def _raw_predict(self, Xnew, full_cov=False, kern=None):
if kern is None:<EOL><INDENT>kern = self.kern<EOL><DEDENT>Kmn = kern.K(Xnew, self.X)<EOL>alpha_kron = self.posterior.alpha<EOL>mu = np.dot(Kmn, alpha_kron)<EOL>mu = mu.reshape(-<NUM_LIT:1>,<NUM_LIT:1>)<EOL>Knm = Kmn.T        <EOL>noise = self.likelihood.variance<EOL>V_kron = self.posterior.V_kron<EOL>Qs = self.posterior.Qs<EOL>QTs = self.posterior.QTs<EOL>A = self.kron_mmprod(QTs, Knm)<EOL>V_kron = V_kron.reshape(-<NUM_LIT:1>, <NUM_LIT:1>)<EOL>A = A / (V_kron + noise)<EOL>A = self.kron_mmprod(Qs, A)<EOL>Kmm = kern.K(Xnew)<EOL>var = np.diag(Kmm - np.dot(Kmn, A)).copy()<EOL>var = var.reshape(-<NUM_LIT:1>, <NUM_LIT:1>)<EOL>return mu, var<EOL>
Make a prediction for the latent function values