* is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

          -20,
          Double.NaN,
          Double.NEGATIVE_INFINITY,
          -0.001);

  public void testFuzzyEqualsFinite() {
    for (double a : FINITE_DOUBLE_CANDIDATES) {
      for (double b : FINITE_DOUBLE_CANDIDATES) {
        for (double tolerance : FINITE_TOLERANCE_CANDIDATES) {
          assertEquals(Math.abs(a - b) <= tolerance, DoubleMath.fuzzyEquals(a, b, tolerance));
        }
      }
    }
  }

        fractionalBuilder.add(x);
      }
    }
    FRACTIONAL_DOUBLE_CANDIDATES = fractionalBuilder.build();
    FINITE_DOUBLE_CANDIDATES =
        Iterables.concat(FRACTIONAL_DOUBLE_CANDIDATES, INTEGRAL_DOUBLE_CANDIDATES);
    POSITIVE_FINITE_DOUBLE_CANDIDATES =
        Iterables.filter(
            FINITE_DOUBLE_CANDIDATES,
            new Predicate<Double>() {
              @Override

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();

    assertThat(TWO_VALUES_STATS.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    // For datasets of many double values created from an array, we test many combinations of finite
    // and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double mean = Stats.of(values.asArray()).mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();

    assertThat(TWO_VALUES_STATS.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    // For datasets of many double values created from an array, we test many combinations of finite
    // and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double mean = Stats.of(values.asArray()).mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();

              // Test for equality to allow non-finite values to match; otherwise, use the finite
              // test.
              return actual.equals(expected)
                  || FINITE_QUANTILE_CORRESPONDENCE.compare(actual, expected);
            }
          },
          "is identical to or " + FINITE_QUANTILE_CORRESPONDENCE);

              // Test for equality to allow non-finite values to match; otherwise, use the finite
              // test.
              return actual.equals(expected)
                  || FINITE_QUANTILE_CORRESPONDENCE.compare(actual, expected);
            }
          },
          "is identical to or " + FINITE_QUANTILE_CORRESPONDENCE);