Hello NuPIC, In hotgym anomaly tutorial Matt changed inferenceType from TemporalMultiStep to TemporalAnomaly to being able detect anomalies. When he then run script to removed all Tuesdays NuPIC adapted to those changes, as it sees more and more of those data, started to consider it as a normal and stop reporting it as an anomaly.
1. I do not want NuPIC to adapt to those changes. Is possible to disable learning in this phase? I want is to create model using 1st dataset, then pass 2nd dataset to this model but further learning will be disabled. So far I know how to: create model_params by running swarm over 1st dataset and pushing this dataset into NuPIC to compute anomaly score. But what I do not know is how to "save" those learned patterns from 1st dataset and detect anomalies using this "trained" version in 2nd dataset. Is this even possible for NuPIC? 2. The one difference between hot gym prediction and hot gym anomaly was changing inferenceType from TemporalMultiStep to TemporalAnomaly in existing model params. So I guess that inferenceType does not affects swarm process because it can be easily turned into something else in existing model if needed? Are all available options under inferenceType using the same algorithm principles under the hood? 3. Based on above: when somebody is talking about creating model he is basically referring not just tuning (e.g. by hand or swarm) parameters inside model_params.py but also in this "training" phase? 4. Where can I find further info regarding inferenceType, the only info that I’ve found is this list [1]? Matt in his hot gym prediction tutorial said that the data are temporal so he has chosen TemporalMultiStep. But how can I know if my data are temporal and not e.g. nontemporal? As a nontemporal data can be considered e.g. those that guys from cortical.io are dealing with? I mean SDRs for particular words where time does not plays crucial role? Is the role of time completely omitted in cortical.io examples? [1] Inference Types - https://github.com/numenta/nupic/wiki/Inference-Types -- Thank you Best Regards Wakan
