Here are examples on how to use MDP for typical machine learning
- Logistic Maps — Using Slow Feature Analysis (SFA) for
processing a non-stationary time series, derived by a logistic map.
- Growing Neural Gas — Capture the topological structure of a
- Locally Linear Embedding — Approximate data with a low-dimensional surface
and reduce its dimensionality by learning a mapping to the surface.
- Fast image filtering using the caching extension — Filter images with 2D wavelets and demonstrate use
of caching extension.
- Handwritten digits classification with MDP and scikits.learn — Use the combined power of MDP and scikits.learn
in an applciation for handwritten digit classification
- hinet_html.py — Get the HTML representation for a simple hinet network.
- benchmark_parallel.py — Simple benchmark to compare the different
schedulers in MDP.
- pp_remote_test.py — Simple test of the remote Parallel Python support,
using the NetworkPPScheduler.
- Slideshow and Double slideshow — Created slideshows of
matplotlib plots, demonstrates the slideshow module in MDP.
- hinetplaner — Interactive HTML/JS/AJAX based GUI for constructing special
hinet networks. This is a complicated example which won’t teach you much
- mnist — Several more example for handwritten digit classification,
this time with Fisher Discriminant Analysis and without scikits.learn.
The following examples use and illustrate BiMDP.