April 27th 2010
Last time, I’ve made a simple example of a gradient-free optimization. Now, I’d like to use the gradient of my function (analytical gradient I’ve computed) to be able to get the global minimum in less iterations.
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Loading ...February 2nd 2010
I’m pleased to announce the first release of one of my projects. This scikits is based on a generic framework that can support unconstrained cost function minimization. It is based on a separation principle and is also completely object oriented.
Several optimizers are available:
The usual criterias can be used:
Different direction searches are available:
Finally several line searches (1D minimization) were coded:
Additional helper classes can be used:
Although it is the 0.1 version, the code is quite stable and is used in the learn scikit.
The package can be easy-installed or can be found on PyPI.
Several tutorials are available or will be available on the future at the following locations:
(2 votes, average: 4.50 out of 5) Loading ...December 22nd 2009
Some months ago, I’ve finished my manifold learning posts serie. As support for the manifold learning toolkit, I’ve also developed an optimization framework, which I’ll be blogging about, starting now.
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(1 votes, average: 3.00 out of 5) Loading ...September 24th 2009
Python can be used for many things, and is mainly known for the shell scripts people wrote. Shai Vangast proposes using the langage for data analysis and visualization.
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(No Ratings Yet) Loading ...June 2nd 2009
I’ve promised to make an update whenever I would find a solution to the problem I had some months ago when I tried to use the latest MKL with numpy. Well, there was a simple hack that did the trick. It is far from being perfect, but at least, the tests pass now.
So the only thing you have to do is to export the LD_PRELOAD variable:
export LD_PRELOAD=/path/to/the/MKL/lib/libmkl_core.so |
(1 votes, average: 5.00 out of 5) Loading ...November 3rd 2008
I’m trying to use the MKL with some programs and libraries, but I encountered something really strange and I’m not alone.
First, what is i_free ? Accoding to Intel, it’s their way to handle memory allocation and deallocation. They are only pointers to the actual memory functions so as to let the user decide if he wants a custom memory handler. Since 10.0.3, Intel changed their model, and the trouble begins.
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(No Ratings Yet) Loading ...November 6th 2007
When moving to Python, the real big problem that arises is the transformation of a Python array into the C++ container the team used for years.
Let’s set some hypothesis :
The first hypothesis is derived from the responsibility principle, the two classes have two distinct responsibilities, the first allocates the data space and allows simple access to it, the second allows usual operations (assignation, comparison tests or iterations for instance).
The second one will be the heart of the wrapper. It allows to change the way data is stored and accessed in a simple way.
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