使用stastmodels的WLS时出错:SVD没有收敛

时间:2014-06-22 20:46:57

标签: python numpy classification linear-regression training-data

我已经在statsmodels中使用WLS(加权最小二乘回归)编写了级联增强分类器的算法,并且已经能够成功运行几次。我用了几百张图片,一切都很好。

但是,我现在用它来训练大约4000张图像的模型。 现在我收到以下错误:

MKL ERROR: Parameter 4 was incorrect on entry to DLASCL.
Traceback (most recent call last):
  File "C:\Users\app\Documents\Python Scripts\gbc_carclassify.py", line 95, in <module>
    gentlebooster.train(X_train,y_train,100)
  File "C:\Users\app\Documents\Python Scripts\gentleboost_c_class.py", line 103, in train
    temp_g = sm.WLS(y1, self.X, w).fit()  # Step 2(a)(ii)
  File "C:\Users\app\Anaconda\lib\site-packages\statsmodels\regression\linear_model.py", line 127, in fit
    self.pinv_wexog = pinv_wexog = np.linalg.pinv(self.wexog)
  File "C:\Users\app\Anaconda\lib\site-packages\numpy\linalg\linalg.py", line 1574, in pinv
    u, s, vt = svd(a, 0)
  File "C:\Users\app\Anaconda\lib\site-packages\numpy\linalg\linalg.py", line 1323, in svd
    raise LinAlgError('SVD did not converge')
numpy.linalg.linalg.LinAlgError: SVD did not converge

可能是什么原因?我以前没见过这个消息:

MKL ERROR: Parameter 4 was incorrect on entry to DLASCL.

错误似乎是由这一行引起的:

 temp_g = sm.WLS(y1, self.X, w).fit() 

0 个答案:

没有答案