ched.
100 loops, best of 3: 1.58 µs per loop
In [8]: %timeit echo_numpy(x)
The slowest run took 58.81 times longer than the fastest. This could
mean that an intermediate result is being cached.
100 loops, best of 3: 474 ns per loop
-Dave
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Sebastian Berg sipsolutions.net> writes:
>
> On Mo, 2015-03-16 at 15:53 +0000, Dave Hirschfeld wrote:
> > I have a number of large arrays for which I want to compute the mean
and
> > standard deviation over a particular axis - e.g. I want to compute
the
> > stat
aller temporary?
For those who like a challenge: is there a faster way to achieve what
I'm after?
Cheers,
Dave
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plenty of memory even were it to require temporaries 5x the
original size.
I don't use einsum that much because I've noticed the performance can be
very problem dependant so I've always profiled it to check. Hopefully
this work will make the performance more consistent, allowing
ies?
Perhaps if you show the code using loops with `np.dot` it will be clearer
how to translate to using `np.einsum`
-Dave
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Julian Taylor googlemail.com> writes:
>
> On 23.10.2014 19:21, Dave Hirschfeld wrote:
> > Hi,
> > I accidentally passed a pandas DatetimeIndex to `np.arange` which
caused
> > it to segfault. It's a pretty dumb thing to do but I don't think it
> > s
#>>> pd.__version__
'0.14.1'
#>>> np.arange(pd.DatetimeIndex([]))
Fatal Python error: Segmentation fault
Current thread 0x1d18:
File "", line 1 in
The exception dialog which pops up contains the following information:
Unhandled ex
oking at the first and last points of the window; if they are
> the same values, then the window is incorrect.
>
If you use signal.get_window(), the default is sym=False:
def get_window(window, Nx, fftbins=True):
# snip
sym = not fftbins
Dave Cook
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http://docs.scipy.org/doc/
-Dave
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),arr.shape(2))
>
>
> Is there something allowing more flexible reshape, like:
>
> arr.reshape(12,-1,...)?
>
> thanks a lot in advance,best,
>
> Chao
>
For the example given the below code works:
In [1]: x = randn(48,5,4,3,2)
In
Julian Taylor googlemail.com> writes:
>
> On 16.05.2014 10:59, Dave Hirschfeld wrote:
> > Julian Taylor googlemail.com> writes:
> >
> > Yes, I'd heard about the improvements and am very excited to try them out
> > since indexing is one of the bottlene
ts 'Oi".
>
> This 50% increase still makes it slower than the simpler indexing
> variant as these have been greatly improved in 1.9 (thanks to
> Sebastian for this :) )
>
Yes, I'd heard about the improvements and am very excited to try them out
since indexing
Sebastian Berg sipsolutions.net> writes:
>
> On Do, 2014-05-15 at 12:31 +0000, Dave Hirschfeld wrote:
> > As can be seen from the code below (or in the notebook linked beneath)
fancy
> > indexing of a structured array is twice as slow as indexing both fields
> > i
combining the arrays into a structured
array and only doing one indexing operation. Unfortunately that doubled the
time that it took!
Is there any reason for this? If not, I'm happy to open an enhancement issue
on GitHub - just let me know.
Thanks,
Dave
In [32]: nrows, ncols = 365, 1
I
Jeff Reback gmail.com> writes:
>
> Dave,
>
> your example is not a problem with numpy per se, rather that the default
generation is in local timezone (same as what python datetime does).
> If you localize to UTC you get the results that you expect.
>
The proble
.array(records, dtype=[('dates', 'M8[h]'),
('values', float)]))
...: df.set_index('dates', inplace=True)
...: df.groupby(lambda d: d.date()).mean()
...:
Out[17]:
values
2014-03-31 1.00
2014-04-01 1.041667
2014-04-02 2.041667
2014-04-03 3.000
alse, it would be much better than either hanging or segfaulting and
people who care about the isfinite cost probably would be linking to a fast
lapack anyway.
-Dave
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array([ 1.e+300, 1.e+000]),
array([[ 1., 0.],
[ 0., 1.]]))
