Hi Bruce,
Sorry for the delay in the answer.
Le 27/01/2012 17:28, Bruce Southey a écrit :
The output is still a covariance so do we really need yet another set
of very similar functions to maintain?
Or can we get away with a new keyword?
The idea of an additional keyword seems appealing.
Le 26/01/2012 19:19, josef.p...@gmail.com a écrit :
The discussion had this reversed, numpy matches the behavior of
MATLAB, while R (statistics) only returns the cross covariance part as
proposed.
I would also say that there was an attempt to match MATLAB behavior.
However, there is big
On Friday, January 27, 2012, Pierre Haessig pierre.haes...@crans.org
wrote:
Le 26/01/2012 19:19, josef.p...@gmail.com a écrit :
The discussion had this reversed, numpy matches the behavior of
MATLAB, while R (statistics) only returns the cross covariance part as
proposed.
I would also say
On 01/27/2012 09:00 AM, Benjamin Root wrote:
On Friday, January 27, 2012, Pierre Haessig pierre.haes...@crans.org
mailto:pierre.haes...@crans.org wrote:
Le 26/01/2012 19:19, josef.p...@gmail.com
mailto:josef.p...@gmail.com a écrit :
The discussion had this reversed, numpy matches the
Le 22/01/2012 01:40, josef.p...@gmail.com a écrit :
same here,
When I rewrote scipy.stats.spearmanr, I matched the numpy behavior for
two arrays, while R only returns the cross-correlation part.
Since I've seen no negative feedback, I jumped to the next step by
creating a Trac account and
On Thu, Jan 26, 2012 at 7:19 AM, Pierre Haessig
pierre.haes...@crans.org wrote:
Le 22/01/2012 01:40, josef.p...@gmail.com a écrit :
same here,
When I rewrote scipy.stats.spearmanr, I matched the numpy behavior for
two arrays, while R only returns the cross-correlation part.
Since I've seen no
26.01.2012 15:57, Bruce Southey kirjoitti:
[clip]
Also I believe that changing the np.cov
function will cause major havoc because numpy and people's code depend
on the current behavior.
Changing the behavior of `cov` is IMHO not really possible at this point
--- the current behavior is not a
Le 26/01/2012 15:57, Bruce Southey a écrit :
Can you please provide a
couple of real examples with expected output that clearly show what
you want?
Hi Bruce,
Thanks for your ticket feedback ! It's precisely because I see a big
potential impact of the proposed change that I send first a ML
Le 26/01/2012 16:50, Pauli Virtanen a écrit :
the current behavior is not a bug,
I completely agree that numpy.cov(m,y) does what it says !
I (and apparently some other people) are only questioning why there is
such a behavior ? Indeed, the second variable `y` is presented as An
additional set
Den 26.01.2012 17:25, skrev Pierre Haessig:
However, in the case this change is not possible, I would see this
solution :
* add and xcov function that does what Elliot and Sturla and I
described, because
The current np.cov implementation returns the cross-covariance the way
it is commonly
On Thu, Jan 26, 2012 at 12:26 PM, Sturla Molden stu...@molden.no wrote:
Den 26.01.2012 17:25, skrev Pierre Haessig:
However, in the case this change is not possible, I would see this
solution :
* add and xcov function that does what Elliot and Sturla and I
described, because
The current
On Thu, Jan 26, 2012 at 10:07 AM, Pierre Haessig
pierre.haes...@crans.org wrote:
Le 26/01/2012 15:57, Bruce Southey a écrit :
Can you please provide a
couple of real examples with expected output that clearly show what
you want?
Hi Bruce,
Thanks for your ticket feedback ! It's precisely
On Thu, Jan 26, 2012 at 3:58 PM, Bruce Southey bsout...@gmail.com wrote:
On Thu, Jan 26, 2012 at 12:45 PM, josef.p...@gmail.com wrote:
On Thu, Jan 26, 2012 at 1:25 PM, Bruce Southey bsout...@gmail.com wrote:
On Thu, Jan 26, 2012 at 10:07 AM, Pierre Haessig
pierre.haes...@crans.org wrote:
Le
On Thu, Jan 26, 2012 at 6:43 PM, josef.p...@gmail.com wrote:
On Thu, Jan 26, 2012 at 3:58 PM, Bruce Southey bsout...@gmail.com wrote:
On Thu, Jan 26, 2012 at 12:45 PM, josef.p...@gmail.com wrote:
On Thu, Jan 26, 2012 at 1:25 PM, Bruce Southey bsout...@gmail.com wrote:
On Thu, Jan 26, 2012 at
I ran into this a while ago and was confused why cov did not behave the way
pierre suggested.
On Jan 21, 2012 12:48 PM, Elliot Saba staticfl...@gmail.com wrote:
Thank you Sturla, that's exactly what I want.
I'm sorry that I was not able to reply for so long, but Pierre's code is
similar to
On Sat, Jan 21, 2012 at 6:26 PM, John Salvatier
jsalv...@u.washington.edu wrote:
I ran into this a while ago and was confused why cov did not behave the way
pierre suggested.
same here,
When I rewrote scipy.stats.spearmanr, I matched the numpy behavior for
two arrays, while R only returns the
Hi Eliot,
Le 19/01/2012 07:50, Elliot Saba a écrit :
I recently needed to calculate the cross-covariance of two random
vectors, (e.g. I have two matricies, X and Y, the columns of which are
observations of one variable, and I wish to generate a matrix pairing
each value of X and Y)
I
Den 20.01.2012 13:39, skrev Pierre Haessig:
I don't see how does your function relates to numpy.cov [1]. Is it an
extended case function or is there a difference in the underlying math ?
If X is rank n x p, then np.cov(X, rowvar=False) is equal
to S after
cX = X -
Greetings,
I recently needed to calculate the cross-covariance of two random vectors,
(e.g. I have two matricies, X and Y, the columns of which are observations
of one variable, and I wish to generate a matrix pairing each value of X
and Y) and so I wrote a small utility function to do so, and
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