def test_predict(self):
# define some easy training data and predict predictive distribution
circle1 = Ring(variance=1, radius=3)
circle2 = Ring(variance=1, radius=10)
n = 100
X = circle1.sample(n / 2).samples
X = vstack((X, circle2.sample(n / 2).samples))
y = ones(n)
y[:n / 2] = -1.0
# plot(X[:n/2,0], X[:n/2,1], 'ro')
# hold(True)
# plot(X[n/2:,0], X[n/2:,1], 'bo')
# hold(False)
# show()
covariance = SquaredExponentialCovariance(1, 1)
likelihood = LogitLikelihood()
gp = GaussianProcess(y, X, covariance, likelihood)
# predict on mesh
n_test = 20
P = linspace(X[:, 0].min() - 1, X[:, 1].max() + 1, n_test)
Q = linspace(X[:, 1].min() - 1, X[:, 1].max() + 1, n_test)
X_test = asarray(list(itertools.product(P, Q)))
# Y_test = exp(LaplaceApproximation(gp).predict(X_test).reshape(n_test, n_test))
Y_train = exp(LaplaceApproximation(gp).predict(X))
print Y_train
print Y_train>0.5
print y
def main():
dist=Ring(dimension=50)
X=dist.sample(10000).samples
#print X[:,2:dist.dimension]
print dist.emp_quantiles(X)
dist2=Banana(dimension=50)
X2=dist2.sample(10000).samples
print dist2.emp_quantiles(X2)
