On 2013-10-18 16:25, chip9m...@gmail.com wrote:
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!
The numpy-discussion mailing list is probably the best place to ask. I recommend
posting a complete working example (with data) that demonstrates the problem.
Use pastebin.com or a similar service if necessary.
http://www.scipy.org/scipylib/mailing-lists.html
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
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