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__)