当我使用盆地跳跃scipy

Question

我不知道为什么这个问题，一直是参数的变量超出范围，现在也问题是代码并给我这个'Bounds'类型的eroor对象没有len（）：

（我希望我能在代码中找到错误或找到使用变量边界的其他好代码）

Traceback (most recent call last):

File "./trigger_SLQ.py", line 160, in <module>
main()

File "./trigger_SLQ.py", line 156, in main
opt(x0)
File "./trigger_SLQ.py", line 148, in opt

res = basinhopping(rmse, x0, minimizer_kwargs=minimizer_kwargs, T=10000.0, 
stepsize=1.0, niter=100, niter_success=200, accept_test=bounds)

File "/usr/lib/python3/dist-packages/scipy/optimize/_basinhopping.py", line 
611, in basinhopping
accept_tests, disp=disp)

File "/usr/lib/python3/dist-packages/scipy/optimize/_basinhopping.py", line 
72, in __init__
minres = minimizer(self.x)

File "/usr/lib/python3/dist-packages/scipy/optimize/_basinhopping.py", line 
280, in __call__
return self.minimizer(self.func, x0, **self.kwargs)

File "/usr/lib/python3/dist-packages/scipy/optimize/_minimize.py", line 455, 
in minimize
constraints, callback=callback, **options)

File "/usr/lib/python3/dist-packages/scipy/optimize/slsqp.py", line 329, in 

_minimize_slsqp

if bounds is None or len(bounds) == 0:

TypeError: object of type 'Bounds' has no len()

我使用此代码

  b0 = (5, 95)

  b1 = (0.10, 0.95)

  b2= (2, 8)

  bounds=((5, 95), (0.10, 0.95), (2, 8))

class Bounds(object):

def __init__(self, xmax=[0,0,2], xmin=[100,0.100,8], stepsize=0.01):
    self.xmax = np.array(xmax)
    self.xmin = np.array(xmin)
    self.stepsize = stepsize

def __call__(self, x):

    min_step = np.maximum(self.xmin - x, -self.stepsize)
    max_step = np.minimum(self.xmax - x, self.stepsize)

    random_step = np.random.uniform(low=min_step, high=max_step, size=x.shape)

    xnew = x + random_step

    return xnew

    bounded_step = Bounds(np.array([b[0] for b in bounds]), np.array([b[1] 
    for b in bounds]), np.array([b[2] for b in bounds]))


def opt(x0):

bounds = Bounds()

minimizer_kwargs = {'method':'SLSQP', 'bounds':bounds,'options':{'maxiter':1000, 'ftol':1e-2, 'eps':1e-2},}
res = basinhopping(rmse, x0, minimizer_kwargs=minimizer_kwargs, T=10000.0, stepsize=1.0, niter=100, niter_success=200, accept_test=mybounds)
print(res.x)

return res.x


def main():

   x0 = np.array((50.0, 0.50, 5.0)) # initial guess
   opt(x0)


if __name__ == "__main__":
   main()