def _getCellsWithFewestSegments(cls, connections, rng, columns,
cellsPerColumn):
"""
For each column, get the cell that has the fewest total basal segments.
Break ties randomly.
@param connections (SparseMatrixConnections)
@param rng (Random)
@param columns (numpy array) Columns to check
@return (numpy array)
One cell for each of the provided columns
"""
candidateCells = np2.getAllCellsInColumns(columns, cellsPerColumn)
# Arrange the segment counts into one row per minicolumn.
segmentCounts = np.reshape(connections.getSegmentCounts(candidateCells),
newshape=(len(columns),
cellsPerColumn))
# Filter to just the cells that are tied for fewest in their minicolumn.
minSegmentCounts = np.amin(segmentCounts, axis=1, keepdims=True)
candidateCells = candidateCells[np.flatnonzero(segmentCounts ==
minSegmentCounts)]
# Filter to one cell per column, choosing randomly from the minimums.
# To do the random choice, add a random offset to each index in-place, using
# casting to floor the result.
(_,
onePerColumnFilter,
numCandidatesInColumns) = np.unique(candidateCells / cellsPerColumn,
return_index=True, return_counts=True)
offsetPercents = np.empty(len(columns), dtype="float32")
rng.initializeReal32Array(offsetPercents)
np.add(onePerColumnFilter,
offsetPercents*numCandidatesInColumns,
out=onePerColumnFilter,
casting="unsafe")
return candidateCells[onePerColumnFilter]
def _getCellsWithFewestSegments(self, columns):
"""
For each column, get the cell that has the fewest total segments (basal or
apical). Break ties randomly.
@param columns (numpy array)
Columns to check
@return (numpy array)
One cell for each of the provided columns
"""
candidateCells = np2.getAllCellsInColumns(columns, self.cellsPerColumn)
# Arrange the segment counts into one row per minicolumn.
segmentCounts = np.reshape(
self.basalConnections.getSegmentCounts(candidateCells) +
self.apicalConnections.getSegmentCounts(candidateCells),
newshape=(len(columns),
self.cellsPerColumn))
# Filter to just the cells that are tied for fewest in their minicolumn.
minSegmentCounts = np.amin(segmentCounts, axis=1, keepdims=True)
candidateCells = candidateCells[np.flatnonzero(segmentCounts ==
minSegmentCounts)]
# Filter to one cell per column, choosing randomly from the minimums.
# To do the random choice, add a random offset to each index in-place, using
# casting to floor the result.
(_,
onePerColumnFilter,
numCandidatesInColumns) = np.unique(candidateCells / self.cellsPerColumn,
return_index=True, return_counts=True)
offsetPercents = np.empty(len(columns), dtype="float32")
self.rng.initializeReal32Array(offsetPercents)
np.add(onePerColumnFilter,
offsetPercents*numCandidatesInColumns,
out=onePerColumnFilter,
casting="unsafe")
return candidateCells[onePerColumnFilter]
def compute(self,
activeColumns,
basalInput,
basalGrowthCandidates,
apicalInput=EMPTY_UINT_ARRAY,
apicalGrowthCandidates=EMPTY_UINT_ARRAY,
learn=True):
"""
@param activeColumns (numpy array)
@param basalInput (numpy array)
@param basalGrowthCandidates (numpy array)
@param apicalInput (numpy array)
@param apicalGrowthCandidates (numpy array)
@param learn (bool)
"""
# Calculate predictions for this timestep
(activeBasalSegments,
matchingBasalSegments,
basalPotentialOverlaps) = self._calculateSegmentActivity(
self.basalConnections, basalInput, self.connectedPermanence,
self.activationThreshold, self.minThreshold)
(activeApicalSegments,
matchingApicalSegments,
apicalPotentialOverlaps) = self._calculateSegmentActivity(
self.apicalConnections, apicalInput, self.connectedPermanence,
self.activationThreshold, self.minThreshold)
predictedCells = self._calculatePredictedCells(activeBasalSegments,
activeApicalSegments)
# Calculate active cells
(correctPredictedCells,
burstingColumns) = np2.setCompare(predictedCells, activeColumns,
predictedCells / self.cellsPerColumn,
rightMinusLeft=True)
newActiveCells = np.concatenate((correctPredictedCells,
np2.getAllCellsInColumns(
burstingColumns, self.cellsPerColumn)))
# Calculate learning
(learningActiveBasalSegments,
learningMatchingBasalSegments,
basalSegmentsToPunish,
newBasalSegmentCells,
learningCells) = self._calculateBasalLearning(
activeColumns, burstingColumns, correctPredictedCells,
activeBasalSegments, matchingBasalSegments, basalPotentialOverlaps)
(learningActiveApicalSegments,
learningMatchingApicalSegments,
apicalSegmentsToPunish,
newApicalSegmentCells) = self._calculateApicalLearning(
learningCells, activeColumns, activeApicalSegments,
matchingApicalSegments, apicalPotentialOverlaps)
# Learn
if learn:
# Learn on existing segments
for learningSegments in (learningActiveBasalSegments,
learningMatchingBasalSegments):
self._learn(self.basalConnections, self.rng, learningSegments,
basalInput, basalGrowthCandidates, basalPotentialOverlaps,
self.initialPermanence, self.sampleSize,
self.permanenceIncrement, self.permanenceDecrement,
self.maxSynapsesPerSegment)
