Example usage for org.apache.mahout.clustering.classify WeightedPropertyVectorWritable WeightedPropertyVectorWritable

Introduction

In this page you can find the example usage for org.apache.mahout.clustering.classify WeightedPropertyVectorWritable WeightedPropertyVectorWritable.

Prototype

public WeightedPropertyVectorWritable() 

Source Link

Usage

From source file:hk.newsRecommender.MatrixAndCluster.java

License:Open Source License

public static void clusterOutput(Configuration conf, Path path) {
    try {//from w w w .ja  v  a 2 s. co  m
        BufferedWriter bw;
        FileSystem fs = FileSystem.get(conf);

        SequenceFile.Reader reader = null;
        reader = new SequenceFile.Reader(fs, path, conf);

        // ?uidOfgrp.txt?? uid \t groupID
        bw = new BufferedWriter(new FileWriter(new File("C:\\Users\\Hk\\Desktop\\ClusterPointsInfo.txt")));
        HashMap<String, Integer> clusterIds;
        clusterIds = new HashMap<String, Integer>(120);
        IntWritable key = new IntWritable();
        WeightedPropertyVectorWritable value = new WeightedPropertyVectorWritable();
        //         WeightedVectorWritable value = new WeightedVectorWritable();
        while (reader.next(key, value)) {
            NamedVector vector = (NamedVector) value.getVector();
            // VectorName
            String vectorName = vector.getName();
            System.out.println(vectorName + "\t" + key.toString());
            bw.write(vectorName + "\t" + key.toString() + "\n");
            // ?group?
            if (clusterIds.containsKey(key.toString())) {
                clusterIds.put(key.toString(), clusterIds.get(key.toString()) + 1);
            } else
                clusterIds.put(key.toString(), 1);
        }
        bw.flush();
        reader.close();
        // ?group?grpSize
        bw = new BufferedWriter(new FileWriter(new File("C:\\Users\\Hk\\Desktop\\ClusterPointsSize.txt")));
        Set<String> keys = clusterIds.keySet();
        for (String k : keys) {
            System.out.println(k + " " + clusterIds.get(k));
            bw.write(k + " " + clusterIds.get(k) + "\n");
        }
        bw.flush();
        bw.close();
    } catch (IOException e) {
        e.printStackTrace();
    }
}

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From source file:hk.newsRecommender.MatrixAndCluster.java

License:Open Source License

public static void clusterOutput2(Configuration conf, Path path) {
    try {//from w  ww  . j a  va2 s.  c o  m
        FileSystem fs = FileSystem.get(conf);
        SequenceFile.Reader reader = new SequenceFile.Reader(fs, path, conf);
        IntWritable key = new IntWritable();
        WeightedPropertyVectorWritable value = new WeightedPropertyVectorWritable();
        while (reader.next(key, value)) {
            System.out.println(value.toString() + " belongs to cluster " + key.toString());
        }
        reader.close();
    } catch (IOException e) {
        e.printStackTrace();
    }
}

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From source file:sigis.kmeansmultiplek.AnotherKmeans.java

private void readAndPrintOutputValues(final Configuration configuration) throws IOException {
    final Path input = new Path(OUTPUT_PATH + "/" + Cluster.CLUSTERED_POINTS_DIR + "/part-m-00000");
    //final Path input = new Path(OUTPUT_PATH + "/" + Cluster.FINAL_ITERATION_SUFFIX + "/part-r-00000");
    System.out.println(Cluster.FINAL_ITERATION_SUFFIX);
    System.out.println(Cluster.CLUSTERED_POINTS_DIR);

    final SequenceFile.Reader reader = new SequenceFile.Reader(configuration, SequenceFile.Reader.file(input));

    final IntWritable key = new IntWritable();
    final WeightedPropertyVectorWritable value = new WeightedPropertyVectorWritable();

    while (reader.next(key, value)) {
        LOG.info("{} belongs to cluster {}", value.toString(), key.toString());
        System.out.println(value.toString().substring(18, 28));
        LOG.info("belongs to cluster {}", key.toString());

    }//  www .  j a v  a  2 s  . com
    reader.close();
}

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