def predict_noiseless(self, Xnew, full_cov=False, kern=None):
        """
        Predict the underlying function  f at the new point(s) Xnew.

        :param Xnew: The points at which to make a prediction
        :type Xnew: np.ndarray (Nnew x self.input_dim)
        :param full_cov: whether to return the full covariance matrix, or just the diagonal
        :type full_cov: bool
        :param kern: The kernel to use for prediction (defaults to the model kern).

        :returns: (mean, var):
            mean: posterior mean, a Numpy array, Nnew x self.input_dim
            var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise

           If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim.
           If self.input_dim == 1, the return shape is Nnew x Nnew.
           This is to allow for different normalizations of the output dimensions.
        """
        # Predict the latent function values
        mu, var = self._raw_predict(Xnew, full_cov=full_cov, kern=kern)

        # Un-apply normalization
        if self.normalizer is not None:
            mu, var = self.normalizer.inverse_mean(mu), self.normalizer.inverse_variance(var)

        return mu, var
def predict_noiseless ( self , Xnew , full_cov = False , kern = None ) : mu , var = self . _raw_predict ( Xnew , full_cov = full_cov , kern = kern ) if self . normalizer is not None : mu , var = self . normalizer . inverse_mean ( mu ) , self . normalizer . inverse_variance ( var ) return mu , var
Predict the underlying function  f at the new point(s) Xnew.

        :param Xnew: The points at which to make a prediction
        :type Xnew: np.ndarray (Nnew x self.input_dim)
        :param full_cov: whether to return the full covariance matrix, or just the diagonal
        :type full_cov: bool
        :param kern: The kernel to use for prediction (defaults to the model kern).

        :returns: (mean, var):
            mean: posterior mean, a Numpy array, Nnew x self.input_dim
            var: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise

           If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim.
           If self.input_dim == 1, the return shape is Nnew x Nnew.
           This is to allow for different normalizations of the output dimensions.