def updateSpatialElements(self, active_columns):
"""
Update elements regarding spatial pooler
"""
# Update proximal segments and synapses according to active columns
for col_idx in range(len(self.columns)):
column = self.columns[col_idx]
# Update proximal segment
segment = column.segment
if active_columns[col_idx] == 1:
segment.is_active.setForCurrStep(True)
else:
segment.is_active.setForCurrStep(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.temporal_pooler.predictiveCells:
segment.is_predicted.setForCurrStep(True)
# Update proximal synapses
if segment.is_active.atCurrStep(
) or segment.is_predicted.atCurrStep():
permanences_synapses = []
self.spatial_pooler.getPermanence(col_idx,
permanences_synapses)
connected_synapses = []
self.spatial_pooler.getConnectedSynapses(
col_idx, connected_synapses)
for syn_idx in range(len(permanences_synapses)):
# Get the proximal synapse given its position in the input map
# Create a new one if it doesn't exist
synapse = segment.getSynapse(syn_idx)
# Update proximal synapse
if permanences_synapses[syn_idx] > 0.0:
if synapse == None:
# Create a new synapse to a input element
# An input element is a column if feeder is a region
# or then a bit if feeder is a sensor
synapse = Synapse()
synapse.input_elem = self.input_map[syn_idx]
synapse.index_sp = syn_idx
segment.synapses.append(synapse)
# Update state
synapse.is_removed.setForCurrStep(False)
synapse.permanence.setForCurrStep(
permanences_synapses[syn_idx])
if connected_synapses[syn_idx] == 1:
synapse.is_connected.setForCurrStep(True)
else:
synapse.is_connected.setForCurrStep(False)
else:
if synapse != None:
synapse.is_removed.setForCurrStep(True)
def updateTemporalElements(self):
"""
Update elements regarding temporal pooler
"""
# Update cells, distal segments and synapses according to active columns
for col_idx in range(len(self.columns)):
column = self.columns[col_idx]
# Mark proximal segment and its connected synapses as predicted
if column.segment.is_predicted.atCurrStep():
for synapse in column.segment.synapses:
if synapse.is_connected.atCurrStep():
synapse.is_predicted.setForCurrStep(True)
synapse.input_elem.is_predicted.setForCurrStep(True)
# Mark proximal segment and its connected synapses that were predicted but are not active now
if column.segment.is_predicted.atPreviousStep():
if not column.segment.is_active.atCurrStep():
column.segment.is_falsely_predicted.setForCurrStep(True)
for synapse in column.segment.synapses:
if (synapse.is_predicted.atPreviousStep()
and not synapse.is_connected.atCurrStep()) or (
synapse.is_connected.atCurrStep() and synapse.
input_elem.is_falsely_predicted.atCurrStep()):
synapse.is_falsely_predicted.setForCurrStep(True)
for cell in column.cells:
cell_idx = cell.index
# Update cell's states
if cell_idx in self.temporal_pooler.winnerCells:
cell.is_learning.setForCurrStep(True)
if cell_idx in self.temporal_pooler.activeCells:
cell.is_active.setForCurrStep(True)
if cell_idx in self.temporal_pooler.predictiveCells:
cell.is_predicted.setForCurrStep(True)
if cell.is_predicted.atPreviousStep(
) and not cell.is_active.atCurrStep():
cell.is_falsely_predicted.setForCurrStep(True)
# Get the indexes of the distal segments of this cell
segments_for_cell = self.temporal_pooler.connections.segmentsForCell(
cell_idx)
# Add the segments that appeared after last iteration
for seg_idx in segments_for_cell:
# Check if segment already exists in the cell
seg_found = False
for segment in cell.segments:
if segment.index_tp == seg_idx:
seg_found = True
break
# If segment is new, add it to cell
if not seg_found:
segment = Segment(SegmentType.DISTAL)
segment.index_tp = seg_idx
cell.segments.append(segment)
# Update distal segments
for segment in cell.segments:
seg_idx = segment.index_tp
# If segment not found in segments indexes returned in last iteration mark it as removed
if seg_idx in segments_for_cell:
# Update segment's state
if seg_idx in self.temporal_pooler.activeSegments:
segment.is_active.setForCurrStep(True)
else:
segment.is_active.setForCurrStep(False)
# Get the indexes of the synapses of this segment
synapses_for_segment = self.temporal_pooler.connections.synapsesForSegment(
seg_idx)
# Add the synapses that appeared after last iteration
for syn_idx in synapses_for_segment:
# Check if synapse already exists in the segment
syn_found = False
for synapse in segment.synapses:
if synapse.index_tp == syn_idx:
syn_found = True
break
# If synapse is new, add it to segment
if not syn_found:
synapse = Synapse()
synapse.index_tp = syn_idx
segment.synapses.append(synapse)
# Update synapses
for synapse in segment.synapses:
syn_idx = synapse.index_tp
# If synapse not found in synapses indexes returned in last iteration mark it as removed
if syn_idx in synapses_for_segment:
# Update synapse's state
synapse_data = self.temporal_pooler.connections.dataForSynapse(
syn_idx)
synapse.permanence.setForCurrStep(
synapse_data.permanence)
if synapse_data.permanence >= self.distal_syn_connected_perm:
synapse.is_connected.setForCurrStep(True)
else:
synapse.is_connected.setForCurrStep(False)
# Get cell given cell's index
source_col_idx = synapse_data.presynapticCell / self.cells_per_column
source_cell_rel_idx = synapse_data.presynapticCell % self.cells_per_column
source_cell = self.columns[
source_col_idx].cells[source_cell_rel_idx]
synapse.input_elem = source_cell
else:
synapse.is_removed.setForCurrStep(True)
else:
segment.is_removed.setForCurrStep(True)
