/**
* Activates all of the cells in an unpredicted active column,
* chooses a winner cell, and, if learning is turned on, either adapts or
* creates a segment. growSynapses is invoked on this segment.
* </p><p>
* <b>Pseudocode:</b>
* </p><p>
* <pre>
* mark all cells as active
* if there are any matching distal dendrite segments
* find the most active matching segment
* mark its cell as a winner cell
* (learning)
* grow and reinforce synapses to previous winner cells
* else
* find the cell with the least segments, mark it as a winner cell
* (learning)
* (optimization) if there are previous winner cells
* add a segment to this winner cell
* grow synapses to previous winner cells
* </pre>
* </p>
*
* @param conn Connections instance for the TM
* @param column Bursting {@link Column}
* @param matchingSegments List of matching {@link DistalDendrite}s
* @param prevActiveCells Active cells in `t-1`
* @param prevWinnerCells Winner cells in `t-1`
* @param permanenceIncrement Amount by which permanences of synapses
* are decremented during learning
* @param permanenceDecrement Amount by which permanences of synapses
* are incremented during learning
* @param random Random number generator
* @param learn Whether or not learning is enabled
*
* @return Tuple containing:
* cells list of the processed column's cells
* bestCell the best cell
*/
public BurstingResult BurstColumn(Connections conn, Column column, List <DistalDendrite> matchingSegments,
ICollection <Cell> prevActiveCells, ICollection <Cell> prevWinnerCells, double permanenceIncrement, double permanenceDecrement,
Random random, bool learn)
{
IList <Cell> cells = column.Cells;
Cell leastUsedCell = null;
//
// Matching segments result from number of potential synapses. These are segments with number of potential
// synapses permanence higher than some minimum threshold value.
// Potential synapses are synapses from presynaptc cells connected to the active cell.
// In other words, presynaptic cells define a statistical prediction that active cell will become the active in the next cycle.
// Bursting will create new segments if there are no matching segments until some matching segments appear.
// Once that happen, segment adoption will start.
// If some matching segments exist, bursting will grab the segment with most potential synapses and adapt it.
if (matchingSegments != null && matchingSegments.Count > 0)
{
DistalDendrite maxPotentialSeg = getSegmentwithHighesPotential(conn, matchingSegments);
for (int i = 0; i < matchingSegments.Count; i++)
{
matchingSegments[i].getIndex();
}
leastUsedCell = maxPotentialSeg.GetParentCell();
if (learn)
{
adaptSegment(conn, maxPotentialSeg, prevActiveCells, permanenceIncrement, permanenceDecrement);
int nGrowDesired = conn.getMaxNewSynapseCount() - conn.getLastActivity().PotentialSynapses[maxPotentialSeg.getIndex()];
if (nGrowDesired > 0)
{
growSynapses(conn, prevWinnerCells, maxPotentialSeg, conn.getInitialPermanence(),
nGrowDesired, random);
}
}
}
else
{
leastUsedCell = this.GetLeastUsedCell(conn, cells, random);
if (learn)
{
int nGrowExact = Math.Min(conn.getMaxNewSynapseCount(), prevWinnerCells.Count);
if (nGrowExact > 0)
{
DistalDendrite bestSegment = conn.CreateDistalSegment(leastUsedCell);
growSynapses(conn, prevWinnerCells, bestSegment, conn.getInitialPermanence(),
nGrowExact, random);
}
}
}
return(new BurstingResult(cells, leastUsedCell));
}
/**
* Activates all of the cells in an unpredicted active column,
* chooses a winner cell, and, if learning is turned on, either adapts or
* creates a segment. growSynapses is invoked on this segment.
* </p><p>
* <b>Pseudocode:</b>
* </p><p>
* <pre>
* mark all cells as active
* if there are any matching distal dendrite segments
* find the most active matching segment
* mark its cell as a winner cell
* (learning)
* grow and reinforce synapses to previous winner cells
* else
* find the cell with the least segments, mark it as a winner cell
* (learning)
* (optimization) if there are previous winner cells
* add a segment to this winner cell
* grow synapses to previous winner cells
* </pre>
* </p>
*
* @param conn Connections instance for the TM
* @param column Bursting {@link Column}
* @param matchingSegments List of matching {@link DistalDendrite}s
* @param prevActiveCells Active cells in `t-1`
* @param prevWinnerCells Winner cells in `t-1`
* @param permanenceIncrement Amount by which permanences of synapses
* are decremented during learning
* @param permanenceDecrement Amount by which permanences of synapses
* are incremented during learning
* @param random Random number generator
* @param learn Whether or not learning is enabled
*
* @return Tuple containing:
* cells list of the processed column's cells
* bestCell the best cell
*/
public Tuple BurstColumn(Connections conn, Column column, List <DistalDendrite> matchingSegments,
HashSet <Cell> prevActiveCells, HashSet <Cell> prevWinnerCells, double permanenceIncrement, double permanenceDecrement,
IRandom random, bool learn)
{
IList <Cell> cells = column.GetCells();
Cell bestCell = null;
if (matchingSegments.Any())
{
int[] numPoten = conn.GetLastActivity().numActivePotential;
Comparison <DistalDendrite> cmp = (dd1, dd2) => numPoten[dd1.GetIndex()] - numPoten[dd2.GetIndex()];
var sortedSegments = new List <DistalDendrite>(matchingSegments);
sortedSegments.Sort(cmp);
DistalDendrite bestSegment = sortedSegments.Last();
bestCell = bestSegment.GetParentCell();
if (learn)
{
AdaptSegment(conn, bestSegment, prevActiveCells, permanenceIncrement, permanenceDecrement);
int nGrowDesired = conn.GetMaxNewSynapseCount() - numPoten[bestSegment.GetIndex()];
if (nGrowDesired > 0)
{
GrowSynapses(conn, prevWinnerCells, bestSegment, conn.GetInitialPermanence(),
nGrowDesired, random);
}
}
}
else
{
bestCell = LeastUsedCell(conn, cells, random);
if (learn)
{
int nGrowExact = Math.Min(conn.GetMaxNewSynapseCount(), prevWinnerCells.Count);
if (nGrowExact > 0)
{
DistalDendrite bestSegment = conn.CreateSegment(bestCell);
GrowSynapses(conn, prevWinnerCells, bestSegment, conn.GetInitialPermanence(),
nGrowExact, random);
}
}
}
return(new Tuple(cells, bestCell));
}
