使用python scipy.optimize.basinhopping在exp中溢出

Question

I am using scipy.optimize.basinhopping in order to fit a simple exponential function (a exp(-b time)) to real data. I try to have appropriate initial guesses (for a and b) but in some iterations (for some values basinhopping guesses) "overflow in exp" occurs. I know that it is because of a very large answer to be calculated by exp. By the way the result is something absolutely wrong. Is there anyway to ask the code to ignore those error containing guesses in order to prevent wrong results in output? + time goes from 0 to something around e+06 Thanks for your care and help

Answer 1

here is my code. after running, I get overflow error for some values for bk, so the resulting value for ret is absolutely wrong, something far far from the correct answer. :(

def model(bk):
    s = 0
    realData = data()
    modelData = []
    modelData.append(realData[0])
    for time in range(len(realData) - 1):
        x = realData[0] * np.exp((bk[0] * np.exp(bk[1]*time))*time)
        y = 1 - realData[0] + x
        i = x / y
        modelData.append(i)
        s+=np.abs(i-realData[time])

    return(s)
def optimize():
    bk0 = [1,-1]
    minimizer_kwargs = {"method" : "BFGS"}
    ret = basinhopping(model, bk0, minimizer_kwargs=minimizer_kwargs, niter=100)
    print(ret)

optimize()