On 2010-11-22, at 2:51 AM, Hagen Fürstenau wrote:
but this is bound to be inefficient as soon as the vector of
probabilities gets large, especially if you want to draw multiple samples.
Have I overlooked something or should this be added?
I think you misunderstand the point of multinomial
ISTM that this elementary functionality deserves an implementation
that's as fast as it can be.
To substantiate this, I just wrote a simple implementation of
categorical in numpy/random/mtrand.pyx and it's more than 8x faster
than your version for multiple samples of the same distribution and
On Mon, Nov 22, 2010 at 6:05 AM, Hagen Fürstenau ha...@zhuliguan.net wrote:
ISTM that this elementary functionality deserves an implementation
that's as fast as it can be.
To substantiate this, I just wrote a simple implementation of
categorical in numpy/random/mtrand.pyx and it's more than
On Mon, Nov 22, 2010 at 2:30 AM, Pauli Virtanen p...@iki.fi wrote:
Sun, 21 Nov 2010 23:26:37 -0700, Charles R Harris wrote:
[clip]
Yes, indexing is known to be slow, although I don't recall the precise
reason for that. Something to do with way integers are handled or some
such. There was
On Sun, Nov 21, 2010 at 5:56 PM, Robert Kern robert.k...@gmail.com wrote:
On Sun, Nov 21, 2010 at 19:49, Keith Goodman kwgood...@gmail.com wrote:
But this sample gives a difference:
a = np.random.rand(100)
a.var()
0.080232196646619805
var(a)
0.080232196646619791
As you know, I'm
On Mon, Nov 22, 2010 at 11:03 AM, Keith Goodman kwgood...@gmail.com wrote:
On Sun, Nov 21, 2010 at 5:56 PM, Robert Kern robert.k...@gmail.com
wrote:
On Sun, Nov 21, 2010 at 19:49, Keith Goodman kwgood...@gmail.com
wrote:
But this sample gives a difference:
a = np.random.rand(100)
On Mon, Nov 22, 2010 at 12:07 PM, Benjamin Root ben.r...@ou.edu wrote:
On Mon, Nov 22, 2010 at 11:03 AM, Keith Goodman kwgood...@gmail.com wrote:
On Sun, Nov 21, 2010 at 5:56 PM, Robert Kern robert.k...@gmail.com
wrote:
On Sun, Nov 21, 2010 at 19:49, Keith Goodman kwgood...@gmail.com
On Mon, Nov 22, 2010 at 9:13 AM, josef.p...@gmail.com wrote:
Two pass would provide precision that we would expect in numpy, but I
don't know if anyone ever tested the NIST problems for basic
statistics.
Here are the results for their most difficult dataset. But I guess
running one test
On Mon, Nov 22, 2010 at 10:51 AM, josef.p...@gmail.com wrote:
On Mon, Nov 22, 2010 at 1:39 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Nov 22, 2010 at 10:32 AM, josef.p...@gmail.com wrote:
On Mon, Nov 22, 2010 at 1:26 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Nov 22, 2010
On Mon, Nov 22, 2010 at 1:51 PM, josef.p...@gmail.com wrote:
On Mon, Nov 22, 2010 at 1:39 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Nov 22, 2010 at 10:32 AM, josef.p...@gmail.com wrote:
On Mon, Nov 22, 2010 at 1:26 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Nov 22, 2010
On Mon, Nov 22, 2010 at 11:00 AM, josef.p...@gmail.com wrote:
I don't think that works for complex numbers.
(statsmodels has now a preference that calculations work also for
complex numbers)
I'm only supporting int32, int64, float64 for now. Getting the other
ints and floats should be easy.
On Mon, Nov 22, 2010 at 1:59 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Nov 22, 2010 at 10:51 AM, josef.p...@gmail.com wrote:
On Mon, Nov 22, 2010 at 1:39 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Nov 22, 2010 at 10:32 AM, josef.p...@gmail.com wrote:
On Mon, Nov 22, 2010
On 11/21/10 11:37 AM, Ernest Adrogué wrote:
so you want
t[:,x,y]
I tried that, but it's not the same:
In [307]: t[[0,1],x,y]
Out[307]: array([1, 7])
In [308]: t[:,x,y]
Out[308]:
array([[1, 3],
[5, 7]])
what is your t? Here's my example, which I think matches what you asked
On Mon, Nov 22, 2010 at 2:04 PM, Keith Goodman kwgood...@gmail.com wrote:
On Mon, Nov 22, 2010 at 11:00 AM, josef.p...@gmail.com wrote:
I don't think that works for complex numbers.
