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?
>
>
