/**
* @brief Gets the cross differences between the training and test inputs.
* @param [in] pXs The M test inputs
* @param [in] coord Corresponding coordinate
* @return An matrix pointer
* \f[
* \mathbf{D} \in \mathbb{R}^{N \times M}, \quad
* \mathbf{D}_{ij} = \mathbf{x}_i^c - \mathbf{z}_j^c
* \f]
* @todo Include this matrix as a member variable like m_pDeltaXXList
*/
MatrixPtr pDeltaXXs(const TestData<Scalar> &testData, const int coord) const
{
assert(m_pX && testData.M() > 0);
assert(D() == testData.D());
return PairwiseOp<Scalar>::delta(m_pX, testData.pXs(), coord); // NxM
}
/**
* @brief Gets the cross squared distances between the training and test inputs
* @param [in] pXs The M test inputs
* @return An matrix pointer
* \f[
* \mathbf{R^2} \in \mathbb{R}^{N \times M}, \quad
* \mathbf{R^2}_{ij} = (\mathbf{x}_i - \mathbf{z}_j)^\text{T}(\mathbf{x}_i - \mathbf{z}_j)
* \f]
* @todo Include this matrix as a member variable like m_pSqDistXX
*/
MatrixPtr pSqDistXXs(const TestData<Scalar> &testData) const
{
assert(m_pX && testData.M() > 0);
assert(D() == testData.D());
return PairwiseOp<Scalar>::sqDist(m_pX, testData.pXs()); // NxM
}