No, they heavily changed how to link against mkl in 10. There is a whole
chapter about it in the releases notes.
Yes, I read it, but it appears to me that the new layer libraries
are an option, and that the legacy link format still works. From
chapter 3:
Pure layered libraries give more
Hi,
Does numpy have some sort of generalised inner product? For example I have
arrays
a.shape = (5,6,7)
b.shape = (8,7,9,10)
and I want to perform a product over the 3rd axis of a and the 2nd of b,
i.e.
c[i,j,k,l,m] = sum (over x) of a[i,j,x] * b[k,x,l,m]
I guess I could do it with swapaxes
I've been using git-svn, so I suppose I'm pulling the last rev that
was in 1.1.x. Checked out the RC, looks like there are more unit
tests, but they all still pass for me:
In [2]: numpy.test()
Numpy is installed in /Library/Python/2.5/site-packages/numpy
Numpy version 1.1.0.dev5142
Python version
On Mon, May 19, 2008 at 4:23 AM, Peter Creasey
[EMAIL PROTECTED] wrote:
Hi,
Does numpy have some sort of generalised inner product? For example I have
arrays
a.shape = (5,6,7)
b.shape = (8,7,9,10)
and I want to perform a product over the 3rd axis of a and the 2nd of b,
i.e.
rex wrote:
I always remove the build directory (if I forget the much faster
compilation reminds me). Do you mean remove the installed numpy?
Yes.
Did that, built numpy again, and it fails numpy.test() exactly as before.
I changed site.cfg to:
[mkl]
library_dirs =
def noncentral_chisquare(self, df, nonc, size=None):
Noncentral Chi^2 distribution.
noncentral_chisquare(df, nonc, size=None) - random values
cdef ndarray odf, ononc
cdef double fdf, fnonc
fdf = PyFloat_AsDouble(df)
fnonc =
Please follow exactly my instruction, otherwise, we cannot compare what
we are doing: use exactly the same site.cfg as me.
OK, I used the same MKL version you did (10.0.1.014), the same
site.cfg, and set .bashrc to do:
source /opt/intel/mkl/10.0.1.014/tools/environment/mklvars32.sh
and
I am running on Windows Xp, Intel Xeon CPU. I'd like to fill in a few more
things here. If I send 0 in the second and third argument of svd then I get
the singular_values, but if its 1 then the problem persists. I've tried this
on sparse and non-sparse matrices. This is with the latest windows
Next step is to try icc instead of gcc, and if that works, try
the latest MKL (10.0.3.020).
OK, either I've got a corrupted copy of MKL 10.0.3.020, or it has
a problem. Building with icc MKL 10.0.1.014 works.
Erik, are you reading this? If so, roll back to MKL 10.0.014 and it
should work,
Thank You.. The problem is resolved
On Mon, May 19, 2008 at 10:31 AM, Bruce Southey [EMAIL PROTECTED] wrote:
Nripun Sredar wrote:
I am running on Windows Xp, Intel Xeon CPU. I'd like to fill in a few
more things here. If I send 0 in the second and third argument of svd
then I get the
2008/5/19 Orest Kozyar [EMAIL PROTECTED]:
Given a slice, such as s_[..., :-2:], is it possible to take the
complement of this slice? Specifically, s_[..., ::-2]. I have a
series of 2D arrays that I need to split into two subarrays via
slicing where the members of the second array are all the
I'm here... i am rolling back now and will post my results...
e
On Mon, May 19, 2008 at 9:22 AM, rex [EMAIL PROTECTED] wrote:
Next step is to try icc instead of gcc, and if that works, try
the latest MKL (10.0.3.020).
OK, either I've got a corrupted copy of MKL 10.0.3.020, or it has
a
Message: 1
Date: Mon, 19 May 2008 09:20:21 -0400
From: Neal Becker [EMAIL PROTECTED]
Subject: [Numpy-discussion] noncentral_chisquare buglet?
