relational.classifier
Class ProbRNMultiHopMultiLabel

java.lang.Object
  extended byweka.classifiers.Classifier
      extended byrelational.classifier.RNCMultiLabel
          extended byrelational.classifier.ProbRNMultiLabel
              extended byrelational.classifier.ProbRNMultiHopMultiLabel
All Implemented Interfaces:
java.lang.Cloneable, weka.core.OptionHandler, java.io.Serializable

public class ProbRNMultiHopMultiLabel
extends ProbRNMultiLabel

See Also:
Serialized Form

Field Summary
 
Fields inherited from class relational.classifier.ProbRNMultiLabel
priors, train
 
Fields inherited from class relational.classifier.RNCMultiLabel
maxProb, mostProbableClass, numClasses, numInClasses, numWithoutNeighbors
 
Constructor Summary
ProbRNMultiHopMultiLabel()
           
ProbRNMultiHopMultiLabel(double minWeight, int neighb)
           
 
Method Summary
 double[] distributionForInstance(edu.uci.ics.jung.graph.impl.SparseGraph graph, weka.core.Instance inst, java.util.HashMap id2ClassProb, int label, java.util.HashMap weights, java.util.HashMap normWeights, double minWeight, java.lang.String type, int numNeighbors, int hops)
          Calculates the class probability distribution for an instance using PRNMultiHop (binary classification)
 
Methods inherited from class relational.classifier.ProbRNMultiLabel
buildClassifier, classifyInstance, distributionForInstance, distributionForInstanceGeometricMean
 
Methods inherited from class relational.classifier.RNCMultiLabel
classifyInstance, computeNumLabeledInstances, computePriors, distributionForInstance
 
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, distributionForInstance, forName, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

ProbRNMultiHopMultiLabel

public ProbRNMultiHopMultiLabel()

ProbRNMultiHopMultiLabel

public ProbRNMultiHopMultiLabel(double minWeight,
                                int neighb)
Method Detail

distributionForInstance

public double[] distributionForInstance(edu.uci.ics.jung.graph.impl.SparseGraph graph,
                                        weka.core.Instance inst,
                                        java.util.HashMap id2ClassProb,
                                        int label,
                                        java.util.HashMap weights,
                                        java.util.HashMap normWeights,
                                        double minWeight,
                                        java.lang.String type,
                                        int numNeighbors,
                                        int hops)
                                 throws java.lang.Exception
Calculates the class probability distribution for an instance using PRNMultiHop (binary classification)

Parameters:
graph - - graph (jung)
inst - - instance to be classified
id2ClassProb - - initialization of test instances
label - - the category to be considered
weights - - the weights of the edges in the graph
normWeights - - normalized weights
minWeight - - the minimal weight which an edge has to have in order to be considered in PRNMultiHop
type - - indicates the type of problem (heterogenous or homogenous)
numNeighbors - - determines the number of neighbors for which additional indirect neighbors will be considered using PRNMultiHop
Returns:
class probability distribution
Throws:
java.lang.Exception