Interesting! rank of the whole minus last row numpy.linalg.matrix_rank(users_elements_matrix[:,0:42]) is 42
but also rank of whole is numpy.linalg.matrix_rank(users_elements_matrix[:,0:43]) is 42 but what does that mean?! could you explain briefly what now? thank you! On Friday, October 18, 2013 5:44:31 PM UTC+2, Oscar Benjamin wrote: > On 18 October 2013 16:36, <chip9m...@gmail.com> wrote: > > > one more thing. > > > > > > the problem is not in the last column, if I use it in regression (only that > > column, or with a few others) I will get the results. But if I use all 43 > > columns python breaks! > > > > Have you tried testing the rank with numpy.linalg.matrix_rank? I'm > > guessing that the extra row makes the matrix singular (up to floating > > point error). > > > > > > Oscar -- https://mail.python.org/mailman/listinfo/python-list
