Positive definite kernels; and their derivatives.

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Based on previous discussion with @sathvikbhagavan , derivatives for positive definite kernels (Kriging, Squared exponential functions, etc. is of interest.)

Kernels in the package include:

1. Radials
2. Inverse distance
3. Wenland
4. Kriging

Not in the package

5. Matern kernels
6. Wavelet features
7. Weighted random features (different from reservoir computing features)

Derivatives of order I, II, III and IV are required.
Since kernels are explicitly known and symbolics is not part of the package yet, separate dispatch functions identified by a Differential operator struct for the following kernels will be built.

• Kriging
• Subset of radial
• Subset of inverse distance
• Weak derivatives of Wenland
• Matern
• Wavelets
• weighted random features

To do so, the following is necessary:

• A derivative operator(s) struct
• An implementation of wavelet features
• An implementation of weighted random features
• An implementation of Matern kernel features

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I will check this off, as I make PRs. :)