I think that "reset()" is a function of the nodes in the Network API, and "resetSequenceStates()" is a function of the OPF model instance. If you are working with an OPF model, use the latter to reset a sequence. It has a bit more overloaded meaning in the Network API depending on the region implementation.
--------- Matt Taylor OS Community Flag-Bearer Numenta On Fri, May 6, 2016 at 2:38 AM, Wakan Tanka <[email protected]> wrote: > Hello David, > do you mean resetSequenceStates()? > > On Tue, Apr 26, 2016 at 6:17 PM, cogmission (David Ray) < > [email protected]> wrote: > >> Alexandre, are you calling "reset()" after the 20,000 5's then one 6? The >> "reset()" lets the HTM know that the pattern has concluded and may help >> yield better results? >> >> Cheers, >> David >> >> On Tue, Apr 26, 2016 at 10:03 AM, Alexandre Vivmond <[email protected]> >> wrote: >> >>> Here are parameters that I'm using for running a swarm >>> >>> SWARM_CONFIG = { >>> "includedFields": [ >>> { >>> "fieldName": "value", >>> "fieldType": "float", >>> "maxValue": 6.0, >>> "minValue": 5.0 >>> } >>> ], >>> "streamDef": { >>> "info": "value", >>> "version": 1, >>> "streams": [ >>> { >>> "info": "Values", >>> "source": "file://values.csv", >>> "columns": [ >>> "*" >>> ] >>> } >>> ] >>> }, >>> >>> "inferenceType": "TemporalAnomaly", >>> "inferenceArgs": { >>> "predictionSteps": [ >>> 1 >>> ], >>> "predictedField": "value" >>> }, >>> "iterationCount": -1, >>> "swarmSize": "medium" >>> } >>> >>> >>> And here is the generated model_params.py file output >>> >>> MODEL_PARAMS = {'aggregationInfo': {'days': 0, >>> 'fields': [], >>> 'hours': 0, >>> 'microseconds': 0, >>> 'milliseconds': 0, >>> 'minutes': 0, >>> 'months': 0, >>> 'seconds': 0, >>> 'weeks': 0, >>> 'years': 0}, >>> 'model': 'CLA', >>> 'modelParams': {'anomalyParams': {u'anomalyCacheRecords': None, >>> u'autoDetectThreshold': None, >>> u'autoDetectWaitRecords': None}, >>> 'clParams': {'alpha': 0.00634375, >>> 'clVerbosity': 0, >>> 'regionName': 'CLAClassifierRegion', >>> 'steps': '1'}, >>> 'inferenceType': 'TemporalAnomaly', >>> 'sensorParams': {'encoders': {u'value': {'clipInput': >>> True, >>> 'fieldname': >>> 'value', >>> 'maxval': 6.0, >>> 'minval': 5.0, >>> 'n': 22, >>> 'name': >>> 'value', >>> 'type': >>> 'ScalarEncoder', >>> 'w': 21}}, >>> 'sensorAutoReset': None, >>> 'verbosity': 0}, >>> 'spEnable': True, >>> 'spParams': {'columnCount': 2048, >>> 'globalInhibition': 1, >>> 'inputWidth': 0, >>> 'maxBoost': 2.0, >>> 'numActiveColumnsPerInhArea': 40, >>> 'potentialPct': 0.8, >>> 'seed': 1956, >>> 'spVerbosity': 0, >>> 'spatialImp': 'cpp', >>> 'synPermActiveInc': 0.05, >>> 'synPermConnected': 0.1, >>> 'synPermInactiveDec': 0.09376875}, >>> 'tpEnable': True, >>> 'tpParams': {'activationThreshold': 12, >>> 'cellsPerColumn': 32, >>> 'columnCount': 2048, >>> 'globalDecay': 0.0, >>> 'initialPerm': 0.21, >>> 'inputWidth': 2048, >>> 'maxAge': 0, >>> 'maxSegmentsPerCell': 128, >>> 'maxSynapsesPerSegment': 32, >>> 'minThreshold': 9, >>> 'newSynapseCount': 20, >>> 'outputType': 'normal', >>> 'pamLength': 1, >>> 'permanenceDec': 0.1, >>> 'permanenceInc': 0.1, >>> 'seed': 1960, >>> 'temporalImp': 'cpp', >>> 'verbosity': 0}, >>> 'trainSPNetOnlyIfRequested': False}, >>> 'predictAheadTime': None, >>> 'version': 1} >>> >>> On Tue, Apr 26, 2016 at 4:33 PM, Matthew Taylor <[email protected]> >>> wrote: >>> >>>> What are the encoder parameters you're using to encode these numbers? >>>> 5 and 6 might be close enough that they get encoded as the same bit >>>> array. What are your min/max values for the scalar encoder? Or are yo >>>> using another encoder? >>>> --------- >>>> Matt Taylor >>>> OS Community Flag-Bearer >>>> Numenta >>>> >>>> >>>> On Tue, Apr 26, 2016 at 3:32 AM, Alexandre Vivmond <[email protected]> >>>> wrote: >>>> > I've got a question regarding patterns and noise. I've experimented a >>>> bit >>>> > with HTM now, and I can get it to learn a wide variety of varying >>>> patterns >>>> > such as for example: 1, 2, 3, 1, 2, 3, 1,... or 5, 6, 5, 6, 5, 6, ... >>>> but >>>> > patterns such as 5, 5, 6, 5, 5, 6, ... or 5, 5, 5, 5, 5, 5, 5, 5, 5, >>>> 6, 5, >>>> > 5, 5, 5, 5, 5, 5, 5, 5, 6, ... are things that HTM struggles with, >>>> which is >>>> > understandable considering HTM is really good at creating "links" >>>> between >>>> > values with respect to time and context. But the previously mentioned >>>> > example makes it really hard to create "links" between self-repeating >>>> > values, even though HTM can manage to differ between contexts. So what >>>> > exactly is the "line" between a pattern and noise? I fed HTM 20000 >>>> values of >>>> > 10 fives followed by one 6 (5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 5, ...) >>>> and it >>>> > still didn't manage to learn that pattern. Any ideas? >>>> >>>> >>> >> >> >> -- >> *With kind regards,* >> >> David Ray >> Java Solutions Architect >> >> *Cortical.io <http://cortical.io/>* >> Sponsor of: HTM.java <https://github.com/numenta/htm.java> >> >> [email protected] >> http://cortical.io >> > > > > -- > Best Regards > > Name: Wakan Tanka a.k.a. Wakatana a.k.a. MackoP00h > Location: Europe > Note: I'm non native English speaker so please bare with me ;) > Contact: > [email protected] > http://stackoverflow.com/users/1616488/wakan-tanka > https://github.com/wakatana > https://twitter.com/MackoP00h >
