我有一个关于如何在 Python 中使用 Levenberg-Marquardt 优化方法的问题。 SCIPY库里有很多optimization methods .
我尝试了两种方法(Nelder-Mead 和 Basin-hopping),并且都可以很好地使用以下命令:
# Nelder-Mead
res0_10 = optimize.minimize(f0_10, x0, method='Nelder-Mead', options={'disp': True, 'maxiter': 2000})
# Basin-hopping
res0_10 = optimize.basinhopping(f0_10, x0, niter=100, disp=True)
使用Levenberg-Marquardt时出现的问题(我只复制了错误的部分,因为程序很长)
def f0_10(x):
m, u, z, s = x
for i in range(alt_max):
if i==alt_min: suma=0
if i > alt_min:
suma = suma + (B(x, i)-b0_10(x, i))**2
return np.sqrt(suma/alt_max)
x0 = np.array([40., 0., 500., 50.])
res0_10 = root(f0_10, x0, jac=True, method='lm')
我只更改最后一句(res0_10 = root...
)。程序编译得很好,但是当我执行程序时:
Exception in Tkinter callback
Traceback (most recent call last):
File "C:\Users\Quini SB\AppData\Local\Enthought\Canopy\App\appdata\canopy-1.7.4.3348.win-x86_64\lib\lib-tk\Tkinter.py", line 1536, in __call__
return self.func(*args)
File "C:\Users\Quini SB\Desktop\tfg\Steyn - levmar.py", line 384, in askopenfilename
res0_10 = root(f0_10, x0, jac=True, method='lm')
File "C:\Users\Quini SB\AppData\Local\Enthought\Canopy\User\lib\site-packages\scipy\optimize\_root.py", line 188, in root
sol = _root_leastsq(fun, x0, args=args, jac=jac, **options)
File "C:\Users\Quini SB\AppData\Local\Enthought\Canopy\User\lib\site-packages\scipy\optimize\_root.py", line 251, in _root_leastsq
factor=factor, diag=diag)
File "C:\Users\Quini SB\AppData\Local\Enthought\Canopy\User\lib\site-packages\scipy\optimize\minpack.py", line 377, in leastsq
shape, dtype = _check_func('leastsq', 'func', func, x0, args, n)
File "C:\Users\Quini SB\AppData\Local\Enthought\Canopy\User\lib\site-packages\scipy\optimize\minpack.py", line 26, in _check_func
res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
File "C:\Users\Quini SB\AppData\Local\Enthought\Canopy\User\lib\site-packages\scipy\optimize\optimize.py", line 64, in __call__
self.jac = fg[1]
IndexError: invalid index to scalar variable.
为什么会出现这个错误?
最佳答案
来自文档:
jac : bool or callable, optional
If jac is a Boolean and is True, fun is assumed to return the value
of Jacobian along with the objective function. If False, the
Jacobian will be estimated numerically. jac can also be a callable
returning the Jacobian of fun. In this case, it must accept the
same arguments as fun.
因此,您的函数“f0_10”需要返回两个值,因为您将 jac
设置为 True
关于Python:Scipy.optimize Levenberg-marquardt 方法,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/39345139/