Example usage for org.apache.mahout.common.distance ManhattanDistanceMeasure ManhattanDistanceMeasure

Introduction

In this page you can find the example usage for org.apache.mahout.common.distance ManhattanDistanceMeasure ManhattanDistanceMeasure.

Prototype

ManhattanDistanceMeasure

Source Link

Usage

From source file:DisplayKMeans.java

License:Apache License

public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);// w  w  w  . j a v a2s . c o m
    HadoopUtil.delete(conf, output);

    RandomUtils.useTestSeed();
    generateSamples();
    writeSampleData(samples);
    boolean runClusterer = true;
    double convergenceDelta = 0.001;
    int numClusters = 2;
    int maxIterations = 10;
    if (runClusterer) {
        runSequentialKMeansClusterer(conf, samples, output, measure, numClusters, maxIterations,
                convergenceDelta);
    } else {
        runSequentialKMeansClassifier(conf, samples, output, measure, numClusters, maxIterations,
                convergenceDelta);
    }
    new DisplayKMeans();
}

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From source file:DisplayFuzzyKMeans.java

License:Apache License

public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();

    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, output);//from  www.j a  v a2s.  com
    HadoopUtil.delete(conf, samples);
    RandomUtils.useTestSeed();
    DisplayClustering.generateSamples();
    writeSampleData(samples);
    boolean runClusterer = true;
    int maxIterations = 10;
    float threshold = 0.001F;
    float m = 1.1F;
    if (runClusterer) {
        runSequentialFuzzyKClusterer(conf, samples, output, measure, maxIterations, m, threshold);
    } else {
        int numClusters = 3;
        runSequentialFuzzyKClassifier(conf, samples, output, measure, numClusters, maxIterations, m, threshold);
    }
    new DisplayFuzzyKMeans();
}

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From source file:cc.recommenders.mining.calls.DistanceMeasureFactory.java

License:Open Source License

public DistanceMeasure get() {
    switch (options.getDistanceMeasure()) {
    case COSINE:/*from  www. j ava 2  s.c o  m*/
        return new CosineDistanceMeasure();
    case MANHATTAN:
        return new ManhattanDistanceMeasure();
    }
    throw new RuntimeException("unknown distance measure");
}

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From source file:curation.mahout_test.DisplayKMeans.java

License:Apache License

public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);/*from  ww  w . j  a v a  2  s  .  c  o  m*/
    HadoopUtil.delete(conf, output);

    RandomUtils.useTestSeed();
    generateSamples();
    writeSampleData(samples);
    boolean runClusterer = true;
    double convergenceDelta = 0.001;
    int numClusters = 3;
    int maxIterations = 10;
    if (runClusterer) {
        runSequentialKMeansClusterer(conf, samples, output, measure, numClusters, maxIterations,
                convergenceDelta);
    } else {
        runSequentialKMeansClassifier(conf, samples, output, measure, numClusters, maxIterations,
                convergenceDelta);
    }
    new DisplayKMeans();
}

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From source file:io.github.thushear.display.DisplayFuzzyKMeans.java

License:Apache License

public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();

    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);//from  w w w. j  ava 2s .  c  om
    HadoopUtil.delete(conf, output);
    RandomUtils.useTestSeed();
    DisplayClustering.generateSamples();
    writeSampleData(samples);
    boolean runClusterer = false;
    int maxIterations = 10;
    if (runClusterer) {
        runSequentialFuzzyKClusterer(conf, samples, output, measure, maxIterations);
    } else {
        int numClusters = 3;
        runSequentialFuzzyKClassifier(conf, samples, output, measure, numClusters, maxIterations);
    }
    new DisplayFuzzyKMeans();
}

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From source file:io.github.thushear.display.DisplayKMeans.java

License:Apache License

public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);/*  www. ja v a2  s. c o m*/
    HadoopUtil.delete(conf, output);

