u'c0_dayOfWeek': None,
u'c1': {
'name': 'c1',
'clipInput': True,
'n': 275,
'fieldname': 'c1',
'w': 21,
'type': 'AdaptiveScalarEncoder'
},
u'c0_weekend': None
}
},
'inferenceType': 'NontemporalMultiStep',
'spParams': {
'synPermInactiveDec': 0.052500000000000005
},
'tmParams': {
'minThreshold': 11,
'activationThreshold': 14,
'pamLength': 3
},
'clParams': {
'alpha': 0.050050000000000004
}
},
'dataPath': 'data/a.csv',
}
mod = importBaseDescription('../base.py', config)
locals().update(mod.__dict__)
# 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/
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.exp_description_helpers import importBaseDescription
# the sub-experiment configuration
config = \
{
'dataSource': 'file://' + os.path.join(os.path.dirname(__file__),
'../datasets/simple_3.csv'),
'modelParams': { 'clParams': { 'verbosity': 0, 'steps': '1,3'},
'sensorParams': { 'encoders': { }, 'verbosity': 0},
'spParams': { },
'tmParams': { }},
'predictedField': 'field2',
'predictionSteps': [1, 3]}
mod = importBaseDescription('../base/description.py', config)
locals().update(mod.__dict__)
'tmParams': {
'minThreshold': 11,
'activationThreshold': 14,
'pamLength': 3
},
'sensorParams': {
'encoders': {
u'cpu': {
'maxval': 100.0,
'name': 'cpu',
'clipInput': True,
'minval': 0.0,
'n': 296,
'fieldname': 'cpu',
'w': 21,
'type': 'ScalarEncoder'
}
}
},
'spParams': {
'synPermInactiveDec': 0.055135
},
'clParams': {
'alpha': 0.055045000000000004
}
},
}
mod = importBaseDescription('../cpu_model_store/description.py', config)
locals().update(mod.__dict__)
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.exp_description_helpers import importBaseDescription
# the sub-experiment configuration
config = \
{ 'modelParams': { 'clParams': { 'verbosity': 0},
'sensorParams': { 'encoders': { 'consumption': { 'clipInput': True,
'fieldname': u'consumption',
'n': 28,
'name': u'consumption',
'type': 'AdaptiveScalarEncoder',
'w': 21},
'timestamp_dayOfWeek': None,
'timestamp_timeOfDay': { 'fieldname': u'timestamp',
'name': u'timestamp_timeOfDay',
'timeOfDay': ( 21,
8),
'type': 'DateEncoder'},
'timestamp_weekend': None},
'verbosity': 0},
'spParams': { },
'tmParams': { 'activationThreshold': 14,
'minThreshold': 12,
'verbosity': 0}},
'numRecords': 16000}
mod = importBaseDescription('../hotgym/description.py', config)
locals().update(mod.__dict__)
#
# 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/
# ----------------------------------------------------------------------
"""This file defines parameters for a prediction experiment."""
import os
from nupic.frameworks.opf.exp_description_helpers import importBaseDescription
# the sub-experiment configuration
config = \
{ 'modelParams': { 'clParams': { },
'sensorParams': { 'encoders': { }},
'spParams': { },
'tmParams': { 'activationThreshold': 12}}}
mod = importBaseDescription('./base.py', config)
locals().update(mod.__dict__)
#
# 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/
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.exp_description_helpers import importBaseDescription
# the sub-experiment configuration
config = \
{
'dataSource': 'file://' + os.path.join(os.path.dirname(__file__),
'../datasets/category_TM_0.csv'),
'modelParams': { 'clParams': { 'verbosity': 0},
'sensorParams': { 'encoders': { }, 'verbosity': 0},
'spParams': { },
'tmEnable': True,
'tmParams': { }}}
mod = importBaseDescription('../base_category/description.py', config)
locals().update(mod.__dict__)
# 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/
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
###############################################################################
# IMPORTANT!!!
# This params file is dynamically generated by the RunExperimentPermutations
# script. Any changes made manually will be over-written the next time
# RunExperimentPermutations is run!!!
###############################################################################
from nupic.frameworks.opf.exp_description_helpers import importBaseDescription
# the sub-experiment configuration
config ={
'aggregationInfo' : {'seconds': 0, 'fields': [], 'months': 0, 'days': 0, 'years': 0, 'hours': 0, 'microseconds': 0, 'weeks': 0, 'minutes': 0, 'milliseconds': 0},
'modelParams' : {'tmParams': {'minThreshold': 10, 'activationThreshold': 13, 'pamLength': 2}, 'sensorParams': {'encoders': {u'timestamp_dayOfWeek': {'dayOfWeek': (21, 4.5265359696838647), 'name': 'timestamp', 'fieldname': 'timestamp', 'type': 'DateEncoder'}, u'timestamp_weekend': {'name': 'timestamp', 'type': 'DateEncoder', 'fieldname': 'timestamp', 'weekend': (21, 1)}, u'timestamp_timeOfDay': None, u'Ct': {'maxval': 6.0, 'name': 'Ct', 'clipInput': True, 'minval': 0, 'n': 92, 'fieldname': 'Ct', 'w': 21, 'type': 'ScalarEncoder'}}}, 'spParams': {'synPermInactiveDec': 0.083048695689323701}, 'clParams': {'alpha': 0.021640002042688296}},
}
mod = importBaseDescription('..\description.py', config)
locals().update(mod.__dict__)
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.exp_description_helpers import importBaseDescription
# the sub-experiment configuration
config = \
{
'dataSource': 'file://' + os.path.join(os.path.dirname(__file__),
'../datasets/scalar_SP_0.csv'),
'modelParams': { 'clParams': { 'verbosity': 0},
'inferenceType': 'NontemporalClassification',
'sensorParams': { 'encoders': { 'field1': { 'clipInput': True,
'fieldname': 'field1',
'maxval': 0.10000000000000001,
'minval': 0.0,
'n': 11,
'name': 'field1',
'type': 'AdaptiveScalarEncoder',
'w': 7}},
'verbosity': 0},
'spEnable': False,
'spParams': { 'spVerbosity': 0},
'tmEnable': False,
'tmParams': { }}}
mod = importBaseDescription('../base_scalar/description.py', config)
locals().update(mod.__dict__)
#
# 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/
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.exp_description_helpers import importBaseDescription
# the sub-experiment configuration
config = \
{
'dataSource': 'file://' + os.path.join(os.path.dirname(__file__),
'../datasets/category_TM_1.csv'),
'modelParams': { 'clParams': { 'verbosity': 0},
'sensorParams': { 'encoders': { }, 'verbosity': 0},
'spParams': { 'spVerbosity': 0},
'tmEnable': True,
'tmParams': { 'verbosity': 0}}}
mod = importBaseDescription('../base_category/description.py', config)
locals().update(mod.__dict__)
