Hey all, As far as I’ve gathered, when HTM can’t make a proper prediction, it simply passes through the last input it’s seen and uses that as a prediction (which is why plots comparing predictions with actual values usually start out with a lag).
The problem with this approach is that when using multiple HTM regions each trained on their own data in a classification setup, a region that is totally confused by the sequence it’s seeing (since it never learned it) will end up outputting predictions that are delayed inputs and the final prediction sequence will have a similarity to the original sequence that you wouldn’t expect an untrained region to have. More concretely, in my application, I’m passing sequences of 2D coordinates that trace a number. Even though I only train a single region to produce low anomaly for that number, ALL other regions output a predicted sequence that looks similar i.e. region assigned to recognize ‘2’ outputs a trace that looks like a ‘1’ when a ‘1’ sequence is shown to it even though it’s never seen a ‘1’!! So do all other regions. I think this is what’s causing my classification results (based on anomaly) to be so sub par. Is that a right assessment of the consequences of using this feed-through approach? If so, where exactly is the code can I make a change to prevent HTM from doing it? Thank you, Nicholas
