On Saturday 26 February 2011 02:58:19 Bruce Southey wrote:
> On 02/25/2011 02:01 AM, Algis Kabaila wrote:
>
> I just build numpy and scipy from source so I do not know how
> you get Python 3 or which Ubuntu versions include recent
> numpy versions (there is a upcoming release that will
> probably
On Fri, Feb 25, 2011 at 12:52 PM, Joe Kington wrote:
> Do you expect to have very large integer values, or only values over a
> limited range?
>
> If your integer values will fit in into 16-bit range (or even 32-bit, if
> you're on a 64-bit machine, the default dtype is float64...) you can
> pote
Do you expect to have very large integer values, or only values over a
limited range?
If your integer values will fit in into 16-bit range (or even 32-bit, if
you're on a 64-bit machine, the default dtype is float64...) you can
potentially halve your memory usage.
I.e. Something like:
data = nump
A topic that often comes up on the list is that arr.sum(axis=-1) is
faster than arr.sum(axis=0). For C ordered arrays, moving along the
last axis moves the smallest amount in memory. And moving small
amounts in memory keeps the data in cache longer. Can I use that fact
to speed up calculations alon
On 02/25/2011 02:01 AM, Algis Kabaila wrote:
> On Friday 25 February 2011 18:54:13 Scott Sinclair wrote:
>> On 25 February 2011 06:22, Algis Kabaila
> wrote:
>>> On Friday 25 February 2011 14:44:07 Algis Kabaila wrote:
>>> PS: a little investigation shows that my version of numpy
>>> is 1.3.0 and
Hi
Is it possible to load a text file 664 MB large with integer values and
about 98% sparse? numpy.loadtxt() shows a memory error.
If it's not possible, what alternatives could I have?
The usable RAM on my machine running Windows 7 is 3.24 GB.
Thanks.
___
Gaël, Olivier,
I finally got working it. I don't compute the nearest value but the mean.
Works like a charm ;-)
Thanks anyway.
Cheers,
--
Fred
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2011/2/25 Gael Varoquaux :
> On Fri, Feb 25, 2011 at 10:36:42AM +0100, Fred wrote:
>> I have a big array (44 GB) I want to decimate.
>
>> But this array has a lot of NaN (only 1/3 has value, in fact, so 2/3 of
>> NaN).
>
>> If I "basically" decimate it (a la NumPy, ie data[::nx, ::ny, ::nz], for
>>
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 arr
Le 25/02/2011 10:42, Gael Varoquaux a écrit :
> 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 data.shape = (
On Fri, Feb 25, 2011 at 10:36:42AM +0100, Fred wrote:
> I have a big array (44 GB) I want to decimate.
> But this array has a lot of NaN (only 1/3 has value, in fact, so 2/3 of
> NaN).
> If I "basically" decimate it (a la NumPy, ie data[::nx, ::ny, ::nz], for
> instance), the decimated array wi
Hi there,
I have a big array (44 GB) I want to decimate.
But this array has a lot of NaN (only 1/3 has value, in fact, so 2/3 of
NaN).
If I "basically" decimate it (a la NumPy, ie data[::nx, ::ny, ::nz], for
instance), the decimated array will also have a lot of NaN.
What I would like to have
Pauli Virtanen wrote:
> Thu, 24 Feb 2011 16:47:14 +, Pauli Virtanen wrote:
>> Another possible reason is that Numpy was installed wrong (as the
>> numpy.__config__ module is apparently missing). Numpy needs to be
>> installed via "python setup.py install", manually copying the "numpy"
>> direct
On Friday 25 February 2011 18:54:13 Scott Sinclair wrote:
> On 25 February 2011 06:22, Algis Kabaila
wrote:
> > On Friday 25 February 2011 14:44:07 Algis Kabaila wrote:
> > PS: a little investigation shows that my version of numpy
> > is 1.3.0 and scipy is 0.7.2 - so ubuntu binaries are way
> > b
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