Examples of ClusteringPolicy

org.apache.mahout.clustering.ClusteringPolicy
A ClusteringPolicy captures the semantics of assignment of points to clusters
org.apache.mahout.clustering.iterator.ClusteringPolicy
A ClusteringPolicy captures the semantics of assignment of points to clusters

Examples of org.apache.mahout.clustering.ClusteringPolicy

    ClusterClassifier prior = new ClusterClassifier(initialClusters);
    Path priorClassifier = new Path(output, "clusters-0");
    writeClassifier(prior, conf, priorClassifier);
    
    int maxIter = 10;
    ClusteringPolicy policy = new KMeansClusteringPolicy();
    new ClusterIterator(policy).iterateSeq(samples, priorClassifier, output, maxIter);
    for (int i = 1; i <= maxIter; i++) {
      ClusterClassifier posterior = readClassifier(conf, new Path(output, "classifier-" + i));
      CLUSTERS.add(posterior.getModels());
    }

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Examples of org.apache.mahout.clustering.ClusteringPolicy

    }
    ClusterClassifier prior = new ClusterClassifier(initialClusters);
    Path priorClassifier = new Path(output, "classifier-0");
    writeClassifier(prior, conf, priorClassifier);
    
    ClusteringPolicy policy = new FuzzyKMeansClusteringPolicy();
    new ClusterIterator(policy).iterateSeq(samples, priorClassifier, output, maxIterations);
    for (int i = 1; i <= maxIterations; i++) {
      ClusterClassifier posterior = readClassifier(conf, new Path(output, "classifier-" + i));
      CLUSTERS.add(posterior.getModels());
    }

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Examples of org.apache.mahout.clustering.ClusteringPolicy

    Path output = new Path("output");
    Path priorClassifier = new Path(output, "clusters-0");
    Configuration conf = new Configuration();
    writeClassifier(prior, conf, priorClassifier);
    
    ClusteringPolicy policy = new DirichletClusteringPolicy(numClusters, numIterations);
    new ClusterIterator(policy).iterateSeq(samples, priorClassifier, output, numIterations);
    for (int i = 1; i <= numIterations; i++) {
      ClusterClassifier posterior = readClassifier(conf, new Path(output, "classifier-" + i));
      List<Cluster> clusters = Lists.newArrayList();
      for (Cluster cluster : posterior.getModels()) {

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Examples of org.apache.mahout.clustering.ClusteringPolicy

    }
    ClusterClassifier prior = new ClusterClassifier(initialClusters);
    Path priorClassifier = new Path(output, "classifier-0");
    writeClassifier(prior, conf, priorClassifier);
    
    ClusteringPolicy policy = new FuzzyKMeansClusteringPolicy();
    new ClusterIterator(policy).iterate(samples, priorClassifier, output, maxIterations);
    for (int i = 1; i <= maxIterations; i++) {
      ClusterClassifier posterior = readClassifier(conf, new Path(output, "classifier-" + i));
      CLUSTERS.add(posterior.getModels());
    }

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Examples of org.apache.mahout.clustering.ClusteringPolicy

    Path output = new Path("output");
    Path priorClassifier = new Path(output, "clusters-0");
    Configuration conf = new Configuration();
    writeClassifier(prior, conf, priorClassifier);
    
    ClusteringPolicy policy = new DirichletClusteringPolicy(numClusters, numIterations);
    new ClusterIterator(policy).iterate(samples, priorClassifier, output, numIterations);
    for (int i = 1; i <= numIterations; i++) {
      ClusterClassifier posterior = readClassifier(conf, new Path(output, "classifier-" + i));
      List<Cluster> clusters = new ArrayList<Cluster>();    
      for (Cluster cluster : posterior.getModels()) {

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Examples of org.apache.mahout.clustering.ClusteringPolicy

    ClusterClassifier prior = new ClusterClassifier(initialClusters);
    Path priorClassifier = new Path(output, "clusters-0");
    writeClassifier(prior, conf, priorClassifier);
    
    int maxIter = 10;
    ClusteringPolicy policy = new KMeansClusteringPolicy();
    new ClusterIterator(policy).iterate(samples, priorClassifier, output, maxIter);
    for (int i = 1; i <= maxIter; i++) {
      ClusterClassifier posterior = readClassifier(conf, new Path(output, "classifier-" + i));
      CLUSTERS.add(posterior.getModels());
    }

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Examples of org.apache.mahout.clustering.iterator.ClusteringPolicy

  }
  
  private static void classifyClusterSeq(Configuration conf, Path input, Path clusters, Path output,
      Double clusterClassificationThreshold, boolean emitMostLikely) throws IOException {
    List<Cluster> clusterModels = populateClusterModels(clusters, conf);
    ClusteringPolicy policy = ClusterClassifier.readPolicy(finalClustersPath(conf, clusters));
    ClusterClassifier clusterClassifier = new ClusterClassifier(clusterModels, policy);
    selectCluster(input, clusterModels, clusterClassifier, output, clusterClassificationThreshold, emitMostLikely);
    
  }

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Examples of org.apache.mahout.clustering.iterator.ClusteringPolicy

    clusterModels = Lists.newArrayList();
    
    if (clustersIn != null && !clustersIn.isEmpty()) {
      Path clustersInPath = new Path(clustersIn);
      clusterModels = populateClusterModels(clustersInPath, conf);
      ClusteringPolicy policy = ClusterClassifier
          .readPolicy(finalClustersPath(clustersInPath));
      clusterClassifier = new ClusterClassifier(clusterModels, policy);
    }
    clusterId = new IntWritable();
  }

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Examples of org.apache.mahout.clustering.iterator.ClusteringPolicy

    if (clusters.isEmpty()) {
      throw new IllegalStateException("No input clusters found in " + clustersIn + ". Check your -c argument.");
    }
    
    Path priorClustersPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR);
    ClusteringPolicy policy = new KMeansClusteringPolicy(convergenceDelta);
    ClusterClassifier prior = new ClusterClassifier(clusters, policy);
    prior.writeToSeqFiles(priorClustersPath);
    
    if (runSequential) {
      ClusterIterator.iterateSeq(conf, input, priorClustersPath, output, maxIterations);

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Examples of org.apache.mahout.clustering.iterator.ClusteringPolicy

    if (clusters.isEmpty()) {
      throw new IllegalStateException("No input clusters found in " + clustersIn + ". Check your -c argument.");
    }
    
    Path priorClustersPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR);   
    ClusteringPolicy policy = new FuzzyKMeansClusteringPolicy(m, convergenceDelta);
    ClusterClassifier prior = new ClusterClassifier(clusters, policy);
    prior.writeToSeqFiles(priorClustersPath);
    
    if (runSequential) {
      ClusterIterator.iterateSeq(conf, input, priorClustersPath, output, maxIterations);

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