public void testRecycleLeastRecentlyActiveSegmentToMakeRoomForNewSegment()
{
TemporalMemory tm = new TemporalMemory();
Connections cn = new Connections();
Parameters p = getDefaultParameters(null, KEY.CELLS_PER_COLUMN, 1);
p = getDefaultParameters(p, KEY.INITIAL_PERMANENCE, 0.5);
p = getDefaultParameters(p, KEY.PERMANENCE_INCREMENT, 0.02);
p = getDefaultParameters(p, KEY.PERMANENCE_DECREMENT, 0.02);
p.Set(KEY.MAX_SEGMENTS_PER_CELL, 2);
p.apply(cn);
tm.init(cn);
int[] prevActiveColumns1 = { 0, 1, 2 };
int[] prevActiveColumns2 = { 3, 4, 5 };
int[] prevActiveColumns3 = { 6, 7, 8 };
int[] activeColumns = { 9 };
Cell cell9 = cn.getCell(9);
tm.Compute(prevActiveColumns1, true);
tm.Compute(activeColumns, true);
Assert.AreEqual(1, cn.getSegments(cell9).Count);
DistalDendrite oldestSegment = cn.getSegments(cell9)[0];
tm.reset(cn);
tm.Compute(prevActiveColumns2, true);
tm.Compute(activeColumns, true);
Assert.AreEqual(2, cn.getSegments(cell9).Count);
//Set<Cell> oldPresynaptic = cn.getSynapses(oldestSegment)
// .stream()
// .map(s->s.getPresynapticCell())
// .collect(Collectors.toSet());
var oldPresynaptic = cn.getSynapses(oldestSegment).Select(s => s.getPresynapticCell()).ToList();
tm.reset(cn);
tm.Compute(prevActiveColumns3, true);
tm.Compute(activeColumns, true);
Assert.AreEqual(2, cn.getSegments(cell9).Count);
// Verify none of the segments are connected to the cells the old
// segment was connected to.
foreach (DistalDendrite segment in cn.getSegments(cell9))
{
//Set<Cell> newPresynaptic = cn.getSynapses(segment)
// .stream()
// .map(s->s.getPresynapticCell())
// .collect(Collectors.toSet());
var newPresynaptic = cn.getSynapses(segment).Select(s => s.getPresynapticCell()).ToList();
Assert.IsTrue(areDisjoined <Cell>(oldPresynaptic, newPresynaptic));
}
}
public void LongerSequenceExperiment()
{
int inputBits = 1024;
bool learn = true;
Parameters p = Parameters.getAllDefaultParameters();
p.Set(KEY.RANDOM, new ThreadSafeRandom(42));
p.Set(KEY.INPUT_DIMENSIONS, new int[] { inputBits });
p.Set(KEY.CELLS_PER_COLUMN, 10);
p.Set(KEY.COLUMN_DIMENSIONS, new int[] { 2048 });
CortexNetwork net = new CortexNetwork("my cortex");
List <CortexRegion> regions = new List <CortexRegion>();
CortexRegion region0 = new CortexRegion("1st Region");
regions.Add(region0);
SpatialPoolerMT sp1 = new SpatialPoolerMT();
TemporalMemory tm1 = new TemporalMemory();
var mem = new Connections();
p.apply(mem);
sp1.init(mem, UnitTestHelpers.GetMemory());
tm1.init(mem);
Dictionary <string, object> settings = new Dictionary <string, object>()
{
{ "W", 21 },
{ "N", inputBits },
{ "Radius", -1.0 },
{ "MinVal", 0.0 },
// { "MaxVal", 20.0 },
{ "Periodic", false },
{ "Name", "scalar" },
{ "ClipInput", false },
};
double max = 50;
List <double> lst = new List <double>();
for (double i = 0; i < max; i++)
{
lst.Add(i);
}
settings["MaxVal"] = max;
EncoderBase encoder = new ScalarEncoder(settings);
CortexLayer <object, object> layer1 = new CortexLayer <object, object>("L1");
//
// NewBorn learning stage.
region0.AddLayer(layer1);
layer1.HtmModules.Add("encoder", encoder);
layer1.HtmModules.Add("sp", sp1);
HtmClassifier <double, ComputeCycle> cls = new HtmClassifier <double, ComputeCycle>();
double[] inputs = lst.ToArray();
//
// This trains SP.
foreach (var input in inputs)
{
Debug.WriteLine($" ** {input} **");
for (int i = 0; i < 3; i++)
{
var lyrOut = layer1.Compute((object)input, learn) as ComputeCycle;
}
}
// Here we add TM module to the layer.
layer1.HtmModules.Add("tm", tm1);
//
// Now, training with SP+TM. SP is pretrained on pattern.
for (int i = 0; i < 200; i++)
{
foreach (var input in inputs)
{
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
cls.Learn(input, lyrOut.ActiveCells.ToArray(), lyrOut.predictiveCells.ToArray());
Debug.WriteLine($"-------------- {input} ---------------");
if (learn == false)
{
Debug.WriteLine($"Inference mode");
}
Debug.WriteLine($"W: {Helpers.StringifyVector(lyrOut.WinnerCells.Select(c => c.Index).ToArray())}");
Debug.WriteLine($"P: {Helpers.StringifyVector(lyrOut.predictiveCells.Select(c => c.Index).ToArray())}");
Debug.WriteLine($"Current Input: {input} \t| Predicted Input: {cls.GetPredictedInputValue(lyrOut.predictiveCells.ToArray())}");
}
if (i == 50)
{
Debug.WriteLine("Stop Learning From Here. Entering inference mode.");
learn = false;
}
tm1.reset(mem);
}
cls.TraceState();
Debug.WriteLine("------------------------------------------------------------------------\n----------------------------------------------------------------------------");
}
