[DataRow(81, 450, 1000)] //Sparsity - 0.18
//[DataRow(9, 90, 1000)] //Sparsity - 0.10
//[DataRow(9, 70, 1000)] //Sparsity - 0.128
//[DataRow(9, 60, 1000)] //Sparsity - 0.15
//[DataRow(9, 45, 1000)] //Sparsity - 0.20
//[DataRow(9, 40, 1000)] //Sparsity - 0.225
//[DataRow(9, 36, 1000)] //Sparsity - 0.25
//[DataRow(9, 30, 1000)] //Sparsity - 0.30
public void HtmSparsity_1(int W, int InputB, int loop)
{
string filename = "Sparsity_" + W + "." + InputB + ".csv";
using (StreamWriter writer = new StreamWriter(filename))
{
Debug.WriteLine($"Learning Cycles: {460}");
Debug.WriteLine("Cycle;Similarity");
//Parent loop / Outer loop
//This loop defines the number of times the experiment will run for the given data
for (int j = 0; j < loop; j++)
{
int inputBits = InputB;
bool learn = true;
Parameters p = Parameters.getAllDefaultParameters();
p.Set(KEY.RANDOM, new ThreadSafeRandom(42));
p.Set(KEY.INPUT_DIMENSIONS, new int[] { inputBits });
p.Set(KEY.CELLS_PER_COLUMN, 5);
p.Set(KEY.COLUMN_DIMENSIONS, new int[] { 500 });
CortexNetwork net = new CortexNetwork("my cortex");
List <CortexRegion> regions = new List <CortexRegion>();
CortexRegion region0 = new CortexRegion("1st Region");
regions.Add(region0);
SpatialPoolerMT sp1 = new SpatialPoolerMT();
TemporalMemory tm1 = new TemporalMemory();
var mem = new Connections();
p.apply(mem);
sp1.Init(mem, UnitTestHelpers.GetMemory());
tm1.Init(mem);
Dictionary <string, object> settings = new Dictionary <string, object>()
{
{ "W", W },
{ "N", inputBits },
{ "Radius", -1.0 },
{ "MinVal", 0.0 },
// { "MaxVal", 20.0 },
{ "Periodic", false },
{ "Name", "scalar" },
{ "ClipInput", false },
};
double max = 10;
List <double> lst = new List <double>();
for (double i = max - 1; i >= 0; i--)
{
lst.Add(i);
}
settings["MaxVal"] = max;
EncoderBase encoder = new ScalarEncoder(settings);
CortexLayer <object, object> layer1 = new CortexLayer <object, object>("L1");
//
// NewBorn learning stage.
region0.AddLayer(layer1);
layer1.HtmModules.Add("encoder", encoder);
layer1.HtmModules.Add("sp", sp1);
HtmClassifier <double, ComputeCycle> cls = new HtmClassifier <double, ComputeCycle>();
double[] inputs = lst.ToArray();
//
// This trains SP.
foreach (var input in inputs)
{
Debug.WriteLine($" ** {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);
int cycle = 0;
int matches = 0;
double lastPredictedValue = 0;
//
// Now, training with SP+TM. SP is pretrained on pattern.
//Child loop / Inner loop
for (int i = 0; i < 460; i++)
{
matches = 0;
cycle++;
foreach (var input in inputs)
{
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
cls.Learn(input, lyrOut.ActiveCells.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())}");
var predictedValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray());
Debug.WriteLine($"Current Input: {input} \t| - Predicted value in previous cycle: {lastPredictedValue} \t| Predicted Input for the next cycle: {predictedValue}");
if (input == lastPredictedValue)
{
matches++;
Debug.WriteLine($"Match {input}");
}
else
{
Debug.WriteLine($"Missmatch Actual value: {input} - Predicted value: {lastPredictedValue}");
}
lastPredictedValue = predictedValue;
}
if (i == 500)
{
Debug.WriteLine("Stop Learning From Here. Entering inference mode.");
learn = false;
}
//tm1.reset(mem);
Debug.WriteLine($"Cycle: {cycle}\tMatches={matches} of {inputs.Length}\t {(double)matches / (double)inputs.Length * 100.0}%");
if ((double)matches / (double)inputs.Length == 1)
{
writer.WriteLine($"{cycle}");
break;
}
}
}
Debug.WriteLine("New Iteration");
}
//cls.TraceState();
Debug.WriteLine("------------------------------------------------------------------------\n----------------------------------------------------------------------------");
}