To: numpy-discussion@scipy.org
Message-ID: [EMAIL PROTECTED]
Content-Type: text/plain; charset=us-ascii
def noncentral_chisquare(self, df, nonc,
All,
* I've just noticed that the page describing RecordArrays
(http://www.scipy.org/RecordArrays) is not listed under the Cookbook: should
this be changed ? Shouldn't there be at least a link in the documentation
page ?
* Same problem with Subclasses (http://www.scipy.org/Subclasses)
* I was
On Mon, May 19, 2008 at 9:34 AM, Orest Kozyar [EMAIL PROTECTED] wrote:
Given a slice, such as s_[..., :-2:], is it possible to take the
complement of this slice? Specifically, s_[..., ::-2].
Hmm, that doesn't look like the complement. Did you mean s_[..., -2:]
and s_[..., :-2]?
I have a
On Mon, May 19, 2008 at 11:33 AM, Peck, Jon [EMAIL PROTECTED] wrote:
Message: 1
Date: Mon, 19 May 2008 09:20:21 -0400
From: Neal Becker [EMAIL PROTECTED]
Subject: [Numpy-discussion] noncentral_chisquare buglet?
To: numpy-discussion@scipy.org
Message-ID: [EMAIL PROTECTED]
Content-Type:
Hi Pierre
2008/5/19 Pierre GM [EMAIL PROTECTED]:
* I've just noticed that the page describing RecordArrays
(http://www.scipy.org/RecordArrays) is not listed under the Cookbook: should
this be changed ? Shouldn't there be at least a link in the documentation
page ?
How about we add those
Jarrod Millman wrote:
Please test the release candidate:
svn co http://svn.scipy.org/svn/numpy/tags/1.1.0rc1 1.1.0rc1
Also please review the release notes:
http://projects.scipy.org/scipy/numpy/milestone/1.1.0
I am going to ask Chris and David to create Windows and Mac binaries,
which I
Hi -
First off, I know that optimization is evil, and I should make sure
that everything works as expected prior to bothering with squeezing
out extra performance, but the situation is that this particular block
of code works, but it is about half as fast with numpy as in matlab,
and I'm
If you don't mind fancy indexing, you can convert your index arrays
into boolean form:
complement = A==A
complement[idx] = False
This actually would work perfectly for my purposes. I don't really
need super-fancy indexing.
Given a slice, such as s_[..., :-2:], is it possible to take the
Ticket 793 has a patch, submitted by Alan McIntyre, waiting for review from
someone C-API-wise.
Cheers,
David
2008/5/19 Neal Becker [EMAIL PROTECTED]:
Jarrod Millman wrote:
Please test the release candidate:
svn co http://svn.scipy.org/svn/numpy/tags/1.1.0rc1 1.1.0rc1
Also please
On Mon, May 19, 2008 at 7:08 PM, James Snyder [EMAIL PROTECTED] wrote:
for n in range(0,time_milliseconds):
self.u = self.expfac_m * self.prev_u +
(1-self.expfac_m) * self.aff_input[n,:]
self.v = self.u + self.sigma *
Also you could use xrange instead of range...
Again, not sure of the size of the effect but it seems to be
recommended by the docstring.
Robin
___
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Numpy-discussion@scipy.org
for n in range(0,time_milliseconds):
self.u = self.expfac_m * self.prev_u +
(1-self.expfac_m) * self.aff_input[n,:]
self.v = self.u + self.sigma *
np.random.standard_normal(size=(1,self.naff))
self.theta = self.expfac_theta * self.prev_theta -
2008/5/19 Orest Kozyar [EMAIL PROTECTED]:
If you don't mind fancy indexing, you can convert your index arrays
into boolean form:
complement = A==A
complement[idx] = False
This actually would work perfectly for my purposes. I don't really
need super-fancy indexing.
Heh. Actually fancy
I've built a Mac binary for the 1.1 release candidate. Mac users,
please test it from:
https://cirl.berkeley.edu/numpy/numpy-1.1.0rc1-py2.5-macosx10.5.dmg
This is for the MacPython installed from python.org.
