class ColumnPooler(object):
"""
This class constitutes a temporary implementation for a cross-column pooler.
The implementation goal of this class is to prove basic properties before
creating a cleaner implementation.
"""
def __init__(self,
inputWidth,
lateralInputWidths=(),
cellCount=4096,
sdrSize=40,
onlineLearning = False,
maxSdrSize = None,
minSdrSize = None,
# Proximal
synPermProximalInc=0.1,
synPermProximalDec=0.001,
initialProximalPermanence=0.6,
sampleSizeProximal=20,
minThresholdProximal=10,
connectedPermanenceProximal=0.50,
predictedInhibitionThreshold=20,
# Distal
synPermDistalInc=0.1,
synPermDistalDec=0.001,
initialDistalPermanence=0.6,
sampleSizeDistal=20,
activationThresholdDistal=13,
connectedPermanenceDistal=0.50,
distalSegmentInhibitionFactor=0.999,
inertiaFactor=1.,
seed=42):
"""
Parameters:
----------------------------
@param inputWidth (int)
The number of bits in the feedforward input
@param lateralInputWidths (list of ints)
The number of bits in each lateral input
@param sdrSize (int)
The number of active cells in an object SDR
@param onlineLearning (Bool)
Whether or not the column pooler should learn in online mode.
@param maxSdrSize (int)
The maximum SDR size for learning. If the column pooler has more
than this many cells active, it will refuse to learn. This serves
to stop the pooler from learning when it is uncertain of what object
it is sensing.
@param minSdrSize (int)
The minimum SDR size for learning. If the column pooler has fewer
than this many active cells, it will create a new representation
and learn that instead. This serves to create separate
representations for different objects and sequences.
If online learning is enabled, this parameter should be at least
inertiaFactor*sdrSize. Otherwise, two different objects may be
incorrectly inferred to be the same, as SDRs may still be active
enough to learn even after inertial decay.
@param synPermProximalInc (float)
Permanence increment for proximal synapses
@param synPermProximalDec (float)
Permanence decrement for proximal synapses
@param initialProximalPermanence (float)
Initial permanence value for proximal synapses
@param sampleSizeProximal (int)
Number of proximal synapses a cell should grow to each feedforward
pattern, or -1 to connect to every active bit
@param minThresholdProximal (int)
Number of active synapses required for a cell to have feedforward
support
@param connectedPermanenceProximal (float)
Permanence required for a proximal synapse to be connected
@param predictedInhibitionThreshold (int)
How much predicted input must be present for inhibitory behavior
to be triggered. Only has effects if onlineLearning is true.
@param synPermDistalInc (float)
Permanence increment for distal synapses
@param synPermDistalDec (float)
Permanence decrement for distal synapses
@param sampleSizeDistal (int)
Number of distal synapses a cell should grow to each lateral
pattern, or -1 to connect to every active bit
@param initialDistalPermanence (float)
Initial permanence value for distal synapses
@param activationThresholdDistal (int)
Number of active synapses required to activate a distal segment
@param connectedPermanenceDistal (float)
Permanence required for a distal synapse to be connected
@param distalSegmentInhibitionFactor (float)
The proportion of the highest number of active lateral segments
necessary for a cell not to be inhibited (must be <1).
@param inertiaFactor (float)
The proportion of previously active cells that remain
active in the next timestep due to inertia (in the absence of
inhibition). If onlineLearning is enabled, should be at most
1 - learningTolerance, or representations may incorrectly become
mixed.
