70e4d8aee5070b3b6fdf7e4739b42178a2d34f28,GPflowOpt/acquisition.py,Acquisition,_optimize_all,#Acquisition#,45

Before Change

        self._optimize_all()

    def _optimize_all(self):
        for model in self.models:
            // If likelihood variance is close to zero, updating data may result in non-invertible K
            // Increase likelihood variance a bit.
            model.likelihood.variance = 4.0
            model.optimize()

    def _build_acquisition_wrapper(self, Xcand, gradients=True):
        acq = self.build_acquisition(Xcand)
        if gradients:
            return acq, tf.gradients(acq, [Xcand], name="acquisition_gradient")[0]

After Change

            except:
                // After data update, the starting point for the hypers may result in non-invertible K
                // Reset the hypers to the values when the acquisition function was initialized.
                model.set_state(hypers)
                model.optimize()

    def _build_acquisition_wrapper(self, Xcand, gradients=True):
        acq = self.build_acquisition(Xcand)

Instances