April 27th 2010
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:
- Nelder-Mead or simplex minimization
- Unconstrained gradient-based minimization
The usual criterias can be used:
- Iteration limit
- Parameter change (relative and absolute)
- Cost function changer (relative and absolute)
- Composite criterion generation (AND/OR)
Different direction searches are available:
- Several conjugate-gradient (Fletcher-Reeves, …)
- Decorators for selecting part of the gradient
- Marquardt step
Finally several line searches (1D minimization) were coded:
- Fibonacci and gold number methods (exact line searches)
- Wolfe-Powell soft and strong rules
- Goldstein line search
- Cubic interpolation
Additional helper classes can be used:
- Finite difference differentation (central and forward)
- Quadratic cost (for least square estimation)
- Levenberg-Marquardt approximation for least square estimation
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:
December 22nd 2009
September 24th 2009
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:
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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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 :
- there is a separation between the class containing the data and the class that uses the data (iterators, …)
- the containing class can be changed (policy or strategy pattern)
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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