On Sun, Nov 13, 2011 at 12:32:10PM -0700, Charles R Harris wrote:
Congratulations to all. This looks like a nice release.
And congratulations to Ralf for moving this forward!
G
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On Tue, Nov 15, 2011 at 05:46:52PM +0100, Andreas Müller wrote:
My question was more along the lines of why doesn't numpy do the online
algorithm.
It's probably a matter of nobody having had the time and the urge to code
it, _and_ do all the extra steps necessary to integrate to the core
On Tue, Nov 15, 2011 at 05:57:14PM +, Robert Kern wrote:
Actually, last time I suggested it, it was brought up that the online
algorithms can be worse numerically. I'll try to find the thread.
Indeed, especially for smallish datasets where the memory overhead is not
an issue. I think that
On Thu, Dec 15, 2011 at 04:36:24PM +, Robert Kern wrote:
More explicitly, I have some temporary home-made C structure that holds
a pointer to an array. I prepare (using Cython) an numpy.ndarray using
the PyArray_NewFromDescr function. I can delete my temporary C structure
without
On Tue, Jan 03, 2012 at 07:56:52PM -0800, Wonjun, Choi wrote:
what is the best way to pass c, c++ array to numpy in cython?
I don't know if it is the best way, but I wrote a self-contained example
a little while ago, to explain to people one way of doing it:
On Thu, Jan 05, 2012 at 11:26:05AM +0900, 최원준 wrote:
it seems like you recommend below way.
Cython example of exposing C-computed arrays in Python without data copies
[1]https://gist.github.com/1249305
but it uses malloc. isn't it?
In this example, the data can be allocated the
On Thu, Jan 05, 2012 at 04:04:52PM +0900, 최원준 wrote:
can I pass the array without malloc?
An array is a pointer in C, so yes you can do what you want.
G
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On Tue, Feb 14, 2012 at 09:14:11PM -0600, Bruce Southey wrote:
Ralf,
I will miss you as a numpy release manager!
You have not only done an incredible job but also taken the role to a
higher level.
Your attitude and attention to details has been amazing.
I definitely +1 that. I think that you
On Thu, Feb 16, 2012 at 05:00:29PM +, Nathaniel Smith wrote:
I agree, but the behavior is still surprising -- people reasonably
expect something like svd to be deterministic.
People are wrong then. Trust me, I work enough with ill-conditionned
problems, including SVDs, to know that the
On Sun, Feb 19, 2012 at 05:44:27AM -0500, David Warde-Farley wrote:
I think the comments about the developer audience NumPy will attract are
important. There may be lots of C++ developers out there, but the
intersection of (truly competent in C++) and (likely to involve oneself in
NumPy
On Thu, Feb 23, 2012 at 04:07:04PM -0500, Wes McKinney wrote:
In this last case for example, around 500 MB of RAM is taken up for an
array that should only be about 80-90MB. If you're a data scientist
working in Python, this is _not good_.
But why, oh why, are people storing big data in CSV?
On Sat, Mar 03, 2012 at 04:38:53PM -0800, David Cournapeau wrote:
This is really the kind of code that should be done in cython, as it is
mostly about wrapping C code into the python C API.
+1
Gael
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Hi list,
I just got a new laptop, running Ubuntu 11.10 64bit on an Intel i7.
I am a bit intriged by the test results of numpy on this box.
First of all, master builds and imports OK, but the simplest test case
crashes with a segfault:
import numpy as np
a = np.ones(10, dtype=np.bool)
On Thu, Mar 22, 2012 at 11:20:10AM -0600, Charles R Harris wrote:
This one was fixed a few days ago, so you aren't running the latest.
Indeed, but if I run the latest, any operation on arrays segfaults on me,
so I am running 0.6.1. But you are saying that it's known and fixed.
Great!
That
On Thu, Mar 22, 2012 at 06:24:13PM -0600, Charles R Harris wrote:
Since you are running on Ubuntu, it's probably a good idea to
python setup.py install --user
which will put everything in .local and avoid the mess with dist-packages
and site-packages. Maybe you do that already?
