Package org.apache.mahout.clustering.iterator

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

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    List<Cluster> models = Lists.newArrayList();
    for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(numClusters)) {
      models.add((Cluster) cluster);
    }
   
    ClusterClassifier prior = new ClusterClassifier(models, new DirichletClusteringPolicy(numClusters, alpha0));
    prior.writeToSeqFiles(clustersIn);
   
    if (runSequential) {
      ClusterIterator.iterateSeq(conf, input, clustersIn, output, maxIterations);
    } else {
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   *          execute sequentially if true
   */
  public static void clusterData(Configuration conf, Path input, Path stateIn, Path output, double alpha0,
      int numModels, boolean emitMostLikely, double threshold, boolean runSequential) throws IOException,
      InterruptedException, ClassNotFoundException {
    ClusterClassifier.writePolicy(new DirichletClusteringPolicy(numModels, alpha0), stateIn);
    ClusterClassificationDriver.run(conf, input, output, new Path(output, PathDirectory.CLUSTERED_POINTS_DIRECTORY),
        threshold, emitMostLikely, runSequential);
  }
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    throws IOException {
    List<Cluster> models = Lists.newArrayList();
    for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(numClusters)) {
      models.add((Cluster) cluster);
    }
    ClusterClassifier prior = new ClusterClassifier(models, new DirichletClusteringPolicy(numClusters, alpha0));
    Path priorPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR);
    prior.writeToSeqFiles(priorPath);
    Configuration conf = new Configuration();
    ClusterIterator.iterateSeq(conf, input, priorPath, output, numIterations);
  }
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    ModelDistribution<VectorWritable> modelDist = description.createModelDistribution(new Configuration());
    for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(15)) {
      models.add((Cluster) cluster);
    }
   
    ClusterClassifier classifier = new ClusterClassifier(models, new DirichletClusteringPolicy(15, 1.0));
    ClusterClassifier posterior = ClusterIterator.iterate(sampleData, classifier, 10);
   
    printClusters(posterior.getModels(), DOCS);
  }
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    ModelDistribution<VectorWritable> modelDist = description.createModelDistribution(new Configuration());
    for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(15)) {
      models.add((Cluster) cluster);
    }
   
    ClusterClassifier classifier = new ClusterClassifier(models, new DirichletClusteringPolicy(15, 1.0));
    ClusterClassifier posterior = ClusterIterator.iterate(sampleData, classifier, 10);
   
    printClusters(posterior.getModels(), DOCS);
  }
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    ModelDistribution<VectorWritable> modelDist = description.createModelDistribution(new Configuration());
    for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(15)) {
      models.add((Cluster) cluster);
    }
   
    ClusterClassifier classifier = new ClusterClassifier(models, new DirichletClusteringPolicy(15, 1.0));
    ClusterClassifier posterior = ClusterIterator.iterate(sampleData, classifier, 10);
   
    printClusters(posterior.getModels(), DOCS2);
  }
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    ModelDistribution<VectorWritable> modelDist = description.createModelDistribution(new Configuration());
    for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(15)) {
      models.add((Cluster) cluster);
    }
   
    ClusterClassifier classifier = new ClusterClassifier(models, new DirichletClusteringPolicy(15, 1.0));
    ClusterClassifier posterior = ClusterIterator.iterate(sampleData, classifier, 10);
   
    printClusters(posterior.getModels(), DOCS2);
  }
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    for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(15)) {
      models.add((Cluster) cluster);
    }
   
    ClusterIterator iterator = new ClusterIterator();
    ClusterClassifier classifier = new ClusterClassifier(models, new DirichletClusteringPolicy(15, 1.0));
    ClusterClassifier posterior = iterator.iterate(sampleData, classifier, 10);
   
    printClusters(posterior.getModels(), DOCS);
  }
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    for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(15)) {
      models.add((Cluster) cluster);
    }
   
    ClusterIterator iterator = new ClusterIterator();
    ClusterClassifier classifier = new ClusterClassifier(models, new DirichletClusteringPolicy(15, 1.0));
    ClusterClassifier posterior = iterator.iterate(sampleData, classifier, 10);
   
    printClusters(posterior.getModels(), DOCS);
  }
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    for (Model<VectorWritable> cluster : modelDist.sampleFromPrior(15)) {
      models.add((Cluster) cluster);
    }
   
    ClusterIterator iterator = new ClusterIterator();
    ClusterClassifier classifier = new ClusterClassifier(models, new DirichletClusteringPolicy(15, 1.0));
    ClusterClassifier posterior = iterator.iterate(sampleData, classifier, 10);
   
    printClusters(posterior.getModels(), DOCS2);
  }
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Related Classes of org.apache.mahout.clustering.iterator.DirichletClusteringPolicy

  • org.apache.mahout.clustering.dirichlet.DirichletDriver
  • org.apache.mahout.clustering.dirichlet.TestL1ModelClustering
  • org.apache.mahout.clustering.display.DisplayDirichlet
  • org.apache.mahout.math.DenseVector
  • org.apache.mahout.math.SequentialAccessSparseVector
  • org.apache.mahout.math.Vector
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