Python 运行fmin_l_bfgs时出现索引问题

我试图在python中使用fmin_l_bfgs函数来最大化下面的对数似然函数：

def loglik(x0):
    p = np.zeros((NCS,1)) #vector to hold the probabilities for each observation
    data['v'] = (data.iloc[:, [3,4]]).dot(x0) #calculate determinstic utility
    for i in range(NCS):
        vv = data.v[(data.idcase == i + 1)] 
        vy = data.v[(data.idcase == i + 1) & (data.depvar == 1)]
        p[i][0] = np.maximum(np.exp(vy)/ sum(np.exp(vv)),0.00000001)
    #print("p", p)
    ll = -sum(np.log(p)) #Negative since neg of ll is minimized
    return ll

正在使用的输入数据是：

data = pd.read_csv("drive/My Drive/example_data.csv") #read data
data.iloc[:, [3,4]] = data.iloc[:, [3,4]]/100 #scale costs
B = np.zeros((1,2)) #give starting values of beta; 1xK vector; 2alternatives so 1x2 vector
NCS = data['idcase'].nunique() # number of choice situations in the dataset
x0 = B.T

估计 然而，我一直得到这样的信息：

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-77-2821f2269a8c> in <module>()
     83 print('which is the same as maximizing the log-likelihood.')
     84 
---> 85 optim2 = fmin_l_bfgs_b(loglik, x0, fprime=None, args=(), approx_grad=0, bounds=None, m=10, factr=10000000.0, pgtol=1e-05, epsilon=1e-08, iprint=0, maxfun=15000, maxiter=15000, disp=None, callback=None)
     86 
     87 print(optim2)

4 frames
/usr/local/lib/python3.6/dist-packages/scipy/optimize/optimize.py in __call__(self, x, *args)
     64         self.x = numpy.asarray(x).copy()
     65         fg = self.fun(x, *args)
---> 66         self.jac = fg[1]
     67         return fg[0]
     68 

IndexError: index 1 is out of bounds for axis 0 with size 1#

---------------------------------------------------------------------------
索引器回溯（最后一次最近调用）
在（）
83打印（'这与最大化对数可能性相同'）
84
--->85 optim2=fmin_l_bfgs_b（loglik，x0，fprime=None，args=（），近似梯度=0，边界=None，m=10，factr=10000000.0，pgtol=1e-05，epsilon=1e-08，iprint=0，maxfun=15000，maxiter=15000，disp=None，callback=None）
86
87打印（optim2）
4帧
/调用中的usr/local/lib/python3.6/dist-packages/scipy/optimize/optimize.py（self，x，*args）
64 self.x=numpy.asarray（x）.copy（）
65 fg=自我乐趣（x，*args）
--->66 self.jac=fg[1]
67返回fg[0]
68
索引器：索引1超出大小为1的轴0的界限#

有人能告诉我该怎么办吗？我在使用数值优化方法方面是个新手。

---

谢谢

您是否尝试过

approw_grad=1

？您是否尝试过

approw_grad=1

？

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-77-2821f2269a8c> in <module>()
     83 print('which is the same as maximizing the log-likelihood.')
     84 
---> 85 optim2 = fmin_l_bfgs_b(loglik, x0, fprime=None, args=(), approx_grad=0, bounds=None, m=10, factr=10000000.0, pgtol=1e-05, epsilon=1e-08, iprint=0, maxfun=15000, maxiter=15000, disp=None, callback=None)
     86 
     87 print(optim2)

4 frames
/usr/local/lib/python3.6/dist-packages/scipy/optimize/optimize.py in __call__(self, x, *args)
     64         self.x = numpy.asarray(x).copy()
     65         fg = self.fun(x, *args)
---> 66         self.jac = fg[1]
     67         return fg[0]
     68 

IndexError: index 1 is out of bounds for axis 0 with size 1#