On Wed, Apr 04, 2012 at 04:41:51PM -0700, Abhishek Pratap wrote:
Thanks Chris. So I guess the question becomes how can I efficiently
cluster 1 million x,y coordinates.
Did you try the scikit-learn's implementation of DBSCAN:
http://scikit-learn.org/stable/modules/clustering.html#dbscan
? I am
On Thu, Apr 05, 2012 at 01:05:01PM -0700, Abhishek Pratap wrote:
Also in my case I dont really have a good approximate on value of K in
K-means.
That's a hard problem, for which I have no answer, sorry :$
G
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On Mon, Apr 16, 2012 at 10:40:53PM -0500, Travis Oliphant wrote:
The objectors object to any binary ABI change, but not specifically
three pointers rather than two or one?
Adding pointers is not really an ABI change (but removing them after
they were there would be...) It's really just the
On Tue, Apr 24, 2012 at 05:59:09PM -0600, Charles R Harris wrote:
Travis, if you are playing the BDFL role, then just make the darn decision
and remove the code so we can get on with life. As it is you go back and
forth and that does none of us any good, you're a big guy and you're
rocking the
On Wed, Apr 25, 2012 at 06:03:25AM -0600, Charles R Harris wrote:
Well, you have already appealed to the authority of greater experience, so
it's a bit late to declare disinterest in the subject ;) I mean, at this
point I really would like to see how big your FOSS is.
Chuck, I am not sure that
On behalf of our release manager, Andreas Mueller, and all the
scikit-learn contributors, I am happy to announce the 0.11 beta.
We are doing a quick beta and will hopefuly be releasing the final
version tomorrow. The purpose of this beta is to get feedback on any
release-critical bugs such as
On behalf of Andy Mueller, our release manager, I am happy to announce
the 0.11 release of scikit-learn.
This release includes some major new features such as randomized
sparse models, gradient boosted regression trees, label propagation
and many more. The release also has major
On behalf of Andy Mueller, our release manager, I am happy to announce
the 0.11 release of scikit-learn.
This release includes some major new features such as randomized
sparse models, gradient boosted regression trees, label propagation
and many more. The release also has major
much, it is my pleasure. But it's really a team that you
need to thank: the number of active contributors is huge.
Cheers,
Gael
On Tue, May 8, 2012 at 1:13 AM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
On behalf of Andy Mueller, our release manager, I am happy to announce
On Wed, May 09, 2012 at 02:35:26PM -0500, Travis Oliphant wrote:
Basically it buys not forcing *all* NumPy users (on the C-API level) to
now deal with a masked array. I know this push is a feature that is
part of Mark's intention (as it pushes downstream libraries to think about
On Fri, May 11, 2012 at 09:59:16AM -0500, Travis Oliphant wrote:
Is there a advantage for users by making it a subclass? Nobody is saying
you couldn't 'inherit' the struct (make the ndmask struct be castable to
a PyArrayObject*) even if that is not declared in the Python type object.
I
On Thu, Jun 21, 2012 at 08:59:09PM -0400, Benjamin Root wrote:
munkres seems to be a pure python implementation ;-).
Oops! I could have sworn that I once tried one named munkres that used
numpy. But that was several years ago.
There is a development branch of sk-learn with
On Mon, Jul 16, 2012 at 10:28:19AM -0600, Charles R Harris wrote:
Working lazy imports would be useful to have. Ralf is opposed to the idea
because it caused all sorts of problems on different platforms when it was
tried in scipy. I thought I'd open the topic for discussion so that
On Thu, Aug 02, 2012 at 09:44:53PM +0100, Damon McDougall wrote:
I have a question about the licence for NumPy's codebase. I am currently
writing a library and I'd like to release under some BSD-type licence.
Unfortunately, my choice to link against MIT's FFTW library (released
under the GPL)
Hi list,
I am struggling with offsets on the view of a memmaped array. Consider
the following:
import numpy as np
a = np.memmap('tmp.mmap', dtype=np.float64, shape=50, mode='w+')
a[:] = np.arange(50)
b = a[10:]
Here, I have a.offset == 0 and b.offset == 0. In practice, the data in b
is offset
On Sat, Sep 22, 2012 at 10:15:52AM -0600, Charles R Harris wrote:
Would some sort of 'dup' method be useful?
