336f8e11c48bb4e749b9f389907c450e44f02786,GPy/examples/regression.py,,toy_poisson_rbf_1d_laplace,#Any#Any#,294

Before 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 = 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)

Instances