I’ll try this out Thanks
On 20 Oct 2015, at 1:04 AM, Chetan Surpur <[email protected]<mailto:[email protected]>> wrote: Zvika, On Oct 19, 2015, at 7:06 AM, Zvika Ashani <[email protected]<mailto:[email protected]>> wrote: - Any suggestions on the scaling of the radius parameter ? It can be measured relative to the previous sample or relative to a sample from a few seconds ago, how will that change the outcome? You can use the GeospatialCoordinateEncoder as an analogy. It computes a radius for a given speed, such that the encodings of consecutive readings to be adjacent with some overlap (specially an overlap of 50%) [1]. You can try the same logic for your application. [1] https://github.com/numenta/nupic/blob/master/src/nupic/encoders/geospatial_coordinate.py#L113 - Chetan On 19 Oct 2015, at 4:33 PM, Matthew Taylor <[email protected]<mailto:[email protected]>> wrote: Zvika, Before you try a different encoder, you should attempt to use the CoordinateEncoder directly. It can accept X,Y coordinates and a "radius" which can represent speed. That is what I used to get NuPIC to do anomaly detection on Minecraft XYZ coordinates: https://github.com/nupic-community/mine-hack/blob/master/python/nupic_client.py#L71-L79 And for an anomaly detection model on coordinates, you won't need to swarm because we already have model params that work well detection these types of anomalies here: https://github.com/nupic-community/mine-hack/blob/master/python/model_params/model_params.py. You should be able to re-use those model params (maybe with a few string replacements). --------- Matt Taylor OS Community Flag-Bearer Numenta On Mon, Oct 19, 2015 at 5:05 AM, Zvika Ashani <[email protected]<mailto:[email protected]>> wrote: Hi Nupic, I am trying to see if I can do anomaly detection over a data set that represents object tracks. Each object track is a list of data points that have the following information: - timestamp - position (x,y between 0 and 1) - speed I want to learn a large number for such tracks and then look for anomalies in new tracks. This is kind of like the nupic.geospatial example but the position data is in a different coordinate system. I am looking at using a vector encoder with each sample being [x,y,speed] and then feeding each track as a separate sequence into the model. Questions: - is this the correct approach or is there some better way of encoding the data? - is it possible to swarm over this to find the best model? Thanks, Zvika