@param seed (int)
Random number generator seed
"""
assert distalSegmentInhibitionFactor > 0.0
assert distalSegmentInhibitionFactor < 1.0
assert maxSdrSize is None or maxSdrSize >= sdrSize
assert minSdrSize is None or minSdrSize <= sdrSize
self.inputWidth = inputWidth
self.cellCount = cellCount
self.sdrSize = sdrSize
self.onlineLearning = onlineLearning
if maxSdrSize is None:
self.maxSdrSize = sdrSize
else:
self.maxSdrSize = maxSdrSize
if minSdrSize is None:
self.minSdrSize = sdrSize
else:
self.minSdrSize = minSdrSize
self.synPermProximalInc = synPermProximalInc
self.synPermProximalDec = synPermProximalDec
self.initialProximalPermanence = initialProximalPermanence
self.connectedPermanenceProximal = connectedPermanenceProximal
self.sampleSizeProximal = sampleSizeProximal
self.minThresholdProximal = minThresholdProximal
self.predictedInhibitionThreshold = predictedInhibitionThreshold
self.synPermDistalInc = synPermDistalInc
self.synPermDistalDec = synPermDistalDec
self.initialDistalPermanence = initialDistalPermanence
self.connectedPermanenceDistal = connectedPermanenceDistal
self.sampleSizeDistal = sampleSizeDistal
self.activationThresholdDistal = activationThresholdDistal
self.distalSegmentInhibitionFactor = distalSegmentInhibitionFactor
self.inertiaFactor = inertiaFactor
self.activeCells = numpy.empty(0, dtype="uint32")
self._random = Random(seed)
# These sparse matrices will hold the synapses for each segment.
# Each row represents one segment on a cell, so each cell potentially has
# 1 proximal segment and 1+len(lateralInputWidths) distal segments.
self.proximalPermanences = SparseMatrix(cellCount, inputWidth)
self.internalDistalPermanences = SparseMatrix(cellCount, cellCount)
self.distalPermanences = tuple(SparseMatrix(cellCount, n)
for n in lateralInputWidths)
self.useInertia=True
def compute(self, feedforwardInput=(), lateralInputs=(),
feedforwardGrowthCandidates=None, learn=True,
predictedInput = None,):
"""
Runs one time step of the column pooler algorithm.
@param feedforwardInput (sequence)
Sorted indices of active feedforward input bits
@param lateralInputs (list of sequences)
For each lateral layer, a list of sorted indices of active lateral
input bits
@param feedforwardGrowthCandidates (sequence or None)
Sorted indices of feedforward input bits that active cells may grow
new synapses to. If None, the entire feedforwardInput is used.
@param learn (bool)
If True, we are learning a new object
@param predictedInput (sequence)
Sorted indices of predicted cells in the TM layer.
"""
if feedforwardGrowthCandidates is None:
feedforwardGrowthCandidates = feedforwardInput
if not learn:
self._computeInferenceMode(feedforwardInput, lateralInputs)
elif not self.onlineLearning:
self._computeLearningMode(feedforwardInput, lateralInputs,
feedforwardGrowthCandidates)
else:
if (predictedInput is not None and
len(predictedInput) > self.predictedInhibitionThreshold):
predictedActiveInput = numpy.intersect1d(feedforwardInput,
predictedInput)
predictedGrowthCandidates = numpy.intersect1d(
feedforwardGrowthCandidates, predictedInput)
self._computeInferenceMode(predictedActiveInput, lateralInputs)
self._computeLearningMode(predictedActiveInput, lateralInputs,
feedforwardGrowthCandidates)
elif not self.minSdrSize <= len(self.activeCells) <= self.maxSdrSize:
# If the pooler doesn't have a single representation, try to infer one,
# before actually attempting to learn.
self._computeInferenceMode(feedforwardInput, lateralInputs)
self._computeLearningMode(feedforwardInput, lateralInputs,
feedforwardGrowthCandidates)
else:
# If there isn't predicted input and we have a single SDR,
# we are extending that representation and should just learn.
self._computeLearningMode(feedforwardInput, lateralInputs,
feedforwardGrowthCandidates)
def _computeLearningMode(self, feedforwardInput, lateralInputs,
feedforwardGrowthCandidates):
"""
Learning mode: we are learning a new object in an online fashion. If there
is no prior activity, we randomly activate 'sdrSize' cells and create
connections to incoming input. If there was prior activity, we maintain it.
If we have a union, we simply do not learn at all.
These cells will represent the object and learn distal connections to each
other and to lateral cortical columns.
