List of cluster() Examples

Examples of cluster()

cc.mallet.cluster.Clusterer.cluster()
Return a clustering of an InstanceList
cc.mallet.cluster.GreedyAgglomerativeByDensity.cluster()
co.paralleluniverse.galaxy.Grid.cluster()
Returns the grid's cluster management and node lifecycle service. @return The grid's cluster management and node lifecycle service.
com.dotcms.repackage.org.elasticsearch.client.AdminClient.cluster()
edu.cmu.sphinx.speakerid.SpeakerIdentification.cluster()
@param inputFileName The name of the file used for diarization @return A cluster for each speaker found in the input file
edu.gslis.ttg.clusters.clusterers.SimpleJaccardClusterer.cluster()
edu.ucla.sspace.clustering.Clustering.cluster()
Clusters the set of rows in the given {@code Matrix} without a specifiednumber of clusters (optional operation). The set of cluster assignments are returned for each row in the matrix. @param matrix the {@link Matrix} whose row data points are to beclustered @param props the properties to use for any parameters each clusteringalgorithm may need @return an array of {@link Assignment} instances that indicate zero ormore clusters to which each row belongs.
edu.ucla.sspace.graph.LinkClustering.cluster()
Computes the similarity of the graph's edges and merges them until the specified number of clusters has been reached. @param numClusters the number of clusters to return
net.kuujo.vertigo.network.NetworkContext.cluster()
Returns the address of the cluster to which the network belongs. @return The address of the cluster to which the network belongs.
org.apache.kafka.common.requests.MetadataResponse.cluster()
org.apache.mahout.clustering.dirichlet.DirichletClusterer.cluster()
Iterate over the sample data, obtaining cluster samples periodically and returning them. @param numIterations the int number of iterations to perform @return a List>> of the observed models
org.apache.mahout.clustering.streaming.cluster.BallKMeans.cluster()
Clusters the datapoints in the list doing either random seeding of the centroids or k-means++. @param datapoints the points to be clustered. @return an UpdatableSearcher with the resulting clusters.
org.apache.mahout.clustering.streaming.cluster.StreamingKMeans.cluster()
Cluster the rows of a matrix, treating them as Centroids with weight 1. @param data matrix whose rows are to be clustered. @return the UpdatableSearcher containing the resulting centroids.

Examples of edu.gslis.ttg.clusters.clusterers.SimpleJaccardClusterer.cluster()

      
      // sweep through jaccard steps, calculating F1
      double maxF1 = 0;
      double maxF1Threshold = 1;
      for (double j = 1.0; j >= 0.0; j -= stepSize) { // for each jaccard threshold step
        Clusters clusters = clusterer.cluster(j);
        
        // all clusters are created now, get a finalized set of results
        Set<Long> allResults = new HashSet<Long>(seenResults.keySet());
        allResults.removeAll(clusters.getAllClusteredResults()); // allResults includes unclustered plus one representative from each cluster
        for (Cluster c : clusters) {

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Examples of edu.gslis.ttg.clusters.clusterers.SimpleJaccardClusterer.cluster()

      client = new TrecSearchThriftClient(params.getParamValue(HOST_OPTION), testingPort, group, token);
      searcher = new SimpleSearcher(client, numResults);
      Map<Long, TResult> seenResults = searcher.search(query);
      
      SimpleJaccardClusterer clusterer = new SimpleJaccardClusterer(new ArrayList<TResult>(seenResults.values()));
      Clusters clusters = clusterer.cluster(averageThreshold);
      
      // all clusters are created now, get a finalized set of results
      Set<Long> allResults = new HashSet<Long>(seenResults.keySet());
      allResults.removeAll(clusters.getAllClusteredResults()); // allResults includes unclustered plus one representative from each cluster
      for (Cluster c : clusters) {

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Examples of edu.ucla.sspace.clustering.Clustering.cluster()

