dora/active_sampling/gp_sampler.py
def learn_hyperparams(self, verbose=False, ftol=1e-15, maxiter=2000):
        """
        Learn the kernel hyperparameters from the data collected so far.

        Equivalent to training the Gaussian process used for the sampler
        The training result is summarised by the hyperparameters of the kernel

        .. note :: Learns common hyperparameters between all tasks

        .. note :: [Properties Modified]
                    (None)

        Parameters
        ----------
        verbose : bool, optional
            To log the progress of the hyperparameter learning stage
        ftol : float, optional
            The tolerance level to reach for the learning objective
        maxiter : int, optional
            The maximum number of iterations allowed for optimisation

        Returns
        -------
        list
            A list of hyperparameters with each element being the
            hyperparameters of each corresponding task
        """
        self.update_y_mean()

        logging.info('Training hyperparameters...')
        snlml = gp.criterions.stacked_negative_log_marginal_likelihood
        hyperparams = gp.learn(self.X(), self.y(), self.kerneldef,
                               opt_criterion=snlml,
                               verbose=verbose, ftol=ftol, maxiter=maxiter)
        logging.info('Done.')

        return [hyperparams for i_task in range(self.n_tasks)]

        Learn the kernel hyperparameters from the data collected so far.

        Equivalent to training the Gaussian process used for the sampler
        The training result is summarised by the hyperparameters of the kernel

        .. note :: Learns common hyperparameters between all tasks

        .. note :: [Properties Modified]
                    (None)

        Parameters
        ----------
        verbose : bool, optional
            To log the progress of the hyperparameter learning stage
        ftol : float, optional
            The tolerance level to reach for the learning objective
        maxiter : int, optional
            The maximum number of iterations allowed for optimisation

        Returns
        -------
        list
            A list of hyperparameters with each element being the
            hyperparameters of each corresponding task
        
Learn the kernel hyperparameters from the data collected so far.
Equivalent to training the Gaussian process used for the sampler
The training result is summarised by the hyperparameters of the kernel

note :: Learns common hyperparameters between all tasks

note :: [Properties Modified]
(None)

Parameters

verbose : bool, optional
To log the progress of the hyperparameter learning stage
ftol : float, optional
The tolerance level to reach for the learning objective
maxiter : int, optional
The maximum number of iterations allowed for optimisation

Returns

list
A list of hyperparameters with each element being the
hyperparameters of each corresponding task
Learn the kernel hyperparameters from the data collected so far . Equivalent to training the Gaussian process used for the sampler The training result is summarised by the hyperparameters of the kernel note :: Learns common hyperparameters between all tasks note :: [ Properties Modified ] ( None ) Parameters verbose : bool optional To log the progress of the hyperparameter learning stage ftol : float optional The tolerance level to reach for the learning objective maxiter : int optional The maximum number of iterations allowed for optimisation Returns list A list of hyperparameters with each element being the hyperparameters of each corresponding task
def learn_hyperparams(self, verbose=False, ftol=1e-15, maxiter=2000):
        self.update_y_mean()
        logging.info('Training hyperparameters...')
        snlml = gp.criterions.stacked_negative_log_marginal_likelihood
        hyperparams = gp.learn(self.X(), self.y(), self.kerneldef,
                               opt_criterion=snlml,
                               verbose=verbose, ftol=ftol, maxiter=maxiter)
        logging.info('Done.')
        return [hyperparams for i_task in range(self.n_tasks)]
def learn_hyperparams ( self , verbose = False , ftol = 1e-15 , maxiter = 2000 ) : self . update_y_mean ( ) logging . info ( 'Training hyperparameters...' ) snlml = gp . criterions . stacked_negative_log_marginal_likelihood hyperparams = gp . learn ( self . X ( ) , self . y ( ) , self . kerneldef , opt_criterion = snlml , verbose = verbose , ftol = ftol , maxiter = maxiter ) logging . info ( 'Done.' ) return [ hyperparams for i_task in range ( self . n_tasks ) ]
Learn the kernel hyperparameters from the data collected so far.
Learn the kernel hyperparameters from the data collected so far .
"""
        Learn the kernel hyperparameters from the data collected so far.

        Equivalent to training the Gaussian process used for the sampler
        The training result is summarised by the hyperparameters of the kernel

        .. note :: Learns common hyperparameters between all tasks

        .. note :: [Properties Modified]
                    (None)

        Parameters
        ----------
        verbose : bool, optional
            To log the progress of the hyperparameter learning stage
        ftol : float, optional
            The tolerance level to reach for the learning objective
        maxiter : int, optional
            The maximum number of iterations allowed for optimisation

        Returns
        -------
        list
            A list of hyperparameters with each element being the
            hyperparameters of each corresponding task
        """
self
verbose
ftol
maxiter