/**
* @brief Self covariance matrix between the training data, K(X, X) or its partial derivative
* @note It calls the protected general member function,
* CovMaterniso::K(const Hyp, const MatrixConstPtr, const int)
* which only depends on pair-wise absolute distances.\n\n
* Only this function returns partial derivatives
* since they are used for learning hyperparameters with training data.
* @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] pdHypIndex (Optional) Hyperparameter index for partial derivatives
* - pdHypIndex = -1: return \f$\mathbf{K}(\mathbf{X}, \mathbf{X})\f$ (default)
* - pdHypIndex = 0: return \f$\frac{\partial \mathbf{K}}{\partial \log(l)}\f$
* - pdHypIndex = 1: return \f$\frac{\partial \mathbf{K}}{\partial \log(\sigma_f)}\f$
* @return An NxN matrix pointer\n
* N: The number of training data
*/
static MatrixPtr K(const Hyp &logHyp,
TrainingData<Scalar> &trainingData,
const int pdHypIndex = -1)
{
// Assertions only in the begining of the public static member functions which can be accessed outside.
// The hyparparameter index should be less than the number of hyperparameters
assert(pdHypIndex < logHyp.size());
// K(r)
return K(logHyp, trainingData.pAbsDistXX(), pdHypIndex);
}
