4143f005403808d11928b9772a9a40679a4d441e,GPy/models/bayesian_gplvm_minibatch.py,BayesianGPLVMMiniBatch,_outer_init_full_values,#BayesianGPLVMMiniBatch#,136

Before Change

            return dict(meangrad=np.zeros(self.X.mean.shape),
                        vargrad=np.zeros(self.X.variance.shape))
        else:
            return dict(Xgrad=np.zeros(self.X.shape))

    def parameters_changed(self):
        super(BayesianGPLVMMiniBatch,self).parameters_changed()
        if isinstance(self.inference_method, VarDTC_minibatch):

After Change

            self.X.gradient = full_values["Xgrad"]

    def _outer_init_full_values(self):
        full_values = super(BayesianGPLVMMiniBatch, self)._outer_init_full_values()
        //if self.has_uncertain_inputs():
            //return dict(meangrad=np.zeros(self.X.mean.shape),
                        //vargrad=np.zeros(self.X.variance.shape))
        //else:
            //return dict(Xgrad=np.zeros(self.X.shape))
        return full_values

    def parameters_changed(self):
        super(BayesianGPLVMMiniBatch,self).parameters_changed()
        if isinstance(self.inference_method, VarDTC_minibatch):

Instances