What do you mean by dup?
G
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On Sat, Sep 22, 2012 at 06:30:27PM +0200, Olivier Grisel wrote:
The only clean solution would be to make `numpy.memmap` use a wrapper
buffer object that would keep track of the filename and offset
attributes instead of using a `mmap.mmap` instance directly.
Indeed, Olivier and I have been
On Sat, Sep 22, 2012 at 11:16:27AM -0600, Charles R Harris wrote:
I think this is a bug, taking a view should probably update the offset.
OK, we can include a fix for that alongside with the patch to keep track
of the filename.
Cheers,
Gaël
___
On Sat, Sep 22, 2012 at 12:30:46PM -0600, Charles R Harris wrote:
As to the 1.7 release, I've been thinking we are violating the release
early, release often maxim. Bugs trickle in at a constant rate and if we
wait to fix them all we wait forever. So while it would be nice to
On Sat, Sep 22, 2012 at 12:19:53PM -0600, Charles R Harris wrote:
But you loose the pointer to the filename and the offset. In previous
versions of numpy c.base used to be the np.memmap instance from which
c is an array view. That allowed to make efficient pickling without
Hi list,
I think that I am hit a memory leak with numpy master. The following code
enables to reproduce it:
import numpy as np
n = 100
m = np.eye(n)
for i in range(3):
#np.linalg.slogdet(m)
t, result_t
Hi Fred,
On Mon, Sep 24, 2012 at 02:17:16PM -0400, Frédéric Bastien wrote:
with numpy '1.6.1', I have no problem.
With numpy 1.7.0b2, I can reproduce the problem.
OK, thanks. I think that I'll start a bisect to figure out when it crept
in.
Gael
___
On Mon, Sep 24, 2012 at 07:59:11PM +0100, Nathaniel Smith wrote:
which means I probably forgot a DECREF while doing the
PyArray_Diagonal changes...
Yep: https://github.com/numpy/numpy/pull/457
Awesome. I can confirm that this fixes the problem. Script below to check.
You are my hero!
Gael
Hi numpy developers,
First of all, thanks a lot for the hard work you put in numpy. I know
very well that maintaining such a core library is a lot of effort and a
service to the community. But with great dedication, comes great
responsibility :).
I find that Numpy is a bit of a wild horse, a
On Fri, Sep 28, 2012 at 07:37:35PM -0600, Charles R Harris wrote:
+1, I think we should endeavor to have a respectful and welcoming
community.
With a bit of humour now and then among the old timers, no? Look, I've been
outright insulted on the list and I don't recall anyone weighing
Hi Nathaniel,
First of all, thanks for your investment in numpy. You have really been
moving the project forward lately.
On Sat, Sep 29, 2012 at 03:03:01AM +0100, Nathaniel Smith wrote:
I have just fixed a fairly nasty bug in scikit-learn that was
introduced by change of semantics in
On Sat, Sep 29, 2012 at 03:09:08PM -0700, Ondřej Čertík wrote:
Chuck, Gael, here is my todo list for the 1.7.0 release:
https://github.com/numpy/numpy/issues/396
I have created issues and mentionned them in the comments on your issue.
Cheers,
Gaël
On Sun, Sep 30, 2012 at 07:17:42PM +0100, Nathaniel Smith wrote:
Is there anything better to do than simply revert np.copy() to its
traditional behaviour and accept that np.copy(a) and a.copy() will
continue to have different semantics indefinitely?
Have np.copy take an 'order=None', which
On Sun, Sep 30, 2012 at 10:30:52PM +0200, Han Genuit wrote:
Also, considering that this behaviour already exists in past versions
of NumPy, namely 1.6,
I just checked: in numpy 1.6.1, the behaviour is to create an endless
chain of base.base.base...
