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public GetActiveSynapses ( int t, bool learning, bool connectedOnly ) : List |
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| t | int | |
| learning | bool | |
| connectedOnly | bool | |
| 리턴 | List |
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// Return a segmentUpdate data structure containing a list of proposed changes to segment.
// Let activeSynapses be the list of active synapses where the originating cells have
// their activeState output = 1 at time step t.
// (This list is empty if s = -1 since the segment doesn't exist.) newSynapses is an optional argument
// that defaults to false. If newSynapses is true, then newSynapseCount - count(activeSynapses) synapses
// are added to activeSynapses. These synapses are randomly chosen from the set of cells that have
// learnState output = 1 at time step t.
public SegmentUpdate GetSegmentActiveSynapses(int t, HTMSegment segment = null, bool newSynapses = false)
{
SegmentUpdate segmentUpdate = new SegmentUpdate();
segmentUpdate.ActiveSynapses = new List <HTMSynapse>();
segmentUpdate.NewSynapses = new List <HTMCell>();
segmentUpdate.AddNewSynapses = newSynapses;
segmentUpdate.Segment = segment;
if (segmentUpdate.Segment != null)
{
segmentUpdate.ActiveSynapses = segment.GetActiveSynapses(t, false, false);
}
if (!newSynapses)
{
return(segmentUpdate);
}
int newSynapseCount = Math.Max(0, _newSynapseCount - segmentUpdate.ActiveSynapses.Count);
//List<HTMCell> learningCells = GetRandomActiveCells(t);
List <HTMCell> learningCells = GetRandomLearningCells(t);
newSynapseCount = Math.Min(newSynapseCount, learningCells.Count);
// Truncate the array of learning cells. To do : get a random sample of the array.
for (int i = 0; i < newSynapseCount; i++)
{
segmentUpdate.NewSynapses.Add(learningCells[i]);
}
return(segmentUpdate);
}
public void ComputeOverlap()
{
_overlap = _proximalSegment.GetActiveSynapses(0, false, false).Count;
if (_overlap < _region.MinOverlap)
{
_overlap = 0;
}
else
{
_overlap *= _boost;
}
}
// Return a segmentUpdate data structure containing a list of proposed changes to segment.
// Let activeSynapses be the list of active synapses where the originating cells have
// their activeState output = 1 at time step t.
// (This list is empty if s = -1 since the segment doesn't exist.) newSynapses is an optional argument
// that defaults to false. If newSynapses is true, then newSynapseCount - count(activeSynapses) synapses
// are added to activeSynapses. These synapses are randomly chosen from the set of cells that have
// learnState output = 1 at time step t.
public SegmentUpdate GetSegmentActiveSynapses(int t, HTMSegment segment = null, bool newSynapses = false)
{
SegmentUpdate segmentUpdate = new SegmentUpdate();
segmentUpdate.ActiveSynapses = new List<HTMSynapse>();
segmentUpdate.NewSynapses = new List<HTMCell>();
segmentUpdate.AddNewSynapses = newSynapses;
segmentUpdate.Segment = segment;
if (segmentUpdate.Segment != null)
segmentUpdate.ActiveSynapses = segment.GetActiveSynapses(t, false, false);
if (!newSynapses)
return segmentUpdate;
int newSynapseCount = Math.Max(0, _newSynapseCount - segmentUpdate.ActiveSynapses.Count);
//List<HTMCell> learningCells = GetRandomActiveCells(t);
List<HTMCell> learningCells = GetRandomLearningCells(t);
newSynapseCount = Math.Min(newSynapseCount, learningCells.Count);
// Truncate the array of learning cells. To do : get a random sample of the array.
for (int i = 0 ; i < newSynapseCount; i++)
segmentUpdate.NewSynapses.Add(learningCells[i]);
return segmentUpdate;
}