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 _temporal_phase3(htm, updateSegments):
'Phase 3, p42'
for cell in htm.cells:
if cell.learning:
for synapseStates in updateSegments[cell]:
Segment.adapt_up(synapseStates)
updateSegments.reset(cell)
elif not cell.predicting and cell.predicted:
for synapseStates in updateSegments[cell]:
Segment.adapt_down(synapseStates)
updateSegments.reset(cell)
return updateSegments
def _temporal_phase3(htm, updateSegments):
'Phase 3, p42'
for cell in htm.cells:
if cell.learning:
for synapseStates in updateSegments[cell]:
Segment.adapt_up(synapseStates)
updateSegments.reset(cell)
elif not cell.predicting and cell.predicted:
for synapseStates in updateSegments[cell]:
Segment.adapt_down(synapseStates)
updateSegments.reset(cell)
return updateSegments
def testAdaptation(self):
seg = Segment()
#for now, just don't crash
seg.adapt_up([])
seg.adapt_down([])
def testAdaptation(self):
seg = Segment()
#for now, just don't crash
seg.adapt_up([])
seg.adapt_down([])
