifiers.multilabel.MultiLabelClassifier
debugTipText, getDebug, setDebug

Methods inherited from interface weka.classifiers.Classifier
getCapabilities

- Constructor Detail
  - HARAMNetwork
```
public HARAMNetwork(int fnumFeatures,
                    int fnumClasses,
                    double fro,
                    double fthreshold,
                    double cvig)
```
  - HARAMNetwork
```
public HARAMNetwork()
```
- Method Detail
  - buildClassifier
```
public void buildClassifier(weka.core.Instances D)
                     throws Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    instances - set of instances serving as training data
    
    Throws:
    
    Exception - if the classifier has not been generated successfully
  - updateClassifier
```
public void updateClassifier(weka.core.Instance instance)
                      throws Exception
```
    Updates the classifier with the given instance.
    
    Specified by:
    
    updateClassifier in interface weka.classifiers.UpdateableClassifier
    
    Parameters:
    
    instance - the new training instance to include in the model
    
    Throws:
    
    Exception - if the instance could not be incorporated in the model.
  - PrepareHClusters
```
public void PrepareHClusters()
```
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    Exception - if there is a problem generating the prediction
  - ARAMm_Ranking2Class
```
public double[] ARAMm_Ranking2Class(double[] rankings)
```
  - classifyInstance
```
public double classifyInstance(weka.core.Instance instance)
                        throws Exception
```
    Classifies the given test instance. The instance has to belong to a dataset when it's being classified. Note that a classifier MUST implement either this or distributionForInstance().
    
    Specified by:
    
    classifyInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    classifyInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the predicted most likely class for the instance or Instance.missingValue() if no prediction is made
    
    Throws:
    
    Exception - if an error occurred during the prediction
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.SingleClassifierEnhancer
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options. 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.
    
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.SingleClassifierEnhancer
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.SingleClassifierEnhancer
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - toString
```
public String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a description of the classifier as a string.
  - main
```
public static void main(String[] argv)
```
  - isThreaded
```
public boolean isThreaded()
```
    Specified by:
    
    isThreaded in interface MultiLabelClassifierThreaded
  - setThreaded
```
public void setThreaded(boolean setv)
```
    Specified by:
    
    setThreaded in interface MultiLabelClassifierThreaded
  - distributionForInstanceM
```
public double[][] distributionForInstanceM(weka.core.Instances i)
                                    throws Exception
```
    Specified by:
    
    distributionForInstanceM in interface MultiLabelClassifierThreaded
    
    Throws:
    
    Exception
  - getVigilancy
```
public double getVigilancy()
```
  - setVigilancy
```
public void setVigilancy(double vigilancy)
```
  - setThreshold
```
public void setThreshold(double fthreshold)
```
  - getThreshold
```
public double getThreshold()
```
  - setClusterVigilance
```
public void setClusterVigilance(double fclustervig)
```
  - getClusterVigilance
```
public double getClusterVigilance()
```
  - globalInfo
```
public String globalInfo()
```
    Description to display in the GUI.
    
    Returns:
    
    the description
  - getTechnicalInformation
```
public weka.core.TechnicalInformation getTechnicalInformation()
```
    Specified by:
    
    getTechnicalInformation in interface weka.core.TechnicalInformationHandler
  - setSeed
```
public void setSeed(int seed)
```
    Specified by:
    
    setSeed in interface weka.core.Randomizable
  - getSeed
```
public int getSeed()
```
    Specified by:
    
    getSeed in interface weka.core.Randomizable
  - getModel
```
public String getModel()
```
    Description copied from interface: MultiLabelClassifier
    
    Returns a string representation of the model.
    
    Specified by:
    
    getModel in interface MultiLabelClassifier
    
    Returns:
    
    the model

Class HARAMNetwork

Field Summary

Fields inherited from class meka.classifiers.multilabel.neurofuzzy.ARAMNetworkClass

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class meka.classifiers.multilabel.neurofuzzy.ARAMNetworkClass

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface meka.classifiers.multilabel.MultiLabelClassifier

Methods inherited from interface weka.classifiers.Classifier

Constructor Detail

HARAMNetwork

HARAMNetwork

Method Detail

buildClassifier

updateClassifier

PrepareHClusters

distributionForInstance

ARAMm_Ranking2Class

classifyInstance

listOptions

setOptions

getOptions

toString

main

isThreaded

setThreaded

distributionForInstanceM

getVigilancy

setVigilancy

setThreshold

getThreshold

setClusterVigilance

getClusterVigilance

globalInfo

getTechnicalInformation

setSeed

getSeed

getModel