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,
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,
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 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 == '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 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, 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 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 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 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 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 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 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)
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)
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)
