}

  public void testMappingAnd_samePointTwice() {
    double x = 1.2;
    double y = 3.4;
    assertThrows(
        IllegalArgumentException.class, () -> LinearTransformation.mapping(x, y).and(x, y));
  }

  public void testMappingWithSlope_regular() {
    double x1 = 1.2;
    double y1 = 3.4;
    double xDelta = -5.6;
    double slope = -7.8;

   */
  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(
      Collection<Integer> indexes, int scale, double[] dataset);

  static double getMinValue(double[] array, int from) {

    final double slope;
    final double yIntercept;

    @LazyInit @Nullable LinearTransformation inverse;

    RegularLinearTransformation(double slope, double yIntercept) {
      this.slope = slope;
      this.yIntercept = yIntercept;
      this.inverse = null; // to be lazily initialized
    }

    RegularLinearTransformation(double slope, double yIntercept, LinearTransformation inverse) {

      Double[] suffix = {86.0, 99.0};
      Double[] all = concat(concat(prefix, elements), suffix);
      return makeArray(all).subArray(2, elements.length + 2).asList();
    }
  }

  @J2ktIncompatible
  @GwtIncompatible // used only from suite
  @AndroidIncompatible
  private static Double[] concat(Double[] a, Double[] b) {
    return ObjectArrays.concat(a, b, Double.class);
  }

  @Param QuantilesAlgorithm algorithm;

  private final double[][] datasets = new double[0x100][];

  @BeforeExperiment
  void setUp() {
    Random rng = new Random();
    for (int i = 0; i < 0x100; i++) {
      datasets[i] = new double[datasetSize];
      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private double[] dataset(int i) {

  @Param QuantilesAlgorithm algorithm;

  private final double[][] datasets = new double[0x100][];

  @BeforeExperiment
  void setUp() {
    Random rng = new Random();
    for (int i = 0; i < 0x100; i++) {
      datasets[i] = new double[datasetSize];
      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private double[] dataset(int i) {

 *
 * @author Louis Wasserman
 */
@NullUnmarked
public class DoubleMathRoundingBenchmark {
  private static final double[] doubleInIntRange = new double[ARRAY_SIZE];
  private static final double[] doubleInLongRange = new double[ARRAY_SIZE];
  private static final double[] positiveDoubles = new double[ARRAY_SIZE];

  @Param({"DOWN", "UP", "FLOOR", "CEILING", "HALF_EVEN", "HALF_UP", "HALF_DOWN"})
  RoundingMode mode;

        case HALF_UP:
          return Double.MAX_VALUE * sign(x);
        case FLOOR:
          return (roundArbitrarily == Double.POSITIVE_INFINITY)
              ? Double.MAX_VALUE
              : Double.NEGATIVE_INFINITY;
        case CEILING:
          return (roundArbitrarily == Double.POSITIVE_INFINITY)
              ? Double.POSITIVE_INFINITY
              : -Double.MAX_VALUE;
        case UP:

        assertEquals(0, new BigDecimal("0.01").compareTo(BigDecimalConversionUtil.toBigDecimal(Double.valueOf(0.01D))));
        assertEquals(0, new BigDecimal("0.001").compareTo(BigDecimalConversionUtil.toBigDecimal(Double.valueOf(0.001D))));
        assertEquals(0, new BigDecimal("0.0001").compareTo(BigDecimalConversionUtil.toBigDecimal(Double.valueOf(0.0001D))));

          .toString();
    }
  }

  double sumOfProductsOfDeltas() {
    return sumOfProductsOfDeltas;
  }

  private static double ensurePositive(double value) {
    if (value > 0.0) {
      return value;
    } else {
      return Double.MIN_VALUE;
    }
  }

  private static double ensureInUnitRange(double value) {
    if (value >= 1.0) {
      return 1.0;
    }