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 -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations.html
