} else {
      // This is a generalized version of the calculation in add(double, double) above. Note that
      // non-finite inputs will have sumOfProductsOfDeltas = NaN, so non-finite values will result
      // in NaN naturally.
      sumOfProductsOfDeltas +=
          values.sumOfProductsOfDeltas()
              + (values.xStats().mean() - xStats.mean())
                  * (values.yStats().mean() - yStats.mean())

      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);

    }
    return false;
  }

  /**
   * Returns the base 2 logarithm of a double value.
   *
   * <p>Special cases:
   *
   * <ul>
   *   <li>If {@code x} is NaN or less than zero, the result is NaN.
   *   <li>If {@code x} is positive infinity, the result is positive infinity.
   *   <li>If {@code x} is positive or negative zero, the result is negative infinity.
   * </ul>
   *

     *    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.

   *
   * <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();
  }

            lt=lt,
            le=le,
            min_length=min_length,
            max_length=max_length,
            discriminator=discriminator,
            multiple_of=multiple_of,
            allow_nan=allow_inf_nan,
            max_digits=max_digits,
            decimal_places=decimal_places,
            **extra,
        )
        if examples is not None:
            kwargs["examples"] = examples

   * the arithmetic mean of the population.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains {@link Double#NaN} then the result is {@link Double#NaN}. If it
   * contains both {@link Double#POSITIVE_INFINITY} and {@link Double#NEGATIVE_INFINITY} then the
   * result is {@link Double#NaN}. If it contains {@link Double#POSITIVE_INFINITY} and finite values

        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

    }

    @Override
    public double slope() {
      return NaN;
    }

    @Override
    public double transform(double x) {
      return NaN;
    }

    @Override
    public LinearTransformation inverse() {
      return this;
    }

    @Override
    public String toString() {
      return "NaN";
    }
  }

        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