def updateSpatialElements(self, activeColumns):
"""
Update elements regarding spatial pooler
"""
# Update proximal segments and synapses according to active columns
for colIdx in range(len(self.columns)):
column = self.columns[colIdx]
# Update proximal segment
segment = column.segment
if activeColumns[colIdx] == 1:
segment.isActive[maxStoredSteps - 1] = True
else:
segment.isActive[maxStoredSteps - 1] = False
# Check if proximal segment is predicted by check if the column has any predicted cell
for cell in column.cells:
if cell.index in self.temporalPooler.predictiveCells:
segment.isPredicted[maxStoredSteps - 1] = True
# Update proximal synapses
if segment.isActive[maxStoredSteps -
1] or segment.isPredicted[maxStoredSteps - 1]:
permanencesSynapses = []
self.spatialPooler.getPermanence(colIdx, permanencesSynapses)
connectedSynapses = []
self.spatialPooler.getConnectedSynapses(
colIdx, connectedSynapses)
for synIdx in range(len(permanencesSynapses)):
# Get the proximal synapse given its position in the input map
# Create a new one if it doesn't exist
synapse = segment.getSynapse(synIdx)
# Update proximal synapse
if permanencesSynapses[synIdx] > 0.:
if synapse == None:
# Create a new synapse to a input element
# An input element is a column if child is a region
# or then a bit if child is a sensor
synapse = Synapse()
synapse.inputElem = self._inputMap[synIdx]
synapse.indexSP = synIdx
segment.synapses.append(synapse)
# Update state
synapse.isRemoved[maxStoredSteps - 1] = False
synapse.permanence[maxStoredSteps -
1] = permanencesSynapses[synIdx]
if connectedSynapses[synIdx] == 1:
synapse.isConnected[maxStoredSteps - 1] = True
else:
synapse.isConnected[maxStoredSteps - 1] = False
else:
if synapse != None:
synapse.isRemoved[maxStoredSteps - 1] = True
def performSpatialProcess(self, input): """ Perfoms spatial process. """ # Initialize the vector for representing the current record columnDimensions = (self.width, self.height) columnNumber = numpy.array(columnDimensions).prod() activeColumns = numpy.zeros(columnNumber) # Send input to Spatial Pooler and get processed output (i.e. the active columns) self.spatialPooler.compute(input, self.enableSpatialPooling, activeColumns) # Update proximal segments and synapses according to active columns for colIdx in range(len(self.columns)): column = self.columns[colIdx] # Update proximal segment segment = column.segment if activeColumns[colIdx] == 1: segment.isActive[maxTimeSteps - 1] = True else: segment.isActive[maxTimeSteps - 1] = False # Update proximal synapses permanencesSynapses = [] self.spatialPooler.getPermanence(colIdx, permanencesSynapses) connectedSynapses = [] self.spatialPooler.getConnectedSynapses(colIdx, connectedSynapses) for synIdx in range(len(permanencesSynapses)): # Get the proximal synapse connected to the input elem # Create a new one if it doesn't exist inputElem = self._inputMap[synIdx] synapse = segment.getSynapse(inputElem) # Update proximal synapse if permanencesSynapses[synIdx] > 0.: if synapse == None: # Create a new synapse to a input element # An input element is a column if child is a region # or then a bit if child is a sensor synapse = Synapse() synapse.inputElem = inputElem segment.synapses.append(synapse) # Update state synapse.permanence[maxTimeSteps - 1] = permanencesSynapses[synIdx] if connectedSynapses[synIdx] == 1: synapse.isConnected[maxTimeSteps - 1] = True else: synapse.isConnected[maxTimeSteps - 1] = False else: if synapse != None: synapse.isRemoved[maxTimeSteps - 1] = True
