Python PyRegion.PyRegion类代码示例

  def __init__(self,
               columnCount,   # Number of columns in the SP, a required parameter
               inputWidth,    # Size of inputs to the SP, a required parameter
               spatialImp=getDefaultSPImp(),   #'py', 'cpp'
               **kwargs):

    if columnCount <= 0 or inputWidth <=0:
      raise TypeError("Parameters columnCount and inputWidth must be > 0")

    # Pull out the spatial arguments automatically
    # These calls whittle down kwargs and create instance variables of SPRegion
    self.SpatialClass = getSPClass(spatialImp)
    sArgTuples = _buildArgs(self.SpatialClass.__init__, self, kwargs)

    # Make a list of automatic spatial arg names for later use
    self._spatialArgNames = [t[0] for t in sArgTuples]

    # Learning and SP parameters.
    # By default we start out in stage learn with inference disabled
    self.learningMode   = True
    self.inferenceMode  = False
    self.anomalyMode    = False
    self.topDownMode    = False
    self.columnCount    = columnCount
    self.inputWidth     = inputWidth

    PyRegion.__init__(self, **kwargs)

    # Initialize all non-persistent base members, as well as give
    # derived class an opportunity to do the same.
    self._loaded = False
    self._initializeEphemeralMembers()

    # Debugging support, used in _conditionalBreak
    self.breakPdb = False
    self.breakKomodo = False

    # Defaults for all other parameters
    self.logPathInput = ''
    self.logPathOutput = ''
    self.logPathOutputDense = ''
    self._fpLogSPInput = None
    self._fpLogSP = None
    self._fpLogSPDense = None


    #
    # Variables set up in initInNetwork()
    #

    # Spatial instance
    self._sfdr                = None

    # Spatial pooler's bottom-up output value: hang on to this  output for
    # top-down inference and for debugging
    self._spatialPoolerOutput = None

    # Spatial pooler's bottom-up input: hang on to this for supporting the
    # spInputNonZeros parameter
    self._spatialPoolerInput  = None

  def __init__(self,
               columnCount=2048,
               cellsPerColumn=16,
               activationThreshold=13,
               initialPermanence=0.21,
               connectedPermanence=0.50,
               minThreshold=10,
               maxNewSynapseCount=20,
               permanenceIncrement=0.10,
               permanenceDecrement=0.10,
               predictedSegmentDecrement=0.0,
               seed=42,
               defaultOutputType = "active",
               **kwargs):
    # Defaults for all other parameters
    self.columnCount = columnCount
    self.cellsPerColumn = cellsPerColumn
    self.inputWidth = self.columnCount*self.cellsPerColumn
    self.activationThreshold = activationThreshold
    self.initialPermanence = initialPermanence
    self.connectedPermanence = connectedPermanence
    self.minThreshold = minThreshold
    self.maxNewSynapseCount = maxNewSynapseCount
    self.permanenceIncrement = permanenceIncrement
    self.permanenceDecrement = permanenceDecrement
    self.predictedSegmentDecrement = predictedSegmentDecrement
    self.seed = seed
    self.learningMode = True
    self.inferenceMode = True
    self.defaultOutputType = defaultOutputType

    PyRegion.__init__(self, **kwargs)

  def __init__(self, columnCount, inputWidth, historyLength,
               minHistory, poolerType, **kwargs):

    if columnCount <= 0 or inputWidth <=0:
      raise TypeError("Parameters columnCount and inputWidth must be > 0")
    # Pull out the pooler arguments automatically
    # These calls whittle down kwargs and create instance variables of TemporalPoolerRegion
    self._poolerType = poolerType
    self._poolerClass = _getPoolerClass(poolerType)
    pArgTuples = _buildArgs(self._poolerClass, self, kwargs)

    # include parent spatial pooler parameters
    if poolerType == "union" or poolerType == "unionMonitored":
      pArgTuplesSP = _buildArgs(_getParentSpatialPoolerClass(poolerType), self, kwargs)
      # Make a list of automatic pooler arg names for later use
      self._poolerArgNames = [t[0] for t in pArgTuples] + [t[0] for t in pArgTuplesSP]
    else:
      self._poolerArgNames = [t[0] for t in pArgTuples]

    PyRegion.__init__(self, **kwargs)

    # Defaults for all other parameters
    self.learningMode = True
    self.inferenceMode = True
    self._inputWidth = inputWidth
    self._columnCount = columnCount
    self._historyLength = historyLength
    self._minHistory = minHistory