for learningSegments in (learningActiveApicalSegments,
learningMatchingApicalSegments):
self._learn(self.apicalConnections, self.rng, learningSegments,
apicalInput, apicalGrowthCandidates,
apicalPotentialOverlaps, self.initialPermanence,
self.sampleSize, self.permanenceIncrement,
self.permanenceDecrement, self.maxSynapsesPerSegment)
# Punish incorrect predictions
if self.predictedSegmentDecrement != 0.0:
self.basalConnections.adjustActiveSynapses(
basalSegmentsToPunish, basalInput, -self.predictedSegmentDecrement)
self.apicalConnections.adjustActiveSynapses(
apicalSegmentsToPunish, apicalInput, -self.predictedSegmentDecrement)
# Grow new segments
if len(basalGrowthCandidates) > 0:
self._learnOnNewSegments(self.basalConnections, self.rng,
newBasalSegmentCells, basalGrowthCandidates,
self.initialPermanence, self.sampleSize,
self.maxSynapsesPerSegment)
if len(apicalGrowthCandidates) > 0:
self._learnOnNewSegments(self.apicalConnections, self.rng,
newApicalSegmentCells, apicalGrowthCandidates,
self.initialPermanence, self.sampleSize,
self.maxSynapsesPerSegment)
# Save the results
self.activeCells = newActiveCells
self.winnerCells = learningCells
self.prevPredictedCells = predictedCells
def activateCells(self,
activeColumns,
basalReinforceCandidates,
apicalReinforceCandidates,
basalGrowthCandidates,
apicalGrowthCandidates,
learn=True):
"""
Activate cells in the specified columns, using the result of the previous
'depolarizeCells' as predictions. Then learn.
@param activeColumns (numpy array)
List of active columns
@param basalReinforceCandidates (numpy array)
List of bits that the active cells may reinforce basal synapses to.
@param apicalReinforceCandidates (numpy array)
List of bits that the active cells may reinforce apical synapses to.
@param basalGrowthCandidates (numpy array or None)
List of bits that the active cells may grow new basal synapses to.
@param apicalGrowthCandidates (numpy array or None)
List of bits that the active cells may grow new apical synapses to
@param learn (bool)
Whether to grow / reinforce / punish synapses
"""
# Calculate active cells
(correctPredictedCells,
burstingColumns) = np2.setCompare(self.predictedCells, activeColumns,
self.predictedCells / self.cellsPerColumn,
rightMinusLeft=True)
newActiveCells = np.concatenate((correctPredictedCells,
np2.getAllCellsInColumns(
burstingColumns, self.cellsPerColumn)))
# Calculate learning
(learningActiveBasalSegments,
learningActiveApicalSegments,
learningMatchingBasalSegments,
learningMatchingApicalSegments,
basalSegmentsToPunish,
apicalSegmentsToPunish,
newSegmentCells,
learningCells) = self._calculateLearning(activeColumns,
burstingColumns,
correctPredictedCells,
self.activeBasalSegments,
self.activeApicalSegments,
self.matchingBasalSegments,
self.matchingApicalSegments,
self.basalPotentialOverlaps,
self.apicalPotentialOverlaps)
if learn:
# Learn on existing segments
for learningSegments in (learningActiveBasalSegments,
learningMatchingBasalSegments):
self._learn(self.basalConnections, self.rng, learningSegments,
basalReinforceCandidates, basalGrowthCandidates,
self.basalPotentialOverlaps,
self.initialPermanence, self.sampleSize,
self.permanenceIncrement, self.permanenceDecrement,
self.maxSynapsesPerSegment)
for learningSegments in (learningActiveApicalSegments,
learningMatchingApicalSegments):
self._learn(self.apicalConnections, self.rng, learningSegments,
apicalReinforceCandidates, apicalGrowthCandidates,
self.apicalPotentialOverlaps, self.initialPermanence,
self.sampleSize, self.permanenceIncrement,
self.permanenceDecrement, self.maxSynapsesPerSegment)
# Punish incorrect predictions
if self.basalPredictedSegmentDecrement != 0.0:
self.basalConnections.adjustActiveSynapses(
basalSegmentsToPunish, basalReinforceCandidates,
-self.basalPredictedSegmentDecrement)
if self.apicalPredictedSegmentDecrement != 0.0:
self.apicalConnections.adjustActiveSynapses(
apicalSegmentsToPunish, apicalReinforceCandidates,
-self.apicalPredictedSegmentDecrement)
# Only grow segments if there is basal *and* apical input.
if len(basalGrowthCandidates) > 0 and len(apicalGrowthCandidates) > 0:
self._learnOnNewSegments(self.basalConnections, self.rng,
newSegmentCells, basalGrowthCandidates,
self.initialPermanence, self.sampleSize,
self.maxSynapsesPerSegment)
self._learnOnNewSegments(self.apicalConnections, self.rng,
newSegmentCells, apicalGrowthCandidates,
self.initialPermanence, self.sampleSize,
self.maxSynapsesPerSegment)
# Save the results
newActiveCells.sort()
learningCells.sort()
self.activeCells = newActiveCells
self.winnerCells = learningCells
self.predictedActiveCells = correctPredictedCells
def compute(self,
activeColumns,
basalInput,
apicalInput,
basalGrowthCandidates=None,
apicalGrowthCandidates=None,
learn=True):
"""
Perform one timestep. Use the basal and apical input to form a set of
predictions, then activate the specified columns.
