lancered wrote: > > > So, can you tell me what goes wrong? Is this a bug in > Numpy.linalg? How to deal with this situation? If you need, I can > post the matrix I used below, but it is so long,so not at the moment. >
As you discovered, it is very likely your problem is a very high condition number. The easiest thing to do is to use numpy.linalg.pinv to perform a pseudo-inverse which will only use the singular-values that are well-conditioned to compute the inverse. This will still not give you an exact identity, but at least you will know you aren't amplifiying low-valued singular vectors. -Travis -- http://mail.python.org/mailman/listinfo/python-list
