private void RunSpStabilityExperiment_1(int inputBits, Parameters p, EncoderBase encoder, List <double> inputValues)
{
string path = nameof(SpatialPooler_Stability_Experiment_1);
if (Directory.Exists(path))
{
Directory.Delete(path, true);
}
while (true)
{
Directory.CreateDirectory(path);
if (Directory.Exists(path) == false)
{
Thread.Sleep(300);
}
else
{
break;
}
}
Stopwatch sw = new Stopwatch();
sw.Start();
bool learn = true;
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();
var mem = new Connections();
p.apply(mem);
sp1.Init(mem, UnitTestHelpers.GetMemory());
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 = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int maxSPLearningCycles = 10000;
List <(double Element, (int Cycle, double Similarity)[] Oscilations)> oscilationResult = new List <(double Element, (int Cycle, double Similarity)[] Oscilations)>();
/// <summary>
///
/// </summary>
private void RunExperiment(int inputBits, Parameters p, EncoderBase encoder, List <double> inputValues)
{
Stopwatch sw = new Stopwatch();
sw.Start();
//INeuroVisualizer vis = new WSNeuroVisualizer();
//vis.InitModelAsync(new NeuroModel(null, (new long [10, 0]), 6));
int maxMatchCnt = 0;
bool learn = true;
//INeuroVisualizer vis = new WSNeuroVisualizer();
//GenerateNeuroModel model = new GenerateNeuroModel();
//vis.InitModel(model.CreateNeuroModel(new int[] { 1}, (long[,])p[KEY.COLUMN_DIMENSIONS], (int)p[KEY.CELLS_PER_COLUMN]));
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);
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 = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int maxSPLearningCycles = 5;
List <(double Element, (int Cycle, double Similarity)[] Oscilations)> oscilationResult = new List <(double Element, (int Cycle, double Similarity)[] Oscilations)>();
/// <summary>
///
/// </summary>
private void RunSpStabilityExperiment(int inputBits, Parameters p, EncoderBase encoder, List <double> inputValues)
{
Stopwatch sw = new Stopwatch();
sw.Start();
bool learn = true;
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();
var mem = new Connections();
p.apply(mem);
sp1.Init(mem, UnitTestHelpers.GetMemory());
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 = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int maxSPLearningCycles = 25000;
List <(double Element, (int Cycle, double Similarity)[] Oscilations)> oscilationResult = new List <(double Element, (int Cycle, double Similarity)[] Oscilations)>();
/// <summary>
///
/// </summary>
private void RunExperiment(int inputBits, Parameters p, EncoderBase encoder, List <double> inputValues)
{
Stopwatch sw = new Stopwatch();
sw.Start();
int maxMatchCnt = 0;
bool learn = true;
CortexNetwork net = new CortexNetwork("my cortex");
List <CortexRegion> regions = new List <CortexRegion>();
CortexRegion region0 = new CortexRegion("1st Region");
regions.Add(region0);
var mem = new Connections();
p.apply(mem);
//bool isInStableState = false;
//HtmClassifier<double, ComputeCycle> cls = new HtmClassifier<double, ComputeCycle>();
HtmClassifier <string, ComputeCycle> cls = new HtmClassifier <string, ComputeCycle>();
var numInputs = inputValues.Distinct().ToList().Count;
TemporalMemory tm1 = new TemporalMemory();
HomeostaticPlasticityController hpa = new HomeostaticPlasticityController(mem, numInputs * 55, (isStable, numPatterns, actColAvg, seenInputs) =>
{
if (isStable)
{
// Event should be fired when entering the stable state.
Debug.WriteLine($"STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
// Ideal SP should never enter unstable state after stable state.
Debug.WriteLine($"INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
Assert.IsTrue(numPatterns == numInputs);
//isInStableState = true;
cls.ClearState();
tm1.Reset(mem);
}, numOfCyclesToWaitOnChange: 25);
SpatialPoolerMT sp1 = new SpatialPoolerMT(hpa);
sp1.Init(mem, UnitTestHelpers.GetMemory());
tm1.Init(mem);
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("tm", tm1);
double[] inputs = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int cycle = 0;
int matches = 0;
string lastPredictedValue = "0";
Dictionary <double, List <List <int> > > activeColumnsLst = new Dictionary <double, List <List <int> > >();
foreach (var input in inputs)
{
if (activeColumnsLst.ContainsKey(input) == false)
{
activeColumnsLst.Add(input, new List <List <int> >());
}
}
int maxCycles = 3500;
int maxPrevInputs = inputValues.Count - 1;
List <string> previousInputs = new List <string>();
previousInputs.Add("-1.0");
//
// Now training with SP+TM. SP is pretrained on the given input pattern.
