On Thu, Aug 25, 2011 at 2:23 PM, srean wrote:
> Hi,
>
> I would like to know a bit about how the installation process works. Could
> you point me to a resource. In particular I want to know how the site.cfg
> configuration works. Is it numpy/scipy specific or is it standard with
> distutils. I g
On Thu, Aug 25, 2011 at 2:10 PM, Paul Menzel
wrote:
> is there an easy way to also save the indexes of an array (columns, rows
> or both) when outputting it to a text file. For saving an array to a
> file I only found `savetxt()` [1] which does not seem to have such an
> option. Adding indexes man
On Fri, Aug 26, 2011 at 2:57 PM, Charles R Harris
wrote:
>
>
> On Fri, Aug 26, 2011 at 12:38 PM, Mark Janikas wrote:
>>
>> Charles! That looks like it could be a winner! It looks like you always
>> choose the last column of the U matrix and ID the columns that have the same
>> values? It works
On Fri, Aug 26, 2011 at 11:47 AM, Christopher Jordan-Squire wrote:
> Regarding ufuncs and NA's, all the mechanics of handling NA from a
> ufunc are in the PyUFunc_FromFuncAndData function, right? So the ufunc
> creation docs don't have to be updated to include NA's?
>
That's correct, any ufunc w
On Fri, Aug 26, 2011 at 12:38 PM, Mark Janikas wrote:
> Charles! That looks like it could be a winner! It looks like you always
> choose the last column of the U matrix and ID the columns that have the same
> values? It works when I add extra columns as well! BTW, sorry for my lack
> of knowl
Regarding ufuncs and NA's, all the mechanics of handling NA from a
ufunc are in the PyUFunc_FromFuncAndData function, right? So the ufunc
creation docs don't have to be updated to include NA's?
-Chris JS
On Wed, Aug 24, 2011 at 7:08 PM, Mark Wiebe wrote:
> I've added C-API documentation to the m
Charles! That looks like it could be a winner! It looks like you always
choose the last column of the U matrix and ID the columns that have the same
values? It works when I add extra columns as well! BTW, sorry for my lack of
knowledge... but what was the point of the dot multiply at the end
On Fri, Aug 26, 2011 at 1:41 PM, Mark Janikas wrote:
> I wonder if my last statement is essentially the only answer... which I
> wanted to avoid...
>
> Should I just use combinations of the columns and try and construct the
> corrcoef() (then ID whether NaNs are present), or use the condition nu
On Fri, Aug 26, 2011 at 11:41 AM, Mark Janikas wrote:
> I wonder if my last statement is essentially the only answer... which I
> wanted to avoid...
>
> Should I just use combinations of the columns and try and construct the
> corrcoef() (then ID whether NaNs are present), or use the condition nu
On Fri, Aug 26, 2011 at 7:41 PM, Mark Janikas wrote:
> I wonder if my last statement is essentially the only answer... which I
> wanted to avoid...
>
> Should I just use combinations of the columns and try and construct the
> corrcoef() (then ID whether NaNs are present), or use the condition nu
I wonder if my last statement is essentially the only answer... which I wanted
to avoid...
Should I just use combinations of the columns and try and construct the
corrcoef() (then ID whether NaNs are present), or use the condition number to
ID the singularity? I just wanted to avoid the whole
I actually use the VIF when the design matrix can be inverted I do it the
quick and dirty way as opposed to the step regression:
1. Calc the correlation coefficient of the matrix (w/o the intercept)
2. Return the diagonal of the inversion of the correlation matrix in step 1.
Again, the probl
On Fri, Aug 26, 2011 at 1:10 PM, Mark Janikas wrote:
> Hello All,
>
>
>
> I am trying to identify columns of a matrix that are perfectly collinear.
> It is not that difficult to identify when two columns are identical are have
> zero variance, but I do not know how to ID when the culprit is of a h
As you will note, since most of the functions work on rows, the matrix in
question has been transposed.
From: numpy-discussion-boun...@scipy.org
[mailto:numpy-discussion-boun...@scipy.org] On Behalf Of Mark Janikas
Sent: Friday, August 26, 2011 10:11 AM
To: 'Discussion of Numerical Python'
Subje
Hello All,
I am trying to identify columns of a matrix that are perfectly collinear. It
is not that difficult to identify when two columns are identical are have zero
variance, but I do not know how to ID when the culprit is of a higher order.
i.e. columns 1 + 2 + 3 = column 4. NUM.corrcoef(m
On Fri, Aug 26, 2011 at 07:04, Derek Homeier
wrote:
> On 25.08.2011, at 8:42PM, Chris.Barker wrote:
>
>> On 8/24/11 9:22 AM, Anthony Scopatz wrote:
>>> You can use Python pickling, if you do *not* have a requirement for:
>>
>> I can't recall why, but it seem pickling of numpy arrays has been
>>
On 8/26/11 5:04 AM, Derek Homeier wrote:
> Hmm, the pure Python version might be, but, I've used cPickle for a long time
> and never noted any stability problems.
well, here is the NEP:
https://github.com/numpy/numpy/blob/master/doc/neps/npy-format.txt
It addresses the why's and hows of the for
Hi,
as the subject says, the array_* comparison functions currently do not operate
on structured/record arrays. Pull request
https://github.com/numpy/numpy/pull/146
implements these comparisons.
There are two commits, differing in their interpretation whether two
arrays with different field na
On 25.08.2011, at 8:42PM, Chris.Barker wrote:
> On 8/24/11 9:22 AM, Anthony Scopatz wrote:
>>You can use Python pickling, if you do *not* have a requirement for:
>
> I can't recall why, but it seem pickling of numpy arrays has been
> fragile and not very performant.
>
Hmm, the pure Python v
On 25. aug. 2011, at 23.49, David Warde-Farley wrote:
> On 2011-08-25, at 2:42 PM, Chris.Barker wrote:
>
>> On 8/24/11 9:22 AM, Anthony Scopatz wrote:
>>> You can use Python pickling, if you do *not* have a requirement for:
>>
>> I can't recall why, but it seem pickling of numpy arrays has be
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