In [4]: C
Out[4]:
array([[ inf, 0.],
[ 0., 1.]])
In [5]: np.linalg.svd(C)
Out[5]:
(array([[ 0., 1.],
[ 1., 0.]]),
array([ nan, nan]),
array([[ 0., 1.],
[ 1., 0.]]))
In [6]: np.__version__
Out[6]: '1.7.1'
Regards,
Dave
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04:00:00 2014-03-30 00:00:00
2 2014-03-30 05:00:00 2014-03-30 01:00:00
3 2014-03-30 06:00:00 2014-03-30 02:00:00
4 2014-03-30 07:00:00 2014-03-30 03:00:00
5 2014-10-26 03:00:00 2014-10-25 23:00:00
6 2014-10-26 04:00:00 2014-10-26 00:00:00
7 2014-10-26 05:00:00 2014-10-26 01:0
e+38
> nexp = 8 min=-max
> -----
>
Thanks for that.
Dave Cook
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)
I would expect float32 to be able to represent
numbers with exponents as small as -127.
Thanks,
Dave Cook
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of terminology which I agree is important in helping
understand what's happening.
-Dave
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ely this would result in more breaking changes
than .H returning a view rather than a copy?!
IANAD so I'm happy with whatever the consensus is I just thought I'd put
forward the view from a (specific type of) user perspective.
Regards,
Dave
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fact that A.H will return a copy.
A user coming from Matlab probably doesn't care that it takes a copy but you'd
be hard pressed to convince them there's any benefit of writing
A.conjugate().transpose() over exactly what it looks like in textbooks - A.H
Regards,
Dave
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The example below demonstrates the fact that the datetime64 constructor
ignores the dtype argument if passed in. Is this conscious design decision or
a bug/oversight?
In [55]: from datetime import datetime
...: d = datetime.now()
...:
In [56]: d
Out[56]: datetime.datetime(2013, 6, 12,
would be good to get the utc-everywhere fix for datetime64 in there if
someone has time to look into it.
Thanks,
Dave
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3), so I'll punt,
> but someone can pick it up.
>
> I'll start one for (4) also, though I'm not sure I have much to say,
> other than that I think it's good idea. My naive view is that it would
> be pretty easy, actually, but I could be very wrong th
y useful to me in performance
critical areas of my code. So much so in fact that I've actually rolled my own
functions in cython to do what I need. I'd be happy if the functionality was
available by default in numpy though.
Regards,
Dave
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t,
>
> -Travis
>
That sounds like a good solution.
-Dave
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Nathaniel Smith pobox.com> writes:
>
> On Wed, Apr 3, 2013 at 2:26 PM, Dave Hirschfeld
> gmail.com> wrote:
> >
> > This isn't acceptable for my use case (in a multinational company) and I
found
> > no reasonable way around it other than bypassing the num
2000-02-24 11:00:00 2000-03-01 00:00:00 2000-03-31 00:00:00 0.1425
Oh dear!
This isn't acceptable for my use case (in a multinational company) and I found
no reasonable way around it other than bypassing the numpy conversion entirely
by setting the dtype to object, manually parsing the strings and creating an
array from the list of datetime objects.
Regards,
Dave
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..: it.reset()
...: for values, index_values, out in it:
...: out[...] = values[index_values.argmax()]
...: #
...:
100 loops, best of 3: 5.26 ms per loop
In [48]:
Thanks,
Dave
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3: 1.74 ms per loop
In [2]: %%timeit
...: x = empty(1, dtype=float)
...: x[:] = NaN
...:
1 loops, best of 3: 28 us per loop
In [3]: 1.74e-3/28e-6
Out[3]: 62.142857142857146
Even when not in the mythical "tight loop" setting an array to one and then
multiplyi
---
TypeError Traceback (most recent call last)
in ()
> 1 a[0] = b
TypeError: can't convert complex to float
In [21]: a[0:1] = b
In [22]:
-Dave
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7;t get lost I've opened issue #380 on
GitHub
https://github.com/numpy/numpy/issues/380
-Dave
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Dave Hirschfeld gmail.com> writes:
>
> It seems that reshape doesn't work correctly on an array which has been
> resized using the 0-stride trick e.g.
