'numpy.ndarray' object is not callable 错误 optimize.minimize
'numpy.ndarray' object is not callable error with optimize.minimize
我想用一个简单的 sin^2 函数拟合一组数据，并根据拟合参数确定它的最小值。
这是我的代码：
import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize
data = np.loadtxt('data.txt', usecols=(0,1))
x = data[:,0]*np.pi/180
y = data[:,1]
plt.scatter(x, y, c='red')
def sine(t,a,b,c):
return a*(np.sin(b*(t-c)))**2
params, cov = optimize.curve_fit(sine, x, y, p0=[9500, 0.5, 0])
print(params)
t = np.linspace(0, 2*np.pi/3, 120)
plt.plot(t, sine(t, *params), 'black')
plt.show()
optimize.minimize(sine(t, *params), x0=0)
除了 minimize 调用外一切正常，因为我收到以下错误（带有完整的回溯）：
TypeError Traceback (most recent call last)
~\Documents\CNR\Calibrazione_lamine_20181112\Fit.py in <module>()
23 plt.show()
24
---> 25 optimize.minimize(sine(t, *params), x0=0)
~\Anaconda3\lib\site-packages\scipy\optimize\_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
442 return _minimize_cg(fun, x0, args, jac, callback, **options)
443 elif meth == 'bfgs':
--> 444 return _minimize_bfgs(fun, x0, args, jac, callback, **options)
445 elif meth == 'newton-cg':
446 return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in _minimize_bfgs(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, **unknown_options)
911 else:
912 grad_calls, myfprime = wrap_function(fprime, args)
--> 913 gfk = myfprime(x0)
914 k = 0
915 N = len(x0)
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in function_wrapper(*wrapper_args)
290 def function_wrapper(*wrapper_args):
291 ncalls[0] += 1
--> 292 return function(*(wrapper_args + args))
293
294 return ncalls, function_wrapper
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in approx_fprime(xk, f, epsilon, *args)
686
687 """
--> 688 return _approx_fprime_helper(xk, f, epsilon, args=args)
689
690
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in _approx_fprime_helper(xk, f, epsilon, args, f0)
620 """
621 if f0 is None:
--> 622 f0 = f(*((xk,) + args))
623 grad = numpy.zeros((len(xk),), float)
624 ei = numpy.zeros((len(xk),), float)
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in function_wrapper(*wrapper_args)
290 def function_wrapper(*wrapper_args):
291 ncalls[0] += 1
--> 292 return function(*(wrapper_args + args))
293
294 return ncalls, function_wrapper
TypeError: 'numpy.ndarray' object is not callable.
我遗漏了一些东西，但我不知道是什么。
我正在按照建议添加数据文件来制作此程序运行
0 405
5 20
10 350
15 1380
20 2900
25 4750
30 6450
35 8100
40 9100
45 9800
50 10100
55 10250
60 9400
65 8400
70 6430
75 4900
80 3030
85 1500
90 400
95 17
100 410
105 1550
110 3100
115 4850
120 6780
在 scipy 的文档中，optimize.minimize 函数将 ndarray 或 shape(n) 作为 x, 的输入，而不是整数。我认为错误是从那里引发的，因为在他们的错误跟踪中
--> 913 gfk = myfprime(x0)
此函数引发错误。
文档 link.
minimize 需要一个函数作为第一个参数，但是，您目前传递
sine(t, *params)
这是一个 numpy 数组。
您可以解决此问题并执行以下操作：
print(optimize.minimize(sine, x0=[0], args=tuple(params)))
这将打印
fun: 2.4080485986582715e-12
hess_inv: array([[1.15258817e-05]])
jac: array([8.19961349e-09])
message: 'Optimization terminated successfully.'
nfev: 18
nit: 4
njev: 6
status: 0
success: True
x: array([0.09203053])
我想用一个简单的 sin^2 函数拟合一组数据，并根据拟合参数确定它的最小值。
这是我的代码：
import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize
data = np.loadtxt('data.txt', usecols=(0,1))
x = data[:,0]*np.pi/180
y = data[:,1]
plt.scatter(x, y, c='red')
def sine(t,a,b,c):
return a*(np.sin(b*(t-c)))**2
params, cov = optimize.curve_fit(sine, x, y, p0=[9500, 0.5, 0])
print(params)
t = np.linspace(0, 2*np.pi/3, 120)
plt.plot(t, sine(t, *params), 'black')
plt.show()
optimize.minimize(sine(t, *params), x0=0)
除了 minimize 调用外一切正常，因为我收到以下错误（带有完整的回溯）：
TypeError Traceback (most recent call last)
~\Documents\CNR\Calibrazione_lamine_20181112\Fit.py in <module>()
23 plt.show()
24
---> 25 optimize.minimize(sine(t, *params), x0=0)
~\Anaconda3\lib\site-packages\scipy\optimize\_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
442 return _minimize_cg(fun, x0, args, jac, callback, **options)
443 elif meth == 'bfgs':
--> 444 return _minimize_bfgs(fun, x0, args, jac, callback, **options)
445 elif meth == 'newton-cg':
446 return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in _minimize_bfgs(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, **unknown_options)
911 else:
912 grad_calls, myfprime = wrap_function(fprime, args)
--> 913 gfk = myfprime(x0)
914 k = 0
915 N = len(x0)
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in function_wrapper(*wrapper_args)
290 def function_wrapper(*wrapper_args):
291 ncalls[0] += 1
--> 292 return function(*(wrapper_args + args))
293
294 return ncalls, function_wrapper
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in approx_fprime(xk, f, epsilon, *args)
686
687 """
--> 688 return _approx_fprime_helper(xk, f, epsilon, args=args)
689
690
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in _approx_fprime_helper(xk, f, epsilon, args, f0)
620 """
621 if f0 is None:
--> 622 f0 = f(*((xk,) + args))
623 grad = numpy.zeros((len(xk),), float)
624 ei = numpy.zeros((len(xk),), float)
~\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in function_wrapper(*wrapper_args)
290 def function_wrapper(*wrapper_args):
291 ncalls[0] += 1
--> 292 return function(*(wrapper_args + args))
293
294 return ncalls, function_wrapper
TypeError: 'numpy.ndarray' object is not callable.
我遗漏了一些东西，但我不知道是什么。
我正在按照建议添加数据文件来制作此程序运行
0 405
5 20
10 350
15 1380
20 2900
25 4750
30 6450
35 8100
40 9100
45 9800
50 10100
55 10250
60 9400
65 8400
70 6430
75 4900
80 3030
85 1500
90 400
95 17
100 410
105 1550
110 3100
115 4850
120 6780
在 scipy 的文档中，optimize.minimize 函数将 ndarray 或 shape(n) 作为 x, 的输入，而不是整数。我认为错误是从那里引发的，因为在他们的错误跟踪中
--> 913 gfk = myfprime(x0)
此函数引发错误。
文档 link.
minimize 需要一个函数作为第一个参数，但是，您目前传递
sine(t, *params)
这是一个 numpy 数组。
您可以解决此问题并执行以下操作：
print(optimize.minimize(sine, x0=[0], args=tuple(params)))
这将打印
fun: 2.4080485986582715e-12
hess_inv: array([[1.15258817e-05]])
jac: array([8.19961349e-09])
message: 'Optimization terminated successfully.'
nfev: 18
nit: 4
njev: 6
status: 0
success: True
x: array([0.09203053])