d383403c8e62df942d7fc54da8116ff98cc0b35a,GPy/examples/classification.py,,sparse_crescent_data,#Any#Any#,158

Before Change

    Y[Y.flatten()==-1]=0

    // Kernel object
    kernel = GPy.kern.rbf(data["X"].shape[1]) + GPy.kern.white(data["X"].shape[1])

    // Likelihood object
    distribution = GPy.likelihoods.likelihood_functions.binomial()
    likelihood = GPy.likelihoods.EP(Y, distribution)

After Change

    m = GPy.models.sparse_GP_classification(data["X"], Y)
    m.ensure_default_constraints()
    m[".*len"] = 10.
    m.update_likelihood_approximation()
    m.optimize()
    print(m)
    m.plot()

Instances