>
> In [73]: x = array([5])
>
> In [74]: y = as_strided(x, shape=(10,), strides=(0,))
>
> In [75]: y
>
py().reshape(shape)
else:
return y.reshape(shape)
Regards,
Dave
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ording could be:
IndexError: Index 3 is out of bounds for axis 0 of an array of shape (3,2,4)
-Dave
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Thanks Ralf! I'm interested in unattended/silent installations.
My best,
Dave
---
Date: Sat, 21 Apr 2012 10:48:36 +0200
From: Ralf Gommers
Subject: Re: [Numpy-discussion] Command-line options for (Windows)
NumPy Installe
Hi, is there any documentation available on exactly which command line options
are available from NumPy's 'superpack' installers on Windows? E.g.,
http://docs.scipy.org/doc/numpy/user/install.html mentions an "/arch" flag, but
I'm not seeing anything e
On Thu, Feb 9, 2012 at 9:41 PM, Dave Cook wrote:
>
> Interesting. I should have mentioned that I'm using numpy 1.5.1 on 64-bit
> Ubuntu 10.10. This transpose/compute/transpose trick did not work for me.
>
>
Nor does it work under numpy 1.6.1 built with MKL under Windows 7
T == a.cumsum(0)).all()
> Out[14]: True
>
>
Interesting. I should have mentioned that I'm using numpy 1.5.1 on 64-bit
Ubuntu 10.10. This transpose/compute/transpose trick did not work for me.
In [27]: timeit a.T.cumsum(-1).T
10 loops, best of 3: 18.3 ms per loop
Dave Cook
___
[1]: from cumsumtest import *
True
True
In [2]: timeit npcumsum(a)
100 loops, best of 3: 14.7 ms per loop
In [3]: timeit addaccum(a)
100 loops, best of 3: 15.4 ms per loop
In [4]: timeit loopcumsum(a)
100 loops, best of 3: 2.16 ms per loop
Dave Cook
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functions fuctions were made available in the
np.rec namespace (and possibly deprecate np.lib.recfunctions to avoid
confusion?)
I'm conscious that as a user (not a developer) talk is cheap and I'm happy with
whatever the consensus is. I just thought I'd pipe up since it was only t
x27;2011-12-31T18:00:00.00-0600'],
dtype='datetime64[us]')
>
I would expect the second value of the array to be midnight at the end of the
year. I'm not sure what is actually happening in the above example. What happens
if I email that code to someone in New Zealand wo
e
> added as a new dtype, a dtype like
> [('date', 'M8[D]'), ('event', 'i8[mod 100]')] could replace the current
> 'M8[D]//100'.
Sounds like a cleaner API.
>
>>
>> As Dave H. summarized, we used a basic keyword to do the same
tssort(series):
: indices = series.argsort()
: return ts.time_series(series._series[indices],
: series.dates[indices],
: autosort=False)
:
In [92]: tssort(series)[0:5]
Out[92]:
timeseries([-2.96602612 -2.81612524 -2.61558511 -2.59522921 -2.4899447 ],
dates = [26-Aug-2011 18-Apr-2011 27-Aug-2011 21-Aug-2011 19-Nov-2011],
freq = D)
Pandas seems to offer an even higher level api than scikits.timeseries. I view
them as mostly complementary but I haven't (yet) had much experience with
pandas...
-Dave
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the current date. If the expiry date was accidentally specified at (or converted
to) a monthly frequency and the current date was a daily frequency taking the
difference will give you the wrong number of days until expiry resulting in a
subtly different answer - not good.