def updateSpatialElements(self, activeColumns): """ Update elements regarding spatial pooler """ # Update proximal segments and synapses according to active columns for colIdx in range(len(self.columns)): column = self.columns[colIdx] # Update proximal segment segment = column.segment if activeColumns[colIdx] == 1: segment.isActive[maxPreviousSteps - 1] = True else: segment.isActive[maxPreviousSteps - 1] = False # Check if proximal segment is predicted by check if the column has any predicted cell for cell in column.cells: if cell.index in self.temporalPooler.predictiveCells: segment.isPredicted[maxPreviousSteps - 1] = True # Update proximal synapses if segment.isActive[maxPreviousSteps - 1] or segment.isPredicted[maxPreviousSteps - 1]: permanencesSynapses = [] self.spatialPooler.getPermanence(colIdx, permanencesSynapses) connectedSynapses = [] self.spatialPooler.getConnectedSynapses(colIdx, connectedSynapses) for synIdx in range(len(permanencesSynapses)): # Get the proximal synapse given its position in the input map # Create a new one if it doesn't exist synapse = segment.getSynapse(synIdx) # Update proximal synapse if permanencesSynapses[synIdx] > 0.: if synapse == None: # Create a new synapse to a input element # An input element is a column if child is a region # or then a bit if child is a sensor synapse = Synapse() synapse.inputElem = self._inputMap[synIdx] synapse.indexSP = synIdx segment.synapses.append(synapse) # Update state synapse.isRemoved[maxPreviousSteps - 1] = False synapse.permanence[maxPreviousSteps - 1] = permanencesSynapses[synIdx] if connectedSynapses[synIdx] == 1: synapse.isConnected[maxPreviousSteps - 1] = True else: synapse.isConnected[maxPreviousSteps - 1] = False else: if synapse != None: synapse.isRemoved[maxPreviousSteps - 1] = True
def performTemporalProcess(self): """ Perfoms spatial process. """ # First we should convert from float array to integer set activeColumnsSet = set() for colIdx in range(len(self.columns)): column = self.columns[colIdx] if column.segment.isActive[maxTimeSteps - 1]: activeColumnsSet.add(colIdx) # Send active columns to Temporal Pooler and get processed output (i.e. the predicting cells) self.temporalPooler.compute(activeColumnsSet, self.enableTemporalPooling) # Update cells, distal segments and synapses according to active columns for colIdx in range(len(self.columns)): column = self.columns[colIdx] for cell in column.cells: cellIdx = (colIdx * self.numCellsPerColumn) + column.cells.index(cell) if cell.index == -1: cell.index = cellIdx # Update cell's state following the priority if cellIdx in self.temporalPooler.winnerCells: cell.isLearning[maxTimeSteps - 1] = True if cellIdx in self.temporalPooler.activeCells: cell.isActive[maxTimeSteps - 1] = True if cellIdx in self.temporalPooler.predictiveCells: cell.isPredicted[maxTimeSteps - 1] = True # Mark proximal segment and its connected synapses as predicted column.segment.isPredicted[maxTimeSteps - 1] = True for synapse in column.segment.synapses: if synapse.isConnected[maxTimeSteps - 1]: synapse.isPredicted[maxTimeSteps - 1] = True synapse.inputElem.isPredicted[maxTimeSteps - 1] = True # Get the indexes of the distal segments of this cell segmentsForCell = self.temporalPooler.connections.segmentsForCell(cellIdx) # Add the segments that appeared after last iteration for segIdx in segmentsForCell: # Check if segment already exists in the cell segFound = False for segment in cell.segments: if segment.index == segIdx: segFound = True break # If segment is new, add it to cell if not segFound: segment = Segment(SegmentType.distal) segment.index = segIdx cell.segments.append(segment) # Update distal segments for segment in cell.segments: segIdx = segment.index # If segment not found in segments indexes returned in last iteration mark it as removed if segIdx in segmentsForCell: # Update segment's state if segIdx in self.temporalPooler.activeSegments: segment.isActive[maxTimeSteps - 1] = True else: segment.isActive[maxTimeSteps - 1] = False # Get the indexes of the synapses of this segment synapsesForSegment = self.temporalPooler.connections.synapsesForSegment(segIdx) # Add the synapses that appeared after last iteration for synIdx in