In some sens, what Travis is proposing is
On Wed, Oct 17, 2012 at 10:22:28AM -0500, Travis Oliphant wrote:
There is a small chance this will break someone's code if they relied on this
behavior. I don't believe anyone is currently relying on this behavior -- but
I've been proven wrong before. What do people on this list think?
I
On Thu, Nov 08, 2012 at 11:28:21AM +, Nathaniel Smith wrote:
I think everyone would be very happy to see numpy.dot modified to do
this automatically. But adding a scipy.dot IMHO would be fixing things
in the wrong place and just create extra confusion.
I am not sure I agree: numpy is often
On Thu, Nov 08, 2012 at 11:29:19AM -0600, Anthony Scopatz wrote:
Indeed, there is no reason not to make this available in NumPy.
+1, I agree, this should be a fix in numpy, not scipy.
I agree.
My point was a bit of a side issue: given a user's computer, I have no
garantee that numpy will
On Fri, Nov 09, 2012 at 03:12:42PM +, Nathaniel Smith wrote:
But what if someone compiles numpy against an optimized blas (mkl,
say) and then compiles SciPy against the reference blas? What do you
do then!? ;-)
This could happen. But the converse happens very often. What happens is
that
On Mon, Nov 12, 2012 at 02:27:02PM -0800, Ondřej Čertík wrote:
I successfully defended my Ph.D. thesis last Thursday, I just need to
do some changes to it and submit it and I am done.
Yey! Tag and release (the thesis, I mean)!
Congratulation.
G
PS: It seems to me that it was yesterday that
On Thu, Apr 25, 2013 at 08:10:32PM +0100, Robert Kern wrote:
In my opinion, duplicating functionality under different aliases just
so people can supposedly find things without reading the documentation
is not a viable strategy for building out an API.
+1. It's been my experience over and over
On Wed, Oct 20, 2010 at 09:38:25AM +0200, Gael Varoquaux wrote:
Hum, this page is mentionning a '--with-netlib-lapack' swith the the
configre that is not in the INSTALL.txt of my atlas version.
OK, that's no longer valid.
However, I just realize that atlas now packages the netlib lapack
On Wed, Oct 20, 2010 at 09:43:12AM +0200, Gael Varoquaux wrote:
On Wed, Oct 20, 2010 at 09:38:25AM +0200, Gael Varoquaux wrote:
Hum, this page is mentionning a '--with-netlib-lapack' swith the the
configre that is not in the INSTALL.txt of my atlas version.
OK, that's no longer valid
On Wed, Oct 20, 2010 at 04:41:38PM +0900, David wrote:
- ldd /volatile/varoquau/dev/numpy/numpy/linalg/lapack_lite.so: does it
load the libraries you think are you loading ?
For the dynamic libraries, it seems so. I am using static libraries for
atlas/blas (.a)
- nm atlas_libraries
On Wed, Oct 20, 2010 at 09:13:24AM -0400, Ian Goodfellow wrote:
--with-netlib-lapack is indeed no longer valid.
INSTALL.txt includes a warning that INSTALL.txt is out of date, you should
refer to doc/atlas_install.pdf instead.
Ah, I read too quickly and missed that. Thanks.
The
The 3rd International Conference on Finite Element Methods in Engineering
and Science (FEMTEC 2011, http://hpfem.org/events/femtec-2011/) will have
a track on Open-source projects and Python in scientific computing.
FEMTEC 2011 is co-organized by the University of Nevada (Reno), Desert
Reseach
On Sun, Nov 14, 2010 at 01:29:03PM -0700, Charles R Harris wrote:
I keep getting page does not exist.
It looks like github is having difficulties currently.
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Hi list,
does anybody have, or knows where I can find some N dimensional dichotomy
optimization code in Python (BSD licensed, or equivalent)?
Worst case, it does not look too bad to code, but I am interested by any
advice. I haven't done my reading yet, and I don't know how ill-posed a problem
On Mon, Nov 22, 2010 at 09:12:45PM +0100, Matthieu Brucher wrote:
Hi ;)
Hi bro
does anybody have, or knows where I can find some N dimensional
dichotomy optimization code in Python (BSD licensed, or equivalent)?