        Properties props = System.getProperties();
        Assignments assignments = null;
        if (sparseVectors.size() > 0) {
            SparseMatrix matrix = Matrices.asSparseMatrix(sparseVectors);
            assignments = (numClusters > 0) 
                ? clustering.cluster(matrix, numClusters, props)
                : clustering.cluster(matrix, props);
        } else {
            Matrix matrix = Matrices.asMatrix(vectors);
            assignments = (numClusters > 0) 
                ? clustering.cluster(matrix, numClusters, props)

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Examples of edu.ucla.sspace.clustering.Clustering.cluster()

        Assignments assignments = null;
        if (sparseVectors.size() > 0) {
            SparseMatrix matrix = Matrices.asSparseMatrix(sparseVectors);
            assignments = (numClusters > 0) 
                ? clustering.cluster(matrix, numClusters, props)
                : clustering.cluster(matrix, props);
        } else {
            Matrix matrix = Matrices.asMatrix(vectors);
            assignments = (numClusters > 0) 
                ? clustering.cluster(matrix, numClusters, props)
                : clustering.cluster(matrix, props);

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Examples of edu.ucla.sspace.clustering.Clustering.cluster()

                ? clustering.cluster(matrix, numClusters, props)
                : clustering.cluster(matrix, props);
        } else {
            Matrix matrix = Matrices.asMatrix(vectors);
            assignments = (numClusters > 0) 
                ? clustering.cluster(matrix, numClusters, props)
                : clustering.cluster(matrix, props);
        }


        int a = 0;
        for (String word : words) {

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Examples of edu.ucla.sspace.clustering.Clustering.cluster()

                : clustering.cluster(matrix, props);
        } else {
            Matrix matrix = Matrices.asMatrix(vectors);
            assignments = (numClusters > 0) 
                ? clustering.cluster(matrix, numClusters, props)
                : clustering.cluster(matrix, props);
        }


        int a = 0;
        for (String word : words) {
            Assignment assignment = assignments.get(a++);

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Examples of edu.ucla.sspace.graph.LinkClustering.cluster()

            else
                GraphIO.readUndirected(f, vertexLabels);


            LinkClustering lc = new LinkClustering();
            MultiMap<Integer,Integer> clusterToVertices = 
                lc.cluster(graph, System.getProperties());


            PrintWriter pw = new PrintWriter(
                new BufferedOutputStream(new FileOutputStream(
                                         opts.getPositionalArg(1))));

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Examples of net.kuujo.vertigo.network.NetworkContext.cluster()


  @SuppressWarnings("unchecked")
  private static <T extends Context<T>> T deserialize(String uri, JsonObject context) {
    ContextUri curi = new ContextUri(uri);
    NetworkContext network = SerializerFactory.getSerializer(Context.class).deserializeObject(context, NetworkContext.class);
    if (!curi.getCluster().equals(network.cluster()) || !curi.getNetwork().equals(network.name())) {
      throw new IllegalArgumentException("The given URI does not match the given context configuration");
    }


    if (curi.hasComponent()) {
      ComponentContext<?> component = network.component(curi.getComponent());

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Examples of org.apache.kafka.common.requests.MetadataResponse.cluster()

    }


    private void handleMetadataResponse(RequestHeader header, Struct body, long now) {
        this.metadataFetchInProgress = false;
        MetadataResponse response = new MetadataResponse(body);
        Cluster cluster = response.cluster();
        // don't update the cluster if there are no valid nodes...the topic we want may still be in the process of being
        // created which means we will get errors and no nodes until it exists
        if (cluster.nodes().size() > 0)
            this.metadata.update(cluster, now);
        else

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Examples of org.apache.mahout.clustering.dirichlet.DirichletClusterer.cluster()

    }


    DirichletClusterer dc = new DirichletClusterer(points,
        new GaussianClusterDistribution(new VectorWritable(
            new DenseVector(2))), 1.0, 10, 2, 2);
    List<Cluster[]> result = dc.cluster(20);
    for (Cluster cluster : result.get(result.size() - 1)) {
      System.out.println("Cluster id: " + cluster.getId() + " center: "
          + cluster.getCenter().asFormatString());
    }
  }

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