Zvika, > On Oct 19, 2015, at 7:06 AM, Zvika Ashani <[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 <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 >> >> <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]> 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 >> >
