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Added sampling for predictive quantiles and also mean and variance

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where necessary
This commit is contained in:
Alan Saul 2013-10-28 15:22:06 +00:00
parent 5a924ff5cb
commit 336f8e11c4
4 changed files with 53 additions and 39 deletions
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20
GPy/examples/regression.py
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@ -272,11 +272,10 @@ def toy_rbf_1d_50(max_iters=100):
def toy_poisson_rbf_1d(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
x_len = 400
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.EP(Y,noise_model)
@ -293,11 +292,10 @@ def toy_poisson_rbf_1d(optimizer='bfgs', max_nb_eval_optim=100):
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
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)
@ -309,6 +307,8 @@ def toy_poisson_rbf_1d_laplace(optimizer='bfgs', max_nb_eval_optim=100):
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