def getBaseSpec(cls):
    """
    Doesn't include the spatial, temporal and other parameters

    :returns: (dict) the base Spec for TMRegion.
    """
    spec = dict(
      description=TMRegion.__doc__,
      singleNodeOnly=True,
      inputs=dict(
        bottomUpIn=dict(
          description="""The input signal, conceptually organized as an
                         image pyramid data structure, but internally
                         organized as a flattened vector.""",
          dataType='Real32',
          count=0,
          required=True,
          regionLevel=False,
          isDefaultInput=True,
          requireSplitterMap=False),

        resetIn=dict(
          description="""Effectively a boolean flag that indicates whether
                         or not the input vector received in this compute cycle
                         represents the first training presentation in a
                         new temporal sequence.""",
          dataType='Real32',
          count=1,
          required=False,
          regionLevel=True,
          isDefaultInput=False,
          requireSplitterMap=False),

        sequenceIdIn=dict(
          description="Sequence ID",
          dataType='UInt64',
          count=1,
          required=False,
          regionLevel=True,
          isDefaultInput=False,
          requireSplitterMap=False),

      ),

      outputs=dict(
        bottomUpOut=dict(
          description="""The output signal generated from the bottom-up inputs
                          from lower levels.""",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=True),

        topDownOut=dict(
          description="""The top-down inputsignal, generated from
                        feedback from upper levels""",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=False),

        activeCells=dict(
          description="The cells that are active",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=False),

        predictedActiveCells=dict(
          description="The cells that are active and predicted",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=False),

        anomalyScore = dict(
          description="""The score for how 'anomalous' (i.e. rare) the current
                        sequence is. Higher values are increasingly rare""",
          dataType='Real32',
          count=1,
          regionLevel=True,
          isDefaultOutput=False),

        lrnActiveStateT = dict(
          description="""Active cells during learn phase at time t.  This is
                        used for anomaly classification.""",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=False),

      ),

      parameters=dict(
        breakPdb=dict(
          description='Set to 1 to stop in the pdb debugger on the next compute',
          dataType='UInt32',
          count=1,
          constraints='bool',
          defaultValue=0,
          accessMode='ReadWrite'),

        breakKomodo=dict(
          description='Set to 1 to stop in the Komodo debugger on the next compute',
          dataType='UInt32',
          count=1,
          constraints='bool',
          defaultValue=0,
          accessMode='ReadWrite'),

      ),
      commands = {}
    )

    return spec
def getBaseSpec(cls):
    """
    Doesn't include the spatial, temporal and other parameters

    :returns: (dict) the base Spec for TMRegion.
    """
    spec = dict(
      description=TMRegion.__doc__,
      singleNodeOnly=True,
      inputs=dict(
        bottomUpIn=dict(
          description="""The input signal, conceptually organized as an
                         image pyramid data structure, but internally
                         organized as a flattened vector.""",
          dataType='Real32',
          count=0,
          required=True,
          regionLevel=False,
          isDefaultInput=True,
          requireSplitterMap=False),

        resetIn=dict(
          description="""Effectively a boolean flag that indicates whether
                         or not the input vector received in this compute cycle
                         represents the first training presentation in a
                         new temporal sequence.""",
          dataType='Real32',
          count=1,
          required=False,
          regionLevel=True,
          isDefaultInput=False,
          requireSplitterMap=False),

        sequenceIdIn=dict(
          description="Sequence ID",
          dataType='UInt64',
          count=1,
          required=False,
          regionLevel=True,
          isDefaultInput=False,
          requireSplitterMap=False),