@param activeColumns (numpy array)
List of active columns
@param basalInput (numpy array)
List of active input bits for the basal dendrite segments
@param apicalInput (numpy array)
List of active input bits for the apical dendrite segments
@param basalGrowthCandidates (numpy array or None)
List of bits that the active cells may grow new basal synapses to.
If None, the basalInput is assumed to be growth candidates.
@param apicalGrowthCandidates (numpy array or None)
List of bits that the active cells may grow new apical synapses to
If None, the apicalInput is assumed to be growth candidates.
@param learn (bool)
Whether to grow / reinforce / punish synapses
"""
if basalGrowthCandidates is None:
basalGrowthCandidates = basalInput
if apicalGrowthCandidates is None:
apicalGrowthCandidates = apicalInput
# Calculate predictions for this timestep
(activeBasalSegments, matchingBasalSegments,
basalPotentialOverlaps) = self._calculateSegmentActivity(
self.basalConnections, basalInput, self.connectedPermanence,
self.activationThresholdBasal, self.minThresholdBasal)
(activeApicalSegments, matchingApicalSegments,
apicalPotentialOverlaps) = self._calculateSegmentActivity(
self.apicalConnections, apicalInput, self.connectedPermanence,
self.activationThresholdApical, self.minThresholdApical)
# Take union of predictions
predictedCells = np.union1d(
self.basalConnections.mapSegmentsToCells(activeBasalSegments),
self.apicalConnections.mapSegmentsToCells(activeApicalSegments))
# Calculate active cells
(correctPredictedCells, burstingColumns) = np2.setCompare(
predictedCells,
activeColumns,
predictedCells / self.cellsPerColumn,
rightMinusLeft=True)
newActiveCells = np.concatenate(
(correctPredictedCells,
np2.getAllCellsInColumns(burstingColumns, self.cellsPerColumn)))
# Calculate learning
(learningActiveBasalSegments, learningActiveApicalSegments,
learningMatchingBasalSegments, learningMatchingApicalSegments,
basalSegmentsToPunish, apicalSegmentsToPunish,
newSegmentCells, learningCells) = self._calculateLearning(
activeColumns, burstingColumns, correctPredictedCells,
activeBasalSegments, activeApicalSegments, matchingBasalSegments,
matchingApicalSegments, basalPotentialOverlaps,
apicalPotentialOverlaps)
if learn:
# Learn on existing segments
for learningSegments in (learningActiveBasalSegments,
learningMatchingBasalSegments):
self._learn(self.basalConnections, self.rng, learningSegments,
basalInput, basalGrowthCandidates,
basalPotentialOverlaps, self.initialPermanence,
self.sampleSize, self.permanenceIncrement,
self.permanenceDecrement,
self.maxSynapsesPerSegment)
for learningSegments in (learningActiveApicalSegments,
learningMatchingApicalSegments):
self._learn(self.apicalConnections, self.rng, learningSegments,
apicalInput, apicalGrowthCandidates,
apicalPotentialOverlaps, self.initialPermanence,
self.sampleSize, self.permanenceIncrement,
self.permanenceDecrement,
self.maxSynapsesPerSegment)
# Punish incorrect predictions
if self.basalPredictedSegmentDecrement != 0.0:
self.basalConnections.adjustActiveSynapses(
basalSegmentsToPunish, basalInput,
-self.basalPredictedSegmentDecrement)
if self.apicalPredictedSegmentDecrement != 0.0:
self.apicalConnections.adjustActiveSynapses(
apicalSegmentsToPunish, apicalInput,
-self.apicalPredictedSegmentDecrement)
# Only grow segments if there is basal *and* apical input.
if len(basalGrowthCandidates) > 0 and len(
apicalGrowthCandidates) > 0:
self._learnOnNewSegments(self.basalConnections, self.rng,
newSegmentCells,
basalGrowthCandidates,
self.initialPermanence,
self.sampleSize,
self.maxSynapsesPerSegment)
self._learnOnNewSegments(self.apicalConnections, self.rng,
newSegmentCells,
apicalGrowthCandidates,
self.initialPermanence,
self.sampleSize,
self.maxSynapsesPerSegment)
# Save the results
newActiveCells.sort()
learningCells.sort()
self.activeCells = newActiveCells
self.winnerCells = learningCells
self.predictedCells = predictedCells
self.activeBasalSegments = activeBasalSegments
self.activeApicalSegments = activeApicalSegments
