List of clusterInstance() Examples

Examples of clusterInstance()

weka.clusterers.AbstractClusterer.clusterInstance()
Classifies a given instance. Either this or distributionForInstance() needs to be implemented by subclasses. @param instance the instance to be assigned to a cluster @return the number of the assigned cluster as an integer @exception Exception if instance could not be clusteredsuccessfully
weka.clusterers.EM.clusterInstance()
weka.clusterers.FarthestFirst.clusterInstance()
Classifies a given instance. @param instance the instance to be assigned to a cluster @return the number of the assigned cluster as an integerif the class is enumerated, otherwise the predicted value @throws Exception if instance could not be classifiedsuccessfully
weka.clusterers.SimpleKMeans.clusterInstance()
Classifies a given instance. @param instance the instance to be assigned to a cluster @return the number of the assigned cluster as an intergerif the class is enumerated, otherwise the predicted value @throws Exception if instance could not be classifiedsuccessfully

Examples of weka.clusterers.AbstractClusterer.clusterInstance()

            assert(txn_trace != null);
            Integer base_partition = this.getBasePartition(txn_trace);
            // Skip any txn that executes on a partition that we're not evaluating
            if (this.all_partitions.contains(base_partition) == false) continue;
            
            int c = (int)clusterer.clusterInstance(inst);


            // Debug Stuff
//            Pair<Long, Integer> key = Pair.of((Long)txn_trace.getParam(1), ((Object[])txn_trace.getParam(4)).length);
//            if (key_to_cluster.containsKey(key) == false) key_to_cluster.put(key, new Histogram());
//            key_to_cluster.get(key).put(c);

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Examples of weka.clusterers.AbstractClusterer.clusterInstance()

            assertNotNull(txn_trace);
            Integer base_partition = p_estimator.getBasePartition(txn_trace);
            assertNotNull(base_partition);
            assertEquals(base_partition.intValue(), (int)inst.value(base_partition_idx));


            int c = clusterer.clusterInstance(inst);
            Histogram<Integer> h = p_c_xref.get(base_partition);
            if (h == null) {
                h = new ObjectHistogram<Integer>();
                p_c_xref.put(base_partition, h);
            }

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Examples of weka.clusterers.EM.clusterInstance()

    try {
      clusterer.setOptions(options);
      clusterer.buildClusterer(dataset);
      Enumeration e = dataset.enumerateInstances();
      while (e.hasMoreElements()) {
        clusters.add(clusterer.clusterInstance((Instance) e
            .nextElement()));
      }
      ClusteringResult result = new ClusteringResult(clusters, null,
          clusterer.numberOfClusters(), parameters.getParameter(
              EMClustererParameters.visualization).getValue());

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Examples of weka.clusterers.FarthestFirst.clusterInstance()

    try {
      clusterer.setOptions(options);
      clusterer.buildClusterer(dataset);
      Enumeration e = dataset.enumerateInstances();
      while (e.hasMoreElements()) {
        clusters.add(clusterer.clusterInstance((Instance) e
            .nextElement()));
      }
      ClusteringResult result = new ClusteringResult(clusters, null,
          clusterer.numberOfClusters(), parameters.getParameter(
              EMClustererParameters.visualization).getValue());

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Examples of weka.clusterers.SimpleKMeans.clusterInstance()

    try {
      clusterer.setOptions(options);
      clusterer.buildClusterer(dataset);
      Enumeration e = dataset.enumerateInstances();
      while (e.hasMoreElements()) {
        clusters.add(clusterer.clusterInstance((Instance) e
            .nextElement()));
      }
      ClusteringResult result = new ClusteringResult(clusters, null,
          clusterer.numberOfClusters(), parameters.getParameter(
              EMClustererParameters.visualization).getValue());

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