*    value (whether it is NaN or infinity).
     * 2. If the new value is finite and the previous mean is non-finite then the mean is unchanged
     *    (whether it is NaN or infinity).
     * 3. If both the previous mean and the new value are non-finite and...
     * 3a. ...either or both is NaN (so mean != value) then the new mean is NaN.

      assertWithMessage("" + value)
          .that(Floats.indexOf(new float[] {5f, value, value, 5f}, new float[] {value, value}))
          .isEqualTo(1);
    }
    assertThat(Floats.indexOf(new float[] {5f, NaN, NaN, 5f}, new float[] {NaN, NaN}))
        .isEqualTo(-1);
  }

  public void testLastIndexOf() {
    assertThat(Floats.lastIndexOf(EMPTY, (float) 1)).isEqualTo(-1);

   *
   * <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();
  }

 * uniform distribution on [0,1]."
 *
 * <h3>Handling of non-finite values</h3>
 *
 * <p>If any values in the input are {@link Double#NaN NaN} then all values returned are {@link
 * Double#NaN NaN}. (This is the one occasion when the behaviour is not the same as you'd get from
 * sorting with {@link java.util.Arrays#sort(double[]) Arrays.sort(double[])} or {@link

        assertFalse(DoubleMath.fuzzyEquals(Double.NaN, a, tolerance));
      }
    }
  }

  public void testFuzzyEqualsTwoNaNs() {
    for (double tolerance : TOLERANCE_CANDIDATES) {
      assertTrue(DoubleMath.fuzzyEquals(Double.NaN, Double.NaN, tolerance));
    }
  }

  public void testFuzzyEqualsZeroTolerance() {
    // make sure we test -0 tolerance

          .that(Doubles.indexOf(new double[] {5.0, value, value, 5.0}, new double[] {value, value}))
          .isEqualTo(1);
    }
    assertThat(Doubles.indexOf(new double[] {5.0, NaN, NaN, 5.0}, new double[] {NaN, NaN}))
        .isEqualTo(-1);
  }

  public void testLastIndexOf() {
    assertThat(Doubles.lastIndexOf(EMPTY, (double) 1)).isEqualTo(-1);

          .that(Doubles.indexOf(new double[] {5.0, value, value, 5.0}, new double[] {value, value}))
          .isEqualTo(1);
    }
    assertThat(Doubles.indexOf(new double[] {5.0, NaN, NaN, 5.0}, new double[] {NaN, NaN}))
        .isEqualTo(-1);
  }

  public void testLastIndexOf() {
    assertThat(Doubles.lastIndexOf(EMPTY, (double) 1)).isEqualTo(-1);

        assertFalse(DoubleMath.fuzzyEquals(Double.NaN, a, tolerance));
      }
    }
  }

  public void testFuzzyEqualsTwoNaNs() {
    for (double tolerance : TOLERANCE_CANDIDATES) {
      assertTrue(DoubleMath.fuzzyEquals(Double.NaN, Double.NaN, tolerance));
    }
  }

  public void testFuzzyEqualsZeroTolerance() {
    // make sure we test -0 tolerance

   * written to or read from it. For example, whether {@code list.get(0) == list.get(0)} is true for
   * the returned list is unspecified.
   *
   * <p>The returned list may have unexpected behavior if it contains {@code NaN}, or if {@code NaN}
   * is used as a parameter to any of its methods.
   *
   * <p>The returned list is serializable.
   *
   * @param backingArray the array to back the list
   * @return a list view of the array
   */

                return input.doubleValue() > 0.0;
              }
            });
    DOUBLE_CANDIDATES_EXCEPT_NAN = Iterables.concat(FINITE_DOUBLE_CANDIDATES, INFINITIES);
    ALL_DOUBLE_CANDIDATES = Iterables.concat(DOUBLE_CANDIDATES_EXCEPT_NAN, asList(Double.NaN));
  }