args=(data, y, sigmoid_cache, self.tau), maxiter=iters)else:# BFGS gives me a numerical problems with the dataset ../datasets/logreg/ex2data1.txt# ret = opt.fmin_bfgs(error, theta0, gradient,# args=(data, y, sigmoid_cache), maxiter=iters)# this worksret = opt.fmin_ncg(error, t...
# 需要导入模块: from scipy import optimize [as 别名]# 或者: from scipy.optimize importfmin_ncg[as 别名]deftest_ncg(self, use_wrapper=False):""" line-search Newton conjugate gradient optimization routine """ifuse_wrapper: opts = {'maxiter': self.maxiter,'disp':False,'return_all':False} ...
scipy.optimize.fmin_tnc(func,x0,fprime=None,args=(),approx_grad=0,bounds=None,epsilon=1e-08,scale=None,offset=None,messages=15,maxCGit=-1,maxfun=None,eta=-1,stepmx=0,accuracy=0,fmin=0,ftol=-1,xtol=-1,pgtol=-1,rescale=-1,disp=None,callback=None)[source] Minimize a function ...
if method in ["fmin", "fmin_powell"]: result = fmin_func(f, init_point) #参数为目标函数和初始值 elif method in ["fmin_cg", "fmin_bfgs", "fmin_l_bfgs_b", "fmin_tnc"]: result = fmin_func(f, init_point, fprime) #参数为目标函数、初始值和导函数(导函数可选?) elif method in...
fmin_ncg else: raise ValueError('optimizer method not available') #TODO: add other optimization options and results return optimizer(self.gmmobjective_cu, start, args=(), **optim_args) Example #10Source File: test_optimize.py From Computable with MIT License 5 votes def test_bfgs(self, ...
方法三:scipy.optimize.fmin_cobyla 这个方法只能实现不等约束,但是也能用了 fmin_cobyla(func, x0, cons, args=(), consargs=None, rhobeg=1.0,rhoend=1e-4, maxfun=1000,disp=None, catol=2e-4) fun:求最小值的目标函数 x0:每个变量的初始值 ...
如何强制scipy.optimize.fmin_l_bfgs_b使用“dtype=float32” 、、、 我试图用Python中的GPU计算来优化函数,所以我更喜欢用dtype=float32将所有数据存储为ndarray。当我使用scipy.optimize.fmin_l_bfgs_b时,我注意到优化器总是将一个float64 (在我的64位机器上)参数传递给我的目标函数和梯度函数,即使当我将floa...
As I said we use parameter transformation a lot in statsmodels but with the fmin_xxx optimizers. http://wwwasdoc.web.cern.ch/wwwasdoc/minuit/node5.html Which is also our usecase and recommended in our main textbook. (We use internally a switch to turn the constraints on or off in som...
底层算法是截断牛顿算法,也称为牛顿共轭梯度算法。此方法与 scipy.optimize.fmin_ncg 的不同之处在于 它包装了算法的 C 实现 它允许给每个变量一个上限和下限。 该算法通过像无约束截断牛顿一样确定下降方向来合并边界约束,但永远不会采用足够大的步长以留下可行的 x 的空间。该算法跟踪一组当前活动的约束,并在...
fmin , fmin_powell , fmin_cg , fmin_bfgs , fmin_ncg 非线性最小二乘最小化器: scipy.optimize.leastsq 受约束的多元优化器: fmin_l_bfgs_b , fmin_tnc , fmin_cobyla 全局优化器: basinhopping , brute , differential_evolution 局部标量最小化器: fminbound , brent , golden , bracket N...