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June 19, 2017 20:16
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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/ | |
# ---------------------------------------------------------------------- | |
from nupic.data.inference_shifter import InferenceShifter | |
from nupic.frameworks.opf.model_factory import ModelFactory | |
# Create and run the model: | |
MODEL_PARAMS = { | |
"model": "HTMPrediction", | |
"version": 1, | |
"predictAheadTime": None, | |
"modelParams": { | |
"inferenceType": "TemporalMultiStep", | |
"sensorParams": { | |
"verbosity" : 0, | |
"encoders": { | |
"char": { | |
"fieldname": u"char", | |
"name": u"char", | |
"type": "CategoryEncoder", | |
"categoryList": list(set(map(str.strip, open("input_test.txt").readlines()))), | |
"w": 21 | |
} | |
}, | |
"sensorAutoReset" : None, | |
}, | |
"spEnable": True, | |
"spParams": { | |
"spVerbosity" : 0, | |
"globalInhibition": 1, | |
"columnCount": 2048, | |
"inputWidth": 0, | |
"numActiveColumnsPerInhArea": 40, | |
"seed": 1956, | |
"columnDimensions": 0.5, | |
"synPermConnected": 0.1, | |
"synPermActiveInc": 0.1, | |
"synPermInactiveDec": 0.01, | |
}, | |
"tmEnable" : True, | |
"tmParams": { | |
"verbosity": 0, | |
"columnCount": 2048, | |
"cellsPerColumn": 32, | |
"inputWidth": 2048, | |
"seed": 1960, | |
"temporalImp": "cpp", | |
"newSynapseCount": 20, | |
"maxSynapsesPerSegment": 32, | |
"maxSegmentsPerCell": 128, | |
"initialPerm": 0.21, | |
"permanenceInc": 0.1, | |
"permanenceDec" : 0.1, | |
"globalDecay": 0.0, | |
"maxAge": 0, | |
"minThreshold": 12, | |
"activationThreshold": 16, | |
"outputType": "normal", | |
"pamLength": 1, | |
}, | |
"clParams": { | |
"implementation": "py", | |
"regionName" : "SDRClassifierRegion", | |
"verbosity" : 0, | |
"alpha": 0.0001, | |
"steps": "1,2,3", | |
}, | |
"trainSPNetOnlyIfRequested": False, | |
}, | |
} | |
model = ModelFactory.create(MODEL_PARAMS) | |
model.enableInference({"predictedField": "char"}) | |
shifter = InferenceShifter() | |
out = open("results.txt", "wt") | |
my_str = ''.join(['abcd '] * 10) | |
for c in my_str: | |
modelInput = {"char": c} | |
#result = shifter.shift(model.run(modelInput)) | |
result = model.run(modelInput) | |
out.write("%s\n" % | |
([c] + [result.inferences["multiStepBestPredictions"]])) | |
out.close() |
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