Here is a print of the total PARAMS dict I’m using:
AGGREGATIONINFO
seconds
-- 0
fields
-- [[u'device_temp_c', 'sum'], [u'libversion', 'sum'], [u'media_wear_pct', 'sum'], [u'power_on_hours', 'sum'], [u'write_amp', 'sum']]
months
-- 0
days
-- 0
years
-- 0
hours
-- 0
microseconds
-- 0
weeks
-- 0
minutes
-- 0
milliseconds
-- 0
('model', ' -- ', 'HTMPrediction')
('version', ' -- ', 1)
('predictAheadTime', ' -- ', 'null')
MODELPARAMS
sensorParams
---- verbosity = 0
-------- media_wear_pct = {'maxval': 2472.0, 'name': u'media_wear_pct', 'clipInput': True, 'minval': 666.0, 'n': 231, 'fieldname': u'media_wear_pct', 'w': 21, 'type': 'ScalarEncoder'}
-------- _classifierInput = {'maxval': 2472.0, 'classifierOnly': True, 'name': u'media_wear_pct', 'clipInput': True, 'minval': 666.0, 'n': 231, 'fieldname': u'media_wear_pct', 'w': 21, 'type': 'ScalarEncoder'}
---- sensorAutoReset = None
spParams
---- columnCount = 2048
---- spVerbosity = 0
---- localAreaDensity = -1.0
---- spatialImp = cpp
---- inputWidth = 946
---- synPermInactiveDec = 0.005
---- synPermConnected = 0.1
---- synPermActiveInc = 0.04
---- seed = 1965
---- numActiveColumnsPerInhArea = 40
---- boostStrength = 3.0
---- globalInhibition = 1
---- potentialPct = 0.85
trainSPNetOnlyIfRequested
-- False
clParams
---- maxCategoryCount = 1000
---- implementation = cpp
---- verbosity = 0
---- steps = 1
---- alpha = 0.005
---- regionName = SDRClassifierRegion
tmParams
---- columnCount = 2048
---- activationThreshold = 16
---- pamLength = 1
---- cellsPerColumn = 32
---- permanenceInc = 0.1
---- minThreshold = 12
---- 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
tmEnable
-- True
spEnable
-- True
inferenceType
-- TemporalAnomaly