Smt with combination of kernels

[NicolasJeanGonel]

Added ways to combine kernels to create more complex kernels. For now only addition and multiplication are possible. Several important points should be noted :

• As they are, Pow_Exp kernels are not usable in a combination as they lack the computation of the power for the distance and should be rewritten to include them.
• In order to normalize the resulting kernels in the case of addition, a dummy kernel is multiplied to the resulting kernel which can be a bit heavy and confusing for the user.
• Chained derivatives for the addition and multiplication are theoretically working but based on personnal tests felt very slow and inefficient, with disappointing results. Either there is a better way to compute them, or it is preferable to stay with gradient-free optimizer with a combined kernel.
• It seems that these complex kernels can cause more local minimums, making it so that the order of addition and multiplication can create different results ; this issue is still investigated.