    # pooler instance
    self._pooler = None

  def __init__(self,
               moduleCount,
               cellsPerAxis,
               scale,
               orientation,
               anchorInputSize,
               activeFiringRate,
               bumpSigma,
               activationThreshold=10,
               initialPermanence=0.21,
               connectedPermanence=0.50,
               learningThreshold=10,
               sampleSize=20,
               permanenceIncrement=0.1,
               permanenceDecrement=0.0,
               maxSynapsesPerSegment=-1,
               bumpOverlapMethod="probabilistic",
               learningMode=False,
               seed=42,
               dualPhase=True,
               dimensions=2,
               **kwargs):
    if moduleCount <= 0 or cellsPerAxis <= 0:
      raise TypeError("Parameters moduleCount and cellsPerAxis must be > 0")
    if moduleCount != len(scale) or moduleCount != len(orientation):
      raise TypeError("scale and orientation arrays len must match moduleCount")
    if dimensions < 2:
      raise TypeError("dimensions must be >= 2")

    self.moduleCount = moduleCount
    self.cellsPerAxis = cellsPerAxis
    self.cellCount = cellsPerAxis * cellsPerAxis
    self.scale = list(scale)
    self.orientation = list(orientation)
    self.anchorInputSize = anchorInputSize
    self.activeFiringRate = activeFiringRate
    self.bumpSigma = bumpSigma
    self.activationThreshold = activationThreshold
    self.initialPermanence = initialPermanence
    self.connectedPermanence = connectedPermanence
    self.learningThreshold = learningThreshold
    self.sampleSize = sampleSize
    self.permanenceIncrement = permanenceIncrement
    self.permanenceDecrement = permanenceDecrement
    self.maxSynapsesPerSegment = maxSynapsesPerSegment
    self.bumpOverlapMethod = bumpOverlapMethod
    self.learningMode = learningMode
    self.dualPhase = dualPhase
    self.dimensions = dimensions
    self.seed = seed

    # This flag controls whether the region is processing sensation or movement
    # on dual phase configuration
    self._sensing = False

    self._modules = None

    self._projection = None

    PyRegion.__init__(self, **kwargs)

  def __init__(self,

               columnCount,   # Number of columns in the SP, a required parameter
               inputWidth,    # Size of inputs to the SP, a required parameter
               cellsPerColumn, # Number of cells per column, required

               # Constructor arguments are picked up automatically. There is no
               # need to add them anywhere in TMRegion, unless you need to do
               # something special with them. See docstring above.

               orColumnOutputs=False,
               cellsSavePath='',
               temporalImp=gDefaultTemporalImp,
               anomalyMode=False,
               computePredictedActiveCellIndices=False,

               **kwargs):
    # Which Temporal implementation?
    TemporalClass = _getTPClass(temporalImp)

    # Make a list of automatic temporal arg names for later use
    # Pull out the temporal arguments automatically
    # These calls whittle down kwargs and create instance variables of TMRegion
    tArgTuples = _buildArgs(TemporalClass.__init__, self, kwargs)

    self._temporalArgNames = [t[0] for t in tArgTuples]

    self.learningMode   = True      # Start out with learning enabled
    self.inferenceMode  = False
    self.anomalyMode    = anomalyMode
    self.computePredictedActiveCellIndices = computePredictedActiveCellIndices
    self.topDownMode    = False
    self.columnCount    = columnCount
    self.inputWidth     = inputWidth
    self.outputWidth    = columnCount * cellsPerColumn
    self.cellsPerColumn = cellsPerColumn

    PyRegion.__init__(self, **kwargs)

    # Initialize all non-persistent base members, as well as give
    # derived class an opportunity to do the same.
    self._loaded = False
    self._initialize()

    # Debugging support, used in _conditionalBreak
    self.breakPdb = False
    self.breakKomodo = False

    # TMRegion only, or special handling
    self.orColumnOutputs = orColumnOutputs
    self.temporalImp = temporalImp

    # Various file names
    self.storeDenseOutput = False
    self.logPathOutput = ''
    self.cellsSavePath = cellsSavePath
    self._fpLogTPOutput = None