Thanks,
Chris
On Sat, May 17, 2008 at 9:01 PM, Jarrod Millman [EMAIL PROTECTED]
Hi,
I think my understanding is somehow incomplete... It's not clear to me
why (simplified case)
a[curidx,:] = scalar * a[2-curidx,:]
should be faster than
a = scalar * b
In both cases I thought the scalar multiplication results in a new
array (new memory allocated) and then the difference
2008/5/19 James Snyder [EMAIL PROTECTED]:
First off, I know that optimization is evil, and I should make sure
that everything works as expected prior to bothering with squeezing
out extra performance, but the situation is that this particular block
of code works, but it is about half as fast
Robin wrote:
Also you could use xrange instead of range...
Again, not sure of the size of the effect but it seems to be
recommended by the docstring.
No, it is going away in Python 3.0, and its only real benefit is a
memory saving in extreme cases.
From the Python library docs:
The
On Mon, May 19, 2008 at 12:53 PM, Robin [EMAIL PROTECTED] wrote:
Hi,
I think my understanding is somehow incomplete... It's not clear to me
why (simplified case)
a[curidx,:] = scalar * a[2-curidx,:]
should be faster than
a = scalar * b
In both cases I thought the scalar multiplication
On May 19, 2008, at 3:39 PM, Christopher Burns wrote:
I've built a Mac binary for the 1.1 release candidate. Mac users,
please test it from:
https://cirl.berkeley.edu/numpy/numpy-1.1.0rc1-py2.5-macosx10.5.dmg
This is for the MacPython installed from python.org.
Thanks,
Chris
I tried
On Mon, May 19, 2008 at 3:20 PM, Tommy Grav [EMAIL PROTECTED] wrote:
==
FAIL: test_basic (numpy.core.tests.test_multiarray.TestView)
--
Traceback (most recent
On Mon, May 19, 2008 at 3:35 PM, Robert Kern [EMAIL PROTECTED] wrote:
Endianness issues. Probably bugs in the code.
By which I meant test code. numpy itself is fine and is working
correctly. The tests themselves incorrectly assume little-endianness.
--
Robert Kern
I have come to believe that
On Mon, May 19, 2008 at 3:38 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Mon, May 19, 2008 at 3:35 PM, Robert Kern [EMAIL PROTECTED] wrote:
Endianness issues. Probably bugs in the code.
By which I meant test code. numpy itself is fine and is working
correctly. The tests themselves incorrectly
On May 19, 2008, at 4:38 PM, Robert Kern wrote:
On Mon, May 19, 2008 at 3:35 PM, Robert Kern [EMAIL PROTECTED]
wrote:
Endianness issues. Probably bugs in the code.
By which I meant test code. numpy itself is fine and is working
correctly. The tests themselves incorrectly assume
Anne Archibald wrote:
2008/5/19 James Snyder [EMAIL PROTECTED]:
I can provide the rest of the code if needed, but it's basically just
filling some vectors with random and empty data and initializing a few
things.
It would kind of help, since it would make it clearer what's a scalar
and
Thanks Tommy! Robert has already committed a fix.
On Mon, May 19, 2008 at 1:42 PM, Tommy Grav [EMAIL PROTECTED] wrote:
On May 19, 2008, at 4:38 PM, Robert Kern wrote:
On Mon, May 19, 2008 at 3:35 PM, Robert Kern [EMAIL PROTECTED]
wrote:
Endianness issues. Probably bugs in the code.
By
On Mon, May 19, 2008 at 2:53 PM, Christopher Barker [EMAIL PROTECTED]
wrote:
Anne Archibald wrote:
2008/5/19 James Snyder [EMAIL PROTECTED]:
I can provide the rest of the code if needed, but it's basically just
filling some vectors with random and empty data and initializing a few
On Mon, May 19, 2008 at 5:27 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
The latest versions of Matlab use the ziggurat method to generate random
normals and it is faster than the method used in numpy. I have ziggurat code
at hand, but IIRC, Robert doesn't trust the method ;)
Well, I
Separating the response into 2 emails, here's the aspect that comes
from implementations of random:
In short, that's part of the difference. I ran these a few times to
check for consistency.
