*
 * @author Louis Wasserman
 */
@NullUnmarked
public class DoubleMathBenchmark {
  private static final double[] positiveDoubles = new double[ARRAY_SIZE];
  private static final int[] factorials = new int[ARRAY_SIZE];
  private static final double[] doubles = new double[ARRAY_SIZE];

  @BeforeExperiment
  void setUp() {
    for (int i = 0; i < ARRAY_SIZE; i++) {

    }
  }

  private static double[] longsToDoubles(long[] longs) {
    int len = longs.length;
    double[] doubles = new double[len];
    for (int i = 0; i < len; i++) {
      doubles[i] = longs[i];
    }
    return doubles;
  }

  private static double[] intsToDoubles(int[] ints) {
    int len = ints.length;
    double[] doubles = new double[len];
    for (int i = 0; i < len; i++) {

    double expectedReportedFpp = (double) knownNumberOfFalsePositives / numInsertions;
    double actualReportedFpp = bf.expectedFpp();
    assertThat(actualReportedFpp).isWithin(0.00033).of(expectedReportedFpp);
  }

  /** Sanity checking with many combinations of false positive rates and expected insertions */
  public void testBasic() {
    for (double fpr = 0.0000001; fpr < 0.1; fpr *= 10) {

        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))));

    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) {

        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:

   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link
   * Double#NEGATIVE_INFINITY}, or {@link Double#NaN}) then the result is {@link Double#NaN}.
   *
   * @throws IllegalStateException if the dataset is empty
   */
  public double populationCovariance() {
    checkState(count() != 0);
    return sumOfProductsOfDeltas / count();
  }

   * each other or identical non-finite values.
   */
  private static final Correspondence<Double, Double> QUANTILE_CORRESPONDENCE =
      Correspondence.from(
          new BinaryPredicate<Double, Double>() {
            @Override
            public boolean apply(@Nullable Double actual, @Nullable Double expected) {
              // Test for equality to allow non-finite values to match; otherwise, use the finite

   * each other or identical non-finite values.
   */
  private static final Correspondence<Double, Double> QUANTILE_CORRESPONDENCE =
      Correspondence.from(
          new BinaryPredicate<Double, Double>() {
            @Override
            public boolean apply(@Nullable Double actual, @Nullable Double expected) {
              // Test for equality to allow non-finite values to match; otherwise, use the finite

        .setExpectation(Double.POSITIVE_INFINITY, UP, CEILING)
        .roundUnnecessaryShouldThrow()
        .test();
  }

  public void testRoundToDouble_wayTooBig() {
    BigDecimal bi = BigDecimal.valueOf(2).pow(2 * Double.MAX_EXPONENT);
    new RoundToDoubleTester(bi)
        .setExpectation(Double.MAX_VALUE, DOWN, FLOOR, HALF_EVEN, HALF_UP, HALF_DOWN)