def __call__(self,x,cov=False):
assert self._L is not None
ks = _sparse_k(self._a, self._l2, x, self._x)
# calculate the mean
f = ks.dot(np.atleast_2d(self._alpha).T)[:,0]
if not cov:
return f
kss = _sparse_k(self._a, self._l2, x, x)
kik = np.zeros(ks.shape)
for i in xrange(ks.shape[0]):
kik[i,:] = self._L.solve(np.array(ks[i,:].todense())[0])
v = kss - ks.dot(kik.T)
return f,v
def K(self,x,y=None):
if y is None:
y = x
b = _sparse_k(self._a, self._l2, x, y) \
+ self._s2 * sp.identity(len(x),format="csc")
return b
