def __init__(self, region, srcPos, pos):
"""
Construct a new Column for the given parent region at source row/column
position srcPos and column grid position pos.
@param region the parent Region this Column belongs to.
@param srcPos a tuple (srcX,srcY) of this Column's 'center' position
in terms of the proximal-synapse input space.
@param pos a tuple(x,y) of this Column's position within the
Region's column grid.
"""
self.region = region #parent region
self.cells = [Cell(self, i)
for i in xrange(region.cellsPerCol)] #Sequence cells
self.isActive = False #whether or not this Column is currently active.
#The list of potential synapses and their permanence values.
self.proximalSegment = Segment(region.segActiveThreshold)
#The boost value for column c as computed during learning.
# used to increase the overlap value for inactive columns.
self.boost = 1.0
#A sliding average representing how often column c has been active
# after inhibition (e.g. over the last 1000 iterations).
self.activeDutyCycle = 1.0
#A sliding average representing how often column c has had
# significant overlap (i.e. greater than minOverlap) with its inputs
# (e.g. over the last 1000 iterations).
self.overlapDutyCycle = 1.0
self.overlap = 0 #the last computed input overlap for the Column.
self.ix = srcPos[0] #'input' row and col
self.iy = srcPos[1]
self.cx = pos[0] #'column grid' row and col
self.cy = pos[1]
def createSegment(self, learningCells):
"""
Create a new segment for this Cell. The new segment will initially connect to
at most newSynapseCount synapses randomly selected from the set of cells that
were in the learning state at t-1 (specified by the learningCells parameter).
@param learningCells: the set of available learning cells to add to the segment.
@return the segment that was just created.
"""
newSegment = Segment(self.column.region.segActiveThreshold)
newSegment.createSynapsesToLearningCells(learningCells)
self.segments.append(newSegment)
return newSegment
