Hi, So based on these changes what error metric was used for evaluating the effectiveness of the model and how was this model found to be better? Were different permutations run programmatically? Which data set was used to evaluate the performance , is it publicly available?
Thanks, Manal On Wed, Nov 19, 2014 at 11:57 PM, Chetan Surpur <[email protected]> wrote: > Hi Manal, > > I started with the model params for hotgym, and updated a couple > parameters for better performance. > > In particular, > > 1. Removed the timestamp parameter (leaving only the geospatial encoder's > output as a parameter) > 2. Matched encoder width to number of columns (2048, same as hotgym > example), keeping sparsity the same > 3. Updated parameters: minThreshold 9 => 3, activationThreshold 12 => 6 > (for faster prediction) > > - Chetan > > > On Nov 19, 2014, at 5:21 AM, Manal Bhingardeve <[email protected]> > wrote: > > Hi, > > Could you share more details about the process that was followed to derive > the parameters in the > > model_params.py (Geospatial Tracking application) file. Is there a > generic process that can be used for prediction on multiple fields? > > Thanks, > Manal > > On Thu, Nov 13, 2014 at 10:41 PM, Matthew Taylor <[email protected]> wrote: > >> The swarming process optimizes for prediction on one specific field in >> the input data. So it does not always produce model params that are >> best for anomaly detection. Chetan used some hand-created model params >> for that example application. >> --------- >> Matt Taylor >> OS Community Flag-Bearer >> Numenta >> >> >> On Thu, Nov 13, 2014 at 9:07 AM, Manal Bhingardeve >> <[email protected]> wrote: >> > Hi, >> > >> > In the NuPIC Geospatial Tracking application example how is the >> > model_params.py file obtained? Shouldn't there be a swarm file? >> > >> > Thanks, >> > Manal >> > >> >> > >
