def testAggregation(self):
seg = Segment()
self.assertFalse(seg.active)
firing = Mock({'is_firing': True})
off = Mock({'is_firing': False})
seg.add_synapse(off)
self.assertFalse(seg.active)
onRequired = int(FRACTION_SEGMENT_ACTIVATION_THRESHOLD * 100)
offRequired = 99 - onRequired
#this isn't a hard requirement, just a limitation of the test
assert (onRequired >= 2)
for _ in xrange(offRequired):
seg.add_synapse(off)
self.assertFalse(seg.active)
for _ in xrange(onRequired - 1):
seg.add_synapse(firing)
self.assertFalse(seg.active)
seg.add_synapse(firing)
self.assert_(seg.active)
def __init__(self, htm, x, y, cellsPerColumn):
'''
'''
self.htm = htm
self.cells = [Cell(self, i) for i in xrange(cellsPerColumn)]
self.overlap = 0
#synapses on input/proximal dendrites, forced equal for whole column, in equivalent of single segment
self.segment = Segment(distal=False)
self.boost = 1
self.x = x
self.y = y
self.dutyCycleMin = 0
self.dutyCycleActive = 0
self.dutyCycleOverlap = 0
self.active = False
def __init__(self, column=None, layer=None):
'''
@param layer the inner layer of the cell, so an HTM with 3 cells per column
would have cells with layers 0, 1 and 2
'''
self.column = column
self.layer = layer
self.active = False
self.__wasActive = False #read-only
self.predicting = False
self.__predicted = False #read-only
self.learning = False
self.__wasLearning = False #read-only
self.predictingNext = None #cached-val
self.__predictedNext = False #read-only
self.segments = [Segment() for i in xrange(SEGMENTS_PER_CELL)]
def create_segment(self, htm, nextStep):
'''
@param htm: the htm network that contains the cell
@param nextStep: boolean for whether this segment indicates predicted
firing in the very next time step
'''
seg = Segment(nextStep=nextStep)
#randomly choose input cells, from active non-self cells
synapseLen = self.__createSynapses(seg, htm.cells,
SYNAPSES_PER_SEGMENT,
lambda c: c.wasLearning)
if not nextStep and synapseLen < SYNAPSES_PER_SEGMENT:
addSynapseLen = SYNAPSES_PER_SEGMENT - synapseLen
activeSynapseLen = self.__createSynapses(seg, htm.cells,
addSynapseLen,
lambda c: c.wasActive)
self.segments.append(seg)
return seg
def testAdaptation(self):
seg = Segment()
#for now, just don't crash
seg.adapt_up([])
seg.adapt_down([])