I don't know any code, but it should be too difficult by bgoing
through a
On Mon, Nov 22, 2010 at 11:12:26PM +0100, Matthieu Brucher wrote:
It seems that a simplex is what you need.
Ha! I am learning new fancy words. Now I can start looking clever.
I realize that maybe I should rephrase my question to try and draw more
out of the common wealth of knowledge on
On Mon, Nov 22, 2010 at 11:12:26PM +0100, Matthieu Brucher wrote:
It seems that a simplex is what you need. It uses the barycenter (more
or less) to find a new point in the simplex. And it works well only in
convex functions (but in fact almost all functions have an issue with
this :D)
One
On Tue, Nov 23, 2010 at 08:21:55AM +0100, Matthieu Brucher wrote:
optimize.fmin can be enough, I don't know it well enough. Nelder-Mead
is not a constrained optimization algorithm, so you can't specify an
outer hull.
I saw that, after a bit more reading.
As for the integer part, I don't know
On Tue, Nov 23, 2010 at 11:13:23AM +0100, Sebastian Walter wrote:
I'm not familiar with dichotomy optimization.
Several techniques have been proposed to solve the problem: genetic
algorithms, simulated annealing, Nelder-Mead and Powell.
To be honest, I find it quite confusing that these
On Tue, Nov 23, 2010 at 11:37:02AM +0100, Sebastian Walter wrote:
min_x f(x)
s.t. lo = Ax + b = up
0 = g(x)
0 = h(x)
No constraints.
didn't you say that you operate only in some convex hull?
No. I have an initial guess that allows me to specify a convex hull
On Tue, Nov 23, 2010 at 02:47:10PM +0100, Sebastian Walter wrote:
Well, I don't know what the best method is to solve your problem, so
take the following with a grain of salt:
Wouldn't it be better to change the model than modifying the
optimization algorithm?
In this case, that's not
On Tue, Nov 23, 2010 at 10:19:06AM -0500, josef.p...@gmail.com wrote:
I have a Nelder-Mead that seems to be working quite well on a few toy
problems.
Assuming your function is well behaved, one possible idea is to try
replacing the integer objective function with a continuous
On Tue, Nov 23, 2010 at 07:14:56PM +0100, Matthieu Brucher wrote:
Jumping in a little late, but it seems that simulated annealing might
be a decent method here: take random steps (drawing from a
distribution of integer step sizes), reject steps that fall outside
the fitting range, and
On Sat, Dec 04, 2010 at 10:25:52AM +0100, Fabian Pedregosa wrote:
The correct command is sudo gcc_select mp-gcc45 which effectively
does all the symbolic links for you and works like a charm, so please
ignore my previous patch.
I am not a mac user, so I guess that my opinion is not very
On Thu, Jan 13, 2011 at 10:49:17AM -0800, David Cortesi wrote:
As to why I'm using Python 3, it's because I'm starting a new project
with no prior dependencies and want the current and future language --
which is now TWO FRAKKIN' YEARS OLD! -- but that's a rant for another
time.
Oh, you're
On Thu, Jan 27, 2011 at 12:18:30AM +0100, Hanno Klemm wrote:
interesting idea. Given the fact that in 2-d euclidean metric, the
Einstein summation conventions are only a way to write out
conventional matrix multiplications, do you consider at some point to
include a non-euclidean metric
Hi list,
This is just a note that an extra track at FEMTEC, a conference for
computational methods in engineering and science, is open for open source
scientific software. The organisers have a taste for Python, so if you
want to submit a paper on numerical methods with Python, this is an
On Sun, Jan 30, 2011 at 04:15:34PM +0100, Sturla Molden wrote:
Den 30.01.2011 07:28, skrev Algis Kabaila:
Why not simply numply.linalg.cond? This gives the condition
number directly (and presumably performs the inspection of
sv's). Or do you think that sv's give more useful information?
On Tue, Feb 22, 2011 at 09:59:26PM +, Pauli Virtanen wrote:
Probably because the numpy binary that the author was using was compiled
without a blas implementation, and just using numpy's internal
lapack_lite. This is a common problem in real life.
