>
> QR without column pivoting is a nice option for
>"safe" problems, but it doesn't
>provide a reliable indication of rank
>reduction. I also don't find pinv useful
>once the rank goes down, since it relies on
> Euclidean distance having relevance in
>parameter space and that is seldom a so
On Wed, Jan 9, 2013 at 1:29 AM, Till Stensitz wrote:
> Nathaniel Smith pobox.com> writes:
>
>
> >
> > An obvious thing is that it always computes residuals, which could be
> > costly; if your pinv code isn't doing that then it's not really
> > comparable. (Though might still be well-suited for y
Nathaniel Smith pobox.com> writes:
>
> An obvious thing is that it always computes residuals, which could be
> costly; if your pinv code isn't doing that then it's not really
> comparable. (Though might still be well-suited for your actual
> problem.)
>
> Depending on how well-conditioned your
On Tue, Jan 8, 2013 at 11:17 AM, Till Stensitz wrote:
> Hi,
> i did some profiling and testing of my data-fitting code.
> One of its core parts is doing some linear least squares,
> until now i used np.linalg.lstsq. Most of time the size
> a is (250, 7) and of b is (250, 800).
>
> Today i compare
On Tue, Jan 8, 2013 at 6:17 PM, Till Stensitz wrote:
> Hi,
> i did some profiling and testing of my data-fitting code.
> One of its core parts is doing some linear least squares,
> until now i used np.linalg.lstsq. Most of time the size
> a is (250, 7) and of b is (250, 800).
>
> Today i compared
On Tue, Jan 8, 2013 at 1:17 PM, Till Stensitz wrote:
> Hi,
> i did some profiling and testing of my data-fitting code.
> One of its core parts is doing some linear least squares,
> until now i used np.linalg.lstsq. Most of time the size
> a is (250, 7) and of b is (250, 800).
My guess is that thi
Hi,
i did some profiling and testing of my data-fitting code.
One of its core parts is doing some linear least squares,
until now i used np.linalg.lstsq. Most of time the size
a is (250, 7) and of b is (250, 800).
Today i compared it to using pinv manually,
to my surprise, it is much faster. I
