MDP: A Simple Feed Forward Network

[blaine]

Hey everyone,
  I was hoping to see some people out on the python list that are
familiar with MDP (Modular Toolkit for Data Processing -
http://mdp-toolkit.sourceforge.net/)?

  I am wanting to develop a very simple feed forward network.  This
network would consist of a few input neurons, some hidden neurons, and
a few output neurons.  There is no learning involved.  This network is
being used as a gene selection network in a genetic simulator where we
are evolving the weights and connectivity.

  There are many different types of nodes listed as being supported,
but i can't figure out the best one to use for this case.  In this
situation, we only want to iterate through the network X times.  (In
the simples version, with no cycles, this would mean that once the
output nodes are calculated there would be no additional calculations
since the system would be stable and non-learning).  Node types are
listed at the bottom here: http://mdp-toolkit.sourceforge.net/tutorial.html#quick-start

  In the more complex version, we would have the same model but
instead of having straight connectivity all the way through, we would
add a few cycles in the hidden layer so that a few neurons would feed
back into themselves on the next time step.  This could also be
connected to a 'selector' layer, that feeds back on the hidden layer
as well.  Since we are only running this a finite number of times, the
system would not spiral out into instability.

  Any suggestions for which node types to use, or possibly what other
libraries would be helpful? I realize that due to the relative
simplicity of this network I could hand code this from scratch.  MDP
just looks extremely handy and efficient and I'd like to use it if
possible.

Simple Network:

(H is interconnected fully with the Input and Output layer)
I -> H -> O
I -> H -> O
I -> H -> O

Cycled Network (Trying to show that the first hidden neuron is
connected back to itself)
     v---|
I -> H -> O
I -> H -> O
I -> H -> O

Complex Network (Trying to show that the first hidden neuron is
connected to another hidden neuron S that connects back to the input
of H.  S would be interconnected with the other hidden neurons as
well)
         v--- S <--|
I ->    H         -> O
I ->    H         -> O
I ->    H         -> O

Thanks!
-Blaine