gradient - Openmdao の kriged 関数の勾配

Question

私は現在、クリギング関数を使用する多重勾配降下法アルゴリズムをコーディングしています。私の問題は、kriged 関数の勾配を取得する方法が見つからないことです (linearize を使用しようとしましたが、それを機能させる方法がわかりません)。

    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