private void ActivateCellsInColumn(Connections conn, bool learn, Pair <Column, List <List <object> > > tuple, ComputeCycle cycle, ConcurrentDictionary <int, ComputeCycle> cycles, ISet <Cell> prevActiveCells, ISet <Cell> prevWinnerCells, double permanenceIncrement, double permanenceDecrement)
{
ColumnData activeColumnData = new ColumnData();
activeColumnData.Set(tuple);
if (activeColumnData.IsExistAnyActiveCol(cIndexofACTIVE_COLUMNS))
{
// If there are some active segments on the column already...
if (activeColumnData.ActiveSegments != null && activeColumnData.ActiveSegments.Count > 0)
{
//Debug.Write("A");
List <Cell> cellsOwnersOfActSegs = ActivatePredictedColumn(conn, activeColumnData.ActiveSegments,
activeColumnData.MatchingSegments, prevActiveCells, prevWinnerCells,
permanenceIncrement, permanenceDecrement, learn, cycle.ActiveSynapses);
ComputeCycle colCycle = new ComputeCycle();
cycles[tuple.Key.Index] = colCycle;
foreach (var item in cellsOwnersOfActSegs)
{
colCycle.ActiveCells.Add(item);
colCycle.WinnerCells.Add(item);
}
}
else
{
Debug.Write("M");
//
// If no active segments are detected (start of learning) then all cells are activated
// and a random single cell is chosen as a winner.
BurstingResult burstingResult = BurstColumn(conn, activeColumnData.Column(), activeColumnData.MatchingSegments,
prevActiveCells, prevWinnerCells, permanenceIncrement, permanenceDecrement, conn.HtmConfig.Random,
learn);
ComputeCycle colCycle = new ComputeCycle();
cycles[tuple.Key.Index] = colCycle;
//
// Here we activate all cells by putting them to list of active cells.
foreach (var item in burstingResult.Cells)
{
colCycle.ActiveCells.Add(item);
}
colCycle.WinnerCells.Add((Cell)burstingResult.BestCell);
}
}
else
{
if (learn)
{
PunishPredictedColumn(conn, activeColumnData.ActiveSegments, activeColumnData.MatchingSegments,
prevActiveCells, prevWinnerCells, conn.HtmConfig.PredictedSegmentDecrement);
}
}
}
/**
* Calculate the active cells, using the current active columns and dendrite
* segments. Grow and reinforce synapses.
*
* <pre>
* Pseudocode:
* for each column
* if column is active and has active distal dendrite segments
* call activatePredictedColumn
* if column is active and doesn't have active distal dendrite segments
* call burstColumn
* if column is inactive and has matching distal dendrite segments
* call punishPredictedColumn
*
* </pre>
*
* @param conn
* @param activeColumnIndices
* @param learn
*/
protected ComputeCycle ActivateCells(Connections conn, int[] activeColumnIndices, bool learn)
{
ComputeCycle cycle = new ComputeCycle();
ColumnData activeColumnData = new ColumnData();
List <Cell> prevActiveCells = conn.getActiveCells();
List <Cell> prevWinnerCells = conn.getWinnerCells();
// The list of active columns.
List <Column> activeColumns = new List <Column>();
foreach (var indx in activeColumnIndices.OrderBy(i => i))
{
activeColumns.Add(conn.getColumn(indx));
}
//Func<Object, Column> segToCol = segment => ((DistalDendrite)segment).getParentCell().getParentColumnIndex();
Func <Object, Column> segToCol = (segment) =>
{
var colIndx = ((DistalDendrite)segment).GetParentCell().getParentColumnIndex();
var parentCol = this.connections.getMemory().GetColumn(colIndx);
return(parentCol);
};
Func <object, Column> times1Fnc = x => (Column)x;
var list = new Pair <List <object>, Func <object, Column> > [3];
list[0] = new Pair <List <object>, Func <object, Column> >(Array.ConvertAll(activeColumns.ToArray(), item => (object)item).ToList(), times1Fnc);
list[1] = new Pair <List <object>, Func <object, Column> >(Array.ConvertAll(conn.getActiveSegments().ToArray(), item => (object)item).ToList(), segToCol);
list[2] = new Pair <List <object>, Func <object, Column> >(Array.ConvertAll(conn.getMatchingSegments().ToArray(), item => (object)item).ToList(), segToCol);
GroupBy2 <Column> grouper = GroupBy2 <Column> .of(list);
double permanenceIncrement = conn.HtmConfig.PermanenceIncrement;
double permanenceDecrement = conn.HtmConfig.PermanenceDecrement;
//
// Grouping by columns, which have active and matching segments.
foreach (var tuple in grouper)
{
Console.Write(":");
activeColumnData = activeColumnData.Set(tuple);
if (activeColumnData.IsExistAnyActiveCol(cIndexofACTIVE_COLUMNS))
{
// If there are some active segments on the column already...
if (activeColumnData.ActiveSegments != null && activeColumnData.ActiveSegments.Count > 0)
{
Debug.Write(".");
List <Cell> cellsOwnersOfActSegs = ActivatePredictedColumn(conn, activeColumnData.ActiveSegments,
activeColumnData.MatchingSegments, prevActiveCells, prevWinnerCells,
permanenceIncrement, permanenceDecrement, learn);
foreach (var item in cellsOwnersOfActSegs)
{
cycle.ActiveCells.Add(item);
cycle.WinnerCells.Add(item);
}
//foreach (var item in cellsOwnersOfActSegs)
//{
// cycle.WinnerCells.Add(item);
//}
}
else
{
Debug.Write("B.");
//
// If no active segments are detected (start of learning) then all cells are activated
// and a random single cell is chosen as a winner.
BurstingResult burstingResult = BurstColumn(conn, activeColumnData.Column(), activeColumnData.MatchingSegments,
prevActiveCells, prevWinnerCells, permanenceIncrement, permanenceDecrement, conn.getRandom(),
learn);
//
// Here we activate all cells by putting them to list of active cells.
foreach (var item in burstingResult.Cells)
{
cycle.ActiveCells.Add(item);
}
cycle.WinnerCells.Add((Cell)burstingResult.BestCell);
}
}
else
{
if (learn)
{
punishPredictedColumn(conn, activeColumnData.ActiveSegments, activeColumnData.MatchingSegments,
prevActiveCells, prevWinnerCells, conn.getPredictedSegmentDecrement());
}
}
}
//int[] arr = new int[cycle.winnerCells.Count];
//int count = 0;
//foreach (Cell activeCell in cycle.winnerCells)
//{
// arr[count] = activeCell.Index;
// count++;
//}
return(cycle);
}