Parameters:
----------------------------
@param feedforwardInput (sequence)
Sorted indices of active feedforward input bits
@param lateralInputs (list of sequences)
For each lateral layer, a list of sorted indices of active lateral
input bits
@param feedforwardGrowthCandidates (sequence or None)
Sorted indices of feedforward input bits that the active cells may
grow new synapses to. This is assumed to be the predicted active
cells of the input layer.
"""
prevActiveCells = self.activeCells
# If there are not enough previously active cells, then we are no longer on
# a familiar object. Either our representation decayed due to the passage
# of time (i.e. we moved somewhere else) or we were mistaken. Either way,
# create a new SDR and learn on it.
# This case is the only way different object representations are created.
if len(self.activeCells) < self.minSdrSize:
self.activeCells = _sampleRange(self._random,
0, self.numberOfCells(),
step=1, k=self.sdrSize)
self.activeCells.sort()
# If we have a union of cells active, don't learn. This primarily affects
# online learning.
if len(self.activeCells) > self.maxSdrSize:
return
# Finally, now that we have decided which cells we should be learning on, do
# the actual learning.
if len(feedforwardInput) > 0:
self._learn(self.proximalPermanences, self._random,
self.activeCells, feedforwardInput,
feedforwardGrowthCandidates, self.sampleSizeProximal,
self.initialProximalPermanence, self.synPermProximalInc,
self.synPermProximalDec, self.connectedPermanenceProximal)
# External distal learning
for i, lateralInput in enumerate(lateralInputs):
self._learn(self.distalPermanences[i], self._random,
self.activeCells, lateralInput, lateralInput,
self.sampleSizeDistal, self.initialDistalPermanence,
self.synPermDistalInc, self.synPermDistalDec,
self.connectedPermanenceDistal)
# Internal distal learning
self._learn(self.internalDistalPermanences, self._random,
self.activeCells, prevActiveCells, prevActiveCells,
self.sampleSizeDistal, self.initialDistalPermanence,
self.synPermDistalInc, self.synPermDistalDec,
self.connectedPermanenceDistal)
def _computeInferenceMode(self, feedforwardInput, lateralInputs):
"""
Inference mode: if there is some feedforward activity, perform
spatial pooling on it to recognize previously known objects, then use
lateral activity to activate a subset of the cells with feedforward
support. If there is no feedforward activity, use lateral activity to
activate a subset of the previous active cells.
Parameters:
----------------------------
@param feedforwardInput (sequence)
Sorted indices of active feedforward input bits
@param lateralInputs (list of sequences)
For each lateral layer, a list of sorted indices of active lateral
input bits
"""
prevActiveCells = self.activeCells
# Calculate the feedforward supported cells
overlaps = self.proximalPermanences.rightVecSumAtNZGteThresholdSparse(
feedforwardInput, self.connectedPermanenceProximal)
feedforwardSupportedCells = numpy.where(
overlaps >= self.minThresholdProximal)[0]
# Calculate the number of active segments on each cell
numActiveSegmentsByCell = numpy.zeros(self.cellCount, dtype="int")
overlaps = self.internalDistalPermanences.rightVecSumAtNZGteThresholdSparse(
prevActiveCells, self.connectedPermanenceDistal)
numActiveSegmentsByCell[overlaps >= self.activationThresholdDistal] += 1
for i, lateralInput in enumerate(lateralInputs):
overlaps = self.distalPermanences[i].rightVecSumAtNZGteThresholdSparse(
lateralInput, self.connectedPermanenceDistal)
numActiveSegmentsByCell[overlaps >= self.activationThresholdDistal] += 1
chosenCells = []
minNumActiveCells = int(self.sdrSize * 0.75)
numActiveSegsForFFSuppCells = numActiveSegmentsByCell[
feedforwardSupportedCells]
# First, activate the FF-supported cells that have the highest number of
# lateral active segments (as long as it's not 0)
if len(feedforwardSupportedCells) == 0:
pass
else:
