Python statsmodel.api fit（）引发溢出错误

我使用Logistic回归进行Mnist数字分类，并使用statsmodel.api库拟合参数，但Logit.fit（）仍会抛出溢出警告。下面是我在Windows10、python 2.7上使用从下载的库时遇到的错误

C:\Python27\lib\site packages\statsmodels\discrete\discrete_model.py:1213:RuntimeWarning:exp-return 1/（1+np.exp（-X））中遇到溢出C:\Python27\lib\site packages\statsmodels\discrete\discrete_model.py:1263:RuntimeWarning:在日志返回np.sum（np.log（self.cdf（q*np.dot（X，params））中遇到零）警告：已超过最大迭代次数。
当前函数值：inf
迭代次数：35次回溯（最后一次调用）：文件“code.py”，第44行，在
result1=logit1.fit（）文件“C:\Python27\lib\site packages\statsmodels\discrete\discrete\u model.py”，第1376行，在fit中
disp=disp，callback=callback，**kwargs）文件“C:\Python27\lib\site packages\statsmodels\discrete\discrete_model.py”，第203行
disp=disp，callback=callback，**kwargs）文件“C:\Python27\lib\site packages\statsmodels\base\model.py”，第434行，在fit中
Hinv=np.linalg.inv（-retvals['Hessian']）/nobs文件“C:\Python27\lib\site packages\numpy\linalg\linalg.py”，第526行，inv
ainv=\u umath\u linalg.inv（a，signature=signature，extrobj=extrobj）文件“C:\Python27\lib\site packages\numpy\linalg\linalg.py”，第90行，单数
raise LinAlgError（“奇异矩阵”）numpy.linalg.linalg.linalgeror:奇异矩阵

请提供您的代码和数据。（请参阅）问题不是溢出，而是“奇异矩阵”，请参阅此答案，请提供您的代码和数据。（参见）问题不是溢出，而是“奇异矩阵”，请参见此答案

C:\Python27\lib\site-packages\statsmodels\discrete\discrete_model.py:1213: RuntimeWarning: overflow encountered in exp   return 1/(1+np.exp(-X)) C:\Python27\lib\site-packages\statsmodels\discrete\discrete_model.py:1263: RuntimeWarning: divide by zero encountered in log   return np.sum(np.log(self.cdf(q*np.dot(X,params)))) Warning: Maximum number of iterations has been exceeded.
         Current function value: inf
         Iterations: 35 Traceback (most recent call last):   File "code.py", line 44, in <module>
    result1 = logit1.fit()   File "C:\Python27\lib\site-packages\statsmodels\discrete\discrete_model.py", line 1376, in fit
    disp=disp, callback=callback, **kwargs)   File "C:\Python27\lib\site-packages\statsmodels\discrete\discrete_model.py", line 203, in fit
    disp=disp, callback=callback, **kwargs)   File "C:\Python27\lib\site-packages\statsmodels\base\model.py", line 434, in fit
    Hinv = np.linalg.inv(-retvals['Hessian']) / nobs   File "C:\Python27\lib\site-packages\numpy\linalg\linalg.py", line 526, in inv
    ainv = _umath_linalg.inv(a, signature=signature, extobj=extobj)   File "C:\Python27\lib\site-packages\numpy\linalg\linalg.py", line 90, in _raise_linalgerror_singular
    raise LinAlgError("Singular matrix") numpy.linalg.linalg.LinAlgError: Singular matrix