NB: The timeseries Date/Date
Christopher Barker noaa.gov> writes:
>
> Dave Hirschfeld wrote:
> > That would be one way of dealing with irregularly spaced data. I would argue
> > that the example is somewhat back-to-front though. If something happens
> > twice a month it's not occuring
Robert Kern gmail.com> writes:
>
> On Tue, Jun 7, 2011 at 07:34, Dave Hirschfeld gmail.com>
wrote:
>
> > I'm not convinced about the events concept - it seems to add complexity
> > for something which could be accomplished better in other ways. A [Y]//4
>
I'm not sure how this could be implemented in practice except for some vague
thoughts about providing hooks where users could provide functions which
converted to and from an integer representation for their particular
calendar. Creating a weekday calendar would be a good test-case for such
an API.
Apologies for the very long post! I guess it can be mostly summarised as
you've got a pretty good template for functionality in scikits.timeseries!
Pandas/statsmodels may have more sophisticated requirements though so their
input on the finance/econometric side would be useful...
-Dave
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Disclaimer: I'm on windows (Win7 x64)
Following the instructions at:
http://docs.scipy.org/doc/numpy/dev/gitwash/following_latest.html
I got the following (rather unhelpful) error message:
C:\dev\src>git clone git://github.com/numpy/numpy.git
Cloning into numpy...
github.com[0: 207.97.227.239]:
Jean-Luc Menut free.fr> writes:
>
> I have a little question about the speed of numpy vs IDL 7.0.
>
> Here the IDL result:
> % Compiled module: $MAIN$.
> 2.837
>
> The python code:
> from numpy import *
> from time import time
> time1 = time()
> for j in range(1):
> for i i
e-01
...
In [7]: data = np.loadtxt('dummy_data.txt')
In [8]: data.shape
Out[8]: (1,)
In [9]: data = data.reshape(100, 100)
In [10]: data.shape
Out[10]: (100, 100)
In [11]: np.allclose(dummy_data, data)
Out[11]: True
HTH,
Dave
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In [28]: print("%f %g %e %d" % (w,x,y,z,))
68.00 64 1.30e+01 57
In [29]: w, x, y, z
Out[29]: (68, 64, 13, 57)
For a file I would use np.savetxt
HTH,
Dave
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http
gt;
Only slightly more convenient is:
a.reshape(a.shape[0], -1).sum(-1)
HTH,
Dave
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Richard D. Moores gmail.com> writes:
>
> On Mon, Jul 19, 2010 at 06:03, Dave gmail.com> wrote:
> > My bad - typo. The command to build numpy should have been:
> >
> > C:\Python31\python setup.py bdist_wininst
>
> I tried that. See the attached.
>
>
Richard D. Moores gmail.com> writes:
>
> >
> > The commands should therefore be:
> > cd c:\SVNRepository\numpy
> > C:\Python31>python setup.py bdist_wininst
>
> Dave, I got:
> c:\SVNRepository\numpy>C:\Python31>python setup.py bdist_wininst
>
un
which itself takes an argument telling it what you want to do - e.g.
build/install/bdist_wininst.
The commands should therefore be:
cd c:\SVNRepository\numpy
C:\Python31>python setup.py bdist_wininst
Assuming you have a C compiler on your system (and in your path) this will
then build numpy and create an windows installer (the bdist_wininst
argument passed to setup.py) in the dist directory which you can then use
to install numpy.
HTH,
Dave
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bbox.width/dpi, 1.25*bbox.height/dpi)
show()
finally:
interactive(IS_INTERACTIVE)
NB: Sympy provides the latex function to convert the equation objects into
latex as well as other ways to display the objects in the sympy.printing
module. It shouldn't be too hard to do so
00 | Austin | TX | 78701
Do participants in this Webinar need to have GoToMeeting software
installed and if yes, do they need to purchase this software?
GoToMeeting does require installing some local software, but it
anks, sorry for all the emails.
Dave
On 9/23/09, Dave Wood wrote:
>
> Appologies for the multiple posts, people. My posting to the forum was
> pending for a long time, so I deleted it and tried emailing directly. I
> didn't think they'd all be sent out.
> Gokan, thank
the information. To fix my problem, I tried taking out the row
names (leaving only numerical information), and converting the 2D list to
floats. I still had the same problem.
On 9/23/09, Christopher Barker wrote:
>
> Dave Wood wrote:
> > Well, I suppose they are all considered to be strin
sn't seem like the expected behaviour though and the error message
seems wrong.
