They are very large numbers indeed. Thanks for giving me a wake up call.
Currently my data is represented as vectors in a vectorset, a typical
sparse representation.
I reduced the problem significantly by removing lots of noise. I'm
basically recording traces of a terms occurrence throughout a
Hi out there,
this is the code segment
if m maxN and n maxN and self.activeWide[m+1, n+1]:
try:
deltaX = x[m+1] - x[m]
except TypeError:
print '-' * 40
i.e. for example from flat array [1, 2, 3] obtain
array([[ 1.],
[ 2.],
[ 3.]])
I have numpy v 1.0.1
Thx, D.
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On Sun, May 13, 2007 at 02:36:39PM +0300, dmitrey wrote:
i.e. for example from flat array [1, 2, 3] obtain
array([[ 1.],
[ 2.],
[ 3.]])
I have numpy v 1.0.1
Thx, D.
Use newaxis:
In [1]: a = array([1., 2., 3.])
In [2]: a
Out[2]: array([ 1., 2., 3.])
In [3]: a[:,newaxis]
On Sunday 13 May 2007 7:36:39 am dmitrey wrote:
i.e. for example from flat array [1, 2, 3] obtain
array([[ 1.],
[ 2.],
[ 3.]])
a=array([1,2,3])
a.shape=(len(a),1)
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Hello all
On Sat, 12 May 2007, Charles R Harris wrote:
On 5/12/07, Albert Strasheim [EMAIL PROTECTED] wrote:
I've more or less finished my quick triage effort.
Thanks, Albert. The tickets look much better organized now.
My pleasure. Stefan van der Walt has also gotten in on the act and
On Sun, May 13, 2007 at 07:46:47AM -0400, Darren Dale wrote:
On Sunday 13 May 2007 7:36:39 am dmitrey wrote:
i.e. for example from flat array [1, 2, 3] obtain
array([[ 1.],
[ 2.],
[ 3.]])
a=array([1,2,3])
a.shape=(len(a),1)
Or just
a.shape = (-1,1)
Cheers
Stéfan
On 5/13/07, Dave P. Novakovic [EMAIL PROTECTED] wrote:
They are very large numbers indeed. Thanks for giving me a wake up call.
Currently my data is represented as vectors in a vectorset, a typical
sparse representation.
I reduced the problem significantly by removing lots of noise. I'm
Is it possible somehow to speedup numpy 1.0.3 appearing in Linux update
channels? (as for me I'm interested in Ubuntu/Kubuntu, currently there
is v 1.0.1)
I tried to compile numpy 1.0.2, but, as well as in Octave compiling, it
failed because c compiler can't create executable. gcc
On Sun, May 13, 2007 at 06:19:30PM +0300, dmitrey wrote:
Is it possible somehow to speedup numpy 1.0.3 appearing in Linux update
channels? (as for me I'm interested in Ubuntu/Kubuntu, currently there
is v 1.0.1)
I tried to compile numpy 1.0.2, but, as well as in Octave compiling, it
hi all,
does anyone know howto copy an instance of class, that contains multiple
subfields, for example
myObj.field1.subfield2 = 'asdf'
myObj.field4.subfield8 = numpy.mat('1 2 3; 4 5 6')
I tried
from copy import copy
myObjCopy = copy(myObj)
but it seems that it doesn't work correctly
Thx, D.
Hi all,
In the numpy.sctypes dictionary, there are two entries for uint32:
In [2]: N.sctypes['uint']
Out[2]:
[type 'numpy.uint8',
type 'numpy.uint16',
type 'numpy.uint32',
type 'numpy.uint32',
type 'numpy.uint64']
Comparing the dtypes of the two types gives the correct answer:
In [3]: sc
Stefan van der Walt wrote:
Hi all,
In the numpy.sctypes dictionary, there are two entries for uint32:
In [2]: N.sctypes['uint']
Out[2]:
[type 'numpy.uint8',
type 'numpy.uint16',
type 'numpy.uint32',
type 'numpy.uint32',
type 'numpy.uint64']
Comparing the dtypes of the two
Stefan van der Walt wrote:
On Sun, May 13, 2007 at 06:19:30PM +0300, dmitrey wrote:
Is it possible somehow to speedup numpy 1.0.3 appearing in Linux update
channels? (as for me I'm interested in Ubuntu/Kubuntu, currently there
is v 1.0.1)
I tried to compile numpy 1.0.2, but, as well as
Hi,
you have a problem with your Ubuntu installation, not with numpy.
Matthieu
2007/5/13, dmitrey [EMAIL PROTECTED]:
Stefan van der Walt wrote:
On Sun, May 13, 2007 at 06:19:30PM +0300, dmitrey wrote:
Is it possible somehow to speedup numpy 1.0.3 appearing in Linux update
channels? (as
Hi Dmitrey
On Sun, May 13, 2007 at 08:21:15PM +0300, dmitrey wrote:
Many people here are compiling numpy fine under Ubuntu. Do you have
write permissions to the output directory? What is the compiler error
given?
Sorry, I meant compiling Python2.5 and Octave, not numpy Octave
It's alwys helpful if you can include a self contained example so it's easy
to figure out exactly what you are getting at. I say that because I'm not
entirely sure of the context here -- it appears that this is not numpy
related issue at all, but rather a general python question. If so, I think
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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There are definitely elements of spectral graph theory in my research
too. I'll summarise
We are interested in seeing the each eigenvector from svd can
represent in a semantic space
In addition to this we'll be testing it against some algorithms like
concept indexing (uses a bipartitional
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