Continued writing a unit test for destin so I could figure out why the beliefs were not updating like before. Found a few things that changed that affected it. The default starvation trace coefficient was set lower so it would take longer for other centroids to be adjusted. The default temperature I set for my destin network using the new boltzman distribution was set too low so that over time the beliefs would be smoothed out to uniform and stop evolving. Andrew implemented a new rule for the rate of adjusting centroids to be proportional to 1/(centroid update count) so the centroids would eventually stop moving. Will have to think about what the best time is to stop centroid training in the robot vision experiments.
So to get the beliefs more reactive again I reverted the starvation trace coefficient to be higher, turned off applying the bolzman distribution to the beliefs and set the learning rate to be fixed (not going got check that in because I dont want to mess up Andrew's experiments, but I suppose we can make that stuff configurable which some already is). Basically there's lots of factors to play around with.
Going to move on again to implementing uniform destin.
Making good progress on uniform destin. Continued updating unit test, somewhat following test driven development. Nodes are using the shared centroids to compare their inputs with. Working through the logic to average centroid update vectors in case multiple nodes choose the same shared centroid to update. Also working through the logic on the starvation trace.
Uniform DeSTIN translation invariance through shared centroids is pretty much complete ( see http://wiki.opencog.org/w/DestinOpenCog#Uniform_DeSTIN.2C_Part_One )
For each iteration of the DeSTIN algorithm, each node picks a winning centroid from the shared pool of centroids for the layer. If multiple nodes from a layer pick the same centroid as winner then their movement vectors are averaged and then moved at the end. If a shared centroid is picked by at least one node in the level then its starvation trace credit is reset.
The SaveDestin, LoadDestin, and CreateDestin ( loading from a config file) now works with uniform destin. To switch between uniform destin and classic destin a boolean flag isUniform was added as an argument to InitDestin.
Unfortunately having shared centroids will require a different parallelization strategy when we port it back to CUDA.
Made the centroid update strategy configurable via function pointers to make it easy to switch between a fixed learning rate or a decaying one.
Added Python bindings to DeSTIN to make it easier to interativly experiment with it.
Worked on creating a visualization for DeSTIN. I simply created an image for each layer by taking the winning centroid index of each node and converting it into grayscale pixel. You can watch in time how long it takes for each layer to come "onlnie" as the bottom layers learn first. With uniform DeSTIN turned on a clear picture come through whereas on classic DeSTIN it looks like static because the winning centroid labels at each node mean something different.
Throug the visualization work I discovered that our heirarcy wasn't a 4x1 reduction in a square fashion at each level. Instead it was take in 4x4 square regions in the bottom layer, but then the upperalyers were doing a 4x1 reduction in a linear fashion instead. This was fixed by adding a lookup array for the node inputs to force them to have childing arranged in a square.
I've spent about two days creating code to stream DeSTIN states to a self organizing map to see if the belief states cluster with certain images. Will need to spend more time to interpret the results. Currently however I'm going to be moving on to implementing scale invariance as outlined here: http://wiki.opencog.org/w/DestinOpenCog#Uniform_DeSTIN.2C_Part_Two