synapsesForSegment: # Check if synapse already exists in the segment synFound = False for synapse in segment.synapses: if synapse.index == synIdx: synFound = True break # If synapse is new, add it to segment if not synFound: synapse = Synapse() synapse.index = synIdx segment.synapses.append(synapse) # Update synapses for synapse in segment.synapses: synIdx = synapse.index # If synapse not found in synapses indexes returned in last iteration mark it as removed if synIdx in synapsesForSegment: # Update synapse's state (_, sourceCellIdx, permanence) = self.temporalPooler.connections.dataForSynapse(synIdx) synapse.permanence[maxTimeSteps - 1] = permanence if permanence >= self.distalSynConnectedPerm: synapse.isConnected[maxTimeSteps - 1] = True else: synapse.isConnected[maxTimeSteps - 1] = False # Get cell given cell's index for sourceColumn in self.columns: for sourceCell in sourceColumn.cells: if sourceCell.index == sourceCellIdx: synapse.inputElem = sourceCell else: synapse.isRemoved[maxTimeSteps - 1] = True else: segment.isRemoved[maxTimeSteps - 1] = True
def updateTemporalElements(self):
"""
Update elements regarding temporal pooler
"""
# Update cells, distal segments and synapses according to active columns
for colIdx in range(len(self.columns)):
column = self.columns[colIdx]
# Mark proximal segment and its connected synapses as predicted
if column.segment.isPredicted[maxStoredSteps - 1]:
for synapse in column.segment.synapses:
if synapse.isConnected[maxStoredSteps - 1]:
synapse.isPredicted[maxStoredSteps - 1] = True
synapse.inputElem.isPredicted[maxStoredSteps -
1] = True
for cell in column.cells:
cellIdx = cell.index
# Update cell's states
if cellIdx in self.temporalPooler.winnerCells:
cell.isLearning[maxStoredSteps - 1] = True
if cellIdx in self.temporalPooler.activeCells:
cell.isActive[maxStoredSteps - 1] = True
if cellIdx in self.temporalPooler.predictiveCells:
cell.isPredicted[maxStoredSteps - 1] = True
# Get the indexes of the distal segments of this cell
segmentsForCell = self.temporalPooler.connections.segmentsForCell(
cellIdx)
# Add the segments that appeared after last iteration
for segIdx in segmentsForCell:
# Check if segment already exists in the cell
segFound = False
for segment in cell.segments:
if segment.indexTP == segIdx:
segFound = True
break
# If segment is new, add it to cell
if not segFound:
segment = Segment(SegmentType.distal)
segment.indexTP = segIdx
cell.segments.append(segment)
# Update distal segments
for segment in cell.segments:
segIdx = segment.indexTP
# If segment not found in segments indexes returned in last iteration mark it as removed
if segIdx in segmentsForCell:
# Update segment's state
if segIdx in self.temporalPooler.activeSegments:
segment.isActive[maxStoredSteps - 1] = True
else:
segment.isActive[maxStoredSteps - 1] = False
# Get the indexes of the synapses of this segment
synapsesForSegment = self.temporalPooler.connections.synapsesForSegment(
segIdx)
# Add the synapses that appeared after last iteration
for synIdx in synapsesForSegment:
# Check if synapse already exists in the segment
synFound = False
for synapse in segment.synapses:
if synapse.indexTP == synIdx:
synFound = True
break
# If synapse is new, add it to segment
if not synFound:
synapse = Synapse()
synapse.indexTP = synIdx
segment.synapses.append(synapse)
# Update synapses
for synapse in segment.synapses:
synIdx = synapse.indexTP
# If synapse not found in synapses indexes returned in last iteration mark it as removed
if synIdx in synapsesForSegment:
# Update synapse's state
(
_, sourceCellIdx, permanence
) = self.temporalPooler.connections.dataForSynapse(
synIdx)
synapse.permanence[maxStoredSteps -
1] = permanence
if permanence >= self.distalSynConnectedPerm:
synapse.isConnected[maxStoredSteps -
1] = True
else:
synapse.isConnected[maxStoredSteps -
1] = False
# Get cell given cell's index
for sourceColumn in self.columns:
