})

				indexes[index.Name] = idx
			}
		}
	}
	for _, index := range indexes {
		if index.Class == "UNIQUE" && len(index.Fields) == 1 {
			index.Fields[0].Field.UniqueIndex = index.Name
		}
	}
	return indexes
}

func (schema *Schema) LookIndex(name string) *Index {
	if schema != nil {
		indexes := schema.ParseIndexes()
		for _, index := range indexes {
			if index.Name == name {

    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)

    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)

    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());

    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());

     * @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());
    }

    @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);
    }
  },
  ;

  /**

    @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);
    }
  },
  ;

  /**

import java.util.Random;

/** Benchmarks some algorithms providing the same functionality as {@link Quantiles}. */
public class QuantilesBenchmark {

  private static final ContiguousSet<Integer> ALL_DECILE_INDEXES =
      ContiguousSet.create(Range.closed(0, 10), DiscreteDomain.integers());

  @Param({"10", "100", "1000", "10000", "100000"})
  int datasetSize;

  @Param QuantilesAlgorithm algorithm;

import java.util.Random;

/** Benchmarks some algorithms providing the same functionality as {@link Quantiles}. */
public class QuantilesBenchmark {

  private static final ContiguousSet<Integer> ALL_DECILE_INDEXES =
      ContiguousSet.create(Range.closed(0, 10), DiscreteDomain.integers());

  @Param({"10", "100", "1000", "10000", "100000"})
  int datasetSize;

  @Param QuantilesAlgorithm algorithm;