MATLAB (R2008a:
tic
for i = 1:2000
a = randn(1,13857);
end
toc
Runtime: ~0.733489 s
NumPy
On Mon, May 19, 2008 at 4:36 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Mon, May 19, 2008 at 5:27 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
The latest versions of Matlab use the ziggurat method to generate random
normals and it is faster than the method used in numpy. I have ziggurat
On Mon, May 19, 2008 at 6:39 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
On Mon, May 19, 2008 at 4:36 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Mon, May 19, 2008 at 5:27 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
The latest versions of Matlab use the ziggurat method to generate
On to the code, here's a current implementation, attached. I make no
claims about it being great code, I've modified it so that there is a
weave version and a sans-weave version.
Many of the suggestions make things a bit faster. The weave version
bombs out with a rather long log, which can be
On Mon, May 19, 2008 at 5:52 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Mon, May 19, 2008 at 6:39 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
On Mon, May 19, 2008 at 4:36 PM, Robert Kern [EMAIL PROTECTED]
wrote:
On Mon, May 19, 2008 at 5:27 PM, Charles R Harris
[EMAIL PROTECTED]
On Mon, May 19, 2008 at 6:55 PM, James Snyder [EMAIL PROTECTED] wrote:
Also note, I'm not asking to match MATLAB performance. It'd be nice,
but again I'm just trying to put together decent, fairly efficient
numpy code.
I can cut the time by about a quarter by just using the boolean mask
On Mon, May 19, 2008 at 7:30 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
On Mon, May 19, 2008 at 5:52 PM, Robert Kern [EMAIL PROTECTED] wrote:
On Mon, May 19, 2008 at 6:39 PM, Charles R Harris
[EMAIL PROTECTED] wrote:
On Mon, May 19, 2008 at 4:36 PM, Robert Kern [EMAIL PROTECTED]
Robert Kern wrote:
On Mon, May 19, 2008 at 6:55 PM, James Snyder [EMAIL PROTECTED] wrote:
Also note, I'm not asking to match MATLAB performance. It'd be nice,
but again I'm just trying to put together decent, fairly efficient
numpy code.
I can cut the time by about a quarter by just using
Bruce Southey wrote:
Nripun Sredar wrote:
I am running on Windows Xp, Intel Xeon CPU. I'd like to fill in a few
more things here. If I send 0 in the second and third argument of svd
then I get the singular_values, but if its 1 then the problem
persists. I've tried this on sparse and
Hi,
To build numpy binaries, I have some pretty boring python scripts,
and I think it would be useful to have them somewhere in numpy trunk
(for example in tools). Does anyone have something against it ?
cheers,
David
___
Numpy-discussion
Hi,
Sorry for the delay, but it is now ready: numpy superpack
installers for numpy 1.1.0rc1:
http://www.ar.media.kyoto-u.ac.jp/members/david/archives/numpy-1.1.0rc1-win32-superpack-python2.5.exe
On Mon, May 19, 2008 at 7:54 PM, David Cournapeau
[EMAIL PROTECTED] wrote:
Hi,
To build numpy binaries, I have some pretty boring python scripts,
and I think it would be useful to have them somewhere in numpy trunk
(for example in tools). Does anyone have something against it ?
Hey,
On Mon, May 19, 2008 at 8:54 PM, David Cournapeau
[EMAIL PROTECTED] wrote:
Hi,
To build numpy binaries, I have some pretty boring python scripts,
and I think it would be useful to have them somewhere in numpy trunk
(for example in tools). Does anyone have something against it ?
Nope. Go
CC: numpy-discussion because of other reactions on the subject.
On Tue, May 20, 2008 1:26 am, Robert Kern wrote:
Is this an important bugfix? If not, can you hold off until 1.1.0 is
released?
The patch fixes a long existing and unreported bug in f2py - I think
the bug was introduced when
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