def _updateDutyCycles(self, overlaps, activeColumns):
overlapArray = numpy.zeros(self._numColumns, dtype=realDType)<EOL>activeArray = numpy.zeros(self._numColumns, dtype=realDType)<EOL>overlapArray[overlaps > <NUM_LIT:0>] = <NUM_LIT:1><EOL>activeArray[activeColumns] = <NUM_LIT:1><EOL>period = self._dutyCyclePeriod<EOL>if (period > self._iterationNum):<EOL><INDENT>period = self._iterationNum<EOL><DEDENT>self._overlapDutyCycles = self._updateDutyCyclesHelper(<EOL>self._overlapDutyCycles,<EOL>overlapArray,<EOL>period<EOL>)<EOL>self._activeDutyCycles = self._updateDutyCyclesHelper(<EOL>self._activeDutyCycles,<EOL>activeArray,<EOL>period<EOL>)<EOL>
Updates the duty cycles for each column. The OVERLAP duty cycle is a moving
average of the number of inputs which overlapped with the each column. The
ACTIVITY duty cycles is a moving average of the frequency of activation for
each column.

Parameters:
----------------------------
:param overlaps:
                An array containing the overlap score for each column.
                The overlap score for a column is defined as the number
                of synapses in a "connected state" (connected synapses)
                that are connected to input bits which are turned on.
:param activeColumns:
                An array containing the indices of the active columns,
                the sparse set of columns which survived inhibition