Hello everybody! One strange problem, please help!
I have the following 2D array: users_elements_matrix numpy.shape(users_elements_matrix) is (100,43) and array merged_binary_ratings numpy.shape(merged_binary_ratings) is (100,) Now,when I run: numpy.linalg.lstsq(users_elements_matrix, merged_binary_ratings) i get some ridiculous numbers for coeficients, all are the same and 1.38946385e+15. What is really strange is that if I run numpy.shape(users_elements_matrix[:,0:42]) i get ok numbers. I tested several thing and have examined the matrix, everything is ok with the data. how is it possible that one additional row (variable in linear regression) has such a strange impact?!!? I am loosing my mind here, please help! Thanks! -- https://mail.python.org/mailman/listinfo/python-list
