Hello
NuPIC Are there anyone can answer my questions? Thank you very much. Yajingfu ------------------ ???????? ------------------ ??????: "??????";<[email protected]>; ????????: 2015??9??24??(??????) ????9:26 ??????: "??????"<[email protected]>; ????: ?????? How to calculate "field contribution" Thank you Pascal. From the website, I think the formula is?? C=(E1-E2)/E1 which C represents the contribution of field B for field A, E1 presents the error by A itself, E2 represents the error of A with the help of B. Is it right? If my understanding is right??and assuming there are 5 fields (A,B,C,D,E), and we want to predict A. Does it just calculate the combination of A, AB, AC, AD, AE individually? Moveover, I know for a sequence A1, A2, A3 ... An, it will change the performance (increase or decrease) between synapses and remember a time sequence. How to use sequence B1, B2, B3 ... Bn to help decreasing sequence A' error? I want to know more about the key algorithm. Which code do I need to learn? Are there any suggestion for me? Thank you so much. :) Yajingfu ------------------ ???????? ------------------ ??????: "Pascal Weinberger";<[email protected]>; ????????: 2015??9??24??(??????) ????6:03 ??????: "??????"<[email protected]>; ????: Re: How to calculate "field contribution" For 2) https://github.com/subutai/nupic.subutai/blob/master/swarm_examples/README.md Regarding one, try to completely redoing the installation according to the wiki :) The latest release 0.3.1 should work :) Best, Pascal ____________________________ BE THE CHANGE YOU WANT TO SEE IN THE WORLD ... On 24 Sep 2015, at 05:23, ?????? <[email protected]> wrote: Hello NuPIC It's me again. I have several new questions and hope for explanation. 1. It's about my last question "How to update the newest NuPIC version". I pasted the output of command "py.test --version" but I didn't get any reply later. Are there any new ideas or suggestions? 2. Now I have a dataset with 50 attributes (including attribute 1: date, and attributes 2-50: scalar value). I want to make prediction(attribute 50, for example). If I don't take sequential relationship into consideration, and just use linear regression, there are relationship between attribute 50 and attributes 2-49. If I want to predict attribute 50 at time T depending on previous information, I think I need to use TP of HTM. But I still want to make full use of attribute 2-49 at the same time. I found there is an item "field contribution". I want to know how does it calculate. Does it range from 0 to 1 and 0 represents no contribution and 1 represents strong contribution? 3. Maybe I will get a low contribution of these contributions. Is it possible to encode attributes 2-49 into one long encoding, and predict attribute 50 depending on attributes 2-49 in TP mode? Hoping for your reply and thank you so much. Yajingfu
