Package org.apache.mahout.math.jet.random

Examples of org.apache.mahout.math.jet.random.Exponential

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  }

  @Test
  public void copyLearnsAsExpected() {
    Random gen = RandomUtils.getRandom();
    Exponential exp = new Exponential(0.5, gen);
    Vector beta = new DenseVector(200);
    for (Vector.Element element : beta) {
        int sign = 1;
        if (gen.nextDouble() < 0.5) {
          sign = -1;
        }
      element.set(sign * exp.nextDouble());
    }

    // train one copy of a wrapped learner
    AdaptiveLogisticRegression.Wrapper w = new AdaptiveLogisticRegression.Wrapper(2, 200, new L1());
    for (int i = 0; i < 3000; i++) {
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    public User(InetAddress address, String geoCode, TermGenerator terms, double period) {
        this.terms = terms;
        this.geoCode = geoCode;
        this.address = address;
        this.rate = period;
        this.sessionTimeDistribution = new Exponential(period, RandomUtils.getRandom());

        id = idCounter.addAndGet(1);
        nextSession = sessionTimeDistribution.nextDouble();
    }
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    public JsonNode sample() {
        ArrayNode r = nodeFactory.arrayNode();

        double t = start;
        double averageInterval = TimeUnit.MILLISECONDS.convert(1, TimeUnit.DAYS) / transactionsPerDay.nextDouble();
        Exponential interval = new Exponential(1 / averageInterval, gen);

        Date date = new Date();
        boolean compromised = false;
        while (t < end) {
            ObjectNode transaction = new ObjectNode(nodeFactory);
            t += interval.nextDouble();
            date.setTime((long) t);
            transaction.set("timestamp", new LongNode((long) (t / 1000)));
            transaction.set("date", new TextNode(df.format(date)));
            Integer merchantId = merchant.sample();
            transaction.set("merchant", new IntNode(merchantId));
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Related Classes of org.apache.mahout.math.jet.random.Exponential

  • com.mapr.franz.Traffic
  • com.mapr.synth.samplers.CommonPointOfCompromise
  • com.mapr.synth.User
  • org.apache.mahout.classifier.sgd.AdaptiveLogisticRegressionTest
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