Question

我正在尝试使用scipy.optimize.curve_fit来适应模型函数，但以下代码给出了以下错误：

from scipy.optimize import curve_fit
from math import log10

def my_func(x, alpha):
return [10*log10(alpha*y*y) for y in x]

known_x = [1039.885254, 2256.833008, 6428.667969, 30602.62891] #known x-values
known_y = [31.87999916, 33.63000107, 35, 36.74000168]

popt, pcov = curve_fit(my_func, known_x, known_y)

我得到的错误是：

TypeError: unsupported operand type(s) for -: 'list' and 'list'

我知道相关问题已被问及here和here，但我无法从这些答案中解决问题。

我仔细检查了curve_fit发送到我的函数的参数的类型，我看到alpha作为numpy.float64和x作为{{1}进来}}

感谢您的帮助。

以下是回溯错误：

list

以下是Traceback (most recent call last): File "test.py", line 10, in <module> popt, pcov = curve_fit(my_func, known_x, known_y) File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/optimize/minpack.py", line 506, in curve_fit res = leastsq(func, p0, args=args, full_output=1, **kw) File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/optimize/minpack.py", line 348, in leastsq m = _check_func('leastsq', 'func', func, x0, args, n)[0] File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/optimize/minpack.py", line 14, in _check_func res = atleast_1d(thefunc(*((x0[:numinputs],) + args))) File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/optimize/minpack.py", line 418, in _general_function return function(xdata, *params) - ydata TypeError: unsupported operand type(s) for -: 'list' and 'list'：

_general_function

Answer 1

您需要将list转换为np.array：

def my_func(x, alpha):
    return np.array([10*np.log10(alpha*y*y) for y in x])

known_x = np.array([1039.885254, 2256.833008, 6428.667969, 30602.62891]) #known x-values
known_y = np.array([31.87999916, 33.63000107, 35, 36.74000168])

结果：

(array([ 0.00012562]), array([[  2.38452809e-08]]))

原因很明显，如此消息所示：

TypeError: unsupported operand type(s) for -: 'list' and 'list'

当然，list无法减去list。为此，我们需要它们位于numpy.array

scipy.optimize.curve_fit不支持的操作数类型错误

1 个答案: