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

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

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        History recorder = new History(1, 10, 30).logTicks();

        Random gen = RandomUtils.getRandom();
        ChineseRestaurant topicSampler = new ChineseRestaurant(options.alpha);
        Exponential interval = new Exponential(1, gen);
        double t = 0;
        long t0 = System.nanoTime();
        long messageId = 0;
        while (messageId < options.max) {
            double rate = options.peak * Math.exp(-scale * (Math.cos(2 * Math.PI * t / options.period) + 1));
            double dt = interval.nextDouble() / rate;
            t += dt;
            double now = (System.nanoTime() - t0) / 1e9;
            if (t > now + 0.01) {
                double millis = Math.floor((t - now) * 1000);
                double nanos = Math.rint((t - now) * 1e9 - millis * 1e6);
 
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  @Test
  public void testTrain() {

    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());
    }

    AdaptiveLogisticRegression.Wrapper cl = new AdaptiveLogisticRegression.Wrapper(2, 200, new L1());
    cl.update(new double[]{1.0e-5, 1});

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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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  @Test
  public void testTrain() {

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

    AdaptiveLogisticRegression.Wrapper cl = new AdaptiveLogisticRegression.Wrapper(2, 200, new L1());
    cl.update(new double[]{1.0e-5, 1});

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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.all()) {
        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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  @Test
  public void testTrain() {

    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());
    }

    AdaptiveLogisticRegression.Wrapper cl = new AdaptiveLogisticRegression.Wrapper(2, 200, new L1());
    cl.update(new double[]{1.0e-5, 1});

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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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  @ThreadLeakLingering(linger=1000)
  @Test
  public void testTrain() {

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

    AdaptiveLogisticRegression.Wrapper cl = new AdaptiveLogisticRegression.Wrapper(2, 200, new L1());
    cl.update(new double[]{1.0e-5, 1});

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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.all()) {
        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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  @Test
  public void testTrain() {

    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());
    }

    AdaptiveLogisticRegression.Wrapper cl = new AdaptiveLogisticRegression.Wrapper(2, 200, new L1());
    cl.update(new double[]{1.0e-5, 1});

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