类方法作为模型函数和类方法作为 scipy.optimize 的优化函数

Question

我正在使用 python 来解决优化问题。我想定义一个类来完成这项工作。在类中，我想使用模型函数作为类的方法，例如：

class MyClass(object):
      def f(self,x,parameters):

但我还想在同一个类中定义另一个方法来对 x 上的函数 f 进行优化，例如：

     def Optim_Funtion(self):
         scipy.optimize.minimize(f,x0,'method='Nelder-Mead')

我的问题是如何做到这一点？我是否必须在 Optim_Funtion 方法中将函数 f 作为 self.f 传递？我发现了一个与此相关的问题，但他们从类定义中取出了优化问题： 类方法作为 scipy.optimize.curve_fit 的模型函数 ，这不是我想要做的。

这里是我使用的代码：

class LaserGating:
# Given laser pulse energy and min photon number to be received at a detector, calculate the max distance  


def __init__(self, alpha, PhotonNumber, EnergyMin, EnergyMax, Wavelength,TargetReflection,d):

    self.alpha = alpha
    self.PhotonNumber = PhotonNumber # photon number @detector
    self.EnergyMax = EnergyMax # laser pulse energy max
    self.EnergyMin = EnergyMin # laser pulse energy Min
    self.Wavelength = Wavelength # laser wavelengh
    self.TargetReflection = TargetReflection # target reflection 
    self.d = d # detector size
    self.PhotonEnergy = 1.054e-34*2*np.pi*3.e8/self.Wavelength # energy of a photon at wavelength "Wavelength"
    self.PulseEnergy = self.EnergyMin 
    self.PulseEnergyRange = np.linspace(self.EnergyMin,self.EnergyMax,1000) # array of energy pulse values

    return


def fMin(self,x,PulseEnergy):
    # laser range model: x is the argument (distance) that the function is to be minimized on

    f = self.PhotonNumber - PulseEnergy*self.TargetReflection * ((self.d/x)**2)*np.exp(-self.alpha*x)/self.PhotonEnergy
    return f


def FindDistance(self):
    #find maximale distance given energy and photon number@ detector
    #print self.PulseEnergyRange
    rangeEnergy = self.PulseEnergyRange
    #print rangeEnergy
    testrange = []
    #for testeleements in rangeEnergy:
        #print testeleements

    for elements in rangeEnergy:

        #initial guess. Fixed for the moment but should depend on elements
        x0 = 10.
        #print elements
        # optimisation on x, using elements as arg
        test = scp.optimize.newton(self.fMin,x0,args = (elements,),tol= 1e-3)

        # append answer
        testrange.append(test)

    return testrange

当我运行它时，例如使用：

DistanceRange = LaserGating(0.001,1000,1.e-9,1.e-6,532.e-9,0.2,0.001)
DistanceRange.FindDistance()

我收到以下错误消息：

enter ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-199-597c7ff1bb69> in <module>()
 ----> 1 DistanceRange.FindDistance()

 <ipython-input-194-b1c115d544c0> in FindDistance(self)
 32             x0 = 1000.
 33 
 ---> 34             test = scp.optimize.minimize(self.fMin,x0,args =        (elements),method='Nelder-Mead',tol= 1e-2)
 35             testrange.append(test)
 36             print elements

 C:\Users\spinchip\AppData\Local\Continuum\Anaconda\lib\site-    packages\scipy\optimize\_minimize.pyc in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
411                       callback=callback, **options)
412     elif meth == 'nelder-mead':
--> 413         return _minimize_neldermead(fun, x0, args, callback, **options)
414     elif meth == 'powell':
415         return _minimize_powell(fun, x0, args, callback, **options)

C:\Users\spinchip\AppData\Local\Continuum\Anaconda\lib\site-  packages\scipy\optimize\optimize.pyc in _minimize_neldermead(func, x0, args, callback,   xtol, ftol, maxiter, maxfev, disp, return_all, **unknown_options)
436     if retall:
437         allvecs = [sim[0]]
--> 438     fsim[0] = func(x0)
439     nonzdelt = 0.05
440     zdelt = 0.00025

C:\Users\spinchip\AppData\Local\Continuum\Anaconda\lib\site-  packages\scipy\optimize\optimize.pyc in function_wrapper(*wrapper_args)
279     def function_wrapper(*wrapper_args):
280         ncalls[0] += 1
--> 281         return function(*(wrapper_args + args))
282 
283     return ncalls, function_wrapper

TypeError: fMin() takes exactly 3 arguments (2 given)code here

所以问题在于调用方法时无法识别的附加参数。

在此先感谢您的任何建议，

格雷戈里

Answer 1

传递args = (elements)相当于args = elements，即不创建元组。

要传递 1 元素元组，请执行args = (elements,), 或args = tuple([elements])。