/**
* 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
*/
public void ActivateCells(Connections conn, ComputeCycle cycle, int[] activeColumnIndices, bool learn)
{
ColumnData columnData = new ColumnData();
HashSet <Cell> prevActiveCells = conn.GetActiveCells();
HashSet <Cell> prevWinnerCells = conn.GetWinnerCells();
List <Column> activeColumns = activeColumnIndices
.OrderBy(i => i)
.Select(i => conn.GetColumn(i))
.ToList();
Func <Column, Column> identity = c => c;
Func <DistalDendrite, Column> segToCol = segment => segment.GetParentCell().GetColumn();
//@SuppressWarnings({ "rawtypes" })
GroupBy2 <Column> grouper = GroupBy2 <Column> .Of(
new Tuple <List <object>, Func <object, Column> >(activeColumns.Cast <object>().ToList(), x => identity((Column)x)),
new Tuple <List <object>, Func <object, Column> >(new List <DistalDendrite>(conn.GetActiveSegments()).Cast <object>().ToList(), x => segToCol((DistalDendrite)x)),
new Tuple <List <object>, Func <object, Column> >(new List <DistalDendrite>(conn.GetMatchingSegments()).Cast <object>().ToList(), x => segToCol((DistalDendrite)x)));
double permanenceIncrement = conn.GetPermanenceIncrement();
double permanenceDecrement = conn.GetPermanenceDecrement();
foreach (Tuple t in grouper)
{
columnData = columnData.Set(t);
if (columnData.IsNotNone(ACTIVE_COLUMNS))
{
if (columnData.ActiveSegments().Any())
{
List <Cell> cellsToAdd = ActivatePredictedColumn(conn, columnData.ActiveSegments(),
columnData.MatchingSegments(), prevActiveCells, prevWinnerCells,
permanenceIncrement, permanenceDecrement, learn);
cycle.ActiveCells().UnionWith(cellsToAdd);
cycle.WinnerCells().UnionWith(cellsToAdd);
}
else
{
Tuple cellsXwinnerCell = BurstColumn(conn, columnData.Column(), columnData.MatchingSegments(),
prevActiveCells, prevWinnerCells, permanenceIncrement, permanenceDecrement, conn.GetRandom(),
learn);
cycle.ActiveCells().UnionWith((IEnumerable <Cell>)cellsXwinnerCell.Get(0));
cycle.WinnerCells().Add((Cell)cellsXwinnerCell.Get(1));
}
}
else
{
if (learn)
{
PunishPredictedColumn(conn, columnData.ActiveSegments(), columnData.MatchingSegments(),
prevActiveCells, prevWinnerCells, conn.GetPredictedSegmentDecrement());
}
}
}
}
public void testZeroActiveColumns()
{
TemporalMemory tm = new TemporalMemory();
Connections cn = new Connections();
Parameters p = GetDefaultParameters();
p.Apply(cn);
TemporalMemory.Init(cn);
int[] previousActiveColumns = { 0 };
Cell cell4 = cn.GetCell(4);
DistalDendrite activeSegment = cn.CreateSegment(cell4);
cn.CreateSynapse(activeSegment, cn.GetCell(0), 0.5);
cn.CreateSynapse(activeSegment, cn.GetCell(1), 0.5);
cn.CreateSynapse(activeSegment, cn.GetCell(2), 0.5);
cn.CreateSynapse(activeSegment, cn.GetCell(3), 0.5);
ComputeCycle cc = tm.Compute(cn, previousActiveColumns, true);
Assert.IsFalse(cc.ActiveCells().Count == 0);
Assert.IsFalse(cc.WinnerCells().Count == 0);
Assert.IsFalse(cc.PredictiveCells().Count == 0);
int[] zeroColumns = new int[0];
ComputeCycle cc2 = tm.Compute(cn, zeroColumns, true);
Assert.IsTrue(cc2.ActiveCells().Count == 0);
Assert.IsTrue(cc2.WinnerCells().Count == 0);
Assert.IsTrue(cc2.PredictiveCells().Count == 0);
}
public void testNewSegmentAddSynapsesToAllWinnerCells()
{
TemporalMemory tm = new TemporalMemory();
Connections cn = new Connections();
Parameters p = GetDefaultParameters(null, Parameters.KEY.MAX_NEW_SYNAPSE_COUNT, 4);
p.Apply(cn);
TemporalMemory.Init(cn);
int[] previousActiveColumns = { 0, 1, 2 };
int[] activeColumns = { 4 };