# This loop will select the FF-supported AND laterally-active cells, in
# order of descending lateral activation, until we exceed the
# minNumActiveCells quorum - but will exclude cells with 0 lateral
# active segments.
ttop = numpy.max(numActiveSegsForFFSuppCells)
while ttop > 0 and len(chosenCells) <= minNumActiveCells:
chosenCells = numpy.union1d(chosenCells,
feedforwardSupportedCells[numActiveSegsForFFSuppCells >
self.distalSegmentInhibitionFactor * ttop])
ttop -= 1
# If we still haven't filled the minNumActiveCells quorum, add in the
# FF-supported cells with 0 lateral support AND the inertia cells.
if len(chosenCells) < minNumActiveCells:
if self.useInertia:
prevCells = numpy.setdiff1d(prevActiveCells, chosenCells)
inertialCap = int(len(prevCells) * self.inertiaFactor)
if inertialCap > 0:
numActiveSegsForPrevCells = numActiveSegmentsByCell[prevCells]
# We sort the previously-active cells by number of active lateral
# segments (this really helps). We then activate them in order of
# descending lateral activation.
sortIndices = numpy.argsort(numActiveSegsForPrevCells)[::-1]
prevCells = prevCells[sortIndices]
numActiveSegsForPrevCells = numActiveSegsForPrevCells[sortIndices]
# We use inertiaFactor to limit the number of previously-active cells
# which can become active, forcing decay even if we are below quota.
prevCells = prevCells[:inertialCap]
numActiveSegsForPrevCells = numActiveSegsForPrevCells[:inertialCap]
# Activate groups of previously active cells by order of their lateral
# support until we either meet quota or run out of cells.
ttop = numpy.max(numActiveSegsForPrevCells)
while ttop >= 0 and len(chosenCells) <= minNumActiveCells:
chosenCells = numpy.union1d(chosenCells,
prevCells[numActiveSegsForPrevCells >
self.distalSegmentInhibitionFactor * ttop])
ttop -= 1
# Finally, add remaining cells with feedforward support
remFFcells = numpy.setdiff1d(feedforwardSupportedCells, chosenCells)
# Note that this is 100% of the remaining FF-supported cells, there is no
# attempt to select only certain ones or limit how many come active.
chosenCells = numpy.append(chosenCells, remFFcells)
chosenCells.sort()
self.activeCells = numpy.asarray(chosenCells, dtype="uint32")
def numberOfInputs(self):
"""
Returns the number of inputs into this layer
"""
return self.inputWidth
def numberOfCells(self):
"""
Returns the number of cells in this layer.
@return (int) Number of cells
"""
return self.cellCount
def getActiveCells(self):
"""
Returns the indices of the active cells.
@return (list) Indices of active cells.
"""
return self.activeCells
def numberOfConnectedProximalSynapses(self, cells=None):
"""
Returns the number of proximal connected synapses on these cells.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells. If None return count for all cells.
"""
if cells is None:
cells = xrange(self.numberOfCells())
return _countWhereGreaterEqualInRows(self.proximalPermanences, cells,
self.connectedPermanenceProximal)
def numberOfProximalSynapses(self, cells=None):
"""
Returns the number of proximal synapses with permanence>0 on these cells.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells. If None return count for all cells.
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = 0
for cell in cells:
n += self.proximalPermanences.nNonZerosOnRow(cell)
return n
def numberOfDistalSegments(self, cells=None):
"""
Returns the total number of distal segments for these cells.
A segment "exists" if its row in the matrix has any permanence values > 0.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = 0
for cell in cells:
if self.internalDistalPermanences.nNonZerosOnRow(cell) > 0:
n += 1
for permanences in self.distalPermanences:
if permanences.nNonZerosOnRow(cell) > 0:
n += 1
return n
def numberOfConnectedDistalSynapses(self, cells=None):
"""
Returns the number of connected distal synapses on these cells.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells. If None return count for all cells.
