*/
    public ScaleAndIndex index(int index) {
      return new ScaleAndIndex(scale, index);
    }

    /**
     * Specifies multiple quantile indexes to be calculated, each index being the k in the kth
     * q-quantile.
     *
     * @param indexes the quantile indexes, each of which must be in the inclusive range [0, q] for

     */
    public ScaleAndIndex index(int index) {
      return new ScaleAndIndex(scale, index);
    }

    /**
     * Specifies multiple quantile indexes to be calculated, each index being the k in the kth
     * q-quantile.
     *
     * @param indexes the quantile indexes, each of which must be in the inclusive range [0, q] for

  public void testScale_zero() {
    assertThrows(IllegalArgumentException.class, () -> Quantiles.scale(0));
  }

  public void testScale_negative() {
    assertThrows(IllegalArgumentException.class, () -> Quantiles.scale(-4));
  }

  public void testScale_index_negative() {
    Quantiles.Scale intermediate = Quantiles.scale(10);
    assertThrows(IllegalArgumentException.class, () -> intermediate.index(-1));
  }

      Map<Integer, Double> quantiles = algorithm.multipleQuantiles(indexes, 100, dataset.clone());
      assertWithMessage("Wrong keys from " + algorithm).that(quantiles.keySet()).isEqualTo(indexes);
      for (int i : indexes) {
        assertWithMessage("Mismatch between %s and %s at %s", algorithm, REFERENCE_ALGORITHM, i)
            .that(quantiles.get(i))
            .isWithin(ALLOWED_ERROR)

    }
  },
  ;

  /**
   * Calculates a single quantile. Equivalent to {@code
   * Quantiles.scale(scale).index(index).computeInPlace(dataset)}.
   */
  abstract double singleQuantile(int index, int scale, double[] dataset);

  /**
   * Calculates multiple quantiles. Equivalent to {@code
   * Quantiles.scale(scale).indexes(indexes).computeInPlace(dataset)}.
   */
  abstract Map<Integer, Double> multipleQuantiles(

	for i := 0; i < samples; i++ {
		window := time.Duration(math.Exp(float64(i)/(samples-1)*(logMax-logMin) + logMin))
		if quantiles == nil {
			plot[i] = make([]float64, 2)
			plot[i][1] = mmuCurve.MMU(window)
		} else {
			plot[i] = make([]float64, 1+len(quantiles))
			copy(plot[i][1:], mmuCurve.MUD(window, quantiles))
		}
		plot[i][0] = float64(window)
	}

	// Create JSON response.

}

func (d Data) Median() float64 {
	return d.Quantile(0.5)
}

func (d Data) Mean() float64 {
	total := float64(0)
	for _, v := range d {
		total += v
	}
	return total / float64(len(d))
}

func (d Data) Quantile(phi float64) float64 {
	return d.sorted().quantile(phi)
}

func (d Data) Quantiles(phis ...float64) []float64 {
	return d.sorted().quantiles(phis...)
}

func (d Data) Copy() Data {

		mmuCurve2 := trace.NewMMUCurve(mu)
		quantiles := []float64{0, 1 - .999, 1 - .99}
		for window := time.Microsecond; window < time.Second; window *= 10 {
			mud1 := mmuCurve.MUD(window, quantiles)
			mud2 := mmuCurve2.MUD(window, quantiles)
			for i := range mud1 {
				if !aeq(mud1[i], mud2[i]) {
					t.Errorf("for quantiles %v at window %v, want %v, got %v", quantiles, window, mud2, mud1)
					break
				}
			}
		}

	// distribution quantile is less than the next worst-case mean
	// mutator utilization. At this point, all further
	// contributions to the distribution must be beyond the
	// desired quantile and hence cannot affect it.
	//
	// First, find the highest desired distribution quantile.
	maxQ := quantiles[0]
	for _, q := range quantiles {
		if q > maxQ {
			maxQ = q
		}
	}

import com.google.common.collect.ImmutableSet;
import com.google.common.collect.Range;
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());