public void SerializeSpatialPooler()
{
HomeostaticPlasticityController homeostaticPlasticityActivator = new HomeostaticPlasticityController();
SpatialPooler spatial = new SpatialPooler(homeostaticPlasticityActivator);
using (StreamWriter sw = new StreamWriter($"ser_{nameof(SerializeSpatialPooler)}.txt"))
{
spatial.Serialize(sw);
}
}
public void SerializeEmptySpatialPooler()
{
SpatialPooler spatial = new SpatialPooler();
using (StreamWriter sw = new StreamWriter($"ser_{nameof(SerializeEmptySpatialPooler)}.txt"))
{
spatial.Serialize(sw);
}
//using (StreamReader sr = new StreamReader($"ser_{nameof(SerializeEmptySpatialPooler)}.txt"))
//{
// SpatialPooler spatial1 = SpatialPooler.Deserialize(sr);
// Assert.IsTrue(spatial1.Equals(spatial));
//}
}
public void CategorySequenceExperiment()
{
bool learn = true;
Parameters p = Parameters.getAllDefaultParameters();
p.Set(KEY.RANDOM, new ThreadSafeRandom(42));
p.Set(KEY.INPUT_DIMENSIONS, new int[] { 100 });
p.Set(KEY.CELLS_PER_COLUMN, 30);
string[] categories = new string[] { "A", "B", "C", "D" };
//string[] categories = new string[] { "A", "B", "C", "D", "E", "F", "G", "H", "I", "K", "L" , "M", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "Ö" };
CortexNetwork net = new CortexNetwork("my cortex");
List <CortexRegion> regions = new List <CortexRegion>();
CortexRegion region0 = new CortexRegion("1st Region");
regions.Add(region0);
SpatialPooler sp1 = new SpatialPooler();
TemporalMemory tm1 = new TemporalMemory();
var mem = new Connections();
p.apply(mem);
sp1.init(mem, UnitTestHelpers.GetMemory());
tm1.init(mem);
Dictionary <string, object> settings = new Dictionary <string, object>();
//settings.Add("W", 25);
settings.Add("N", 100);
//settings.Add("Radius", 1);
EncoderBase encoder = new CategoryEncoder(categories, settings);
//encoder.Encode()
CortexLayer <object, object> layer1 = new CortexLayer <object, object>("L1");
region0.AddLayer(layer1);
layer1.HtmModules.Add("encoder", encoder);
layer1.HtmModules.Add("sp", sp1);
//layer1.HtmModules.Add(tm1);
//layer1.Compute();
//IClassifier<string, ComputeCycle> cls = new HtmClassifier<string, ComputeCycle>();
HtmClassifier <string, ComputeCycle> cls = new HtmClassifier <string, ComputeCycle>();
HtmUnionClassifier <string, ComputeCycle> cls1 = new HtmUnionClassifier <string, ComputeCycle>();
//string[] inputs = new string[] { "A", "B", "C", "D" };
string[] inputs = new string[] { "A", "B", "C", "D" };
//
// This trains SP.
foreach (var input in inputs)
{
Debug.WriteLine($" ** {input} **");
for (int i = 0; i < 3; i++)
{
var lyrOut = layer1.Compute((object)input, learn) as ComputeCycle;
}
}
sp1.Serializer("spCSTSerialized.json");
var sp2 = SpatialPooler.Deserializer("spCSTSerialized.json");
layer1.HtmModules.Remove("sp");
layer1.HtmModules.Add("sp", sp2);
// 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;
//cls1.Learn(input, lyrOut.activeCells.ToArray(), learn);
//Debug.WriteLine($"Current Input: {input}");
cls.Learn(input, lyrOut.ActiveCells.ToArray(), lyrOut.predictiveCells.ToArray());
Debug.WriteLine($"Current Input: {input}");
if (learn == false)
{
Debug.WriteLine($"Predict Input When Not Learn: {cls.GetPredictedInputValue(lyrOut.predictiveCells.ToArray())}");
}
else
{
Debug.WriteLine($"Predict Input: {cls.GetPredictedInputValue(lyrOut.predictiveCells.ToArray())}");
}
Debug.WriteLine("-----------------------------------------------------------\n----------------------------------------------------------");
}
if (i == 10)
{
Debug.WriteLine("Stop Learning From Here----------------------------");
learn = false;
}
// tm1.reset(mem);
}
Debug.WriteLine("------------------------------------------------------------------------\n----------------------------------------------------------------------------");
/*
* learn = false;
* for (int i = 0; i < 19; i++)
* {
* foreach (var input in inputs)
* {
* layer1.Compute((object)input, learn);
* }
* }
*/
sp1.Serialize("tm.serialize.json");
}
