def runHotgym(numRecords):
  """Run the Hot Gym example."""

  # Create a data source for the network.
  dataSource = FileRecordStream(streamID=_INPUT_FILE_PATH)
  numRecords = min(numRecords, dataSource.getDataRowCount())
  network = createNetwork(dataSource)

  # Set predicted field
  network.regions["sensor"].setParameter("predictedField", "consumption")

  # Enable learning for all regions.
  network.regions["SP"].setParameter("learningMode", 1)
  network.regions["TM"].setParameter("learningMode", 1)
  network.regions["classifier"].setParameter("learningMode", 1)

  # Enable inference for all regions.
  network.regions["SP"].setParameter("inferenceMode", 1)
  network.regions["TM"].setParameter("inferenceMode", 1)
  network.regions["classifier"].setParameter("inferenceMode", 1)

  results = []
  N = 1  # Run the network, N iterations at a time.
  for iteration in range(0, numRecords, N):
    network.run(N)

    predictionResults = getPredictionResults(network, "classifier")
    oneStep = predictionResults[1]["predictedValue"]
    oneStepConfidence = predictionResults[1]["predictionConfidence"]
    fiveStep = predictionResults[5]["predictedValue"]
    fiveStepConfidence = predictionResults[5]["predictionConfidence"]

    result = (oneStep, oneStepConfidence * 100,
              fiveStep, fiveStepConfidence * 100)
    print "1-step: {:16} ({:4.4}%)\t 5-step: {:16} ({:4.4}%)".format(*result)
    results.append(result)

  return results
def runHotgym(numRecords):
  """Run the Hot Gym example."""

  # Create a data source for the network.
  dataSource = FileRecordStream(streamID=_INPUT_FILE_PATH)
  numRecords = min(numRecords, dataSource.getDataRowCount())
  network = createNetwork(dataSource)

  # Set predicted field
  network.regions["sensor"].setParameter("predictedField", "consumption")

  # Enable learning for all regions.
  network.regions["SP"].setParameter("learningMode", 1)
  network.regions["TM"].setParameter("learningMode", 1)
  network.regions["classifier"].setParameter("learningMode", 1)

  # Enable inference for all regions.
  network.regions["SP"].setParameter("inferenceMode", 1)
  network.regions["TM"].setParameter("inferenceMode", 1)
  network.regions["classifier"].setParameter("inferenceMode", 1)

  results = []
  N = 1  # Run the network, N iterations at a time.
  for iteration in range(0, numRecords, N):
    network.run(N)

    predictionResults = getPredictionResults(network, "classifier")
    oneStep = predictionResults[1]["predictedValue"]
    oneStepConfidence = predictionResults[1]["predictionConfidence"]
    fiveStep = predictionResults[5]["predictedValue"]
    fiveStepConfidence = predictionResults[5]["predictionConfidence"]

    result = (oneStep, oneStepConfidence * 100,
              fiveStep, fiveStepConfidence * 100)
    print "1-step: {:16} ({:4.4}%)\t 5-step: {:16} ({:4.4}%)".format(*result)
    results.append(result)

  return results
def runHotgym ( numRecords ) : # Create a data source for the network. dataSource = FileRecordStream ( streamID = _INPUT_FILE_PATH ) numRecords = min ( numRecords , dataSource . getDataRowCount ( ) ) network = createNetwork ( dataSource ) # Set predicted field network . regions [ "sensor" ] . setParameter ( "predictedField" , "consumption" ) # Enable learning for all regions. network . regions [ "SP" ] . setParameter ( "learningMode" , 1 ) network . regions [ "TM" ] . setParameter ( "learningMode" , 1 ) network . regions [ "classifier" ] . setParameter ( "learningMode" , 1 ) # Enable inference for all regions. network . regions [ "SP" ] . setParameter ( "inferenceMode" , 1 ) network . regions [ "TM" ] . setParameter ( "inferenceMode" , 1 ) network . regions [ "classifier" ] . setParameter ( "inferenceMode" , 1 ) results = [ ] N = 1 # Run the network, N iterations at a time. for iteration in range ( 0 , numRecords , N ) : network . run ( N ) predictionResults = getPredictionResults ( network , "classifier" ) oneStep = predictionResults [ 1 ] [ "predictedValue" ] oneStepConfidence = predictionResults [ 1 ] [ "predictionConfidence" ] fiveStep = predictionResults [ 5 ] [ "predictedValue" ] fiveStepConfidence = predictionResults [ 5 ] [ "predictionConfidence" ] result = ( oneStep , oneStepConfidence * 100 , fiveStep , fiveStepConfidence * 100 ) print "1-step: {:16} ({:4.4}%)\t 5-step: {:16} ({:4.4}%)" . format ( * result ) results . append ( result ) return results
Run the Hot Gym example.
Run the Hot Gym example .