5570e82943d70be03ea18b34a650590884cca02e,GPy/examples/regression.py,,sparse_GP_regression_2D,#Any#Any#Any#,345

Before Change

    // construct kernel
    rbf =  GPy.kern.rbf(2)
    noise = GPy.kern.white(2)
    kernel = rbf + noise

    // create simple GP Model
    m = GPy.models.SparseGPRegression(X,Y,kernel, num_inducing = num_inducing)

    // contrain all parameters to be positive (but not inducing inputs)
    m.set(".*len",2.)

    m.checkgrad()

    // optimize and plot

After Change

    m = GPy.models.SparseGPRegression(X, Y, kernel=rbf, num_inducing=num_inducing)

    // contrain all parameters to be positive (but not inducing inputs)
    m[".*len"] = 2.

    m.checkgrad()

    // optimize and plot

Instances