Hi,
I’ve coded a new release the script create_cpu_percent_model.py with the full set of OPF with default values. So it’s very helpfull for me.
#!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2015, Numenta, Inc. Unless you have purchased from
# Numenta, Inc. a separate commercial license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
"""Create the cpu percent model. See also send_cpu.py and README.md."""
from htmengine.adapters.datasource import createDatasourceAdapter
modelSpec = {
"datasource": "custom",
"metricSpec": {
"metric": "cpu_percent"
},
"modelParams": {
"min": 0,
"max": 100
},
"completeModelParams" : {
"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": "",
"modelParams": {
"sensorParams": {
"verbosity": 0,
"encoders": {
"c0_dayOfWeek": "",
"c0_timeOfDay": {
"type": "DateEncoder",
"timeOfDay": [21, 9.49122334747737],
"fieldname": "c0",
"name": "c0"
},
"c1": {
"fieldname": "c1",
"seed": 42,
"resolution": 0.8771929824561403,
"name": "c1",
"type": "RandomDistributedScalarEncoder"
},
"c0_weekend": ""
},
"sensorAutoReset": ""
},
"anomalyParams": {
"anomalyCacheRecords": "",
"autoDetectThreshold": "",
"autoDetectWaitRecords": 5030
},
"spParams": {
"columnCount": 2048,
"synPermInactiveDec": 0.0005,
"maxBoost": 1,
"spatialImp": "cpp",
"inputWidth": 0,
"spVerbosity": 0,
"synPermConnected": 0.1,
"synPermActiveInc": 0.0015,
"seed": 1956,
"numActiveColumnsPerInhArea": 40,
"globalInhibition": 1,
"potentialPct": 0.8
},
"trainSPNetOnlyIfRequested": "false",
"clParams": {
"alpha": 0.035828933612158,
"regionName": "CLAClassifierRegion",
"steps": "1",
"clVerbosity": 0
},
"tpParams": {
"columnCount": 2048,
"activationThreshold": 13,
"pamLength": 3,
"cellsPerColumn": 32,
"permanenceInc": 0.1,
"minThreshold": 10,
"verbosity": 0,
"maxSynapsesPerSegment": 32,
"outputType": "normal",
"globalDecay": 0,
"initialPerm": 0.21,
"permanenceDec": 0.1,
"seed": 1960,
"maxAge": 0,
"newSynapseCount": 20,
"maxSegmentsPerCell": 128,
"temporalImp": "cpp",
"inputWidth": 2048
},
"clEnable": "false",
"spEnable": "true",
"inferenceType": "TemporalAnomaly",
"tpEnable": "true"
}
},
"inferenceArgs" : {
"predictionSteps": [1],
"predictedField": "c1",
"inputPredictedField": "auto"
},
"timestampFieldName" : "c0",
"valueFieldName" : "c1"
}
}
adapter = createDatasourceAdapter(modelSpec["datasource"])
modelId = adapter.monitorMetric(modelSpec)
print "Model", modelId, "created..."
Feel free to use or to update git .