Градиент криг-функции в Openmdao

В настоящее время я кодирую алгоритм множественного градиентного спуска, где использую функции кригинга. Моя проблема в том, что я не могу найти, как получить градиент функции кригинга (я пытался использовать линеаризацию, но не знаю, как заставить ее работать).

    from __future__ import print_function

from six import moves
from random import shuffle
import sys
import numpy as np
from numpy import linalg as LA
import math
from openmdao.braninkm import F, G, DF, DG

from openmdao.api import Group, Component,IndepVarComp
from openmdao.api import MetaModel
from openmdao.api import KrigingSurrogate, FloatKrigingSurrogate

def rand_lhc(b, k):
    # Calculates a random Latin hypercube set of n points in k dimensions within [0,n-1]^k hypercube.
    arr = np.zeros((2*b, k))
    row = list(moves.xrange(-b, b))
    for i in moves.xrange(k):
        shuffle(row)
        arr[:, i] = row
    return arr/b*1.2


class TrigMM(Group):
    ''' FloatKriging gives responses as floats '''

    def __init__(self):
        super(TrigMM, self).__init__()

        # Create meta_model for f_x as the response
        F_mm = self.add("F_mm", MetaModel())
        F_mm.add_param('X', val=np.array([0., 0.]))
        F_mm.add_output('f_x:float', val=0., surrogate=FloatKrigingSurrogate())
       # F_mm.add_output('df_x:float', val=0., surrogate=KrigingSurrogate().linearize)


        #F_mm.linearize('X', 'f_x:float')
        #F_mm.add_output('g_x:float', val=0., surrogate=FloatKrigingSurrogate())
        print('init ok')
        self.add('p1', IndepVarComp('X', val=np.array([0., 0.])))
        self.connect('p1.X','F_mm.X')       

        # Create meta_model for f_x as the response
        G_mm = self.add("G_mm", MetaModel())
        G_mm.add_param('X', val=np.array([0., 0.]))
        G_mm.add_output('g_x:float', val=0., surrogate=FloatKrigingSurrogate())
        #G_mm.add_output('df_x:float', val=0., surrogate=KrigingSurrogate().linearize)

        #G_mm.linearize('X', 'g_x:float')
        self.add('p2', IndepVarComp('X', val=np.array([0., 0.])))
        self.connect('p2.X','G_mm.X')                 

from openmdao.api import Problem

prob = Problem()
prob.root = TrigMM()
prob.setup()

u=4 
v=3 

#training avec latin hypercube

prob['F_mm.train:X'] = rand_lhc(20,2)
prob['G_mm.train:X'] = rand_lhc(20,2)

#prob['F_mm.train:X'] = rand_lhc(10,2)
#prob['G_mm.train:X'] = rand_lhc(10,2)
#prob['F_mm.linearize:X'] = rand_lhc(10,2)
#prob['G_mm.linearize:X'] = rand_lhc(10,2)
datF=[]
datG=[]
datDF=[]
datDG=[]

for i in range(len(prob['F_mm.train:X'])):
    datF.append(F(np.array([prob['F_mm.train:X'][i]]),u))
    #datG.append(G(np.array([prob['F_mm.train:X'][i]]),v))
data_trainF=np.fromiter(datF,np.float) 

for i in range(len(prob['G_mm.train:X'])):
    datG.append(G(np.array([prob['G_mm.train:X'][i]]),v))   
data_trainG=np.fromiter(datG,np.float) 

prob['F_mm.train:f_x:float'] = data_trainF
#prob['F_mm.train:g_x:float'] = data_trainG
prob['G_mm.train:g_x:float'] = data_trainG