    # Variables set up in initInNetwork()
    self._tfdr = None  # FDRTemporal instance

  def __init__(self,

               # Input sizes
               columnCount,
               basalInputWidth,
               apicalInputWidth=0,

               # TM params
               cellsPerColumn=32,
               activationThreshold=13,
               initialPermanence=0.21,
               connectedPermanence=0.50,
               minThreshold=10,
               reducedBasalThreshold=13, # ApicalTiebreak and ApicalDependent only
               sampleSize=20,
               permanenceIncrement=0.10,
               permanenceDecrement=0.10,
               basalPredictedSegmentDecrement=0.0,
               apicalPredictedSegmentDecrement=0.0,
               learnOnOneCell=False, # ApicalTiebreakCPP only
               maxSegmentsPerCell=255,
               maxSynapsesPerSegment=255, # ApicalTiebreakCPP only
               seed=42,

               # Region params
               implementation="ApicalTiebreak",
               learn=True,
               **kwargs):

    # Input sizes (the network API doesn't provide these during initialize)
    self.columnCount = columnCount
    self.basalInputWidth = basalInputWidth
    self.apicalInputWidth = apicalInputWidth

    # TM params
    self.cellsPerColumn = cellsPerColumn
    self.activationThreshold = activationThreshold
    self.reducedBasalThreshold = reducedBasalThreshold
    self.initialPermanence = initialPermanence
    self.connectedPermanence = connectedPermanence
    self.minThreshold = minThreshold
    self.sampleSize = sampleSize
    self.permanenceIncrement = permanenceIncrement
    self.permanenceDecrement = permanenceDecrement
    self.basalPredictedSegmentDecrement = basalPredictedSegmentDecrement
    self.apicalPredictedSegmentDecrement = apicalPredictedSegmentDecrement
    self.maxSynapsesPerSegment = maxSynapsesPerSegment
    self.maxSegmentsPerCell = maxSegmentsPerCell
    self.learnOnOneCell = learnOnOneCell
    self.seed = seed

    # Region params
    self.implementation = implementation
    self.learn = learn

    PyRegion.__init__(self, **kwargs)

    # TM instance
    self._tm = None

  def __init__(self,
               cellCount=4096,
               inputWidth=16384,
               numOtherCorticalColumns=0,
               sdrSize=40,

               # Proximal
               synPermProximalInc=0.1,
               synPermProximalDec=0.001,
               initialProximalPermanence=0.6,
               sampleSizeProximal=20,
               minThresholdProximal=1,
               connectedPermanenceProximal=0.50,

               # Distal
               synPermDistalInc=0.10,
               synPermDistalDec=0.10,
               initialDistalPermanence=0.21,
               sampleSizeDistal=20,
               activationThresholdDistal=13,
               connectedPermanenceDistal=0.50,
               distalSegmentInhibitionFactor=1.5,

               seed=42,
               defaultOutputType = "active",
               **kwargs):

    # Used to derive Column Pooler params
    self.numOtherCorticalColumns = numOtherCorticalColumns

    # Column Pooler params
    self.inputWidth = inputWidth
    self.cellCount = cellCount
    self.sdrSize = sdrSize
    self.synPermProximalInc = synPermProximalInc
    self.synPermProximalDec = synPermProximalDec
    self.initialProximalPermanence = initialProximalPermanence
    self.sampleSizeProximal = sampleSizeProximal
    self.minThresholdProximal = minThresholdProximal
    self.connectedPermanenceProximal = connectedPermanenceProximal
    self.synPermDistalInc = synPermDistalInc
    self.synPermDistalDec = synPermDistalDec
    self.initialDistalPermanence = initialDistalPermanence
    self.sampleSizeDistal = sampleSizeDistal
    self.activationThresholdDistal = activationThresholdDistal
    self.connectedPermanenceDistal = connectedPermanenceDistal
    self.distalSegmentInhibitionFactor = distalSegmentInhibitionFactor
    self.seed = seed

    # Region params
    self.learningMode = True
    self.defaultOutputType = defaultOutputType

    self._pooler = None

    PyRegion.__init__(self, **kwargs)

  def __init__(self,
               columnCount=2048,
               inputWidth=16384,
               lateralInputWidth=0,
               activationThresholdDistal=13,
               initialPermanence=0.21,
               connectedPermanence=0.50,
               minThresholdProximal=1,
               minThresholdDistal=10,
               maxNewProximalSynapseCount=20,
               maxNewDistalSynapseCount=20,
               permanenceIncrement=0.10,
               permanenceDecrement=0.10,
               predictedSegmentDecrement=0.0,
               synPermProximalInc=0.1,
               synPermProximalDec=0.001,
               initialProximalPermanence = 0.6,
               seed=42,
               numActiveColumnsPerInhArea=40,
               defaultOutputType = "active",
               **kwargs):

