def __init__(
self,
columnCount, # Number of columns in the SP, a required parameter
inputWidth, # Size of inputs to the SP, a required parameter
**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
sArgTuples = _buildArgs(FDRCSpatial2.FDRCSpatial2.__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()
#
# FDRCSpatial 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
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 __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,
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 TPRegion
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
# 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 __init__(self,
# Constructor arguments for FDRSpatial and FDRTemporal are
# picked up automatically. There is no need to add them anywhere
# in CLARegion, unless you need to do something special with them.
# See docstring above.
# These args are used by CLARegion only or need special handling
disableSpatial=False,
disableTemporal=False,
orColumnOutputs=False,
nCellsPerCol=1,
trainingStep = 'temporal',
cellsSavePath='',
statelessMode=False,
storeDenseOutput=False, #DEPRECATED
outputCloningWidth=0,
outputCloningHeight=0,
saveMasterCoincImages = 0,
temporalImp='py', #'py', 'simple' or 'cpp'
spatialImp=getDefaultSPImp(), #'py', 'cpp', or 'oldpy'
computeTopDown = 0,
nMultiStepPrediction = 0,
# We have separate seeds for spatial and temporal
spSeed=-1,
tpSeed=-1,
# Needed for vision framework
bottomUpOut=None,
**kwargs):
#if disableSpatial and disableTemporal:
# raise RuntimeError("Disable both the spatial and temporal components? "
# "That would make it too easy.")
# Make sure our tuple arguments are integers
for name in ['coincidencesShape', 'inputShape']:
if name in kwargs:
(height, width) = kwargs[name]
kwargs[name] = (int(height), int(width))
# Which FDR Temporal implementation?
FDRCSpatialClass = getSPClass(spatialImp)
FDRTemporalClass = _getTPClass(temporalImp)
# Pull out the spatial and temporal arguments automatically
# These calls whittle down kwargs and create instance variables of CLARegion
sArgTuples = _buildArgs(FDRCSpatialClass.__init__, self, kwargs)
tArgTuples = _buildArgs(FDRTemporalClass.__init__, self, kwargs)
# Make a list of automatic spatial and temporal arg names for later use
self._spatialArgNames = [t[0] for t in sArgTuples]
self._temporalArgNames = [t[0] for t in tArgTuples]
# Start out in stage learn
self.learningMode = True
self.inferenceMode = False
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
# CLARegion only, or special handling
self.disableSpatial = disableSpatial
self.saveMasterCoincImages = saveMasterCoincImages
self.disableTemporal = disableTemporal
self.orColumnOutputs = orColumnOutputs
self.nCellsPerCol = nCellsPerCol # Modified in initInNetwork
self.coincidenceCount = self.coincidencesShape[0] * self.coincidencesShape[1]
self.temporalImp = temporalImp
self.spatialImp = spatialImp
self.computeTopDown = computeTopDown
self.nMultiStepPrediction = nMultiStepPrediction
# Handle -1 for cloning sizes, which essentially just means no cloning...
# ...also handle 0, since that's the new default...
if outputCloningWidth in (0, -1):
outputCloningWidth = self.coincidencesShape[1]
if outputCloningHeight in (0, -1):
outputCloningHeight = self.coincidencesShape[0]
self.outputCloningWidth = outputCloningWidth
self.outputCloningHeight = outputCloningHeight
# Make the clone map, which is used by both spatial and temporal components.
self._cloneMap, self._numCloneMasters = self.makeCloneMap(
self.coincidencesShape, outputCloningWidth, outputCloningHeight
)
# Both FDRCSpatial and FDRTemporal
self.tpSeed = tpSeed
self.spSeed = spSeed
self.trainingStep = trainingStep
self.logPathSPInput = ''
self.logPathSP = ''
self.logPathSPDense = ''
self.logPathTP = ''
# Used to save TP cells data structure to auxiliary file
self.cellsSavePath = cellsSavePath
# Instructs node to ignore past temporal state when operating in
# inference mode; i.e., tells node to ignore the actual resetSignal
# input and treat it as if the resetSignal was *always* set (in
# inference mode only)
self.statelessMode = statelessMode
self._hasRunInference = False
# Variables set up in initInNetwork()
self._sfdr = None # FDRCSpatial instance
self._tfdr = None # FDRTemporal instance
self._numOutputs = None # Number of outputs allocated per node
self._spatialPoolerOutput = None # Hang on to this output for debugging
self._tpSeqOutput = None # Hang on to this for supporting the
# tpSeqOutputNonZeros parameter
self._spatialPoolerInput = None # Hang on to this for supporting the
# spInputNonZeros parameter
self._rfOutput = None # Hang on to this for supporting the
# tpOutputNonZeros parameter
# Read-only node parameters
self.activeOutputCount = None
self.cppOutput = None
self.file = None
# For inspector usage
#from dbgp.client import brk; brk(port=9019)
self._spatialSpec, self._temporalSpec, self._otherSpec = \
_getAdditionalSpecs(spatialImp=self.spatialImp, temporalImp=self.temporalImp)
