reflect the GCM // standard and make binary.BigEndian suitable for marshaling these values, the // bits are stored in big endian order. For example: // // the coefficient of x⁰ can be obtained by v.low >> 63. // the coefficient of x⁶³ can be obtained by v.low & 1. // the coefficient of x⁶⁴ can be obtained by v.high >> 63. // the coefficient of x¹²⁷ can be obtained by v.high & 1. type gcmFieldElement struct { low, high uint64 } // GHASH is exposed to allow crypto/cipher to implement non-AES GCM modes....

        assertWithMessage("Pearson's correlation coefficient of %s", values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
        assertWithMessage("Pearson's correlation coefficient by addAll(PairedStats) of %s", values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of %s", values)

reflect the GCM // standard and make binary.BigEndian suitable for marshaling these values, the // bits are stored in big endian order. For example: // // the coefficient of x⁰ can be obtained by v.low >> 63. // the coefficient of x⁶³ can be obtained by v.low & 1. // the coefficient of x⁶⁴ can be obtained by v.high >> 63. // the coefficient of x¹²⁷ can be obtained by v.high & 1. type gcmFieldElement struct { low, high uint64 } // GHASH is exposed to allow crypto/cipher to implement non-AES GCM modes....

        assertWithMessage("Pearson's correlation coefficient of %s", values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
        assertWithMessage("Pearson's correlation coefficient by addAll(PairedStats) of %s", values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of %s", values)

    return sumOfProductsOfDeltas / (count() - 1);
  }

  /**
   * Returns the <a href="http://mathworld.wolfram.com/CorrelationCoefficient.html">Pearson's or
   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code

      if (values.hasAnyNonFinite()) {
        assertWithMessage("Pearson's correlation coefficient of %s", values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of %s", values)
            .that(pearsonsCorrelationCoefficient)
            .isWithin(ALLOWED_ERROR)
            .of(

      if (values.hasAnyNonFinite()) {
        assertWithMessage("Pearson's correlation coefficient of %s", values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of %s", values)
            .that(pearsonsCorrelationCoefficient)
            .isWithin(ALLOWED_ERROR)
            .of(

    1L * 2 * 3 * 4 * 5 * 6 * 7 * 8 * 9 * 10 * 11 * 12 * 13 * 14 * 15 * 16 * 17 * 18 * 19 * 20
  };

  /**
   * Returns {@code n} choose {@code k}, also known as the binomial coefficient of {@code n} and
   * {@code k}, or {@link Long#MAX_VALUE} if the result does not fit in a {@code long}.
   *
   * @throws IllegalArgumentException if {@code n < 0}, {@code k < 0}, or {@code k > n}
   */

        `tf.contrib.data.sloppy_interleave()`.
*   Add `train_and_evaluate` for simple distributed `Estimator` training.
*   Add `tf.spectral.dct` for computing the DCT-II.
*   Add Mel-Frequency Cepstral Coefficient support to `tf.contrib.signal` (with
    GPU and gradient support).
*   Add a self-check on `import tensorflow` for Windows DLL issues.
*   Add NCHW support to `tf.depth_to_space` on GPU.