I think we are aiming at the same thing here: I’m trying to curate the space and mapping so my movements are made slightly more spread in term of their interest: various curves and mapping between the extreme could give me a bit more nuance and control on sub-states.
For instance, and in 1D to express it clearly, if between corner 0 and 1 there are 2 zones of interests, one between 0.2 and 0.21 and one between 0.666 and 0.669, then maybe I’d like to allow my controller to spend most of its run around those places so I can have fun, and take less care in the rest… does it make sense?
I found a cut and paste solution to this. You need to redo the mapping. Copy the output vector at 0.2, which I assume is not currently a vector in your training and then then paste that solution, but now mapped to 0.05. Copy the output at 0.21 and map that to 0.45. Now do the same with Satan’s vector and the sexy one, mapping them closer to 0.5 and 1. Retrain and it should solve your problem.
I don’t have anything useful to contribute to this conversation other than saying it’s interesting, but I’d love to hear some of this and/or read some further explanation about it, perhaps in a separate forum post. That is, if it’s somewhere in a semi-sharable state.
I’m devising as many small examples as possible. m2n mapping will happen soon-ish from our object and there will be simple examples, because i need those to get my head around the affordances…
Cool. Would just love to see some practical examples of the stuff, even if scratchy and so on.
Having many and more examples early in the process (of learning/digesting this stuff) is, I think, more useful than waiting for slick and polished examples at the end of the project.
I agree. At the moment we are confirming the design and challenges of the building blocks of the database, as you saw, and there are plenty of things to fight against already. @tutschku is onto something for instance, and @tedmoore too. it is important that this foundation is solid, then building ML stuff on it is ‘easy’ for someone like @groma and @weefuzzy who used and abused such technologies for years… Sam’s and @tedmoore’s hybrid approaches are good too, as the scikit learn has many, many well documented methods to help thinking and prototyping forward… I’m sure you want to do all this in RT and because of that, it needs to be built on a solid foundation
