dora/active_sampling/gp_sampler.py
def update_regressors(self):
        """
        Update the regressors of the Gaussian process model.

        Only makes sense to do this after hyperparameters are learned

        .. note :: [Properties Modified]
                    regressors

        .. note :: [Further Work] Use Cholesky Update here correctly to cache
                    regressors and improve efficiency
        """
        if self.hyperparams is None:
            return
            # raise ValueError('Hyperparameters are not learned yet.' +
            #                  'Regressors cannot be updated.')

        # Create the regressors if it hasn't already been
        self.regressors = []
        for i_task in range(self.n_tasks):
            self.regressors.append(
                gp.condition(self.X(), self.y()[:, i_task] -
                             self.y_mean[i_task],
                             self.kerneldef, self.hyperparams[i_task]))

        Update the regressors of the Gaussian process model.

        Only makes sense to do this after hyperparameters are learned

        .. note :: [Properties Modified]
                    regressors

        .. note :: [Further Work] Use Cholesky Update here correctly to cache
                    regressors and improve efficiency
        
Update the regressors of the Gaussian process model.
Only makes sense to do this after hyperparameters are learned

note :: [Properties Modified]
regressors

note :: [Further Work] Use Cholesky Update here correctly to cache
regressors and improve efficiency
Update the regressors of the Gaussian process model . Only makes sense to do this after hyperparameters are learned note :: [ Properties Modified ] regressors note :: [ Further Work ] Use Cholesky Update here correctly to cache regressors and improve efficiency
def update_regressors(self):
        if self.hyperparams is None:
            return
        self.regressors = []
        for i_task in range(self.n_tasks):
            self.regressors.append(
                gp.condition(self.X(), self.y()[:, i_task] -
                             self.y_mean[i_task],
                             self.kerneldef, self.hyperparams[i_task]))
def update_regressors ( self ) : if self . hyperparams is None : return self . regressors = [ ] for i_task in range ( self . n_tasks ) : self . regressors . append ( gp . condition ( self . X ( ) , self . y ( ) [ : , i_task ] - self . y_mean [ i_task ] , self . kerneldef , self . hyperparams [ i_task ] ) )
Update the regressors of the Gaussian process model.
Update the regressors of the Gaussian process model .
"""
        Update the regressors of the Gaussian process model.

        Only makes sense to do this after hyperparameters are learned

        .. note :: [Properties Modified]
                    regressors

        .. note :: [Further Work] Use Cholesky Update here correctly to cache
                    regressors and improve efficiency
        """ # raise ValueError('Hyperparameters are not learned yet.' + #                  'Regressors cannot be updated.') # Create the regressors if it hasn't already been
self