Dear all,
I am pleased to announce that BayesPy 0.3 has been released.
BayesPy provides tools for variational Bayesian inference. The user can
easily constuct conjugate exponential family models from nodes and run
approximate posterior inference. BayesPy aims to be efficient and
flexible enough f
Dear all,
I am pleased to announce the release of BayesPy version 0.2.
BayesPy provides tools for Bayesian inference in Python. In particular,
it implements variational message passing framework, which enables
modular and efficient way to construct models and perform approximate
posterior infe
still, any help is appreciated.
-Jaakko
On 08/05/2013 12:53 PM, Jaakko Luttinen wrote:
> Hi,
>
> I'm trying to install NumPy 1.7.1 for Python 3.3 using
>
> pip install numpy
>
> However, I get the following error after a while:
>
> error: numpy.egg-info
Hi,
I'm trying to install NumPy 1.7.1 for Python 3.3 using
pip install numpy
However, I get the following error after a while:
error: numpy.egg-info/dependency_links.txt: Operation not supported
Is this a bug or am I doing something wrong? If it matters, I'm using
virtualenv as I do no
I don't quite understand how einsum handles broadcasting. I get the
following error, but I don't understand why:
In [8]: import numpy as np
In [9]: A = np.arange(12).reshape((4,3))
In [10]: B = np.arange(6).reshape((3,2))
In [11]: np.einsum('ik,k...->i...', A, B)
--
((2,2))
B = np.arange(2*1).reshape((2,1))
gula.matrix_multiply(A, B)
ValueError: On entry to DGEMM parameter number 10 had an illegal value
-Jaakko
On 03/20/2013 03:33 PM, Jaakko Luttinen wrote:
> I tried using this inner1d as an alternative to dot because it uses
> broadcasting. Howe
, there is already an inner1d implemented in
> numpy.core.umath_tests.inner1d
>
> from numpy.core.umath_tests import inner1d
>
> It should do the trick :)
>
> On Thu, Mar 14, 2013 at 12:54 PM, Jaakko Luttinen
> wrote:
>> Answering to myself, this pull request seems
Answering to myself, this pull request seems to implement an inner
product with broadcasting (inner1d) and many other useful functions:
https://github.com/numpy/numpy/pull/2954/
-J
On 03/13/2013 04:21 PM, Jaakko Luttinen wrote:
> Hi!
>
> How can I compute dot product (or similar mul
Hi,
I was wondering if someone could provide some intuition on the
performance of einsum?
I have found that sometimes it is extremely efficient but sometimes it
is several orders of magnitudes slower compared to some other
approaches, for instance, using multiple dot-calls.
My intuition is that
Hi!
How can I compute dot product (or similar multiply&sum operations)
efficiently so that broadcasting is utilized?
For multi-dimensional arrays, NumPy's inner and dot functions do not
match the leading axes and use broadcasting, but instead the result has
first the leading axes of the first inpu
Hi,
I have encountered a very weird behaviour with einsum. I try to compute
something like R*A*R', where * denotes a kind of "matrix
multiplication". However, for particular shapes of R and A, the results
are extremely bad.
I compare two einsum results:
First, I compute in two einsum calls as (R*
ub.com/numpy/numpy/issues/2969
>
> -n
>
> On 25 Feb 2013 13:41, "Jaakko Luttinen" <mailto:jaakko.lutti...@aalto.fi>> wrote:
>
> Hi!
>
> I was wondering if anyone could help me in finding a memory leak problem
> with NumPy. My project is qu
Hi!
I was wondering if anyone could help me in finding a memory leak problem
with NumPy. My project is quite massive and I haven't been able to
construct a simple example which would reproduce the problem..
I have an iterative algorithm which should not increase the memory usage
as the iteration
Hi,
Is this a bug in numpy.einsum?
>>> np.einsum(3, [], 2, [], [])
ValueError: If 'op_axes' or 'itershape' is not NULL in theiterator
constructor, 'oa_ndim' must be greater than zero
I think it should return 6 (i.e., 3*2).
Regards,
Jaakko
___
NumPy-Di
On 01/14/2013 02:44 PM, Matthew Brett wrote:
> On Mon, Jan 14, 2013 at 10:35 AM, Jaakko Luttinen
> wrote:
>> On 01/14/2013 12:53 AM, Matthew Brett wrote:
>>> You might be able to get away without 2to3, using the kind of stuff
>>> that Pauli has used for scipy recent
On 01/14/2013 12:53 AM, Matthew Brett wrote:
> On Sun, Jan 13, 2013 at 10:46 PM, Jaakko Luttinen
> wrote:
>> I'm a bit stuck trying to make numpydoc Python 3 compatible. I made
>> setup.py try to use distutils.command.build_py.build_py_2to3 in order to
>> transform in
On 2013-01-10 17:16, Jaakko Luttinen wrote:
> On 01/10/2013 05:04 PM, Pauli Virtanen wrote:
>> Jaakko Luttinen aalto.fi> writes:
>>> The files in numpy/doc/sphinxext/ and numpydoc/ (from PyPI) are a bit
>>> different. Which ones should be modified?
>>
>>
On 01/10/2013 05:04 PM, Pauli Virtanen wrote:
> Jaakko Luttinen aalto.fi> writes:
>> The files in numpy/doc/sphinxext/ and numpydoc/ (from PyPI) are a bit
>> different. Which ones should be modified?
>
> The stuff in sphinxext/ is the development version of the package o
The files in numpy/doc/sphinxext/ and numpydoc/ (from PyPI) are a bit
different. Which ones should be modified?
-Jaakko
On 01/10/2013 02:04 PM, Pauli Virtanen wrote:
> Hi,
>
> Jaakko Luttinen aalto.fi> writes:
>> I'm trying to use numpydoc (Sphinx extension) for my proje
Hi!
I'm trying to use numpydoc (Sphinx extension) for my project written in
Python 3.2. However, installing numpydoc gives errors shown at
http://pastebin.com/MPED6v9G and although it says "Successfully
installed numpydoc", trying to import numpydoc raises errors..
Could this be fixed or am I doi
On 02/23/2012 03:50 PM, Jaakko Luttinen wrote:
> Hi!
>
> I was wondering whether it would be easy/possible/reasonable to have
> classes for arrays that have special structure in order to use less
> memory and speed up some computations?
>
> For instance:
> - symmetric m
Hi!
I was wondering whether it would be easy/possible/reasonable to have
classes for arrays that have special structure in order to use less
memory and speed up some computations?
For instance:
- symmetric matrix could be stored in almost half the memory required by
a non-symmetric matrix
- diago
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