Regression using RBF kernel

Questions
11

I am no completely sure what is the implementation behind the kernel regression in sklearn, but I manage to make your example work:

# bayesian rbf kernel
with pm.Model() as model:
    # hyper-prior for ls
    ls = pm.Beta("ls", alpha=1e-2, beta=1e-4)
    cov_func = pm.gp.cov.ExpQuad(X.shape[1], ls=ls)

    # Specify the GP.
    gp = pm.gp.Marginal(cov_func=cov_func)

    # Place a GP prior over the function f.
    sigma = pm.HalfNormal("sigma", 1)
    y_ = gp.marginal_likelihood("y", X=X, y=y, noise=sigma)
    # inference
    map1 = pm.find_MAP()
    pred = gp.predict(X_new, point=map1)
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