Example usage for org.apache.mahout.common RandomUtils useTestSeed

Introduction

In this page you can find the example usage for org.apache.mahout.common RandomUtils useTestSeed.

Prototype

public static void useTestSeed() 

Source Link

Usage

From source file:DisplayClustering.java

License:Apache License

public static void main(String[] args) throws Exception {
    RandomUtils.useTestSeed();
    generateSamples();//  w  w w . j  a v a 2  s  .  c  o m
    new DisplayClustering();
}

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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);/*from   ww  w  .  j  ava2 s .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   w  w  w .  j a v a 2  s. c  om
    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:com.clearspring.analytics.stream.quantile.GroupTreeTest.java

License:Apache License

@Before
public void setUp() {
    RandomUtils.useTestSeed();
}

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From source file:com.clearspring.analytics.stream.quantile.TDigestTest.java

License:Apache License

@Before
public void testSetUp() {
    RandomUtils.useTestSeed();
}

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From source file:com.corchado.testRecomender.evaluarPrecionRecallUI.java

private void calcularPrecicionRecall() {
    try {//from  w w  w  .  ja  v a2s  .  c  om

        RandomUtils.useTestSeed();
        RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
        RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                UserNeighborhood neighborhood = new NearestNUserNeighborhood(CantVecindad, similarity, model);
                return new GenericUserBasedRecommender(model, neighborhood, similarity);
            }
        };
        IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 2,
                GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);

        labelPrecicion.setText("Precisin: " + stats.getPrecision());
        labelRecal.setText("Recall: " + stats.getRecall());

    } catch (TasteException ex) {
        Logger.getLogger(evaluarPrecionRecallUI.class.getName()).log(Level.SEVERE, null, ex);
    }
}

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From source file:com.corchado.testRecomender.evaluarRecomendadorUI.java

private void Evaluar() {
    try {//from   w ww  .  jav  a 2 s. c o m

        RandomUtils.useTestSeed();
        RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
        RecommenderBuilder builder = new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {

                //                UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
                UserNeighborhood neighborhood = new NearestNUserNeighborhood(CantVecindad, similarity, model);

                return new GenericUserBasedRecommender(model, neighborhood, similarity);
            }
        };

        double score = evaluator.evaluate(builder, null, model, 0.7, 1.0);
        labelEvaluacion.setText("Evaluacin: " + score);
    } catch (TasteException ex) {
        Logger.getLogger(evaluarRecomendadorUI.class.getName()).log(Level.SEVERE, null, ex);
    }
}

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From source file:com.corchado.testRecomender.recomendador.java

public static void Probar(final Scanner entrada, DataModel model, final UserSimilarity similarity)
        throws IOException, TasteException {
    RandomUtils.useTestSeed();
    RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder builder = new RecommenderBuilder() {
        @Override/*from   ww  w .  j  av  a 2  s  . co m*/
        public Recommender buildRecommender(DataModel model) throws TasteException {

            //                UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
            UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);

            return new GenericUserBasedRecommender(model, neighborhood, similarity);
        }
    };

    double score = evaluator.evaluate(builder, null, model, 0.7, 1.0);
    System.out.println("evaluacion: " + score);
}

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From source file:com.corchado.testRecomender.recomendador.java

public static void evaluarPrecicionRecall(final Scanner entrada, DataModel model,
        final UserSimilarity similarity) throws IOException, TasteException {
    RandomUtils.useTestSeed();
    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
        @Override/* w  w  w .  j  a  va2s .  c  o m*/
        public Recommender buildRecommender(DataModel model) throws TasteException {
            UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
            return new GenericUserBasedRecommender(model, neighborhood, similarity);
        }
    };
    IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 2,
            GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);

    System.out.println("Precision: " + stats.getPrecision());
    System.out.println("Recal: " + stats.getRecall());
}

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From source file:com.mapr.stats.UpperQuantileTest.java

License:Apache License

@Before
public void generate() {
    RandomUtils.useTestSeed();
    uq = new UpperQuantile(101);
    data = new double[1001];
    Random gen = RandomUtils.getRandom();
    for (int i = 0; i < 1001; i++) {
        double x = gen.nextDouble();
        data[i] = x;// www  .  j ava  2 s. c om
        uq.add(x);
    }
    Arrays.sort(data);
}

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