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = _countWhereGreaterEqualInRows(self.internalDistalPermanences, cells,
self.connectedPermanenceDistal)
for permanences in self.distalPermanences:
n += _countWhereGreaterEqualInRows(permanences, cells,
self.connectedPermanenceDistal)
return n
def numberOfDistalSynapses(self, cells=None):
"""
Returns the total number of distal synapses for these cells.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = 0
for cell in cells:
n += self.internalDistalPermanences.nNonZerosOnRow(cell)
for permanences in self.distalPermanences:
n += permanences.nNonZerosOnRow(cell)
return n
def reset(self):
"""
Reset internal states. When learning this signifies we are to learn a
unique new object.
"""
self.activeCells = numpy.empty(0, dtype="uint32")
def getUseInertia(self):
"""
Get whether we actually use inertia (i.e. a fraction of the
previously active cells remain active at the next time step unless
inhibited by cells with both feedforward and lateral support).
@return (Bool) Whether inertia is used.
"""
return self.useInertia
def setUseInertia(self, useInertia):
"""
Sets whether we actually use inertia (i.e. a fraction of the
previously active cells remain active at the next time step unless
inhibited by cells with both feedforward and lateral support).
@param useInertia (Bool) Whether inertia is used.
"""
self.useInertia = useInertia
@staticmethod
def _learn(# mutated args
permanences, rng,
# activity
activeCells, activeInput, growthCandidateInput,
# configuration
sampleSize, initialPermanence, permanenceIncrement,
permanenceDecrement, connectedPermanence):
"""
For each active cell, reinforce active synapses, punish inactive synapses,
and grow new synapses to a subset of the active input bits that the cell
isn't already connected to.
Parameters:
----------------------------
@param permanences (SparseMatrix)
Matrix of permanences, with cells as rows and inputs as columns
@param rng (Random)
Random number generator
@param activeCells (sorted sequence)
Sorted list of the cells that are learning
@param activeInput (sorted sequence)
Sorted list of active bits in the input
@param growthCandidateInput (sorted sequence)
Sorted list of active bits in the input that the activeCells may
grow new synapses to
For remaining parameters, see the __init__ docstring.
"""
permanences.incrementNonZerosOnOuter(
activeCells, activeInput, permanenceIncrement)
permanences.incrementNonZerosOnRowsExcludingCols(
activeCells, activeInput, -permanenceDecrement)
permanences.clipRowsBelowAndAbove(
activeCells, 0.0, 1.0)
if sampleSize == -1:
permanences.setZerosOnOuter(
activeCells, activeInput, initialPermanence)
else:
existingSynapseCounts = permanences.nNonZerosPerRowOnCols(
activeCells, activeInput)
maxNewByCell = numpy.empty(len(activeCells), dtype="int32")
numpy.subtract(sampleSize, existingSynapseCounts, out=maxNewByCell)
permanences.setRandomZerosOnOuter(
activeCells, growthCandidateInput, maxNewByCell, initialPermanence, rng)
class ColumnPooler(object):
"""
This class constitutes a temporary implementation for a cross-column pooler.
The implementation goal of this class is to prove basic properties before
creating a cleaner implementation.
"""
def __init__(self,
inputWidth,
lateralInputWidths=(),
cellCount=4096,
sdrSize=40,
# Proximal
synPermProximalInc=0.1,
synPermProximalDec=0.001,
initialProximalPermanence=0.6,
sampleSizeProximal=20,
minThresholdProximal=10,
connectedPermanenceProximal=0.50,
# Distal
synPermDistalInc=0.1,
synPermDistalDec=0.001,
initialDistalPermanence=0.6,
sampleSizeDistal=20,
activationThresholdDistal=13,
connectedPermanenceDistal=0.50,
distalSegmentInhibitionFactor=1.5,
seed=42):
"""
Parameters:
----------------------------
@param inputWidth (int)
The number of bits in the feedforward input
@param lateralInputWidths (list of ints)
The number of bits in each lateral input
@param sdrSize (int)
The number of active cells in an object SDR
@param synPermProximalInc (float)
Permanence increment for proximal synapses
@param synPermProximalDec (float)
Permanence decrement for proximal synapses
@param initialProximalPermanence (float)
Initial permanence value for proximal synapses
@param sampleSizeProximal (int)
Number of proximal synapses a cell should grow to each feedforward
pattern, or -1 to connect to every active bit
@param minThresholdProximal (int)
Number of active synapses required for a cell to have feedforward
support
@param connectedPermanenceProximal (float)
Permanence required for a proximal synapse to be connected
@param synPermDistalInc (float)
Permanence increment for distal synapses
@param synPermDistalDec (float)
Permanence decrement for distal synapses
@param sampleSizeDistal (int)
Number of distal synapses a cell should grow to each lateral
pattern, or -1 to connect to every active bit
@param initialDistalPermanence (float)
Initial permanence value for distal synapses
@param activationThresholdDistal (int)
Number of active synapses required to activate a distal segment
@param connectedPermanenceDistal (float)
Permanence required for a distal synapse to be connected
@param distalSegmentInhibitionFactor (float)
The minimum ratio of active dendrite segment counts that will lead
to inhibition. For example, with value 1.5, cells with 2 active
segments will be inhibited by cells with 3 active segments, but
cells with 3 active segments will not be inhibited by cells with 4.
