u'c1': {
'name': 'c1',
'clipInput': True,
'n': 275,
'fieldname': 'c1',
'w': 21,
'type': 'AdaptiveScalarEncoder'
},
u'c0_weekend': None
}
},
'inferenceType': 'NontemporalMultiStep',
'spParams': {
'synPermInactiveDec': 0.052500000000000005
},
'tpParams': {
'minThreshold': 11,
'activationThreshold': 14,
'pamLength': 3
},
'clParams': {
'alpha': 0.050050000000000004
}
},
'firstRecord': 250,
'lastRecord': 500,
}
mod = importBaseDescription('../base.py', config)
locals().update(mod.__dict__)
# 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/
# ----------------------------------------------------------------------
## 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.expdescriptionhelpers 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' : {'sensorParams': {'encoders': {'_classifierInput': {'classifierOnly': True, 'clipInput': True, 'n': 275, 'fieldname': 'category', 'w': 21, 'type': 'AdaptiveScalarEncoder'}, u'category': None, u'timestamp_dayOfWeek': None, u'timestamp_timeOfDay': None, u'kw_energy_consumption': {'maxval': 53.0, 'name': 'kw_energy_consumption', 'clipInput': True, 'minval': 0.0, 'n': 272, 'fieldname': 'kw_energy_consumption', 'w': 21, 'type': 'ScalarEncoder'}, u'timestamp_weekend': None}}, 'spParams': {'synPermInactiveDec': 0.05015}, 'tpParams': {'minThreshold': 11, 'activationThreshold': 14, 'pamLength': 3}, 'clParams': {'alpha': 0.050050000000000004}},
}
mod = importBaseDescription('../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 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/
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.expdescriptionhelpers import importBaseDescription
# the sub-experiment configuration
config = \
{ 'claEvalClassification': True,
'dataSource': 'file://' + os.path.join(os.path.dirname(__file__),
'../datasets/scalar_TP_1.csv'),
'modelParams': { 'clParams': { 'clVerbosity': 0},
'sensorParams': { 'encoders': { }, 'verbosity': 0},
'spParams': { },
'tpEnable': True,
'tpParams': { }}}
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 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/
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.expdescriptionhelpers import importBaseDescription
# the sub-experiment configuration
config = \
{ 'claEvalClassification': True,
'dataSource': 'file://' + os.path.join(os.path.dirname(__file__),
'../datasets/category_hub_TP_0.csv'),
'modelParams': { 'clParams': { 'clVerbosity': 0},
'sensorParams': { 'encoders': { }, 'verbosity': 0},
'spParams': { },
'tpEnable': True,
'tpParams': { }}}
mod = importBaseDescription('../base_category/description.py', config)
locals().update(mod.__dict__)
'sensorParams': { 'encoders': { 'consumption': { 'clipInput': True,
'fieldname': u'consumption',
'n': 28,
'name': u'consumption',
'type': 'AdaptiveScalarEncoder',
'w': 21},
'timestamp_dayOfWeek': { 'dayOfWeek': ( 21,
1),
'fieldname': u'timestamp',
'name': u'timestamp_dayOfWeek',
'type': 'DateEncoder'},
'timestamp_timeOfDay': { 'fieldname': u'timestamp',
'name': u'timestamp_timeOfDay',
'timeOfDay': ( 21,
1),
'type': 'DateEncoder'},
'timestamp_weekend': { 'fieldname': u'timestamp',
'name': u'timestamp_weekend',
'type': 'DateEncoder',
'weekend': 21}},
'verbosity': 0},
'spEnable': False,
'spParams': { },
'tpEnable': False,
'tpParams': { 'activationThreshold': 14,
'minThreshold': 12,
'verbosity': 0}}}
mod = importBaseDescription('../hotgym/description.py', config)
locals().update(mod.__dict__)
# 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/
# ----------------------------------------------------------------------
## 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.expdescriptionhelpers import importBaseDescription
# the sub-experiment configuration
config ={
'aggregationInfo' : {'seconds': 0, 'fields': [(u'c1', 'first'), (u'c0', 'first')], 'months': 0, 'days': 0, 'years': 0, 'hours': 1, 'microseconds': 0, 'weeks': 0, 'minutes': 0, 'milliseconds': 0},
'modelParams' : {'sensorParams': {'encoders': {u'c0_timeOfDay': None, 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}, 'tpParams': {'minThreshold': 11, 'activationThreshold': 14, 'pamLength': 3}, 'clParams': {'alpha': 0.050050000000000004}},
'dataPath': 'data/a_plus_b.csv',
}
mod = importBaseDescription('../base.py', config)
locals().update(mod.__dict__)
"inferenceType": "NontemporalMultiStep",
"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": {"spVerbosity": 0},
"tpParams": {"activationThreshold": 14, "minThreshold": 12, "verbosity": 1},
},
"numRecords": 16000,
}
mod = importBaseDescription("../hotgym/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 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/
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.expdescriptionhelpers import importBaseDescription
# the sub-experiment configuration
config = {
"dataSource": "file://" + os.path.join(os.path.dirname(__file__), "../datasets/first_order_0.csv"),
"modelParams": {
"clParams": {"clVerbosity": 0, "steps": "1,2,3"},
"sensorParams": {"encoders": {}, "verbosity": 0},
"spParams": {},
"tpParams": {},
},
"predictionSteps": [1, 2, 3],
}
mod = importBaseDescription("../base/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 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/
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.expdescriptionhelpers import importBaseDescription
# the sub-experiment configuration
config = \
{
'dataSource': 'file://' + os.path.join(os.path.dirname(__file__),
'../datasets/category_TP_1.csv'),
'modelParams': { 'clParams': { 'clVerbosity': 0},
'sensorParams': { 'encoders': { }, 'verbosity': 0},
'spParams': { 'spVerbosity': 0},
'tpEnable': True,
'tpParams': { 'verbosity': 0}}}
mod = importBaseDescription('../base_category/description.py', config)
locals().update(mod.__dict__)
},
u'Ct': {
'maxval': 24000,
'name': 'Ct',
'clipInput': True,
'minval': 0,
'n': 139,
'fieldname': 'Ct',
'w': 21,
'type': 'ScalarEncoder'
},
u'timestamp_weekend': None
}
},
'spParams': {
'synPermInactiveDec': 0.03769285614096052
},
'tpParams': {
'minThreshold': 11,
'activationThreshold': 16,
'pamLength': 4
},
'clParams': {
'alpha': 0.08568919879429909
}
},
}
mod = importBaseDescription('..\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 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/
# ----------------------------------------------------------------------
## This file defines parameters for a prediction experiment.
import os
from nupic.frameworks.opf.expdescriptionhelpers import importBaseDescription
# the sub-experiment configuration
config = \
{
'dataSource': 'file://' + os.path.join(os.path.dirname(__file__),
'../datasets/category_TP_0.csv'),
'modelParams': { 'clParams': { 'clVerbosity': 0},
'sensorParams': { 'encoders': { }, 'verbosity': 0},
'spParams': { },
'tpEnable': True,
'tpParams': { }}}
mod = importBaseDescription('./base_description.py', config)
locals().update(mod.__dict__)