Many thanks,
Dave
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memory error".
Also, I don't really understand why reading in this 57mb txt file is taking
up ~2gb's of RAM.
Any advice? Thanks in advance
Dave
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Charles R Harris gmail.com> writes:
>
>
> Is anyone with this problem *not* running ubuntu?Chuck
>
All I can say is that it (surprisingly?) doesn't appear to affect my windoze
(XP) box.
Python 2.5.4 (r254:67916, Dec 23 2008, 15:10:54) [MSC v.1310 32 bit (Intel)]
In [2]: a=np.random.rand(1
ailable from http://pastebin.com/m5505d4b5
The "errors" seem to be be related to the NaN handling.
Thanks for the help today!
-Dave
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David Cournapeau ar.media.kyoto-u.ac.jp> writes:
>
> Dave wrote:
> > Dave gmail.com> writes:
> >
> >
> >> Work's for me.
> >>
> >> -Dave
> >>
> >>
> >
> > Except now when trying to compile the l
Dave gmail.com> writes:
>
> Work's for me.
>
> -Dave
>
Except now when trying to compile the latest scipy I get the following error:
C:\dev\src\scipy>svn up
Fetching external item into 'doc\sphinxext'
External at revision 7280.
At revision 589
t;
> Ok, not right away, but could you check that r7280 fixed it for you ?
>
> cheers,
>
> David
>
Work's for me.
adding 'SCRIPTS\f2py.py'
creating dist
removing 'build\bdist.win32\wininst' (and everything under it)
Thanks for the quick fix!
-D
s system. If you have Cygwin installed,
you can try compiling with MingW32, by passing "-c mingw32" to setup.py.
I tried without a distutils.cfg file and deleted the build directory both times.
In case it helps the bulid log should be available from
http://p
up.py bdist_wininst fails with
the same error message as before and python setup.py bdist_wininst
--compiler=mingw32 fails with the message:
error: option --compiler not recognized
Is it still possible to create a .exe installer on Windows and if so what are
the commands we need to make
rthon, automatically
handles the ordering conversion, copying if needed, but I try to avoid
the copying as much as possible. It would be very nice if some of the
ordering issues could be handled under the covers by numpy.
Dave
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Hello,
I'm pleased to announce that Enthought Tool Suite (ETS) version 3.3.0
has been tagged and released! Please see below for a partial list of
changes for this release.
PyPi has been updated with the release, including the availability of
both Windows binaries (.egg) and source distributio
27;%g %g\n')
>
> I need a way to reinterpret the complex data as an array with 2 columns to
> make this work. Ideas?
>
Something like?
gen_qpsk = (array([[1,1j]])*np.loadtxt('gen_qpsk.txt')).sum(1)
HTH,
Dave
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re of such
issues and I'm guessing the casting behaviour is a design decision, nevertheless
I thought I'd post to make others aware of such considerations.
HTH,
Dave
Python 2.5.4 (r254:67916, Dec 23 2008, 15:10:54) [MSC v.1310 32 bit (Intel)]
Type "copyright", "credits
Hello,
I'm pleased to announce that Enthought Tool Suite (ETS) version 3.2.0
has been tagged and released!
Source distributions (.tar.gz) have been uploaded to PyPi, and Windows
binaries will be follow shortly. A full install of ETS can be done using
Setuptools via a command like:
easy_install
e any comments, concerns, or bug reports via the EPD Trac
instance at https://svn.enthought.com/epd or via e-mail to
epd-supp...@enthought.com.
-- Dave
About EPD
-
The Enthought Python Distribution (EPD) is a "kitchen-sink-included"
distribution of the Python™ Progr
; its features include
initialization, validation, delegation, notification, and visualization
of typed attributes.
More information is available for all these packages from the Enthought
Tool Suite development home page:
http://code.enthought.com/projects/index.php
--
My system..
Ubuntu v8.0.4; Gnu gcc v4.0.3;g95 v0.92
Python v2.5.2;Numpy v1.2.1;f2py v2.5972
Hope someone can see what's wrong here?
thanks
Dave Lang
..Here is the F90 code...
