Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseVector.dot()

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        state.setFirstPass(false);
        if (DEBUG) {
          if (previousEigen == null) {
            previousEigen = currentEigen.clone();
          } else {
            double dot = currentEigen.dot(previousEigen);
            if (dot > 0.0) {
              dot /= currentEigen.norm(2) * previousEigen.norm(2);
            }
           // log.info("Current pass * previous pass = {}", dot);
          }
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    Vector beta = new DenseVector(new double[]{1, -1, 0, 0.5, -0.5});
    Random gen = RandomUtils.getRandom();
    for (int i = 0; i < n; i++) {
      Vector x = randomVector(gen, 5);

      int target = gen.nextDouble() < beta.dot(x) ? 1 : 0;
      olr.train(target, x);
    }
  }

  private static Vector randomVector(final Random gen, int n) {
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    w.addToVector("and", v3);
    w.addToVector("more", v3);
    assertEquals(0, v3.minus(v2).norm(1), 0);

    // moreover, the locations set in the unweighted case should be the same as in the weighted case
    assertEquals(v3.zSum(), v3.dot(v1), 0);
  }

  @Test
  public void testAsString() {
    Locale.setDefault(Locale.ENGLISH);
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      v.assign(gen);
      long hash = HashedVector.computeHash64(v, projection);
      final int bitDot = Long.bitCount(qhash ^ hash);
      count[bitDot]++;
      if (count[bitDot] < 200) {
        System.out.printf("%d, %.3f\n", bitDot, v.dot(query) / Math.sqrt(v.getLengthSquared() * query.getLengthSquared()));
      }
    }
    for (int i = 0; i < 65; ++i) {
      System.out.printf("%d, %d\n", i, count[i]);
    }
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    for (Vector.Element element : data.all()) {
      element.set(gen.nextDouble() < 0.3 ? 1 : 0);
    }

    double p = 1 / (1 + Math.exp(1.5 - data.dot(beta)));
    int target = 0;
    if (gen.nextDouble() < p) {
      target = 1;
    }
    return new AdaptiveLogisticRegression.TrainingExample(i, null, target, data);
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        state.setFirstPass(false);
        if (DEBUG) {
          if (previousEigen == null) {
            previousEigen = currentEigen.clone();
          } else {
            double dot = currentEigen.dot(previousEigen);
            if (dot > 0.0) {
              dot /= currentEigen.norm(2) * previousEigen.norm(2);
            }
           // log.info("Current pass * previous pass = {}", dot);
          }
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    Vector beta = new DenseVector(new double[]{1, -1, 0, 0.5, -0.5});
    Random gen = RandomUtils.getRandom();
    for (int i = 0; i < n; i++) {
      Vector x = randomVector(gen, 5);

      int target = gen.nextDouble() < beta.dot(x) ? 1 : 0;
      olr.train(target, x);
    }
  }

  private static Vector randomVector(final Random gen, int n) {
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    for (Vector.Element element : data) {
      element.set(gen.nextDouble() < 0.3 ? 1 : 0);
    }

    double p = 1 / (1 + Math.exp(1.5 - data.dot(beta)));
    int target = 0;
    if (gen.nextDouble() < p) {
      target = 1;
    }
    return new AdaptiveLogisticRegression.TrainingExample(i, null, target, data);
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        for (int i = 0; i < 400; i++) {
            Vector v = new DenseVector(4);
            v.assign(randomValue);

            Tuple x = new DefaultTuple();
            x.append(target[v.dot(n) > 0 ? 1 : 0]);
            x.append(PigVector.toBytes(v));
            examples.add(x);
        }
        Tuple data = new DefaultTuple();
        data.append(examples);
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    Vector beta = new DenseVector(new double[]{1, -1, 0, 0.5, -0.5});
    Random gen = RandomUtils.getRandom();
    for (int i = 0; i < n; i++) {
      Vector x = randomVector(gen, 5);

      int target = gen.nextDouble() < beta.dot(x) ? 1 : 0;
      olr.train(target, x);
    }
  }

  private static Vector randomVector(final Random gen, int n) {
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