run_charpred.py

# ----------------------------------------------------------------------
# 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()