Python pyomo中参数的极大似然估计

我想用pyomo从行为数据集估计RL模型的参数

#dummy data
dis_data =  pd.DataFrame([0,1,0,0,0,1], columns=['reward'])
dis_data['Expt']=str(1)
dis_data = dis_data.set_index('Expt')


def max_likelihood_four(x,data):
    lr=x.lr
    sigma=x.sigma
    time = data.shape[0]
    v = np.zeros(([2, time]))
    pr = np.zeros(([2, time]))+0.5
    pr_log = np.zeros(([time]))

    for t in range(time-1):

        pr[1, t] =  1 / (1 + np.exp(-(v[1, t] - v[0, t])/ sigma))
        pr[0, t] = 1-pr[1, t]

        outcome=int(data.ix[t,'reward'])
        v[choice, t + 1] = v[choice, t] + lr * (outcome - v[choice, t])
        v[1 - choice, t + 1] = v[1 - choice, t]

        pr_log[t] = np.log(pr[choice, t])
        # print(i)
    return -np.sum(pr_log)
def pyomo_fit1(df):

    mdl = ConcreteModel()
    mdl.lr    = Var(initialize=np.random.random(1), bounds=(0, 1))
    mdl.sigma = Var(initialize=np.random.random(1), bounds=(0, 10))

    
    residuals = max_likelihood_four(mdl,df)

    mdl.obj = Objective(expr=residulas, sense=minimize)
    SolverFactory('ipopt').solve(mdl)
    return [mdl.lr,mdl.sigma]

parameter_values_1, r1 = pyomo_fit1(dis_data)

我得到这个错误：

“无法将标量组件‘sigma’视为索引组件”

---

你能附上完整的错误信息吗？（使用回溯）只能猜测，因为不熟悉pyomo和相关应用程序-快速搜索类似的“标量”与“索引组件”问题似乎与有关浮点问题的numpy错误有关-请参阅以下（web存档）页面：和