ComputeCycle cc = tm.Compute(cn, previousActiveColumns, true);
List <Cell> prevWinnerCells = new List <Cell>(cc.WinnerCells());
Assert.AreEqual(3, prevWinnerCells.Count);
cc = tm.Compute(cn, activeColumns, true);
List <Cell> winnerCells = new List <Cell>(cc.WinnerCells());
Assert.AreEqual(1, winnerCells.Count);
List <DistalDendrite> segments = winnerCells[0].GetSegments(cn);
Assert.AreEqual(1, segments.Count);
List <Synapse> synapses = segments[0].GetAllSynapses(cn);
List <Cell> presynapticCells = new List <Cell>();
foreach (Synapse synapse in synapses)
{
Assert.AreEqual(0.21, synapse.GetPermanence(), 0.01);
presynapticCells.Add(synapse.GetPresynapticCell());
}
presynapticCells.Sort();
Assert.IsTrue(prevWinnerCells.SequenceEqual(presynapticCells));
}
public void testActiveSegmentGrowSynapsesAccordingToPotentialOverlap()
{
TemporalMemory tm = new TemporalMemory();
Connections cn = new Connections();
Parameters p = GetDefaultParameters(null, Parameters.KEY.CELLS_PER_COLUMN, 1);
p = GetDefaultParameters(p, Parameters.KEY.MIN_THRESHOLD, 1);
p = GetDefaultParameters(p, Parameters.KEY.ACTIVATION_THRESHOLD, 2);
p = GetDefaultParameters(p, Parameters.KEY.MAX_NEW_SYNAPSE_COUNT, 4);
p.Apply(cn);
TemporalMemory.Init(cn);
// Use 1 cell per column so that we have easy control over the winner cells.
int[] previousActiveColumns = { 0, 1, 2, 3, 4 };
HashSet <Cell> prevWinnerCells = new HashSet <Cell>(Arrays.AsList(0, 1, 2, 3, 4)
.Select(i => cn.GetCell(i))
.ToList());
int[] activeColumns = { 5 };
DistalDendrite activeSegment = cn.CreateSegment(cn.GetCell(5));
cn.CreateSynapse(activeSegment, cn.GetCell(0), 0.5);
cn.CreateSynapse(activeSegment, cn.GetCell(1), 0.5);
cn.CreateSynapse(activeSegment, cn.GetCell(2), 0.2);
ComputeCycle cc = tm.Compute(cn, previousActiveColumns, true);
Assert.IsTrue(prevWinnerCells.SetEquals(cc.WinnerCells()));
cc = tm.Compute(cn, activeColumns, true);
HashSet <Cell> presynapticCells = new HashSet <Cell>(cn.GetSynapses(activeSegment)
.Select(s => s.GetPresynapticCell())
.ToList());
Assert.IsTrue(
presynapticCells.Count == 4 && (
!presynapticCells.Except(new List <Cell> {
cn.GetCell(0), cn.GetCell(1), cn.GetCell(2), cn.GetCell(3)
}).Any() ||
!presynapticCells.Except(new List <Cell> {
cn.GetCell(0), cn.GetCell(1), cn.GetCell(2), cn.GetCell(4)
}).Any()));
}
public void testMatchingSegmentAddSynapsesToSubsetOfWinnerCells()
{
TemporalMemory tm = new TemporalMemory();
Connections cn = new Connections();
Parameters p = GetDefaultParameters(null, Parameters.KEY.CELLS_PER_COLUMN, 1);
p = GetDefaultParameters(p, Parameters.KEY.MIN_THRESHOLD, 1);
p.Apply(cn);
TemporalMemory.Init(cn);
int[] previousActiveColumns = { 0, 1, 2, 3 };
HashSet <Cell> prevWinnerCells = cn.GetCellSet(new int[] { 0, 1, 2, 3 });
int[] activeColumns = { 4 };
DistalDendrite matchingSegment = cn.CreateSegment(cn.GetCell(4));
cn.CreateSynapse(matchingSegment, cn.GetCell(0), 0.5);
ComputeCycle cc = tm.Compute(cn, previousActiveColumns, true);
Assert.IsTrue(cc.WinnerCells().SetEquals(prevWinnerCells));
cc = tm.Compute(cn, activeColumns, true);
List <Synapse> synapses = cn.GetSynapses(matchingSegment);
Assert.AreEqual(3, synapses.Count);
synapses.Sort();
foreach (Synapse synapse in synapses)
{
if (synapse.GetPresynapticCell().GetIndex() == 0)
{
continue;
}
Assert.AreEqual(0.21, synapse.GetPermanence(), 0.01);
Assert.IsTrue(synapse.GetPresynapticCell().GetIndex() == 1 ||
synapse.GetPresynapticCell().GetIndex() == 2 ||
synapse.GetPresynapticCell().GetIndex() == 3);
}
}