It doesn't use blas_lite at the
On Fri, Feb 25, 2011 at 10:52:09AM +0100, Fred wrote:
What exactly do you mean by 'decimating'. To me is seems that you are
looking for matrix factorization or matrix completion techniques, which
are trendy topics in machine learning currently.
By decimating, I mean this:
input array
On Fri, Mar 04, 2011 at 11:54:07AM -0800, Christoph Gohlke wrote:
I also ran tests and/or examples of a few 3rd party packages that were
built against numpy 1.5.1: scipy, pygame, PyMOL, numexpr, matplotlib,
basemap, scikits.learn, ETS.mayavi, Bottleneck, pytables, and pandas.
Wow, this is
On Thu, Mar 24, 2011 at 12:06:56PM -0700, Nadav Horesh wrote:
I am looking for a partial least sqaures code refactoring for two (X,Y)
matrices. I found the following, but they not not work for me:
We have had a PLS code contributed in the scikits learn very recently:
On Thu, Mar 24, 2011 at 08:15:12PM +0100, Olivier Grisel wrote:
2011/3/24 Nadav Horesh nad...@visionsense.com:
I am looking for a partial least sqaures code refactoring for two (X,Y)
matrices. I found the following, but they not not work for me:
1. MDP: Factors only one matrix (am I wrong?)
On Thu, Mar 24, 2011 at 01:57:46PM -0700, Nadav Horesh wrote:
The code doc refer to an excellent reference [Wegelin et al. 2000], so
no real problem here. If the reference is critical I would suggest on
of the following:
1. Put a link to the document.
Do you have one? I'd be happy to add
Hey Sturla,
It's really great that you are still working on that. I'll test the code
under Linux.
The scipy community has moved to github. If I create a repository under
github and put the code on it, would you use it? If I find time, I'll add
a setup.py.
Gaël
On Thu, Apr 21, 2011 at 07:25:18AM +0530, pratik wrote:
If the place where he is seeking tenure does not know his name (i.e
hasn't heard of ATLAS) then it is not a good place to seek tenure in :) .
Scholars undervalue code and don't realise the difficulty and the amount
of work it takes to
On Wed, Apr 20, 2011 at 10:49:10PM -0700, Matthew Brett wrote:
Well - thanks for the offer - Clint was asking for individual letters
too, you could email and ask him?
I can do that, and ask around me.
Are you on the math-atlas list?
No I am not.
If not I'll forward you his request...
Hi there,
We are courrently having a discussion on the scikits learn mailing list
about which patterns to adopt for random number generation. One thing
that is absolutely clear is that making the stream of random numbers
reproducible is critical. We have several objects that can serve as random
On Mon, Apr 25, 2011 at 11:05:05AM -0700, T J wrote:
If code A relies on code B (eg, some numpy function) and code B
changes, then the stream of random numbers will no longer be the same.
The point here is that the user wrote code A but depended on code B,
and even though code A was
On Mon, Apr 25, 2011 at 01:23:12PM -0500, Robert Kern wrote:
Yes, that's exactly why we want the different objects to able to recieve
their own PRNG.
But seriously, they are running A+B, the combination of A and B. If
A+B changes to A+B', then the results may be different. That's to be
Following a suggestion by Joseph, I am trying to implement the
Jonker-Volgenant algorithm for best linear assignment in Python, using
numpy. Unsuprisingly, it is proving time-costly. I cannot afford to spend
too much time on this, as it not to solve a problem of mine, but for the
scikits.learn.
Hey
On Mon, May 16, 2011 at 08:49:58AM -0700, Hoyt Koepke wrote:
On Mon, May 16, 2011 at 12:18 AM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
Jonker-Volgenant algorithm for best linear assignment in Python, using
numpy. Unsuprisingly, it is proving time-costly. I cannot afford
On Mon, May 16, 2011 at 09:15:21AM -0700, Hoyt Koepke wrote:
Thanks for the suggestion. Unfortunately, I do not want to add compiled
code (or an external dependency) to the scikit for this solver, as it is
a fairly minor step for us. I particular chances are that it will never
be a
On Mon, May 16, 2011 at 10:03:09AM -0700, Hoyt Koepke wrote:
I might go that way: I already have pure-Python code that implements it
and that I have been using for a year or so.
