(self, X_test, **kwargs)
return mean, var
Returns the predictive mean and variance of the objective function
        at X average over the predictions of all trees.

        Parameters
        ----------
        X_test: np.ndarray (N, D)
            Input test points

        Returns
        ----------
        np.array(1,)
            predictive mean
        np.array(1,)
            predictive variance
Returns the predictive mean and variance of the objective function
        at X average over the predictions of all trees.
Returns the predictive mean and variance of the objective function at X average over the predictions of all trees .
def predict(self, X_test, **kwargs):
        """
        Returns the predictive mean and variance of the objective function
        at X average over the predictions of all trees.

        Parameters
        ----------
        X_test: np.ndarray (N, D)
            Input test points

        Returns
        ----------
        np.array(1,)
            predictive mean
        np.array(1,)
            predictive variance

        """
        mean = np.zeros(X_test.shape[0])
        var = np.zeros(X_test.shape[0])

        for i, x in enumerate(X_test):
            mean[i], var[i] = self.rf.predict_mean_var(x)

        return mean, var
def predict ( self , X_test , * * kwargs ) : mean = np . zeros ( X_test . shape [ 0 ] ) var = np . zeros ( X_test . shape [ 0 ] ) for i , x in enumerate ( X_test ) : mean [ i ] , var [ i ] = self . rf . predict_mean_var ( x ) return mean , var