(self, points)
return res
Return the gradient.

        Parameters
        ----------
        points : ndarray
            The points at which to evaluate the function. One row for each
            data points.

        Returns
        -------
        gradient : ndarray
            The function gradient at the points. A 3D array with the gradient
            at the i-th data points for the j-th output with regard to the k-th
            dimension stored at (i, j, k). The j-th dimension is squeezed out
            for 1D functions.
Return the gradient.
Return the gradient .
def gradient(self, points):
        """Return the gradient.

        Parameters
        ----------
        points : ndarray
            The points at which to evaluate the function. One row for each
            data points.

        Returns
        -------
        gradient : ndarray
            The function gradient at the points. A 3D array with the gradient
            at the i-th data points for the j-th output with regard to the k-th
            dimension stored at (i, j, k). The j-th dimension is squeezed out
            for 1D functions.

        """
        points = np.atleast_2d(points)
        weights, simplices = self._get_weights_gradient(points)
        # Return function values if desired
        res = np.einsum('ijk,ikl->ilj', weights, self.parameters[simplices, :])
        if res.shape[1] == 1:
            res = res.squeeze(axis=1)
        return res
def gradient ( self , points ) : points = np . atleast_2d ( points ) weights , simplices = self . _get_weights_gradient ( points ) # Return function values if desired res = np . einsum ( 'ijk,ikl->ilj' , weights , self . parameters [ simplices , : ] ) if res . shape [ 1 ] == 1 : res = res . squeeze ( axis = 1 ) return res