! PROGRAM TO DEMONSTRATE PYTHON-->F90 AND F90-->PYTHO
e preferred (ie. correct?) f2py
statements that need to be executed to accomplish this (I would be glad to
contribute same to the body of knowledge, if I can ever get it to work).
I will be most grateful for comments on any or all of this.
thanks
Dave Lang
The Fortran Code:
! PROGAM TO
ubscription and installation
support are available for individual commercial use. An enterprise
subscription with support for particular deployment environments is also
available for commercial purchase.
-- Dave
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Hi All,
Using the current trunk, I am getting the following build error:
creating build\temp.win32-2.5\Release\build
creating build\temp.win32-2.5\Release\build\src.win32-2.5
creating build\temp.win32-2.5\Release\build\src.win32-2.5\numpy
creating build\temp.win32-2.5\Release\build\src.win32-2.5\
also
available for commercial purchase. The beta versions of EPD are
available for indefinite free trial.
-- Dave
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e included documentation hasn't been updated to the current
versions of the third-party libraries.
* Some of the product branding is not up-to-date with regard to the
product name change to "EPD with Py2.5", nor with the version number of
4.0.30001 Beta 1.
-- Dave
mbers so a simple "ets up" (or svn up)
should bring you up to date. Others may wish to grab a complete new
checkout via a "ets co ETS". The release branches that had been created
are now removed. The next release is currently expected to be ETS 3.0.1
I'm pleased to announce that Enthought has released the Enthought Python
Distribution (EPD) 2.5.2001 for OS X!
EPD is a Distribution of the Python Programming Language (currently
version 2.5.2) that includes over 60 additional libraries, including ETS
2.7.1. Please visit the EPD website (http://ww
of ETS and its sub-projects happens on the enthought-dev mailing list,
who's home page is at:
https://mail.enthought.com/mailman/listinfo/enthought-dev
Thank you to all who have worked so hard on making this release possible!
-- Dave
Enthought Tool Suite
---
class. Trait attributes defined by a classs
are automatically inherited by any subclass derived from the class.
-- Dave
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This may be of interest,
LLVM support in Mesa, and i believe there is work doing on with LLVM
and python in the pypy camp.
http://zrusin.blogspot.com/2007/05/mesa-and-llvm.html
I just stumbled on this page, while this conversation was happening :)
Dave
On 6/2/07, Bob Lewis <[EMAIL PROTEC
nce of the term in question, I might
change that to 1, hopefully this will reduce the amount of dimensions.
Dave
On 5/15/07, val <[EMAIL PROTECTED]> wrote:
> Dave,
> I'm may be totally wrong, but i have intuitive feeling that
> your problem may be reformulated with f
d out of the docs to build a
space around it.)
Cheers
Dave
On 5/14/07, Charles R Harris <[EMAIL PROTECTED]> wrote:
>
>
> On 5/13/07, Dave P. Novakovic <[EMAIL PROTECTED]> wrote:
> > > Are you trying some sort of principal components analysis?
> >
> > P
> Are you trying some sort of principal components analysis?
PCA is indeed one part of the research I'm doing.
Dave
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/python2.4/site-packages/numpy/linalg/linalg.py", line
575, in svd
vt = zeros((n, nvt), t)
MemoryError
Cheers
Dave
On 5/13/07, Anne Archibald <[EMAIL PROTECTED]> wrote:
> On 12/05/07, Dave P. Novakovic <[EMAIL PROTECTED]> wrote:
>
> > core 2 duo with 4gb RAM.
>
0 non zero elements.
I think I'll try outputting a sparse matrix file and using svdlibc. If
this works I'll wrap svdlibc with ctypes and post the results back
here.
i just wanted to make sure there was absolutely no way of doing it
with sci/numpy before i looked at anything else.
Cheer
errors, or too many dims), I'm starting to feel like I'm in a bit of
trouble here :)
What do people use to do large svd's? I'm not adverse to using another
lib or wrapping something.
Cheers
Dave
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