Example usage for org.apache.mahout.clustering.iterator ClusterIterator iterateSeq

Introduction

In this page you can find the example usage for org.apache.mahout.clustering.iterator ClusterIterator iterateSeq.

Prototype

public static void iterateSeq(Configuration conf, Path inPath, Path priorPath, Path outPath, int numIterations)
        throws IOException 

Source Link

Document

Iterate over data using a prior-trained ClusterClassifier, for a number of iterations using a sequential implementation

Usage

From source file:DisplayKMeans.java

License:Apache License

private static void runSequentialKMeansClassifier(Configuration conf, Path samples, Path output,
        DistanceMeasure measure, int numClusters, int maxIterations, double convergenceDelta)
        throws IOException {
    Collection<Vector> points = Lists.newArrayList();
    for (int i = 0; i < numClusters; i++) {
        points.add(SAMPLE_DATA.get(i).get());
        //      System.out.println(SAMPLE_DATA.get(i).toString());
    }/*w w  w. ja  va2  s . c  om*/
    List<Cluster> initialClusters = Lists.newArrayList();
    int id = 0;
    for (Vector point : points) {
        initialClusters.add(new org.apache.mahout.clustering.kmeans.Kluster(point, id++, measure));
    }
    ClusterClassifier prior = new ClusterClassifier(initialClusters,
            new KMeansClusteringPolicy(convergenceDelta));
    Path priorPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR);
    prior.writeToSeqFiles(priorPath);

    ClusterIterator.iterateSeq(conf, samples, priorPath, output, maxIterations);
    loadClustersWritable(output);
}

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From source file:DisplayFuzzyKMeans.java

License:Apache License

private static void runSequentialFuzzyKClassifier(Configuration conf, Path samples, Path output,
        DistanceMeasure measure, int numClusters, int maxIterations, float m, double threshold)
        throws IOException {
    Collection<Vector> points = Lists.newArrayList();
    for (int i = 0; i < numClusters; i++) {
        points.add(SAMPLE_DATA.get(i).get());
    }//  ww  w  .j av a 2 s  .c om
    List<Cluster> initialClusters = Lists.newArrayList();
    int id = 0;
    for (Vector point : points) {
        initialClusters.add(new SoftCluster(point, id++, measure));
    }
    ClusterClassifier prior = new ClusterClassifier(initialClusters,
            new FuzzyKMeansClusteringPolicy(m, threshold));
    Path priorPath = new Path(output, "classifier-0");
    prior.writeToSeqFiles(priorPath);

    ClusterIterator.iterateSeq(conf, samples, priorPath, output, maxIterations);
    loadClustersWritable(output);
}

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From source file:curation.mahout_test.DisplayKMeans.java

License:Apache License

private static void runSequentialKMeansClassifier(Configuration conf, Path samples, Path output,
        DistanceMeasure measure, int numClusters, int maxIterations, double convergenceDelta)
        throws IOException {
    Collection<Vector> points = Lists.newArrayList();
    for (int i = 0; i < numClusters; i++) {
        points.add(SAMPLE_DATA.get(i).get());
    }/*w w  w.  j  a  v a2s .c o  m*/
    List<Cluster> initialClusters = Lists.newArrayList();
    int id = 0;
    for (Vector point : points) {
        initialClusters.add(new org.apache.mahout.clustering.kmeans.Kluster(point, id++, measure));
    }
    ClusterClassifier prior = new ClusterClassifier(initialClusters,
            new KMeansClusteringPolicy(convergenceDelta));
    Path priorPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR);
    prior.writeToSeqFiles(priorPath);

    ClusterIterator.iterateSeq(conf, samples, priorPath, output, maxIterations);
    loadClustersWritable(output);
}

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From source file:org.aksw.tsoru.textmining.mahout.plot.Display.java

License:Apache License

private static void runSequentialFuzzyKClassifier(Configuration conf, Path samples, Path output,
        DistanceMeasure measure, int numClusters, int maxIterations, float m, double threshold)
        throws IOException {
    Collection<Vector> points = Lists.newArrayList();
    for (int i = 0; i < numClusters; i++) {
        points.add(SAMPLE_DATA.get(i).get());
    }//from   www . j a v  a2  s .  c o m
    List<Cluster> initialClusters = Lists.newArrayList();
    int id = 0;
    for (Vector point : points) {
        initialClusters.add(new SoftCluster(point, id++, measure));
    }
    ClusterClassifier prior = new ClusterClassifier(initialClusters,
            new FuzzyKMeansClusteringPolicy(m, threshold));
    Path priorPath = new Path(output, "classifier-0");
    prior.writeToSeqFiles(priorPath);

    ClusterIterator.iterateSeq(conf, samples, priorPath, output, maxIterations);
    loadClustersWritable(output);

    prior.close();
}

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