Fair enough -- though you'd probably get a big speed up moving to cython.
Indeed. If this is needed, we'll
On Tue, May 17, 2011 at 09:36:40AM -0700, Hoyt Koepke wrote:
OK, your input is making my motivation to fight with Jonker-Volgenant go
down. I'll see with the other people involved if we still target
Jonger-Volgenant, or if we stick with the hungarian algorithm, in which
case the problem is
On Tue, May 17, 2011 at 12:55:39PM -0500, Benjamin Root wrote:
Is this hungarian method in an official scikits package, or is this just
your own thing?
Right now we are playing with the idea of integrating it in the scikits
learn, as it would be useful in a couple of places. I don't know
I Dmitriy,
Welcome.
This is a great initiative. You'll learn by doing, and I am willing to
bet that contributing examples can make you a good programmer.
On Fri, May 27, 2011 at 09:47:24PM +0400, Dmitriy Rybalkin wrote:
I thought I could contribute to the Numpy project by
showing some
On Sun, May 29, 2011 at 07:09:42AM +0400, Dmitriy Rybalkin wrote:
Thank you, Gael, I really hope that I'll improve my programming skills.
Also I have a question: Will the page with my example be deleted in
case the code is not efficient or smth else has been found to be
wrong?
I cannot really
On Thu, Jun 02, 2011 at 03:06:58PM -0500, Mark Wiebe wrote:
Would anyone object to, at least temporarily, tightening up the default
ufunc casting rule to 'same_kind' in NumPy master? It's a one line change,
so would be easy to undo, but such a change is very desirable in my
On Sun, Jun 05, 2011 at 10:43:45PM +0200, Ralf Gommers wrote:
four in scikit-learn (plus two that don't
look related),
Yeah, some of these failures are due to numerical unstabilities in tests
(nasty ones, still fighting) and some simply to crappy code (HMMs are
hopeless :( ).
Now, with
On Sun, Jun 05, 2011 at 04:41:26PM -0600, Charles R Harris wrote:
Now, with regards to the actual failures induced by the new
branch, it took me a while to understand why they where happening,
and now I realise that we probably should have explicit coercions
at these
Hi,
You can probably find some inspiration from
https://github.com/scikit-learn/scikit-learn/blob/master/scikits/learn/feature_extraction/image.py
Gaël
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On Tue, Jun 14, 2011 at 10:29:38AM -0300, Thiago Franco Moraes wrote:
I don't know if I understand. The idea is to use _make_edges_3d to
give me the connectivity, isn't it? Like for example, a 3x3 image:
0, 1, 2
3, 4, 5
6, 7, 8
If I use _make_edges_3d(3, 3) I get:
array([[0, 1, 3, 4, 6,
On Thu, Jun 23, 2011 at 03:53:31PM -0500, Mark Wiebe wrote:
concluded that adding masks to the core ndarray appears is the best way to
deal with the problem in general.
It seems to me that this is going to make the numpy array a way more
complex object. Althought it is currently quite
On Thu, Jun 23, 2011 at 07:51:25PM -0400, josef.p...@gmail.com wrote:
From the perspective of statistical analysis, I don't see much
advantage of this. What to do with nans depends on the analysis, and
needs to be looked at for each case.
From someone who actually sometimes does statistics
On Sat, Jun 25, 2011 at 01:02:07AM +0100, Matthew Brett wrote:
I'm personally worried that the memory overhead of array.masks will
make many of us tend to avoid them. I work with images that can
easily get large enough that I would not want an array-items size byte
array added to my storage.
On Wed, Jul 06, 2011 at 12:26:24PM -0700, Nathaniel Smith wrote:
A mini-NEP for the where= argument to ufuncs
I _love_ this proposal and it would probably be much more useful to me
than the different masked array proposal that are too focused on a
specific usage pattern to answer all my needs.
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