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,   # 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 TPRegion, unless you need to do
               # something special with them. See docstring above.

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

               **kwargs):

    # Which Temporal implementation?
    FDRTemporalClass = _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 TPRegion
    tArgTuples = _buildArgs(FDRTemporalClass.__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.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

    # TPRegion 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 setParameter(self, name, index, value):
    """Set the value of a parameter."""

    if name == "SVDSampleCount":
      self._SVDSampleCount = value
    elif name == "logPath":
      self._logPath = value
    else:
      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.
    """

    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 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 == 'sparseCoincidenceMatrix':
      if not self._sfdr:
        return None
      return self._sfdr.cm
    elif 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 == 'spOverlapDistribution':
      if not self._sfdr:
        return []
      # Get the dense output (will only work if you've done storeDenseOutput)
      try:
        denseOutput = self._sfdr.getDenseOutput()
        if len(denseOutput) == 0:
          raise Exception("storeDenseOutput wasn't set in PY version")
        denseOutput = numpy.array(denseOutput)
      except Exception:
        print "WARNING: You must enable storing dense output in the SP using" \
              "setStoreDenseOutput."
        return []
      winnerIndices = numpy.array(self._spatialPoolerOutput).nonzero()[0]
      overlaps = denseOutput[winnerIndices]

      # Sort descending and convert to list...
      overlaps = sorted(overlaps, reverse=True)

      return overlaps
    elif parameterName == "denseOutput":
      # Must be a list, not a numpy array to make NuPIC happy...
      denseOutput = self._sfdr.getDenseOutput()
      if not isinstance(denseOutput, list):
        assert isinstance(denseOutput, numpy.ndarray)
        denseOutput = list(denseOutput)
      return denseOutput
    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 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)

  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 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 parameter. Most parameters are handled automatically by
      PyRegion's parameter get mechanism. The ones that need special treatment
      are explicitly handled here.
    """

    if parameterName in self._temporalArgNames:
      return getattr(self._tfdr, parameterName)
    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))
    elif name == "inferenceMode":
      self.inferenceMode = bool(int(value))
    else:
      return PyRegion.setParameter(self, name, index, value)

  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 == "trainRecords":
      return self.trainRecords
    elif name == "anomalyThreshold":
      return self.anomalyThreshold
    elif name == "activeColumnCount":
      return self._activeColumnCount
    elif name == "classificationMaxDist":
      return self._classificationMaxDist
    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)

  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 == "trainRecords":
      # Ensure that the trainRecords can only be set to minimum of the ROWID in
      # the saved states
      if not (isinstance(value, float) or isinstance(value, int)):
        raise CLAModelInvalidArgument("Invalid argument type \'%s\'. threshold "
          "must be a number." % (type(value)))

      if len(self._recordsCache) > 0 and value < self._recordsCache[0].ROWID:
        raise CLAModelInvalidArgument("Invalid value. autoDetectWaitRecord "
          "value must be valid record within output stream. Current minimum "
          " ROWID in output stream is %d." % (self._recordsCache[0].ROWID))
      
      self.trainRecords = value
      # Remove any labels before the first cached record (wont be used anymore)
      self._deleteRangeFromKNN(0, self._recordsCache[0].ROWID)
      # Reclassify all states
      self.classifyStates()
    elif name == "anomalyThreshold":
      if not (isinstance(value, float) or isinstance(value, int)):
        raise CLAModelInvalidArgument("Invalid argument type \'%s\'. threshold "
          "must be a number." % (type(value)))
      self.anomalyThreshold = value
      self.classifyStates()
    elif name == "classificationMaxDist":
      if not (isinstance(value, float) or isinstance(value, int)):
        raise CLAModelInvalidArgument("Invalid argument type \'%s\'. "
          "classificationMaxDist must be a number." % (type(value)))
      self._classificationMaxDist = value
      self.classifyStates()
    elif name == "activeColumnCount":
      self._activeColumnCount = value
    else:
      return PyRegion.setParameter(self, name, index, value)

  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":
      if int(value) and not self.learningMode:
        self._restartLearning()
      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
    elif name == "doSelfValidation":
      self.doSelfValidation = value
    else:
      return PyRegion.setParameter(self, name, index, value)

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

    Note: this method may be overridden by derived classes, but if so, then
    the derived class should invoke this base method if 'name'
    is unknown to the derived class.

    @param name -- the name of the parameter to retrieve, as defined
            by the Node Spec.
    """
    if name == "SVDSampleCount":
      return self._SVDSampleCount
    elif name == "SVDDimCount":
      return self._SVDDimCount
    elif name == "fractionOfMax":
      return self._fractionOfMax
    elif name == "trainingSampleCount":
      return self.gettrainingSampleCount()
    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)