    # Modified Column Pooler params
    self.columnCount = columnCount

    # Column Pooler params
    self.inputWidth = inputWidth
    self.lateralInputWidth = lateralInputWidth
    self.activationThresholdDistal = activationThresholdDistal
    self.initialPermanence = initialPermanence
    self.connectedPermanence = connectedPermanence
    self.minThresholdProximal = minThresholdProximal
    self.minThresholdDistal = minThresholdDistal
    self.maxNewProximalSynapseCount = maxNewProximalSynapseCount
    self.maxNewDistalSynapseCount = maxNewDistalSynapseCount
    self.permanenceIncrement = permanenceIncrement
    self.permanenceDecrement = permanenceDecrement
    self.predictedSegmentDecrement = predictedSegmentDecrement
    self.synPermProximalInc = synPermProximalInc
    self.synPermProximalDec = synPermProximalDec
    self.initialProximalPermanence = initialProximalPermanence
    self.seed = seed
    self.numActiveColumnsPerInhArea = numActiveColumnsPerInhArea
    self.maxSynapsesPerSegment = inputWidth

    # Region params
    self.learningMode = True
    self.inferenceMode = True
    self.defaultOutputType = defaultOutputType

    self._pooler = None

    PyRegion.__init__(self, **kwargs)

 def setParameter(self, name, index, value):
   """
   Overrides :meth:`nupic.bindings.regions.PyRegion.PyRegion.setParameter`.
   """
   if name == "learningMode":
     self.learningMode = bool(int(value))
     self._epoch = 0
   elif name == "inferenceMode":
     self._epoch = 0
     if int(value) and not self.inferenceMode:
       self._finishLearning()
     self.inferenceMode = bool(int(value))
   elif name == "distanceNorm":
     self._knn.distanceNorm = value
   elif name == "distanceMethod":
     self._knn.distanceMethod = value
   elif name == "keepAllDistances":
     self.keepAllDistances = bool(value)
     if not self.keepAllDistances:
       # Discard all distances except the latest
       if self._protoScores is not None and self._protoScores.shape[0] > 1:
         self._protoScores = self._protoScores[-1,:]
       if self._protoScores is not None:
         self._protoScoreCount = 1
       else:
         self._protoScoreCount = 0
   elif name == "verbosity":
     self.verbosity = value
     self._knn.verbosity = value
   else:
     return PyRegion.setParameter(self, name, index, value)

 def getParameter(self, parameterName, index=-1):
   """
     Get the value of a NodeSpec parameter. Most parameters are handled
     automatically by PyRegion's parameter get mechanism. The ones that need
     special treatment are explicitly handled here.
   """
   return PyRegion.getParameter(self, parameterName, index)

 def getParameter(self, name, index=-1):
   """
   Overrides :meth:`nupic.bindings.regions.PyRegion.PyRegion.getParameter`.
   """
   # If any spec parameter name is the same as an attribute, this call
   # will get it automatically, e.g. self.learningMode
   return PyRegion.getParameter(self, name, index)

  def getParameter(self, parameterName, index=-1):
    """
      Get the value of a NodeSpec parameter. Most parameters are handled
      automatically by PyRegion's parameter get mechanism. The ones that need
      special treatment are explicitly handled here.
    """

    if parameterName == 'activeOutputCount':
      return self.columnCount
    elif parameterName == 'spatialPoolerInput':
      return list(self._spatialPoolerInput.reshape(-1))
    elif parameterName == 'spatialPoolerOutput':
      return list(self._spatialPoolerOutput)
    elif parameterName == 'spNumActiveOutputs':
      return len(self._spatialPoolerOutput.nonzero()[0])
    elif parameterName == 'spOutputNonZeros':
      return [len(self._spatialPoolerOutput)] + \
              list(self._spatialPoolerOutput.nonzero()[0])
    elif parameterName == 'spInputNonZeros':
      import pdb; pdb.set_trace()
      return [len(self._spatialPoolerInput)] + \
              list(self._spatialPoolerInput.nonzero()[0])
    elif parameterName == 'spLearningStatsStr':
      try:
        return str(self._sfdr.getLearningStats())
      except:
        return str(dict())
    else:
      return PyRegion.getParameter(self, parameterName, index)

  def setParameter(self, name, index, value):
    """
    Set the value of the parameter.

