Question

我想使用简单的sin ^ 2函数拟合一组数据，并希望根据拟合的参数确定其最小值。

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调用之外，其他所有东西都很好，因为出现以下错误（具有完整的追溯）：

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.

我想念一些东西，但我不知道。

我正在按照建议添加数据文件以使该程序运行

Answer 1

minimize期望将函数作为第一个参数，但是，您当前正在传递

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])

Answer 2

在scipy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)

此功能引发了错误。

文档link。

'numpy.ndarray'对象不是带有optimize.minimize的可调用错误

2 个答案: