On Friday, October 18, 2013 5:37:14 PM UTC+2, Robert Kern wrote:
> 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.
>
>
On 18 October 2013 16:52, wrote:
> 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?!
It means that the additio
On 18.10.2013 18:05, Oscar Benjamin wrote:
It means that the additional column is a linear combination of the
existing columns. This means that your system of equations can contain
a contradiction. Essentially you're trying to get the least squares
solution to something like: 3*x + y = 1 1*x +
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, 20
On 18 October 2013 16:36, 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?
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!
whhh?!?!?!
thanks!
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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:
n
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_bi
