> > 1. Is there a string encoder yet? Originally, I wanted to just send the > model the full ski slope line, but converted it to the int array to pass in > scaler values. >
Strings are encoded as categories, so not really. A category is (generally) a non-overlapping set of bits in the encoded vector. This removes a lot of semantic closeness as compared to the scalar encoder. For your world there might be value in having greater similarity at the encoder level between states. > 2. The trained model seems to work for a little bit, but then stops as it > keeps learning during the live run. Learning can be turned off. You need a learning flag field 'L' and when you want to toggle learning on or off you have that field contain a non None value for that record. NOTE: I haven't done this, this is just my reading of clamodel.py (374) and record_stream.py (102). > I would like the model to "see" the results of it's prediction (i.e. how > it moved the skier to complete the feedback loop), but I also want to have > some kind of error value so it knows that it's prediction was not optimal. > Does a mechanism like this exist in the current code? > Could you describe this sequence of events more precisely? > 3. Most of the model settings are just copied directly from the hotgym > example. Should I change some of the values to work better in this > scenario? > Almost certainly, however the best way to find those settings is using swarming ... not sure if that's fully available yet. 4. Any other comments or suggestions to improve the demonstration? > Is your first goal to have it learn a single track (probably simpler) or to learn in general how to deal with any track? Ian
_______________________________________________ nupic mailing list [email protected] http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org
