public void SerializeSegmentActivityTest()
{
SegmentActivity segment = new SegmentActivity();
segment.ActiveSynapses = new Dictionary <int, int>();
segment.ActiveSynapses.Add(23, 1);
segment.ActiveSynapses.Add(24, 2);
segment.ActiveSynapses.Add(35, 3);
segment.PotentialSynapses = new Dictionary <int, int>();
segment.PotentialSynapses.Add(2, 56);
segment.PotentialSynapses.Add(22, 6);
segment.PotentialSynapses.Add(24, 26);
using (StreamWriter sw = new StreamWriter($"ser_{nameof(SerializeSegmentActivityTest)}.txt"))
{
segment.Serialize(sw);
}
using (StreamReader sr = new StreamReader($"ser_{nameof(SerializeSegmentActivityTest)}.txt"))
{
SegmentActivity segment1 = SegmentActivity.Deserialize(sr);
Assert.IsTrue(segment1.Equals(segment));
}
}
public void SerializeSegmentActivityTest()
{
SegmentActivity segment = new SegmentActivity();
using (StreamWriter sw = new StreamWriter($"ser_{nameof(SerializeSegmentActivityTest)}.txt"))
{
segment.Serialize(sw);
}
using (StreamReader sr = new StreamReader($"ser_{nameof(SerializeSegmentActivityTest)}.txt"))
{
SegmentActivity segment1 = SegmentActivity.Deserialize(sr);
Assert.IsTrue(segment1.Equals(segment));
}
}
/**
* Calculate dendrite segment activity, using the current active cells.
*
* <pre>
* Pseudocode:
* for each distal dendrite segment with activity >= activationThreshold
* mark the segment as active
* for each distal dendrite segment with unconnected activity >= minThreshold
* mark the segment as matching
* </pre>
*
* @param conn the Connectivity
* @param cycle Stores current compute cycle results
* @param learn If true, segment activations will be recorded. This information is used
* during segment cleanup.
*/
protected void ActivateDendrites(Connections conn, ComputeCycle cycle, bool learn)
{
SegmentActivity activity = conn.ComputeActivity(cycle.ActiveCells, conn.getConnectedPermanence());
int i = 0;
var activeSegments = new List <DistalDendrite>();
foreach (var item in activity.ActiveSynapses)
{
if (item.Value >= conn.getActivationThreshold())
{
activeSegments.Add(conn.GetSegmentForFlatIdx(item.Key));
}
}
//
// Step through all synapses on active cells and find involved segments.
var matchingSegments = new List <DistalDendrite>();
foreach (var item in activity.PotentialSynapses)
{
if (item.Value >= conn.getMinThreshold())
{
matchingSegments.Add(conn.GetSegmentForFlatIdx(item.Key));
}
}
//
// Step through all synapses on active cells with permanence over threshold (conencted synapses)
// and find involved segments.
activeSegments.Sort(GetComparer(conn.getNextSegmentOrdinal()));
matchingSegments.Sort(GetComparer(conn.getNextSegmentOrdinal()));
cycle.ActiveSegments = activeSegments;
cycle.MatchingSegments = matchingSegments;
conn.lastActivity = activity;
conn.setActiveCells(new List <Cell>(cycle.ActiveCells));
conn.setWinnerCells(new List <Cell>(cycle.WinnerCells));
conn.setActiveSegments(activeSegments);
conn.setMatchingSegments(matchingSegments);
// Forces generation of the predictive cells from the above active segments
conn.clearPredictiveCells();
//ISet<Cell> predictiveCells = conn.getPredictiveCells();
//string[] arr = new string[predictiveCells.Count];
//foreach (Cell c in predictiveCells)
//{
// arr[i] = c.Index + "-" + c.ParentColumnIndex;
// i++;
//}
//Debug.WriteLine($"ACT: {activity.Active.Count}, POT: {activity.Potential.Count}");
//string output = string.Join("", predictiveCells);
//Debug.WriteLine($"Active Segs: {activeSegments.Count} Matching segs: {matchingSegments.Count}, Predicted cells: {Helpers.StringifyVector(arr)}");
//Debug.WriteLine("-----------------------------------------------------\n-----------------------------------------------------");
if (learn)
{
foreach (var segment in activeSegments)
{
conn.recordSegmentActivity(segment);
}
conn.startNewIteration();
}
}