      ),

      outputs=dict(
        bottomUpOut=dict(
          description="""The output signal generated from the bottom-up inputs
                          from lower levels.""",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=True),

        topDownOut=dict(
          description="""The top-down inputsignal, generated from
                        feedback from upper levels""",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=False),

        activeCells=dict(
          description="The cells that are active",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=False),

        predictedActiveCells=dict(
          description="The cells that are active and predicted",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=False),

        anomalyScore = dict(
          description="""The score for how 'anomalous' (i.e. rare) the current
                        sequence is. Higher values are increasingly rare""",
          dataType='Real32',
          count=1,
          regionLevel=True,
          isDefaultOutput=False),

        lrnActiveStateT = dict(
          description="""Active cells during learn phase at time t.  This is
                        used for anomaly classification.""",
          dataType='Real32',
          count=0,
          regionLevel=True,
          isDefaultOutput=False),

      ),

      parameters=dict(
        breakPdb=dict(
          description='Set to 1 to stop in the pdb debugger on the next compute',
          dataType='UInt32',
          count=1,
          constraints='bool',
          defaultValue=0,
          accessMode='ReadWrite'),

        breakKomodo=dict(
          description='Set to 1 to stop in the Komodo debugger on the next compute',
          dataType='UInt32',
          count=1,
          constraints='bool',
          defaultValue=0,
          accessMode='ReadWrite'),

      ),
      commands = {}
    )

    return spec
def getBaseSpec ( cls ) : spec = dict ( description = TMRegion . __doc__ , singleNodeOnly = True , inputs = dict ( bottomUpIn = dict ( description = """The input signal, conceptually organized as an
                         image pyramid data structure, but internally
                         organized as a flattened vector.""" , dataType = 'Real32' , count = 0 , required = True , regionLevel = False , isDefaultInput = True , requireSplitterMap = False ) , resetIn = dict ( description = """Effectively a boolean flag that indicates whether
                         or not the input vector received in this compute cycle
                         represents the first training presentation in a
                         new temporal sequence.""" , dataType = 'Real32' , count = 1 , required = False , regionLevel = True , isDefaultInput = False , requireSplitterMap = False ) , sequenceIdIn = dict ( description = "Sequence ID" , dataType = 'UInt64' , count = 1 , required = False , regionLevel = True , isDefaultInput = False , requireSplitterMap = False ) , ) , outputs = dict ( bottomUpOut = dict ( description = """The output signal generated from the bottom-up inputs
                          from lower levels.""" , dataType = 'Real32' , count = 0 , regionLevel = True , isDefaultOutput = True ) , topDownOut = dict ( description = """The top-down inputsignal, generated from
                        feedback from upper levels""" , dataType = 'Real32' , count = 0 , regionLevel = True , isDefaultOutput = False ) , activeCells = dict ( description = "The cells that are active" , dataType = 'Real32' , count = 0 , regionLevel = True , isDefaultOutput = False ) , predictedActiveCells = dict ( description = "The cells that are active and predicted" , dataType = 'Real32' , count = 0 , regionLevel = True , isDefaultOutput = False ) , anomalyScore = dict ( description = """The score for how 'anomalous' (i.e. rare) the current
                        sequence is. Higher values are increasingly rare""" , dataType = 'Real32' , count = 1 , regionLevel = True , isDefaultOutput = False ) , lrnActiveStateT = dict ( description = """Active cells during learn phase at time t.  This is
                        used for anomaly classification.""" , dataType = 'Real32' , count = 0 , regionLevel = True , isDefaultOutput = False ) , ) , parameters = dict ( breakPdb = dict ( description = 'Set to 1 to stop in the pdb debugger on the next compute' , dataType = 'UInt32' , count = 1 , constraints = 'bool' , defaultValue = 0 , accessMode = 'ReadWrite' ) , breakKomodo = dict ( description = 'Set to 1 to stop in the Komodo debugger on the next compute' , dataType = 'UInt32' , count = 1 , constraints = 'bool' , defaultValue = 0 , accessMode = 'ReadWrite' ) , ) , commands = { } ) return spec
Doesn't include the spatial, temporal and other parameters

    :returns: (dict) the base Spec for TMRegion.
Doesn t include the spatial temporal and other parameters