Package org.apache.mahout.clustering.iterator

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

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  }
 
  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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    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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    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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    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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  }
 
  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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    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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    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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    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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  }
 
  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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    clusterModels = new ArrayList<Cluster>();
   
    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();
    weightedVW = new WeightedVectorWritable(1, null);
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Related Classes of org.apache.mahout.clustering.iterator.ClusteringPolicy

  • org.apache.mahout.clustering.classify.ClusterClassificationDriver
  • org.apache.mahout.clustering.classify.ClusterClassificationMapper
  • org.apache.mahout.clustering.fuzzykmeans.FuzzyKMeansDriver
  • org.apache.mahout.clustering.kmeans.KMeansDriver
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