def __executeOne(self, learning):
from numenta.htm import pool_spatial, pool_temporal
for cell in self.cells:
cell.clockTick()
pool_spatial(self)
self._updateSegments = pool_temporal(self, self._updateSegments, learning=learning)
#non-Numenta optimization:
#track whether cell is predicted next
#penalize near segments for failing immediately
if EXTENSION_NEXT_STEP_PENALTY:
for cell in self.cells:
#mark prediction of immediate next step
cell.predictingNext = False
for segment in cell.segmentsNear:
if segment.active:
cell.predictingNext = True
if cell.predictedNext and not cell.active:
#penalize only active near segments
otherSynapseStates = []
for synapses in self._updateSegments[cell]:
if len(synapses) and synapses[0].segment == segment:
Segment.adapt_down(synapses)
else:
otherSynapseStates.append(synapses)
self._updateSegments.reset(cell)
self._updateSegments.addAll(cell, otherSynapseStates)
def __executeOne(self, learning):
from numenta.htm import pool_spatial, pool_temporal
for cell in self.cells:
cell.clockTick()
pool_spatial(self)
self._updateSegments = pool_temporal(self,
self._updateSegments,
learning=learning)
#non-Numenta optimization:
#track whether cell is predicted next
#penalize near segments for failing immediately
if EXTENSION_NEXT_STEP_PENALTY:
for cell in self.cells:
#mark prediction of immediate next step
cell.predictingNext = False
for segment in cell.segmentsNear:
if segment.active:
cell.predictingNext = True
if cell.predictedNext and not cell.active:
#penalize only active near segments
otherSynapseStates = []
for synapses in self._updateSegments[cell]:
if len(synapses) and synapses[0].segment == segment:
Segment.adapt_down(synapses)
else:
otherSynapseStates.append(synapses)
self._updateSegments.reset(cell)
self._updateSegments.addAll(cell, otherSynapseStates)
def __executeOne(self, learning):
from numenta.htm import pool_spatial, pool_temporal
for cell in self.cells:
cell.clockTick()
pool_spatial(self)
pool_temporal(self, learning=True)