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
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
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
.
But 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/dependency_links.txt: Operation
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)
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
jaakko.lutti...@aalto.fi wrote:
Answering to myself, this pull request seems to implement an inner
product
((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. However, I
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 multiplysum
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
Hi!
How can I compute dot product (or similar multiplysum 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
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!
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
___
On 01/14/2013 02:44 PM, Matthew Brett wrote:
On Mon, Jan 14, 2013 at 10:35 AM, Jaakko Luttinen
jaakko.lutti...@aalto.fi 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 recently:
https
On 2013-01-10 17:16, Jaakko Luttinen wrote:
On 01/10/2013 05:04 PM, Pauli Virtanen wrote:
Jaakko Luttinen jaakko.luttinen at 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
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 jaakko.luttinen at aalto.fi writes:
I'm trying to use numpydoc (Sphinx extension) for my project written
On 01/10/2013 05:04 PM, Pauli Virtanen wrote:
Jaakko Luttinen jaakko.luttinen at 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 on
PyPi, so
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
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
-
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