Пример #1
0
 @Override
 public T next() {
   PeekingIterator<T> nextIter = queue.remove();
   T next = nextIter.next();
   if (nextIter.hasNext()) {
     queue.add(nextIter);
   }
   return next;
 }
Пример #2
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 @Override
 protected T getNext() throws Exception {
   T next = peekingIterator.next();
   try {
     peekingIterator.peek();
     return next;
   } catch (NoSuchElementException ex) {
     return finished();
   }
 }
Пример #3
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 public static void main(String[] args) {
   PeekingIterator a = new PeekingIterator();
   a.list.add(1);
   a.list.add(2);
   a.list.add(3);
   a.list.add(4);
   System.out.println(a.hasNext());
   System.out.println(a.peek());
   System.out.println(a.peek());
   System.out.println(a.next());
 }
Пример #4
0
  /*
   * Get the exponentially-decayed approximate counts of values in multiple buckets. The elements in
   * the provided list denote the upper bound each of the buckets and must be sorted in ascending
   * order.
   *
   * The approximate count in each bucket is guaranteed to be within 2 * totalCount * maxError of
   * the real count.
   */
  public List<Bucket> getHistogram(List<Long> bucketUpperBounds) {
    checkArgument(
        Ordering.natural().isOrdered(bucketUpperBounds),
        "buckets must be sorted in increasing order");

    final ImmutableList.Builder<Bucket> builder = ImmutableList.builder();
    final PeekingIterator<Long> iterator = Iterators.peekingIterator(bucketUpperBounds.iterator());

    final AtomicDouble sum = new AtomicDouble();
    final AtomicDouble lastSum = new AtomicDouble();

    // for computing weighed average of values in bucket
    final AtomicDouble bucketWeightedSum = new AtomicDouble();

    final double normalizationFactor = weight(TimeUnit.NANOSECONDS.toSeconds(ticker.read()));

    postOrderTraversal(
        root,
        new Callback() {
          @Override
          public boolean process(Node node) {

            while (iterator.hasNext() && iterator.peek() <= node.getUpperBound()) {
              double bucketCount = sum.get() - lastSum.get();

              Bucket bucket =
                  new Bucket(
                      bucketCount / normalizationFactor, bucketWeightedSum.get() / bucketCount);

              builder.add(bucket);
              lastSum.set(sum.get());
              bucketWeightedSum.set(0);
              iterator.next();
            }

            bucketWeightedSum.addAndGet(node.getMiddle() * node.weightedCount);
            sum.addAndGet(node.weightedCount);
            return iterator.hasNext();
          }
        });

    while (iterator.hasNext()) {
      double bucketCount = sum.get() - lastSum.get();
      Bucket bucket =
          new Bucket(bucketCount / normalizationFactor, bucketWeightedSum.get() / bucketCount);

      builder.add(bucket);

      iterator.next();
    }

    return builder.build();
  }
Пример #5
0
    @Override
    protected T computeNext() {
      if (queue.isEmpty()) {
        return endOfData();
      }

      PeekingIterator<T> nextIter = queue.poll();
      T next = nextIter.next();

      if (nextIter.hasNext()) {
        queue.add(nextIter);
      }

      return next;
    }
Пример #6
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  /**
   * Gets the values at the specified quantiles +/- maxError. The list of quantiles must be sorted
   * in increasing order, and each value must be in the range [0, 1]
   */
  public List<Long> getQuantiles(List<Double> quantiles) {
    checkArgument(
        Ordering.natural().isOrdered(quantiles), "quantiles must be sorted in increasing order");
    for (double quantile : quantiles) {
      checkArgument(quantile >= 0 && quantile <= 1, "quantile must be between [0,1]");
    }

    final ImmutableList.Builder<Long> builder = ImmutableList.builder();
    final PeekingIterator<Double> iterator = Iterators.peekingIterator(quantiles.iterator());

    postOrderTraversal(
        root,
        new Callback() {
          private double sum = 0;

          @Override
          public boolean process(Node node) {
            sum += node.weightedCount;

            while (iterator.hasNext() && sum > iterator.peek() * weightedCount) {
              iterator.next();

              // we know the max value ever seen, so cap the percentile to provide better error
              // bounds in this case
              long value = Math.min(node.getUpperBound(), max);

              builder.add(value);
            }

            return iterator.hasNext();
          }
        });

    // we finished the traversal without consuming all quantiles. This means the remaining quantiles
    // correspond to the max known value
    while (iterator.hasNext()) {
      builder.add(max);
      iterator.next();
    }

    return builder.build();
  }