create separate modules for kernels

[sampan501]

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[sampan501]

Can enable tests such as: https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf

[sampan501]

also

• Kernel CCA where the two input kernels can be specified.
• The Randomized Dependence Coefficient (RDC). This is simply CCA on random Fourier features.
• The linear-time independence test in this paper. Python code is available from the authors.
• Block-estimator version of HSIC in this paper. This has O(n) runtime complexity (asymptotically).
• Related to the previous point, in fact, all HSIC variants can be supplied with options of incomplete U-statistic estimators that the users can choose from, ranging from O(n) to O(n**2), depending on the computational budget (but trading off the variance). This will be very useful. More details on incomplete U-statistic estimators can be found here.