/**
* 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 TestActivateCorrectlyPredictiveCells()
{
TemporalMemory tm = new TemporalMemory();
Connections cn = new Connections();
Parameters p = GetDefaultParameters();
p.Apply(cn);
TemporalMemory.Init(cn);
int[] previousActiveColumns = { 0 };
int[] activeColumns = { 1 };
Cell cell4 = cn.GetCell(4);
HashSet <Cell> expectedActiveCells = new HashSet <Cell> {
cell4
}; //Stream.of(cell4).collect(Collectors.toSet());
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.IsTrue(cc.PredictiveCells().SetEquals(expectedActiveCells));
ComputeCycle cc2 = tm.Compute(cn, activeColumns, true);
Assert.IsTrue(cc2.ActiveCells().SetEquals(expectedActiveCells));
}
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 testBurstUnpredictedColumns()
{
TemporalMemory tm = new TemporalMemory();
Connections cn = new Connections();
Parameters p = GetDefaultParameters();
p.Apply(cn);
TemporalMemory.Init(cn);
int[] activeColumns = { 0 };
HashSet <Cell> burstingCells = cn.GetCellSet(new int[] { 0, 1, 2, 3 });
ComputeCycle cc = tm.Compute(cn, activeColumns, true);
Assert.IsTrue(cc.ActiveCells().SetEquals(burstingCells));
}
public void testAddSegmentToCellWithFewestSegments()
{
bool grewOnCell1 = false;
bool grewOnCell2 = false;
for (int seed = 0; seed < 100; seed++)
{
TemporalMemory tm = new TemporalMemory();
Connections cn = new Connections();
Parameters p = GetDefaultParameters(null, Parameters.KEY.MAX_NEW_SYNAPSE_COUNT, 4);
p = GetDefaultParameters(p, Parameters.KEY.PREDICTED_SEGMENT_DECREMENT, 0.02);
p = GetDefaultParameters(p, Parameters.KEY.SEED, seed);
p.SetParameterByKey(Parameters.KEY.RANDOM, new XorshiftRandom(seed));
p.Apply(cn);
TemporalMemory.Init(cn);
int[] prevActiveColumns = { 1, 2, 3, 4 };
Cell[] prevActiveCells = { cn.GetCell(4), cn.GetCell(5), cn.GetCell(6), cn.GetCell(7) };
int[] activeColumns = { 0 };
Cell[] nonMatchingCells = { cn.GetCell(0), cn.GetCell(3) };
HashSet <Cell> activeCells = cn.GetCellSet(new int[] { 0, 1, 2, 3 });
DistalDendrite segment1 = cn.CreateSegment(nonMatchingCells[0]);
cn.CreateSynapse(segment1, prevActiveCells[0], 0.5);
DistalDendrite segment2 = cn.CreateSegment(nonMatchingCells[1]);
cn.CreateSynapse(segment2, prevActiveCells[1], 0.5);
tm.Compute(cn, prevActiveColumns, true);
ComputeCycle cc = tm.Compute(cn, activeColumns, true);
Assert.IsTrue(cc.ActiveCells().SetEquals(activeCells));
Assert.AreEqual(3, cn.GetNumSegments());
Assert.AreEqual(1, cn.GetNumSegments(cn.GetCell(0)));
Assert.AreEqual(1, cn.GetNumSegments(cn.GetCell(3)));
Assert.AreEqual(1, cn.GetNumSynapses(segment1));
Assert.AreEqual(1, cn.GetNumSynapses(segment2));
List <DistalDendrite> segments = new List <DistalDendrite>(cn.GetSegments(cn.GetCell(1)));
if (segments.Count == 0)
{
List <DistalDendrite> segments2 = cn.GetSegments(cn.GetCell(2));
Assert.IsFalse(segments2.Count == 0);
grewOnCell2 = true;
segments.AddRange(segments2);
}
else
{
grewOnCell1 = true;
}
Assert.AreEqual(1, segments.Count);
List <Synapse> synapses = segments[0].GetAllSynapses(cn);
Assert.AreEqual(4, synapses.Count);
HashSet <Column> columnCheckList = cn.GetColumnSet(prevActiveColumns);
foreach (Synapse synapse in synapses)
{
Assert.AreEqual(0.2, synapse.GetPermanence(), 0.01);
Column column = synapse.GetPresynapticCell().GetColumn();
Assert.IsTrue(columnCheckList.Contains(column));
columnCheckList.Remove(column);
}
Assert.AreEqual(0, columnCheckList.Count);
}
Assert.IsTrue(grewOnCell1);
Assert.IsTrue(grewOnCell2);
}