(statsmodels has now a preference that calculations work also for
complex numbers)
I'm only supporting
I didn't realize the x's and y's were varying the first time around.
There's probably a way to omit it, but I think the conceptually
simplest way is probably what you had to begin with. Build an index by
saying i = numpy.arange(0, t.shape[0])
then you can do t[i, x,y]
On Mon, Nov 22, 2010 at
On 11/20/10 11:04 PM, Ralf Gommers wrote:
I am pleased to announce the availability of NumPy 1.5.1.
Binaries, sources and release notes can be found at
https://sourceforge.net/projects/numpy/files/.
Thank you to everyone who contributed to this release.
Yes, thanks so much -- in particular
Hi,
On Mon, Nov 22, 2010 at 11:35 AM, Christopher Barker
chris.bar...@noaa.gov wrote:
On 11/20/10 11:04 PM, Ralf Gommers wrote:
I am pleased to announce the availability of NumPy 1.5.1.
Binaries, sources and release notes can be found at
https://sourceforge.net/projects/numpy/files/.
Thank
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
22/11/10 @ 11:08 (-0800), thus spake Christopher Barker:
On 11/21/10 11:37 AM, Ernest Adrogué wrote:
so you want
t[:,x,y]
I tried that, but it's not the same:
In [307]: t[[0,1],x,y]
Out[307]: array([1, 7])
In [308]: t[:,x,y]
Out[308]:
array([[1, 3],
[5, 7]])
what is
2010/11/21 Ernest Adrogué eadro...@gmx.net:
Hi,
Suppose an array of shape (N,2,2), that is N arrays of
shape (2,2). I want to select an element (x,y) from each one
of the subarrays, so I get a 1-dimensional array of length
N. For instance:
In [228]: t=np.arange(8).reshape(2,2,2)
In
22/11/10 @ 11:20 (-0800), thus spake John Salvatier:
I didn't realize the x's and y's were varying the first time around.
There's probably a way to omit it, but I think the conceptually
simplest way is probably what you had to begin with. Build an index by
saying i = numpy.arange(0,
2010/11/22 Gael Varoquaux gael.varoqu...@normalesup.org:
Hi list,
Hi ;)
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 KdTree.
22/11/10 @ 14:04 (-0600), thus spake Robert Kern:
This way, I get the elements (0,1) and (1,1) which is what
I wanted. The question is: is it possible to omit the [0,1]
in the index?
No, but you can write generic code for it:
t[np.arange(t.shape[0]), x, y]
Thank you. This is what I
I think that the only speedup you will get is defining an index only
once and reusing it.
2010/11/22 Ernest Adrogué eadro...@gmx.net:
22/11/10 @ 14:04 (-0600), thus spake Robert Kern:
This way, I get the elements (0,1) and (1,1) which is what
I wanted. The question is: is it possible to omit
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
2010/11/22 Gael Varoquaux gael.varoqu...@normalesup.org:
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
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
2010/11/22 Gael Varoquaux gael.varoqu...@normalesup.org:
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
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 Mon, Nov 22, 2010 at 5:27 PM, Matthieu Brucher
matthieu.bruc...@gmail.com wrote:
2010/11/22 Gael Varoquaux gael.varoqu...@normalesup.org:
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
It's not an error but a harmless (although confusing) warning message.
You should be able to filter it by adding the following to
scipy/__init__.py:
import warnings
warnings.filterwarnings(action='ignore', message='.*__builtin__.file
size changed.*')
Can you check if that works for you?
2010/11/22 Gael Varoquaux gael.varoqu...@normalesup.org:
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
Hi all,
There are some new test errors
==
ERROR: Test with missing and filling values
--
Traceback (most recent call last):
File
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