/// <summary>
/// For this column, return the cell with the best matching <see cref="Segment"/>
/// (at time t-1 or t). Only consider segments that are predicting cell activation
/// to occur in exactly numberPredictionSteps many time steps from now. If no cell
/// has a matching segment, then return the cell with the fewest number of segments.
/// </summary>
/// <param name="t">only consider active segments at time t.</param>
/// <param name="numberPredictionSteps">only consider segments that are predicting
/// cell activation to occur in exactly this many time steps from now.</param>
/// <returns>Tuple containing the best cell and its best <see cref="Segment"/>
/// (may be None).</returns>
internal Tuple <Cell, DistalSegment> GetBestMatchingCell(int t, int numberPredictionSteps)
{
Cell bestCell = null;
DistalSegment bestDistalSegment = null;
int bestNumberActiveSynapses = 0;
// Chooses the cell with the best matching segment.
foreach (var cell in this.Cells)
{
DistalSegment distalSegment = cell.GetBestMatchingDistalSegment(t, numberPredictionSteps);
if (distalSegment != null)
{
int numberActiveSynapses = distalSegment.GetActiveSynapses(t).Count;
if (numberActiveSynapses > bestNumberActiveSynapses)
{
bestCell = cell;
bestDistalSegment = distalSegment;
bestNumberActiveSynapses = numberActiveSynapses;
}
}
}
// If there are no active sequences, return the cell with the fewest number of
// segments
if (bestCell == null)
{
int bestNumberActiveSegments = int.MaxValue;
foreach (var cell in this.Cells)
{
int numberActiveSegments = cell.DistalSegments.Count;
if (numberActiveSegments < bestNumberActiveSegments)
{
bestNumberActiveSegments = numberActiveSegments;
bestCell = cell;
}
}
// Pick a random cell among those with equal segment counts
// TODO: is there a more efficient way to do this maybe?
var candidates = new List <Cell>();
foreach (var cell in this.Cells)
{
if (cell.DistalSegments.Count == bestNumberActiveSegments)
{
candidates.Add(cell);
}
}
if (candidates.Count > 1)
{
bestCell = candidates[this._random.Next(candidates.Count)];
}
// Leave bestSegment null to indicate a new segment is to be added
}
return(new Tuple <Cell, DistalSegment>(bestCell, bestDistalSegment));
}
///<summary>
/// Add a new <see cref="SegmentUpdate"/> object to this <see cref="Cell"/>
/// containing proposed changes to the specified segment.
///</summary>
///<remarks>
/// If the segment is None, then a new segment is to be added, otherwise
/// the specified segment is updated. If the segment exists, find all active
/// synapses for the segment (either at t or t-1)
/// and mark them as needing to be updated. If newSynapses is true, then
/// Region.newNumberSynapses - len(activeSynapses) new synapses are added to the
/// segment to be updated. The (new) synapses are randomly chosen from the set
/// of current learning cells (within Region.localityRadius if set).
///
/// These segment updates are only applied when the applySegmentUpdates
/// method is later called on this Cell.
///</remarks>
internal SegmentUpdate UpdateDistalSegmentActiveSynapses(int t, DistalSegment distalSegment, bool newSynapses = false)
{
// Let ActiveSynapses be the list of active synapses where the originating
// cells have their ActiveState output = true at time step t.
// (This list is empty if segment = null since the segment doesn't exist.)
var activeSynapses = new List <Synapse>();
if (distalSegment != null)
{
activeSynapses = distalSegment.GetActiveSynapses(t);
}
var segmentUpdate = new SegmentUpdate(this, distalSegment, activeSynapses, newSynapses);
this.SegmentUpdates.Add(segmentUpdate);
return(segmentUpdate);
}
///<summary>
/// Add a new <see cref="SegmentUpdate"/> object to this <see cref="Cell"/>
/// containing proposed changes to the specified segment.
///</summary>
///<remarks>
/// If the segment is None, then a new segment is to be added, otherwise
/// the specified segment is updated. If the segment exists, find all active
/// synapses for the segment (either at t or t-1)
/// and mark them as needing to be updated. If newSynapses is true, then
/// Region.newNumberSynapses - len(activeSynapses) new synapses are added to the
/// segment to be updated. The (new) synapses are randomly chosen from the set
/// of current learning cells (within Region.localityRadius if set).
///
/// These segment updates are only applied when the applySegmentUpdates
/// method is later called on this Cell.
///</remarks>
internal SegmentUpdate UpdateDistalSegmentActiveSynapses(int t, DistalSegment distalSegment, bool newSynapses = false)
{
// Let ActiveSynapses be the list of active synapses where the originating
// cells have their ActiveState output = true at time step t.
// (This list is empty if segment = null since the segment doesn't exist.)
var activeSynapses = new List<Synapse>();
if (distalSegment != null)
{
activeSynapses = distalSegment.GetActiveSynapses(t);
}
var segmentUpdate = new SegmentUpdate(this, distalSegment, activeSynapses, newSynapses);
this.SegmentUpdates.Add(segmentUpdate);
return segmentUpdate;
}
