for (int index = 0; index <= 100; index++) {
      indexes.add(index);
      expectedBuilder.put(index, expectedLargeDatasetPercentile(index));
    }
    Random random = new Random(770683168895677741L);
    Collections.shuffle(indexes, random);
    assertThat(percentiles().indexes(Ints.toArray(indexes)).computeInPlace(dataset))
        .comparingValuesUsing(QUANTILE_CORRESPONDENCE)

<p>
If <code>a</code> is not a map:
</p>
<ul>
	<li>the index <code>x</code> must be of integer type or an untyped constant</li>
	<li>a constant index must be non-negative and
	    <a href="#Representability">representable</a> by a value of type <code>int</code></li>
	<li>a constant index that is untyped is given type <code>int</code></li>
	<li>the index <code>x</code> is <i>in range</i> if <code>0 &lt;= x &lt; len(a)</code>,

- ACTION_REQUIRED

     * @param indexes the quantile indexes, each of which must be in the inclusive range [0, q] for
     *     q-quantiles; the order of the indexes is unimportant, duplicates will be ignored, and the
     *     set will be snapshotted when this method is called
     * @throws IllegalArgumentException if {@code indexes} is empty
     */
    public ScaleAndIndexes indexes(int... indexes) {
      return new ScaleAndIndexes(scale, indexes.clone());
    }

        suggester.indexer().deleteBadWord("[");
        suggester.indexer().deleteBadWord("ｴﾝｼﾞﾝ");
        assertEquals(0, suggester.settings().badword().get(false).length);
    }

    @Test
    public void test_popularWords() throws Exception {
        SuggestItem[] items = getPopularWordsItemSet2();
        suggester.indexer().index(items);
        suggester.refresh();

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      return Quantiles.scale(scale).index(index).computeInPlace(dataset);
    }

    @Override
    Map<Integer, Double> multipleQuantiles(
        Collection<Integer> indexes, int scale, double[] dataset) {
      return Quantiles.scale(scale).indexes(indexes).computeInPlace(dataset);
    }
  },
  ;

  /**

    ImmutableSet<Integer> indexes = ImmutableSet.of(50, 90, 99);
    Map<Integer, Double> referenceQuantiles =
        REFERENCE_ALGORITHM.multipleQuantiles(indexes, 100, dataset.clone());
    assertThat(referenceQuantiles.keySet()).isEqualTo(indexes);
    for (QuantilesAlgorithm algorithm : NON_REFERENCE_ALGORITHMS) {
      Map<Integer, Double> quantiles = algorithm.multipleQuantiles(indexes, 100, dataset.clone());

	if !ok || u.Op != token.AND {
		return false
	}
	index, ok := u.X.(*ast.IndexExpr)
	if !ok {
		return false
	}

	addr := ""
	deref := ""
	if p.isVariable(index.X) {
		addr = "&"
		deref = "*"
	}

	fmt.Fprintf(sb, "_cgoIndex%d := %s%s; ", i, addr, gofmtPos(index.X, index.X.Pos()))
	origX := index.X
	index.X = ast.NewIdent(fmt.Sprintf("_cgoIndex%d", i))
	if deref == "*" {

    ImmutableSet<Integer> indexes = ImmutableSet.of(50, 90, 99);
    Map<Integer, Double> referenceQuantiles =
        REFERENCE_ALGORITHM.multipleQuantiles(indexes, 100, dataset.clone());
    assertThat(referenceQuantiles.keySet()).isEqualTo(indexes);
    for (QuantilesAlgorithm algorithm : NON_REFERENCE_ALGORITHMS) {
      Map<Integer, Double> quantiles = algorithm.multipleQuantiles(indexes, 100, dataset.clone());

suggester.indexer().index(new SuggestItem(new String[] { "検索", "エンジン" }, readings, 1, tags, roles, SuggestItem.Kind.DOCUMENT));
```

### Add suggest documents from source of index

```java
DocumentReader reader = new ESSourceReader(
    client,
    suggester.settings(),
    "contentIndexName",
    "contentTypeName");
suggester.indexer().indexFromDocument(reader, 2, 100).getResponse();
```