c46a1aaa40d45512468ca7c3c004656ad2f94afb,GPy/core/gp.py,GP,_log_likelihood_gradients,#GP#,102

Before Change

        Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta
        
        return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))

    def _raw_predict(self, _Xnew, which_parts="all", full_cov=False, stop=False):
        
        Internal helper function for making predictions, does not account

After Change

            //self.likelihood.fit_full(self.kern.K(self.X))
            //self.likelihood._set_params(self.likelihood._get_params())
            dK_dthetaK = self.kern.dK_dtheta
            dL_dthetaK = self.likelihood._Kgradients(dK_dthetaK, self.X.copy())
            dL_dthetaL = self.likelihood._gradients(partial=np.diag(self.dL_dK))
        else:
            dL_dthetaL = self.likelihood._gradients(partial=np.diag(self.dL_dK))

        return np.hstack((dL_dthetaK, dL_dthetaL))

    def _raw_predict(self, _Xnew, which_parts="all", full_cov=False, stop=False):
        
        Internal helper function for making predictions, does not account

Instances