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 How do I create these Kernel functions in Python for Gaussian Process Regression?
Hello all! I am new to this so I would appreciate any help.

I have a dataset of 1031 observed samples of 7 features that form the X and one target variable that forms the Y.

See below for input data and target representation with number of rows (n) and columns (m) mentioned in round brackets as (n x m). Here x1, x2, ..., x7 and y represent column vectors.

X (1031 x 7) = [x1, x2, x3, ..., x7]
Y (1031 x 1) = [y]

I am using Gaussian Process Regressor to train my models. I want to use anisotropic Gaussian and anisotropic exponential correlation functions as kernels. Please see equation 14 and 15 in the attached equation pic for reference.


How do I define these two functions in python such that they are compatible with SKlearns's GPR? Please help.


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