def finishLearning(self):
    """
    Called when learning has been completed. This method just calls
    :meth:`trimSegments` and then clears out caches.
    """
    # Keep weakly formed synapses around because they contain confidence scores
    # for paths out of learned sequenced and produce a better prediction than
    # chance.
    self.trimSegments(minPermanence=0.0001)

    # Update all cached duty cycles for better performance right after loading
    # in the trained network.
    for c, i in itertools.product(xrange(self.numberOfCols),
                                  xrange(self.cellsPerColumn)):
      for segment in self.cells[c][i]:
        segment.dutyCycle()

    # For error checking purposes, make sure no start cell has incoming
    # connections
    if self.cellsPerColumn > 1:
      for c in xrange(self.numberOfCols):
        assert self.getNumSegmentsInCell(c, 0) == 0
def finishLearning(self):
    """
    Called when learning has been completed. This method just calls
    :meth:`trimSegments` and then clears out caches.
    """
    # Keep weakly formed synapses around because they contain confidence scores
    # for paths out of learned sequenced and produce a better prediction than
    # chance.
    self.trimSegments(minPermanence=0.0001)

    # Update all cached duty cycles for better performance right after loading
    # in the trained network.
    for c, i in itertools.product(xrange(self.numberOfCols),
                                  xrange(self.cellsPerColumn)):
      for segment in self.cells[c][i]:
        segment.dutyCycle()

    # For error checking purposes, make sure no start cell has incoming
    # connections
    if self.cellsPerColumn > 1:
      for c in xrange(self.numberOfCols):
        assert self.getNumSegmentsInCell(c, 0) == 0
def finishLearning ( self ) : # Keep weakly formed synapses around because they contain confidence scores # for paths out of learned sequenced and produce a better prediction than # chance. self . trimSegments ( minPermanence = 0.0001 ) # Update all cached duty cycles for better performance right after loading # in the trained network. for c , i in itertools . product ( xrange ( self . numberOfCols ) , xrange ( self . cellsPerColumn ) ) : for segment in self . cells [ c ] [ i ] : segment . dutyCycle ( ) # For error checking purposes, make sure no start cell has incoming # connections if self . cellsPerColumn > 1 : for c in xrange ( self . numberOfCols ) : assert self . getNumSegmentsInCell ( c , 0 ) == 0
Called when learning has been completed. This method just calls
    :meth:`trimSegments` and then clears out caches.
Called when learning has been completed . This method just calls : meth : trimSegments and then clears out caches .