1a135ca9f7d98f63a3183895f27021308da4d9be,grid_parameters.py,,,#,3

Before Change

lls = np.array(lls).reshape(resolution,resolution)
cgs = np.array(cgs,dtype=np.float64).reshape(resolution,resolution)

pb.contourf(xx,yy,lls,np.linspace(-500,560,100),linewidths=2,cmap=pb.cm.jet)
pb.colorbar()
pb.scatter(xx.flatten(),yy.flatten(),10,cgs.flatten(),linewidth=0,cmap=pb.cm.gray)
pb.figure()

After Change

k = GPy.kern.rbf(1)


models = [GPy.models.sparse_GP_regression(X,Y,Z=Z,kernel=k),
    GPy.models.sgp_debugB(X,Y,Z=Z,kernel=k),
    GPy.models.sgp_debugC(X,Y,Z=Z,kernel=k),
    GPy.models.sgp_debugE(X,Y,Z=Z,kernel=k)]
//[m.constrain_fixed("iip") for m in models]
//m.constrain_fixed("white",1e-6)
//[m.constrain_fixed("precision",50) for m in models]
//[m.ensure_default_constraints() for m in models]


xx,yy = np.mgrid[1.5:4:0+resolution*1j,-2:2:0+resolution*1j]

lls = []
cgs = []
for l,v in zip(xx.flatten(),yy.flatten()):
    [m.set("lengthscale",l) for m in models]
    [m.set("rbf_variance",10.**v) for m in models]
    lls.append(models[0].log_likelihood())
    cgs.append([m.checkgrad(verbose=0) for m in models])

lls = np.array(lls).reshape(resolution,resolution)
cgs = np.array(zip(*cgs),dtype=np.float64).reshape(-1,resolution,resolution)

Instances