Karin, You don't need to swarm if you are looking to get anomaly indications on scalar input fields. We have already established a decent set of model params that work for scalar input. All you need to do is update the "encoders" values to fix the min/max for your data (as well as entering the correct field names). See an example here:
https://github.com/numenta/nupic/blob/master/examples/opf/clients/hotgym/anomaly/one_gym/model_params/rec_center_hourly_model_params.py You can use these model params, and update the encoders section highlighted here: https://github.com/numenta/nupic/blob/master/examples/opf/clients/hotgym/anomaly/one_gym/model_params/rec_center_hourly_model_params.py#L21-L48 You'll need to change "kw_energy_consumption" to your input field label, and provide an accurage "minval"/"maxval" for your scalar encoder configuration. Note that these model params include "'inferenceType': 'TemporalAnomaly'", which is what makes this an anomaly detection model instead of a prediction model. Have you watched this? https://www.youtube.com/watch?v=1fU2Mw_l7ro --------- Matt Taylor OS Community Flag-Bearer Numenta On Thu, Nov 5, 2015 at 4:03 AM, Karin Valisova <[email protected]> wrote: > Hello guys, > > I've been playing around with the OPF past days and I'm trying to optimize > the anomaly scores for my data stream. I have read through the code and > wiki, but I still can't figure out properly how this all works. I am looking > for Temporal Anomaly and my data stream is just two columns as in the hotgym > example, one with hourly timestamp and the other containing values. > > 1. If I put on swarming, for example by scripts/run_swarm.py with very basic > .json defining it - without any custom metrics - what are the anomalies I > get? Those are the raw values - the fraction of activated vs. predicted > values, right? > > 2. Now, if I want to try the different types of anomalies, where should I > implement the call in my code? Is it enough to write it into the > model_params.py information and all the following calculations would be > implemented by the CLAmodel? How does this affect the swarming? > > 3. How does metric used affect the anomaly scores? As I understand it, in no > direct way, right? > > Thank you very much for clearing things up a bit for me! > Hope I haven't missed some basic tutorial with all the explanations, though. > > Have a nice day, > Karin > > -- > > datapine GmbH > Skalitzer Straße 33 > 10999 Berlin > > email: [email protected]
