// For the given column, return the cell with the best matching segment.
// If no cell has a matching segment, then return the cell with the fewest number of segments.
public CellSegmentInfo GetBestMatchingCell(bool sequence, int t)
{
HTMCell bestCell = null;
HTMSegment bestSeg = null;
int bestCount = 0;
HTMSegment seg;
int synapseCount;
foreach (HTMCell cell in _cells)
{
seg = cell.GetBestMatchingSegment(t, sequence);
// TO DO . to study in depth. the previsous function is aggressive in finding the segment but
// nevertheless exclude segment with activation under minThreshold, this could bring
// to add step by step similar segments?
if (seg != null)
{
synapseCount = seg.GetActiveSynapses(t, false, false).Count;
if (synapseCount > bestCount)
{
bestCell = cell;
bestSeg = seg;
bestCount = synapseCount;
}
}
}
if (bestCell == null)
{
int fewestSegmentCount = int.MaxValue;
foreach (HTMCell cell in _cells)
{
if (cell.DistalSegments.Count < fewestSegmentCount)
{
fewestSegmentCount = cell.DistalSegments.Count;
bestCell = cell;
//if (cell.DistalSegments.Count > 0)
// bestSeg = cell.DistalSegments[0]; // choose the first segment
}
}
}
CellSegmentInfo cellSegmentInfo = new CellSegmentInfo();
cellSegmentInfo.Cell = bestCell;
cellSegmentInfo.Segment = bestSeg;
return(cellSegmentInfo);
}
public void StepTemporalPooling()
{
// Phase 1
// From the Numenta Docs:
// The first phase calculates the active state for each cell.
// For each winning column we determine which cells should become active.
// If the bottom-up input was predicted by any cell (i.e. its predictiveState was 1 due to
// a sequence segment in the previous time step), then those cells become active (lines 4-9).
// If the bottom-up input was unexpected (i.e. no cells had predictiveState output on),
// then each cell in the column becomes active (lines 11-13).
foreach (HTMColumn col in _activeColumns)
{
bool buPredicted = false;
bool lcChosen = false;
foreach (HTMCell cell in col.Cells)
{
if (cell.GetPredicting(-1))
{
//
_correctPrediction++;
HTMSegment segment = cell.GetActiveSegment(-1);
if (segment != null && segment.IsSequence)
{
buPredicted = true;
cell.SetActive(true);
if (_doTemporalLearning)
{
if (segment.GetActive(-1, true, false))
{
lcChosen = true;
cell.SetLearning(true);
}
}
}
}
}
if (!buPredicted)
{
foreach (HTMCell cell in col.Cells)
{
cell.SetActive(true);
}
}
// If the bottom-up input was not predicted, the best matching cell is chosen as the learning cell and a new segment is added to that cell.
if (_doTemporalLearning)
{
if (!lcChosen)
{
CellSegmentInfo bestCellSegmentInfo = col.GetBestMatchingCell(true, -1);
bestCellSegmentInfo.Cell.SetLearning(true);
SegmentUpdate segmentToUpdate = bestCellSegmentInfo.Cell.GetSegmentActiveSynapses(-1, bestCellSegmentInfo.Segment, true);
segmentToUpdate.IsSequence = true;
if (segmentToUpdate.Segment != null || (segmentToUpdate.AddNewSynapses && segmentToUpdate.NewSynapses.Count > 0)) // queue the update only if necessary
{
bestCellSegmentInfo.Cell.SegmentUpdateList.Add(segmentToUpdate);
}
}
}
}
// Phase 2
// The second phase calculates the predictive state for each cell.
// A cell will turn on its predictive state output if one of its segments becomes active,
// i.e. if enough of its lateral inputs are currently active due to feed-forward input.
// In this case, the cell queues up the following changes:
// a) reinforcement of the currently active segment (lines 47-48),
// and b) reinforcement of a segment that could have predicted this activation,
// i.e. a segment that has a (potentially weak) match to activity during the previous time step (lines 50-53).
foreach (HTMColumn col in _columns)
{
foreach (HTMCell cell in col.Cells)
{
//if (cell.GetActive(0)) // Cells with active dendrite segments are put in the predictive state UNLESS they are already active due to feed-forward input.
// continue;
foreach (HTMSegment seg in cell.DistalSegments)
{
if (!seg.GetActive(0, false, false))
{
continue;
}
cell.SetPredicting(true);
// TO DO : check. Active and predictive state are mutual exclusive?
// if so, the changing from active to predicting must be done
// through a temporary array to avoid cell asymmetrical behavior.
//cell.SetActive(false);
if (_doTemporalLearning)
{
// a) reinforcement of the currently active segment
SegmentUpdate activeSegUpdate = cell.GetSegmentActiveSynapses(0, seg);
cell.SegmentUpdateList.Add(activeSegUpdate);
// b) reinforcement of a segment that could have predicted this activation,
HTMSegment predSegment = cell.GetBestMatchingSegment(-1, true);
if (predSegment == null)
{
continue;
}
SegmentUpdate predSegUpdate = cell.GetSegmentActiveSynapses(-1, predSegment, true);
cell.SegmentUpdateList.Add(predSegUpdate);
}
}
}
}
// Phase 3
// The third and last phase actually carries out learning.
// In this phase segment updates that have been queued up are actually
// implemented once we get feed-forward input and the cell is chosen
// as a learning cell (lines 56-57). Otherwise,
// if the cell ever stops predicting for any reason,
// we negatively reinforce the segments (lines 58-60).
if (_doTemporalLearning)
{
foreach (HTMColumn col in _columns)
{
foreach (HTMCell cell in col.Cells)
{
if (cell.SegmentUpdateList.Count == 0) // if there isn't segment to adapt then continue.
{
continue;
}
if (cell.GetLearning(0))
{
// once we get feed-forward input and the cell is chosen as a learning cell
cell.AdaptSegments(true);
}
else // if (!cell.GetPredicting(0) && cell.GetPredicting(-1))
{
// if the cell ever stops predicting for any reason,
// we negatively reinforce the segments
cell.AdaptSegments(false);
}
}
}
}
}
// For the given column, return the cell with the best matching segment.
// If no cell has a matching segment, then return the cell with the fewest number of segments.
public CellSegmentInfo GetBestMatchingCell(bool sequence, int t)
{
HTMCell bestCell = null;
HTMSegment bestSeg = null;
int bestCount = 0;
HTMSegment seg;
int synapseCount;
foreach (HTMCell cell in _cells)
{
seg = cell.GetBestMatchingSegment(t, sequence);
// TO DO . to study in depth. the previsous function is aggressive in finding the segment but
// nevertheless exclude segment with activation under minThreshold, this could bring
// to add step by step similar segments?
if (seg != null)
{
synapseCount = seg.GetActiveSynapses(t, false, false).Count;
if (synapseCount > bestCount)
{
bestCell = cell;
bestSeg = seg;
bestCount = synapseCount;
}
}
}
if (bestCell == null)
{
int fewestSegmentCount = int.MaxValue;
foreach (HTMCell cell in _cells)
{
if (cell.DistalSegments.Count < fewestSegmentCount)
{
fewestSegmentCount = cell.DistalSegments.Count;
bestCell = cell;
//if (cell.DistalSegments.Count > 0)
// bestSeg = cell.DistalSegments[0]; // choose the first segment
}
}
}
CellSegmentInfo cellSegmentInfo = new CellSegmentInfo();
cellSegmentInfo.Cell = bestCell;
cellSegmentInfo.Segment = bestSeg;
return cellSegmentInfo;
}