    RandomUtils.useTestSeed();
    DisplayClustering.generateSamples();
    writeSampleData(samples);
    boolean runClusterer = false;
    if (runClusterer) {
        int numClusters = 3;
        runSequentialKMeansClusterer(conf, samples, output, measure, numClusters);
    } else {
        int maxIterations = 10;
        runSequentialKMeansClassifier(conf, samples, output, measure, maxIterations);
    }
    new DisplayKMeans();
}

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

License:Apache License

public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();

    Path samples = new Path("samples");
    Path output = new Path("etc/output-performance-x10");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, output);/*from  w  w w .  j a v  a2  s .  c o m*/
    HadoopUtil.delete(conf, samples);
    RandomUtils.useTestSeed();
    generateSamples();
    writeSampleData(samples);
    boolean runClusterer = false;
    int maxIterations = 10;
    float threshold = 0.001F;
    float m = 1.1F;
    if (runClusterer) {
        runSequentialFuzzyKClusterer(conf, samples, output, measure, maxIterations, m, threshold);
    } else {
        int numClusters = 3;
        runSequentialFuzzyKClassifier(conf, samples, output, measure, numClusters, maxIterations, m, threshold);
    }

    loadClustersWritable(output);

    Display d = new Display();
    try {
        // retrieve image
        BufferedImage bi = new BufferedImage(d.getWidth(), d.getHeight(), BufferedImage.TYPE_INT_ARGB);
        d.paint(bi.createGraphics());
        File outputfile = new File("saved.png");
        ImageIO.write(bi, "png", outputfile);
    } catch (IOException e) {
        e.printStackTrace();
    }

}

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From source file:org.conan.mymahout.clustering.display.DisplayCanopy.java

License:Apache License

public static void main(String[] args) throws Exception {
    Path samples = new Path("/samples");
    Path output = new Path("/output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);//from  w ww.  j  av  a  2  s.c  o  m
    HadoopUtil.delete(conf, output);
    RandomUtils.useTestSeed();
    generateSamples();
    writeSampleData(samples);
    CanopyDriver.buildClusters(conf, samples, output, new ManhattanDistanceMeasure(), T1, T2, 0, true);
    loadClustersWritable(output);

    new DisplayCanopy();
}

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From source file:org.conan.mymahout.clustering.display.DisplaySpectralKMeans.java

License:Apache License

public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    Path samples = new Path(SAMPLES);
    Path output = new Path(OUTPUT);
    Path tempDir = new Path(TEMP);
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);/*from  w w  w .  ja v  a2 s .  c om*/
    HadoopUtil.delete(conf, output);

    RandomUtils.useTestSeed();
    DisplayClustering.generateSamples();
    writeSampleData(samples);
    Path affinities = new Path(output, AFFINITIES);
    FileSystem fs = FileSystem.get(output.toUri(), conf);
    if (!fs.exists(output)) {
        fs.mkdirs(output);
    }
    Writer writer = null;
    try {
        writer = Files.newWriter(new File(affinities.toString()), Charsets.UTF_8);
        for (int i = 0; i < SAMPLE_DATA.size(); i++) {
            for (int j = 0; j < SAMPLE_DATA.size(); j++) {
                writer.write(i + "," + j + ','
                        + measure.distance(SAMPLE_DATA.get(i).get(), SAMPLE_DATA.get(j).get()) + '\n');
            }
        }
    } finally {
        Closeables.close(writer, false);
    }
    int maxIter = 10;
    double convergenceDelta = 0.001;
    SpectralKMeansDriver.run(new Configuration(), affinities, output, SAMPLE_DATA.size(), 3, measure,
            convergenceDelta, maxIter, tempDir, false);
    new DisplaySpectralKMeans();
}

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From source file:org.plista.kornakapi.core.training.StramingKMeansClusterer.java

License:Apache License

public StramingKMeansClusterer(StreamingKMeansClassifierModel model, int clusters, long cutoff) {
    this.model = model;
    this.clusters = clusters;
    this.cutoff = cutoff;
    UpdatableSearcher searcher = new FastProjectionSearch(new ManhattanDistanceMeasure(), 10, 10);
    clusterer = new StreamingKMeans(searcher, clusters, cutoff);
}

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