Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.
X = np.linspace(0,10)[:, None]
F = np.round(X*3-4)
F = np.where(F > 0, F, 0)eps = np.random.randint(0,4, F.shape[0])[:, None]
Y = F + eps
noise_model = GPy.likelihoods.poisson()
likelihood = GPy.likelihoods.Laplace(Y,noise_model)
After Change
def toy_poisson_rbf_1d_laplace(optimizer="bfgs", max_nb_eval_optim=100):
Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance.
x_len = 30
X = np.linspace(0, 10, x_len)[:, None]
f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.rbf(1).K(X))
Y = np.array([np.random.poisson(np.exp(f)) for f in f_true])[:,None]
noise_model = GPy.likelihoods.poisson()
likelihood = GPy.likelihoods.Laplace(Y,noise_model)
// create simple GP Model
m = GPy.models.GPRegression(X, Y, likelihood=likelihood)
// optimize
m.optimize(optimizer, max_f_eval=max_nb_eval_optim)
// plot
m.plot()
// plot the real underlying rate function
pb.plot(X, np.exp(f_true), "--k", linewidth=2)
print(m)
return m
