static class ManyValues {

    private final ImmutableList<Double> values;

    ManyValues(double[] values) {
      this.values = ImmutableList.copyOf(Doubles.asList(values));
    }

    ImmutableList<Double> asIterable() {
      return values;
    }

    double[] asArray() {
      return Doubles.toArray(values);
    }

    boolean hasAnyPositiveInfinity() {

  static class ManyValues {

    private final ImmutableList<Double> values;

    ManyValues(double[] values) {
      this.values = ImmutableList.copyOf(Doubles.asList(values));
    }

    ImmutableList<Double> asIterable() {
      return values;
    }

    double[] asArray() {
      return Doubles.toArray(values);
    }

    boolean hasAnyPositiveInfinity() {

      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();
      accumulator.addAll(values.asIterable());
      for (double value : values.asIterable()) {
        accumulatorByAddAllStats.addAll(Stats.of(value));
      }
      double mean = accumulator.mean();
      double meanByAddAllStats = accumulatorByAddAllStats.mean();

    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =
          createFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES);
      PairedStatsAccumulator accumulatorByAddAllPartitionedPairedStats =
          createPartitionedFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES, 2);
      double populationCovariance = accumulator.populationCovariance();

      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();
      accumulator.addAll(values.asIterable());
      for (double value : values.asIterable()) {
        accumulatorByAddAllStats.addAll(Stats.of(value));
      }
      double mean = accumulator.mean();
      double meanByAddAllStats = accumulatorByAddAllStats.mean();

    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double populationVariance = Stats.of(values.asIterable()).populationVariance();
      if (values.hasAnyNonFinite()) {
        assertWithMessage("population variance of %s", values).that(populationVariance).isNaN();
      } else {

    // 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();
      if (values.hasAnyNonFinite()) {
        assertWithMessage("population covariance of %s", values).that(populationCovariance).isNaN();

    // 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();
      if (values.hasAnyNonFinite()) {
        assertWithMessage("population covariance of %s", values).that(populationCovariance).isNaN();

   * returns a {@link FluentIterable} of elements of an anonymous type whose supertypes are the <a
   * href="https://docs.oracle.com/javase/specs/jls/se25/html/jls-4.html#jls-4.9">intersection</a>
   * of the supertypes of {@code A} and the supertypes of {@code B}.
   */
  public void testConcatIntersectionType() {
    Iterable<A> aIterable = ImmutableList.of();