    @param name -- the name of the parameter to update, as defined
            by the Node Spec.
    @param value -- the value to which the parameter is to be set.
    """
    if name == "learningMode":
      self.learningMode = bool(int(value))
      self._epoch = 0
    elif name == "inferenceMode":
      self._epoch = 0
      if int(value) and not self.inferenceMode:
        self._finishLearning()
      self.inferenceMode = bool(int(value))
    elif name == "distanceNorm":
      self._knn.distanceNorm = value
    elif name == "distanceMethod":
      self._knn.distanceMethod = value
    elif name == "keepAllDistances":
      self.keepAllDistances = bool(value)
      if not self.keepAllDistances:
        # Discard all distances except the latest
        if self._protoScores is not None and self._protoScores.shape[0] > 1:
          self._protoScores = self._protoScores[-1,:]
        if self._protoScores is not None:
          self._protoScoreCount = 1
        else:
          self._protoScoreCount = 0
    elif name == "clVerbosity":
      self.verbosity = value
      self._knn.verbosity = value
    else:
      return PyRegion.setParameter(self, name, index, value)

  def __init__(self,
               columnCount=2048,
               cellsPerColumn=32,
               activationThreshold=13,
               initialPermanence=0.21,
               connectedPermanence=0.50,
               minThreshold=10,
               maxNewSynapseCount=20,
               permanenceIncrement=0.10,
               permanenceDecrement=0.10,
               predictedSegmentDecrement=0.0,
               seed=42,
               learnOnOneCell=1,
               formInternalConnections=1,
               formInternalBasalConnections=1,
               defaultOutputType = "active",
               monitor=False,
               implementation="cpp",
               **kwargs):
    # Defaults for all other parameters

    self.columnCount = columnCount
    self.cellsPerColumn = cellsPerColumn
    self.activationThreshold = activationThreshold
    self.initialPermanence = initialPermanence
    self.connectedPermanence = connectedPermanence
    self.minThreshold = minThreshold
    self.maxNewSynapseCount = maxNewSynapseCount
    self.permanenceIncrement = permanenceIncrement
    self.permanenceDecrement = permanenceDecrement
    self.predictedSegmentDecrement = predictedSegmentDecrement
    self.seed = seed
    self.learnOnOneCell = bool(learnOnOneCell)
    self.learningMode = True
    self.inferenceMode = True
    self.formInternalConnections = bool(formInternalConnections)
    self.formInternalBasalConnections = bool(formInternalBasalConnections)
    self.defaultOutputType = defaultOutputType
    self.monitor = monitor
    self.implementation = implementation

    PyRegion.__init__(self, **kwargs)

    # TM instance
    self._tm = None

  def getParameter(self, name, index=-1):
    """
    Get the value of the parameter.

    @param name -- the name of the parameter to retrieve, as defined
            by the Node Spec.
    """
    if name == "patternCount":
      return self._knn._numPatterns
    elif name == "patternMatrix":
      return self._getPatternMatrix()
    elif name == "k":
      return self._knn.k
    elif name == "distanceNorm":
      return self._knn.distanceNorm
    elif name == "distanceMethod":
      return self._knn.distanceMethod
    elif name == "distThreshold":
      return self._knn.distThreshold
    elif name == "inputThresh":
      return self._knn.binarizationThreshold
    elif name == "doBinarization":
      return self._knn.doBinarization
    elif name == "useSparseMemory":
      return self._knn.useSparseMemory
    elif name == "sparseThreshold":
      return self._knn.sparseThreshold
    elif name == "winnerCount":
      return self._knn.numWinners
    elif name == "relativeThreshold":
      return self._knn.relativeThreshold
    elif name == "SVDSampleCount":
      v = self._knn.numSVDSamples
      return v if v is not None else 0
    elif name == "SVDDimCount":
      v = self._knn.numSVDDims
      return v if v is not None else 0
    elif name == "fractionOfMax":
      v = self._knn.fractionOfMax
      return v if v is not None else 0
    elif name == "useAuxiliary":
      return self._useAuxiliary
    elif name == "justUseAuxiliary":
      return self._justUseAuxiliary
    elif name == "doSphering":
      return self._doSphering
    elif name == "cellsPerCol":
      return self._knn.cellsPerCol
    elif name == "maxStoredPatterns":
      return self.maxStoredPatterns
    elif name == 'categoryRecencyList':
      return self._knn._categoryRecencyList
    else:
      # If any spec parameter name is the same as an attribute, this call
      # will get it automatically, e.g. self.learningMode
      return PyRegion.getParameter(self, name, index)