As I said I'm trying out the HTM engine traffic tutorial where all the model params are generated automatically when the client starts offering data to the HTTP API.
Now, I don't know exactly how to interpret the metric records, but I can at least tell that the inferencetype is what it should be. So I've concluded that part of the tutorial that runs the data through HTM Engine probably isn't doing it's job. The logs in supervisord tell me all the services are working fine (though I had to manually open the port for htmengine:model_scheduler). I would assume that the traffic tutorial is configured to do postprocessing, based on the HTM engine's README <https://github.com/numenta/numenta-apps/tree/master/htmengine#post-processing-the-results> . It's probably a silly oversight on my part, but right now I just dont know what to make of this. Hope someone can help out! Kind regards, Casper Rooker [email protected] P.S.: One such model params record in the metrics table: { "inferenceArgs": { "predictionSteps": [ 1 ], "predictedField": "c1", "inputPredictedField": "auto" }, "modelConfig": { "aggregationInfo": { "seconds": 0, "fields": [], "months": 0, "days": 0, "years": 0, "hours": 0, "microseconds": 0, "weeks": 0, "minutes": 0, "milliseconds": 0 }, "model": "CLA", "version": 1, "predictAheadTime": null, "modelParams": { "sensorParams": { "verbosity": 0, "encoders": { "c0_dayOfWeek": null, "c0_timeOfDay": { "fieldname": "c0", "timeOfDay": [ 21, 9.49122334747737 ], "type": "DateEncoder", "name": "c0" }, "c1": { "name": "c1", "resolution": 0.7017543859649122, "seed": 42, "fieldname": "c1", "type": "RandomDistributedScalarEncoder" }, "c0_weekend": null }, "sensorAutoReset": null }, "clEnable": false, "spParams": { "columnCount": 2048, "spVerbosity": 0, "maxBoost": 1.0, "spatialImp": "cpp", "inputWidth": 0, "synPermInactiveDec": 0.0005, "synPermConnected": 0.1, "synPermActiveInc": 0.0015, "seed": 1956, "numActiveColumnsPerInhArea": 40, "globalInhibition": 1, "potentialPct": 0.8 }, "trainSPNetOnlyIfRequested": false, "clParams": { "alpha": 0.035828933612158, "clVerbosity": 0, "steps": "1", "regionName": "CLAClassifierRegion" }, "tpParams": { "columnCount": 2048, "activationThreshold": 13, "pamLength": 3, "cellsPerColumn": 32, "permanenceInc": 0.1, "minThreshold": 10, "verbosity": 0, "maxSynapsesPerSegment": 32, "outputType": "normal", "globalDecay": 0.0, "initialPerm": 0.21, "permanenceDec": 0.1, "seed": 1960, "maxAge": 0, "newSynapseCount": 20, "maxSegmentsPerCell": 128, "temporalImp": "cpp", "inputWidth": 2048 }, "anomalyParams": { "anomalyCacheRecords": null, "autoDetectThreshold": null, "autoDetectWaitRecords": 5030 }, "spEnable": true, "inferenceType": "TemporalAnomaly", "tpEnable": true } }, "inputRecordSchema": [ [ "c0", "datetime", "T" ], [ "c1", "float", "" ] ] } On Mon, Nov 2, 2015 at 12:14 PM, Wakan Tanka <[email protected]> wrote: > On 11/02/2015 11:31 AM, Cas wrote: > >> Hello NuPIC, >> >> I was trying out the HTM engine traffic tutorial today and I got it >> running. Unfortunately the anomaly score is 'none' for all data points. >> >> Do you have any suggestions on how to troubleshoot this? >> >> I know all the services are running, from the looks of the supervisord >> interface. I'd like to see how the data points are being offered to the >> HTM engine for starters, I'm just not sure how to do that. >> >> Kind regards, >> >> Casper Rooker >> [email protected] <mailto:[email protected]> >> > > Hello Casper, > > Be sure that you've set "inferenceType": "TemporalAnomaly" if you have > "inferenceType": "TemporalMultiStep" then you are unable to get anomaly > score just predictions. > > regards > > Wakan Tanka > >
