Here are examples on how to use MDP for typical machine learning applications:

  • 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 data distribution.
  • 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 about MDP.
  • 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.