@param seed (int)
Random number generator seed
"""
self.inputWidth = inputWidth
self.cellCount = cellCount
self.sdrSize = sdrSize
self.synPermProximalInc = synPermProximalInc
self.synPermProximalDec = synPermProximalDec
self.initialProximalPermanence = initialProximalPermanence
self.connectedPermanenceProximal = connectedPermanenceProximal
self.sampleSizeProximal = sampleSizeProximal
self.minThresholdProximal = minThresholdProximal
self.synPermDistalInc = synPermDistalInc
self.synPermDistalDec = synPermDistalDec
self.initialDistalPermanence = initialDistalPermanence
self.connectedPermanenceDistal = connectedPermanenceDistal
self.sampleSizeDistal = sampleSizeDistal
self.activationThresholdDistal = activationThresholdDistal
self.distalSegmentInhibitionFactor = distalSegmentInhibitionFactor
self.activeCells = ()
self._random = Random(seed)
# These sparse matrices will hold the synapses for each segment.
# Each row represents one segment on a cell, so each cell potentially has
# 1 proximal segment and 1+len(lateralInputWidths) distal segments.
self.proximalPermanences = SparseMatrix(cellCount, inputWidth)
self.internalDistalPermanences = SparseMatrix(cellCount, cellCount)
self.distalPermanences = tuple(SparseMatrix(cellCount, n)
for n in lateralInputWidths)
def compute(self, feedforwardInput=(), lateralInputs=(), learn=True):
"""
Runs one time step of the column pooler algorithm.
@param feedforwardInput (iterable)
Indices of active feedforward input bits
@param lateralInputs (list of iterables)
Sets of indices of active lateral input bits, one per lateral layer
@param learn (bool)
If True, we are learning a new object
"""
if learn:
self._computeLearningMode(feedforwardInput, lateralInputs)
else:
self._computeInferenceMode(feedforwardInput, lateralInputs)
def _computeLearningMode(self, feedforwardInput, lateralInputs):
"""
Learning mode: we are learning a new object. If there is no prior
activity, we randomly activate 'sdrSize' cells and create connections to
incoming input. If there was prior activity, we maintain it.
These cells will represent the object and learn distal connections to each
other and to lateral cortical columns.
Parameters:
----------------------------
@param feedforwardInput (iterable)
List of indices of active feedforward input bits
@param lateralInputs (list of iterables)
Lists of indices of active lateral input bits, one per lateral layer
"""
