}

  /** Returns a new bias. */
  private fun adapt(
    delta: Int,
    numpoints: Int,
    first: Boolean,
  ): Int {
    var delta =
      when {
        first -> delta / DAMP
        else -> delta / 2
      }
    delta += (delta / numpoints)
    var k = 0
    while (delta > ((BASE - TMIN) * TMAX) / 2) {
      delta /= (BASE - TMIN)
      k += BASE
    }

    double x1 = 1.2;
    double y1 = 3.4;
    double xDelta = 5.6;
    double yDelta = 7.8;
    LinearTransformation transformation =
        LinearTransformation.mapping(x1, y1).and(x1 + xDelta, y1 + yDelta);
    assertDiagonalLinearTransformation(transformation, x1, y1, xDelta, yDelta);
  }

  public void testMappingAnd_horizontal() {
    double x1 = 1.2;
    double xDelta = 3.4;
    double y = 5.6;

    double x1 = 1.2;
    double y1 = 3.4;
    double xDelta = 5.6;
    double yDelta = 7.8;
    LinearTransformation transformation =
        LinearTransformation.mapping(x1, y1).and(x1 + xDelta, y1 + yDelta);
    assertDiagonalLinearTransformation(transformation, x1, y1, xDelta, yDelta);
  }

  public void testMappingAnd_horizontal() {
    double x1 = 1.2;
    double xDelta = 3.4;
    double y = 5.6;

          case ADD:
            {
              int delta = random.nextInt(10);
              multiset.add(key, delta);
              deltas[keyIndex] += delta;
              break;
            }
          case SET_COUNT:
            {
              int newValue = random.nextInt(3);
              int oldValue = multiset.setCount(key, newValue);
              deltas[keyIndex] += newValue - oldValue;
              break;

                        break;
                      case 5:
                        map.getAndAdd(key, delta);
                        threadSum += delta;
                        break;
                      case 6:
                        long oldValue = map.put(key, delta);
                        threadSum += delta - oldValue;
                        break;
                      case 7:

        double mean = meanAlgorithm.mean(values);
        double sumOfSquaresOfDeltas = 0.0;
        for (double value : values) {
          double delta = value - mean;
          sumOfSquaresOfDeltas += delta * delta;
        }
        return new MeanAndVariance(mean, sumOfSquaresOfDeltas / values.length);
      }
    },
    KAHAN {
      @Override

  private int value;

  Count(int value) {
    this.value = value;
  }

  public int get() {
    return value;
  }

  public void add(int delta) {
    value += delta;
  }

  public int addAndGet(int delta) {
    return value += delta;
  }

  public void set(int newValue) {
    value = newValue;
  }

  public int getAndSet(int newValue) {
    int result = value;
    value = newValue;

  }

  /**
   * @param delta the offset from the current date to use. Negative values yield dates in the past;
   *     positive values yield dates in the future.
   */
  private fun formatDate(
    delta: Long,
    timeUnit: TimeUnit,
  ): String? = formatDate(Date(System.currentTimeMillis() + timeUnit.toMillis(delta)))

  private fun formatDate(date: Date): String? {

   *
   * @param delta the value to add
   * @return the previous value
   */
  @CanIgnoreReturnValue
  public final double getAndAdd(double delta) {
    return getAndAccumulate(delta, Double::sum);
  }

  /**
   * Atomically adds the given value to the current value.
   *
   * @param delta the value to add
   * @return the updated value
   */

        "run again after ${formatDuration(executeNanoTime - now)}"
      } else {
        "scheduled after ${formatDuration(executeNanoTime - now)}"
      }
    }

    // Insert in chronological order. Always compare deltas because nanoTime() is permitted to wrap.
    var insertAt = futureTasks.indexOfFirst { it.nextExecuteNanoTime - now > delayNanos }
    if (insertAt == -1) insertAt = futureTasks.size
    futureTasks.add(insertAt, task)