def test_log_lik_multiple2(self):
n = 3
y = randint(0, 2, n) * 2 - 1
F = randn(10, n)
X = randn(n, 2)
covariance = SquaredExponentialCovariance(sigma=1, scale=1)
likelihood = LogitLikelihood()
gp = GaussianProcess(y, X, covariance, likelihood)
singles = asarray([gp.log_likelihood(f) for f in F])
multiples = gp.log_likelihood_multiple(F)
self.assertLessEqual(norm(singles - multiples), 1e-10)
def test_log_lik_multiple1(self):
n = 3
y = randint(0, 2, n) * 2 - 1
f = randn(n)
X = randn(n, 2)
covariance = SquaredExponentialCovariance(sigma=1, scale=1)
likelihood = LogitLikelihood()
gp = GaussianProcess(y, X, covariance, likelihood)
single = gp.log_likelihood(f)
multiple = gp.log_likelihood_multiple(f.reshape(1, n))
self.assertLessEqual(norm(single - multiple), 1e-10)