prevActiveCells = self.activeCells
# If there are no previously active cells, select random subset of cells.
# Else we maintain previous activity.
if len(self.activeCells) == 0:
self.activeCells = sorted(_sampleRange(self._random,
0, self.numberOfCells(),
step=1, k=self.sdrSize))
if len(feedforwardInput) > 0:
# Proximal learning
self._learn(self.proximalPermanences, self._random,
self.activeCells, sorted(feedforwardInput),
self.sampleSizeProximal, self.initialProximalPermanence,
self.synPermProximalInc, self.synPermProximalDec,
self.connectedPermanenceProximal)
# Internal distal learning
if len(prevActiveCells) > 0:
self._learn(self.internalDistalPermanences, self._random,
self.activeCells, prevActiveCells,
self.sampleSizeDistal, self.initialDistalPermanence,
self.synPermDistalInc, self.synPermDistalDec,
self.connectedPermanenceDistal)
# External distal learning
for i, lateralInput in enumerate(lateralInputs):
self._learn(self.distalPermanences[i], self._random,
self.activeCells, sorted(lateralInput),
self.sampleSizeDistal, self.initialDistalPermanence,
self.synPermDistalInc, self.synPermDistalDec,
self.connectedPermanenceDistal)
def _computeInferenceMode(self, feedforwardInput, lateralInputs):
"""
Inference mode: if there is some feedforward activity, perform
spatial pooling on it to recognize previously known objects, then use
lateral activity to activate a subset of the cells with feedforward
support. If there is no feedforward activity, use lateral activity to
activate a subset of the previous active cells.
Parameters:
----------------------------
@param feedforwardInput (iterable)
Indices of active feedforward input bits
@param lateralInputs (list of iterables)
Sets of indices of active lateral input bits, one per lateral layer
"""
prevActiveCells = self.activeCells
# Calculate the feedforward supported cells
overlaps = self.proximalPermanences.rightVecSumAtNZGteThresholdSparse(
list(feedforwardInput), self.connectedPermanenceProximal)
feedforwardSupportedCells = set(
numpy.where(overlaps >= self.minThresholdProximal)[0])
# Calculate the number of active segments on each cell
numActiveSegmentsByCell = numpy.zeros(self.cellCount, dtype="int")
overlaps = self.internalDistalPermanences.rightVecSumAtNZGteThresholdSparse(
list(prevActiveCells), self.connectedPermanenceDistal)
numActiveSegmentsByCell[overlaps >= self.activationThresholdDistal] += 1
for i, lateralInput in enumerate(lateralInputs):
overlaps = self.distalPermanences[i].rightVecSumAtNZGteThresholdSparse(
list(lateralInput), self.connectedPermanenceDistal)
numActiveSegmentsByCell[overlaps >= self.activationThresholdDistal] += 1
# Activate some of the feedforward supported cells
minNumActiveCells = self.sdrSize / 2
chosenCells = self._chooseCells(feedforwardSupportedCells,
minNumActiveCells, numActiveSegmentsByCell)
# If necessary, activate some of the previously active cells
if len(chosenCells) < minNumActiveCells:
remainingCandidates = [cell for cell in prevActiveCells
if cell not in feedforwardSupportedCells
if numActiveSegmentsByCell[cell] > 0]
chosenCells.extend(self._chooseCells(remainingCandidates,
minNumActiveCells - len(chosenCells),
numActiveSegmentsByCell))
self.activeCells = sorted(chosenCells)
def _chooseCells(self, candidates, n, numActiveSegmentsByCell):
"""
Choose cells to activate, using their active segment counts to determine
inhibition.
Count backwards through the active segment counts. For each count, find all
of the cells that this count is unable to inhibit. Activate these cells.
If there aren't at least n active cells, repeat with the next lowest
segment count.
Parameters:
----------------------------
@param candidates (iterable)
List of cells to consider activating
@param n (int)
Minimum number of cells to activate, if possible
@param numActiveSegmentsByCell (associative)
A mapping from cells to number of active segments.
