dora/active_sampling/gp_sampler.py
def train(self):
        """
        Train the Gaussian process model.

        A wrapper function that learns the hyperparameters and updates the
        regressors, which is equivalent to a fully trained model that is
        ready to perform Inference

        .. note :: [Properties Modified]
                    hyperparameters,
                    regressors
        """
        assert(self.dims is not None)

        if self.kerneldef is None:

            def kerneldef(h, k):
                a = h(1e-3, 1e+2, 1)
                b = [h(1e-2, 1e+3, 1) for _ in range(self.dims)]
                logsigma = h(-6, 2)
                return a * k(gp.kernels.gaussian, b) + \
                    k(gp.kernels.lognoise, logsigma)

            self.kerneldef = kerneldef

        # Learn hyperparameters
        self.hyperparams = self.learn_hyperparams()

        # Update the regressors
        self.update_regressors()

        Train the Gaussian process model.

        A wrapper function that learns the hyperparameters and updates the
        regressors, which is equivalent to a fully trained model that is
        ready to perform Inference

        .. note :: [Properties Modified]
                    hyperparameters,
                    regressors
        
Train the Gaussian process model.
A wrapper function that learns the hyperparameters and updates the
regressors, which is equivalent to a fully trained model that is
ready to perform Inference

note :: [Properties Modified]
hyperparameters,
regressors
Train the Gaussian process model . A wrapper function that learns the hyperparameters and updates the regressors which is equivalent to a fully trained model that is ready to perform Inference note :: [ Properties Modified ] hyperparameters regressors
def train(self):
        assert(self.dims is not None)
        if self.kerneldef is None:
            def kerneldef(h, k):
                a = h(1e-3, 1e+2, 1)
                b = [h(1e-2, 1e+3, 1) for _ in range(self.dims)]
                logsigma = h(-6, 2)
                return a * k(gp.kernels.gaussian, b) + \
                    k(gp.kernels.lognoise, logsigma)
            self.kerneldef = kerneldef
        self.hyperparams = self.learn_hyperparams()
        self.update_regressors()
def train ( self ) : assert ( self . dims is not None ) if self . kerneldef is None : def kerneldef ( h , k ) : a = h ( 1e-3 , 1e+2 , 1 ) b = [ h ( 1e-2 , 1e+3 , 1 ) for _ in range ( self . dims ) ] logsigma = h ( - 6 , 2 ) return a * k ( gp . kernels . gaussian , b ) + k ( gp . kernels . lognoise , logsigma ) self . kerneldef = kerneldef self . hyperparams = self . learn_hyperparams ( ) self . update_regressors ( )
Train the Gaussian process model.
Train the Gaussian process model .
"""
        Train the Gaussian process model.

        A wrapper function that learns the hyperparameters and updates the
        regressors, which is equivalent to a fully trained model that is
        ready to perform Inference

        .. note :: [Properties Modified]
                    hyperparameters,
                    regressors
        """ # Learn hyperparameters # Update the regressors
self