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GP_regression and sparse_GP_regression now only return the full

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posterior covariance matrix when requested.
This commit is contained in:
James Hensman 2012-12-21 11:42:39 +00:00
parent 2999c6d2d6
commit aae006411a
2 changed files with 36 additions and 14 deletions
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13
GPy/models/sparse_GP_regression.py
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@ -171,14 +171,19 @@ class sparse_GP_regression(GP_regression):
def log_likelihood_gradients(self):
return np.hstack([self.dL_dZ().flatten(), self.dL_dbeta(), self.dL_dtheta()])
def _raw_predict(self, Xnew, slices):
def _raw_predict(self, Xnew, slices, full_cov=False):
"""Internal helper function for making predictions, does not account for normalisation"""
Kx = self.kern.K(self.Z, Xnew)
Kxx = self.kern.K(Xnew)
mu = mdot(Kx.T, self.LBL_inv, self.psi1V)
var = Kxx - mdot(Kx.T, (self.Kmmi - self.LBL_inv), Kx) + np.eye(Xnew.shape[0])/self.beta # TODO: This beta doesn't belong here in the EP case.
if full_cov:
Kxx = self.kern.K(Xnew)
var = Kxx - mdot(Kx.T, (self.Kmmi - self.LBL_inv), Kx) + np.eye(Xnew.shape[0])/self.beta # TODO: This beta doesn't belong here in the EP case.
else:
Kxx = self.kern.Kdiag(Xnew)
var = Kxx - np.sum(Kx*np.dot(self.Kmmi - self.LBL_inv, Kx),0) + 1./self.beta # TODO: This beta doesn't belong here in the EP case.
return mu,var
def plot(self, *args, **kwargs):