This can be any data structure that associates an index with a
value. (list, dict, numpy array)
@return (list) Cells to activate
"""
orderedCandidates = sorted(candidates,
key=numActiveSegmentsByCell.__getitem__,
reverse=True)
activeSegmentCounts = sorted(set(numActiveSegmentsByCell[cell]
for cell in candidates),
reverse=True)
chosenCells = []
i = 0
for activeSegmentCount in activeSegmentCounts:
if len(chosenCells) >= n or i >= len(orderedCandidates):
break
if activeSegmentCount == 0:
chosenCells.extend(orderedCandidates[i:])
break
# If one cell has 'distalSegmentInhibitionFactor' * the number of active
# segments of another cell, the latter cell is inhibited.
boundary = float(activeSegmentCount) / self.distalSegmentInhibitionFactor
while (i < len(orderedCandidates) and
numActiveSegmentsByCell[orderedCandidates[i]] > boundary):
chosenCells.append(orderedCandidates[i])
i += 1
return chosenCells
def numberOfInputs(self):
"""
Returns the number of inputs into this layer
"""
return self.inputWidth
def numberOfCells(self):
"""
Returns the number of cells in this layer.
@return (int) Number of cells
"""
return self.cellCount
def getActiveCells(self):
"""
Returns the indices of the active cells.
@return (list) Indices of active cells.
"""
return self.activeCells
def numberOfConnectedProximalSynapses(self, cells=None):
"""
Returns the number of proximal connected synapses on these cells.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells. If None return count for all cells.
"""
if cells is None:
cells = xrange(self.numberOfCells())
return _countWhereGreaterEqualInRows(self.proximalPermanences, cells,
self.connectedPermanenceProximal)
def numberOfProximalSynapses(self, cells=None):
"""
Returns the number of proximal synapses with permanence>0 on these cells.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells. If None return count for all cells.
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = 0
for cell in cells:
n += self.proximalPermanences.nNonZerosOnRow(cell)
return n
def numberOfDistalSegments(self, cells=None):
"""
Returns the total number of distal segments for these cells.
A segment "exists" if its row in the matrix has any permanence values > 0.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = 0
for cell in cells:
if self.internalDistalPermanences.nNonZerosOnRow(cell) > 0:
n += 1
for permanences in self.distalPermanences:
if permanences.nNonZerosOnRow(cell) > 0:
n += 1
return n
def numberOfConnectedDistalSynapses(self, cells=None):
"""
Returns the number of connected distal synapses on these cells.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells. If None return count for all cells.
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = _countWhereGreaterEqualInRows(self.internalDistalPermanences, cells,
self.connectedPermanenceDistal)
for permanences in self.distalPermanences:
n += _countWhereGreaterEqualInRows(permanences, cells,
self.connectedPermanenceDistal)
return n
def numberOfDistalSynapses(self, cells=None):
"""
Returns the total number of distal synapses for these cells.
Parameters:
----------------------------
@param cells (iterable)
Indices of the cells
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = 0
for cell in cells:
n += self.internalDistalPermanences.nNonZerosOnRow(cell)
for permanences in self.distalPermanences:
n += permanences.nNonZerosOnRow(cell)
return n
def reset(self):
"""
Reset internal states. When learning this signifies we are to learn a
unique new object.
"""
self.activeCells = ()
@staticmethod
def _learn(# mutated args
permanences, rng,
# activity
activeCells, activeInput,
# configuration
sampleSize, initialPermanence, permanenceIncrement,
permanenceDecrement, connectedPermanence):
"""
For each active cell, reinforce active synapses, punish inactive synapses,
and grow new synapses to a subset of the active input bits that the cell
isn't already connected to.
Parameters:
----------------------------
@param permanences (SparseMatrix)
Matrix of permanences, with cells as rows and inputs as columns
@param rng (Random)
Random number generator
@param activeCells (sorted sequence)
Sorted list of the cells that are learning
@param activeInput (sorted sequence)
Sorted list of active bits in the input
For remaining parameters, see the __init__ docstring.
"""
permanences.incrementNonZerosOnOuter(
activeCells, activeInput, permanenceIncrement)
permanences.incrementNonZerosOnRowsExcludingCols(
activeCells, activeInput, -permanenceDecrement)
permanences.clipRowsBelowAndAbove(
activeCells, 0.0, 1.0)
if sampleSize == -1:
permanences.setZerosOnOuter(
activeCells, activeInput, initialPermanence)
else:
permanences.increaseRowNonZeroCountsOnOuterTo(
activeCells, activeInput, sampleSize, initialPermanence, rng)