for sourceCell in sourceColumn.cells:
if sourceCell.index == sourceCellIdx:
synapse.inputElem = sourceCell
else:
synapse.isRemoved[maxStoredSteps - 1] = True
else:
segment.isRemoved[maxStoredSteps - 1] = True
def updateTemporalElements(self): """ Update elements regarding temporal pooler """ # Update cells, distal segments and synapses according to active columns for colIdx in range(len(self.columns)): column = self.columns[colIdx] # Mark proximal segment and its connected synapses as predicted if column.segment.isPredicted[maxPreviousSteps - 1]: for synapse in column.segment.synapses: if synapse.isConnected[maxPreviousSteps - 1]: synapse.isPredicted[maxPreviousSteps - 1] = True synapse.inputElem.isPredicted[maxPreviousSteps - 1] = True # Mark proximal segment and its connected synapses that were predicted but are not active now if column.segment.isPredicted[maxPreviousSteps - 2]: if not column.segment.isActive[maxPreviousSteps - 1]: column.segment.isFalselyPredicted[maxPreviousSteps - 1] = True for synapse in column.segment.synapses: if (synapse.isPredicted[maxPreviousSteps - 2] and not synapse.isConnected[maxPreviousSteps - 1]) or (synapse.isConnected[maxPreviousSteps - 1] and synapse.inputElem.isFalselyPredicted[maxPreviousSteps - 1]): synapse.isFalselyPredicted[maxPreviousSteps - 1] = True for cell in column.cells: cellIdx = cell.index # Update cell's states if cellIdx in self.temporalPooler.winnerCells: cell.isLearning[maxPreviousSteps - 1] = True if cellIdx in self.temporalPooler.activeCells: cell.isActive[maxPreviousSteps - 1] = True if cellIdx in self.temporalPooler.predictiveCells: cell.isPredicted[maxPreviousSteps - 1] = True if cell.isPredicted[maxPreviousSteps - 2] and not cell.isActive[maxPreviousSteps - 1]: cell.isFalselyPredicted[maxPreviousSteps - 1] = True # Get the indexes of the distal segments of this cell segmentsForCell = self.temporalPooler.connections.segmentsForCell(cellIdx) # Add the segments that appeared after last iteration for segIdx in segmentsForCell: # Check if segment already exists in the cell segFound = False for segment in cell.segments: if segment.indexTP == segIdx: segFound = True break # If segment is new, add it to cell if not segFound: segment = Segment(SegmentType.distal) segment.indexTP = segIdx cell.segments.append(segment) # Update distal segments for segment in cell.segments: segIdx = segment.indexTP # If segment not found in segments indexes returned in last iteration mark it as removed if segIdx in segmentsForCell: # Update segment's state if segIdx in self.temporalPooler.activeSegments: segment.isActive[maxPreviousSteps - 1] = True else: segment.isActive[maxPreviousSteps - 1] = False # Get the indexes of the synapses of this segment synapsesForSegment = self.temporalPooler.connections.synapsesForSegment(segIdx) # Add the synapses that appeared after last iteration for synIdx in synapsesForSegment: # Check if synapse already exists in the segment synFound = False for synapse in segment.synapses: if synapse.indexTP == synIdx: synFound = True break # If synapse is new, add it to segment if not synFound: synapse = Synapse() synapse.indexTP = synIdx segment.synapses.append(synapse) # Update synapses for synapse in segment.synapses: synIdx = synapse.indexTP # If synapse not found in synapses indexes returned in last iteration mark it as removed if synIdx in synapsesForSegment: # Update synapse's state (_, sourceCellAbsIdx, permanence) = self.temporalPooler.connections.dataForSynapse(synIdx) synapse.permanence[maxPreviousSteps - 1] = permanence if permanence >= self.distalSynConnectedPerm: synapse.isConnected[maxPreviousSteps - 1] = True else: synapse.isConnected[maxPreviousSteps - 1] = False # Get cell given cell's index sourceColIdx = sourceCellAbsIdx / self.numCellsPerColumn sourceCellRelIdx = sourceCellAbsIdx % self.numCellsPerColumn sourceCell = self.columns[sourceColIdx].cells[sourceCellRelIdx] synapse.inputElem = sourceCell else: synapse.isRemoved[maxPreviousSteps - 1] = True else: segment.isRemoved[maxPreviousSteps - 1] = True
