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# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate 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 Affero 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 Affero Public License for more details.
#
# You should have received a copy of the GNU Affero Public License
# along with this program. If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
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': 0.005,
'regionName': 'SDRClassifierRegion',
'implementation': 'py',
'steps': '5'},
'inferenceType': 'TemporalMultiStep',
'sensorParams': {'encoders': {
'_classifierInput': {'classifierOnly': True,
'clipInput': True,
'fieldname': 'passenger_count',
'maxval': 40000,
'minval': 0,
'n': 50 ,
'name': '_classifierInput',
'type': 'ScalarEncoder',
'w': 29},
u'timeofday': {'clipInput': True,
'fieldname': 'timeofday',
'maxval': 1411,
'minval': 0,
'n': 600,
'name': 'timeofday',
'type': 'ScalarEncoder',
'periodic': True,
'verbosity': 0,
'w': 29},
u'dayofweek': {'clipInput': True,
'fieldname': 'dayofweek',
'maxval': 7,
'minval': 0,
'n': 100,
'name': 'dayofweek',
'type': 'ScalarEncoder',
'periodic': True,
'verbosity': 0,
'w': 29},
u'passenger_count': {'clipInput': True,
'fieldname': 'passenger_count',
'maxval': 40000,
'minval': 0,
'n': 109,
'name': 'passenger_count',
'type': 'ScalarEncoder',
'verbosity': 0,
'w': 29}},
'sensorAutoReset': None,
'verbosity': 0},
'spEnable': True,
'spParams': {'columnCount': 2048,
'globalInhibition': 1,
'inputWidth': 0,
'boostStrength': 0.0,
'numActiveColumnsPerInhArea': 40,
'potentialPct': 0.8,
'seed': 1956,
'spVerbosity': 1,
'spatialImp': 'cpp',
'synPermActiveInc': 0.0001,
'synPermConnected': 0.5,
'synPermInactiveDec': 0.0005},
'tpEnable': True,
'tpParams': {'activationThreshold': 15,
'cellsPerColumn': 32,
'columnCount': 2048,
'globalDecay': 0.0,
'initialPerm': 0.21,
'inputWidth': 2048,
'maxAge': 0,
'maxSegmentsPerCell': 128,
'maxSynapsesPerSegment': 128,
'minThreshold': 15,
'newSynapseCount': 32,
'outputType': 'normal',
'pamLength': 1,
'permanenceDec': 0.1,
'permanenceInc': 0.1,
'predictedSegmentDecrement': 0.01,
'seed': 1960,
'temporalImp': 'tm_py',
'verbosity': 0},
'trainSPNetOnlyIfRequested': False},
'predictAheadTime': None,
'version': 1}
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