def _mapPotential(self, index):
centerInput = self._mapColumn(index)<EOL>columnInputs = self._getInputNeighborhood(centerInput).astype(uintType)<EOL>numPotential = int(columnInputs.size * self._potentialPct + <NUM_LIT:0.5>)<EOL>selectedInputs = numpy.empty(numPotential, dtype=uintType)<EOL>self._random.sample(columnInputs, selectedInputs)<EOL>potential = numpy.zeros(self._numInputs, dtype=uintType)<EOL>potential[selectedInputs] = <NUM_LIT:1><EOL>return potential<EOL>
Maps a column to its input bits. This method encapsulates the topology of
the region. It takes the index of the column as an argument and determines
what are the indices of the input vector that are located within the
column's potential pool. The return value is a list containing the indices
of the input bits. The current implementation of the base class only
supports a 1 dimensional topology of columns with a 1 dimensional topology
of inputs. To extend this class to support 2-D topology you will need to
override this method. Examples of the expected output of this method:
* If the potentialRadius is greater than or equal to the largest input
  dimension then each column connects to all of the inputs.
* If the topology is one dimensional, the input space is divided up evenly
  among the columns and each column is centered over its share of the
  inputs.  If the potentialRadius is 5, then each column connects to the
  input it is centered above as well as the 5 inputs to the left of that
  input and the five inputs to the right of that input, wrapping around if
  wrapAround=True.
* If the topology is two dimensional, the input space is again divided up
  evenly among the columns and each column is centered above its share of
  the inputs.  If the potentialRadius is 5, the column connects to a square
  that has 11 inputs on a side and is centered on the input that the column
  is centered above.

Parameters:
----------------------------
:param index:   The index identifying a column in the permanence, potential
                and connectivity matrices.