def updateTemporalElements(self):
"""
Update elements regarding temporal pooler
"""
# Update cells, distal segments and synapses according to active columns
for colIdx in range(len(self.columns)):
column = self.columns[colIdx]
# Mark proximal segment and its connected synapses as predicted
if column.segment.isPredicted.atCurrStep():
for synapse in column.segment.synapses:
if synapse.isConnected.atCurrStep():
synapse.isPredicted.setForCurrStep(True)
synapse.inputElem.isPredicted.setForCurrStep(True)
# Mark proximal segment and its connected synapses that were predicted but are not active now
if column.segment.isPredicted.atPreviousStep():
if not column.segment.isActive.atCurrStep():
column.segment.isFalselyPredicted.setForCurrStep(True)
for synapse in column.segment.synapses:
if (synapse.isPredicted.atPreviousStep()
and not synapse.isConnected.atCurrStep()
) or (
synapse.isConnected.atCurrStep() and
synapse.inputElem.isFalselyPredicted.atCurrStep()):
synapse.isFalselyPredicted.setForCurrStep(True)
for cell in column.cells:
cellIdx = cell.index
# Update cell's states
if cellIdx in self.temporalPooler.winnerCells:
cell.isLearning.setForCurrStep(True)
if cellIdx in self.temporalPooler.activeCells:
cell.isActive.setForCurrStep(True)
if cellIdx in self.temporalPooler.predictiveCells:
cell.isPredicted.setForCurrStep(True)
if cell.isPredicted.atPreviousStep(
) and not cell.isActive.atCurrStep():
cell.isFalselyPredicted.setForCurrStep(True)
# Get the indexes of the distal segments of this cell
segmentsForCell = self.temporalPooler.connections.segmentsForCell(
cellIdx)
# Add the segments that appeared after last iteration
for segIdx in segmentsForCell:
# Check if segment already exists in the cell
segFound = False
for segment in cell.segments:
if segment.indexTP == segIdx:
segFound = True
break
# If segment is new, add it to cell
if not segFound:
segment = Segment(SegmentType.distal)
segment.indexTP = segIdx
cell.segments.append(segment)
# Update distal segments
for segment in cell.segments:
segIdx = segment.indexTP
# If segment not found in segments indexes returned in last iteration mark it as removed
if segIdx in segmentsForCell:
# Update segment's state
if segIdx in self.temporalPooler.activeSegments:
segment.isActive.setForCurrStep(True)
else:
segment.isActive.setForCurrStep(False)
# Get the indexes of the synapses of this segment
synapsesForSegment = self.temporalPooler.connections.synapsesForSegment(
segIdx)
# Add the synapses that appeared after last iteration
for synIdx in synapsesForSegment:
# Check if synapse already exists in the segment
synFound = False
for synapse in segment.synapses:
if synapse.indexTP == synIdx:
synFound = True
break
# If synapse is new, add it to segment
if not synFound:
synapse = Synapse()
synapse.indexTP = synIdx
segment.synapses.append(synapse)
# Update synapses
for synapse in segment.synapses:
synIdx = synapse.indexTP
# If synapse not found in synapses indexes returned in last iteration mark it as removed
if synIdx in synapsesForSegment:
# Update synapse's state
(
_, sourceCellAbsIdx, permanence
) = self.temporalPooler.connections.dataForSynapse(
synIdx)
synapse.permanence.setForCurrStep(permanence)
if permanence >= self.distalSynConnectedPerm:
synapse.isConnected.setForCurrStep(True)
else:
synapse.isConnected.setForCurrStep(False)
# Get cell given cell's index
sourceColIdx = sourceCellAbsIdx / self.numCellsPerColumn
sourceCellRelIdx = sourceCellAbsIdx % self.numCellsPerColumn
sourceCell = self.columns[sourceColIdx].cells[
sourceCellRelIdx]
synapse.inputElem = sourceCell
else:
synapse.isRemoved.setForCurrStep(True)
else:
segment.isRemoved.setForCurrStep(True)