/**
* @brief Cross covariance matrix between the training and test data, Ks(X, Z)
* @note It calls the protected general member function,
* CovMaterniso::K(const Hyp, const MatrixConstPtr, const int)
* which only depends on pair-wise absolute distances.
* @param [in] logHyp The log hyperparameters
* - logHyp(0) = \f$\log(l)\f$
* - logHyp(1) = \f$\log(\sigma_f)\f$
* @param [in] trainingData The training data
* @param [in] testData The test data
* @return An NxM matrix pointer, \f$\mathbf{K}_* = \mathbf{K}(\mathbf{X}, \mathbf{Z})\f$\n
* N: The number of training data\n
* M: The number of test data
*/
static MatrixPtr Ks(const Hyp &logHyp,
const TrainingData<Scalar> &trainingData,
const TestData<Scalar> &testData)
{
// K(r)
return K(logHyp, trainingData.pAbsDistXXs(testData));
}
