def predict(self, X, with_noise=True):
        """
        Predictions with the model. Returns posterior means and standard deviations at X. Note that this is different in GPy where the variances are given.

        Parameters:
            X (np.ndarray) - points to run the prediction for.
            with_noise (bool) - whether to add noise to the prediction. Default is True.
        """
        m, v = self._predict(X, False, with_noise)
        # We can take the square root because v is just a diagonal matrix of variances
        return m, np.sqrt(v)
def predict ( self , X , with_noise = True ) : m , v = self . _predict ( X , False , with_noise ) return m , np . sqrt ( v )
Predictions with the model. Returns posterior means and standard deviations at X. Note that this is different in GPy where the variances are given.

        Parameters:
            X (np.ndarray) - points to run the prediction for.
            with_noise (bool) - whether to add noise to the prediction. Default is True.