Map<Integer, Double> quantiles = algorithm.multipleQuantiles(indexes, 100, dataset.clone());
      assertWithMessage("Wrong keys from " + algorithm).that(quantiles.keySet()).isEqualTo(indexes);
      for (int i : indexes) {
        assertWithMessage("Mismatch between %s and %s at %s", algorithm, REFERENCE_ALGORITHM, i)
            .that(quantiles.get(i))
            .isWithin(ALLOWED_ERROR)

      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of " + values).that(meanByAddAllStats).isNaN();
      } else if (values.hasAnyPositiveInfinity() && values.hasAnyNegativeInfinity()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of " + values).that(meanByAddAllStats).isNaN();

      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();
      } else if (values.hasAnyPositiveInfinity() && values.hasAnyNegativeInfinity()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();
      } else if (values.hasAnyPositiveInfinity()) {
        assertWithMessage("mean of " + values).that(mean).isPositiveInfinity();

      if (values.hasAnyNonFinite()) {
        assertWithMessage("population covariance of " + values).that(populationCovariance).isNaN();
        assertWithMessage("population covariance by addAll(PairedStats) of " + values)
            .that(populationCovarianceByAddAllPartitionedPairedStats)
            .isNaN();
      } else {
        assertWithMessage("population covariance of " + values)

import static com.google.common.truth.Truth.assertWithMessage;

import java.util.concurrent.Executor;
import java.util.concurrent.Future;
import junit.framework.TestCase;

/** Test for {@link ListenableFuture}. */
public class ListenableFutureTest extends TestCase {
  public void testNoNewApis() throws Exception {
    assertWithMessage(

      if (values.hasAnyNonFinite()) {
        assertWithMessage("population covariance of " + values).that(populationCovariance).isNaN();
        assertWithMessage("population covariance by addAll(PairedStats) of " + values)
            .that(populationCovarianceByAddAllPartitionedPairedStats)
            .isNaN();
      } else {
        assertWithMessage("population covariance of " + values)

    Type paramType = getOnlyParameterType();
    TestSubtype spec = method.getAnnotation(TestSubtype.class);
    assertWithMessage("%s is subtype of %s", paramType, returnType)
        .that(TypeToken.of(paramType).isSubtypeOf(returnType))
        .isTrue();
    assertWithMessage("%s is supertype of %s", returnType, paramType)
        .that(TypeToken.of(returnType).isSupertypeOf(paramType))
        .isTrue();

      assertWithMessage("Float value of " + unsignedValue)
          .that(unsignedValue.floatValue())
          .isEqualTo(unsignedValue.bigIntegerValue().floatValue());
    }
  }

  public void testDoubleValue() {
    for (long value : TEST_LONGS) {
      UnsignedLong unsignedValue = UnsignedLong.fromLongBits(value);
      assertWithMessage("Double value of " + unsignedValue)

      if (values.hasAnyNaN()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of " + values).that(meanByAddAllStats).isNaN();
      } else if (values.hasAnyPositiveInfinity() && values.hasAnyNegativeInfinity()) {
        assertWithMessage("mean of " + values).that(mean).isNaN();
        assertWithMessage("mean by addAll(Stats) of " + values).that(meanByAddAllStats).isNaN();

      assertWithMessage("Float value of " + unsignedValue)
          .that(unsignedValue.floatValue())
          .isEqualTo(unsignedValue.bigIntegerValue().floatValue());
    }
  }

  public void testDoubleValue() {
    for (long value : TEST_LONGS) {
      UnsignedLong unsignedValue = UnsignedLong.fromLongBits(value);
      assertWithMessage("Double value of " + unsignedValue)