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle++;
Debug.WriteLine($"-------------- Cycle {cycle} ---------------");
foreach (var input in inputs)
{
Debug.WriteLine($"-------------- {input} ---------------");
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
var activeColumns = layer1.GetResult("sp") as int[];
activeColumnsLst[input].Add(activeColumns.ToList());
previousInputs.Add(input.ToString());
if (previousInputs.Count > maxPrevInputs + 1)
{
previousInputs.RemoveAt(0);
}
string key = GetKey(previousInputs, input);
List <Cell> actCells;
if (lyrOut.ActiveCells.Count == lyrOut.WinnerCells.Count)
{
actCells = lyrOut.ActiveCells;
}
else
{
actCells = lyrOut.WinnerCells;
}
cls.Learn(key, actCells.ToArray());
if (learn == false)
{
Debug.WriteLine($"Inference mode");
}
Debug.WriteLine($"Col SDR: {Helpers.StringifyVector(lyrOut.ActivColumnIndicies)}");
Debug.WriteLine($"Cell SDR: {Helpers.StringifyVector(actCells.Select(c => c.Index).ToArray())}");
if (key == lastPredictedValue)
{
matches++;
Debug.WriteLine($"Match. Actual value: {key} - Predicted value: {lastPredictedValue}");
}
else
{
Debug.WriteLine($"Missmatch! Actual value: {key} - Predicted value: {lastPredictedValue}");
}
if (lyrOut.PredictiveCells.Count > 0)
{
var predictedInputValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray());
Debug.WriteLine($"Current Input: {input} \t| Predicted Input: {predictedInputValue}");
lastPredictedValue = predictedInputValue;
}
else
{
Debug.WriteLine($"NO CELLS PREDICTED for next cycle.");
lastPredictedValue = string.Empty;
}
}
// The brain does not do that this way, so we don't use it.
// tm1.reset(mem);
double accuracy = matches / (double)inputs.Length * 100.0;
Debug.WriteLine($"Cycle: {cycle}\tMatches={matches} of {inputs.Length}\t {accuracy}%");
if (accuracy == 100.0)
{
maxMatchCnt++;
Debug.WriteLine($"100% accuracy reched {maxMatchCnt} times.");
if (maxMatchCnt >= 30)
{
sw.Stop();
Debug.WriteLine($"Exit experiment in the stable state after 30 repeats with 100% of accuracy. Elapsed time: {sw.ElapsedMilliseconds / 1000 / 60} min.");
learn = false;
//var testInputs = new double[] { 0.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 7.0, 1.0, 9.0, 12.0, 11.0, 0.0, 1.0 };
// C-0, D-1, E-2, F-3, G-4, H-5
//var testInputs = new double[] { 0.0, 0.0, 4.0, 4.0, 5.0, 5.0, 4.0, 3.0, 3.0, 2.0, 2.0, 1.0, 1.0, 0.0 };
//// Traverse the sequence and check prediction.
//foreach (var input in inputValues)
//{
// var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
// predictedInputValue = cls.GetPredictedInputValue(lyrOut.predictiveCells.ToArray());
// Debug.WriteLine($"I={input} - P={predictedInputValue}");
//}
/*
* //
* // Here we let the HTM predict sequence five times on its own.
* // We start with last predicted value.
* int cnt = 5 * inputValues.Count;
*
* Debug.WriteLine("---- Start Predicting the Sequence -----");
*
* //
* // This code snippet starts with some input value and tries to predict all next inputs
* // as they have been learned as a sequence.
* // We take a random value to start somwhere in the sequence.
* var predictedInputValue = inputValues[new Random().Next(0, inputValues.Count - 1)].ToString();
*
* List<string> predictedValues = new List<string>();
*
* while (--cnt > 0)
* {
* //var lyrOut = layer1.Compute(predictedInputValue, learn) as ComputeCycle;
* var lyrOut = layer1.Compute(double.Parse(predictedInputValue[predictedInputValue.Length - 1].ToString()), false) as ComputeCycle;
* predictedInputValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray());
* predictedValues.Add(predictedInputValue);
* };
*
* // Now we have a sequence of elements and watch in the trace if it matches to defined input set.
* foreach (var item in predictedValues)
* {
* Debug.Write(item);
* Debug.Write(" ,");
* }*/
break;
}
}
else if (maxMatchCnt > 0)
{
Debug.WriteLine($"At 100% accuracy after {maxMatchCnt} repeats we get a drop of accuracy with {accuracy}. This indicates instable state. Learning will be continued.");
maxMatchCnt = 0;
}
}
Debug.WriteLine("---- cell state trace ----");
cls.TraceState($"cellState_MinPctOverlDuty-{p[KEY.MIN_PCT_OVERLAP_DUTY_CYCLES]}_MaxBoost-{p[KEY.MAX_BOOST]}.csv");
Debug.WriteLine("---- Spatial Pooler column state ----");
foreach (var input in activeColumnsLst)
{
using (StreamWriter colSw = new StreamWriter($"ColumState_MinPctOverlDuty-{p[KEY.MIN_PCT_OVERLAP_DUTY_CYCLES]}_MaxBoost-{p[KEY.MAX_BOOST]}_input-{input.Key}.csv"))
{
Debug.WriteLine($"------------ {input.Key} ------------");
foreach (var actCols in input.Value)
{
Debug.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
colSw.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
}
}
}
Debug.WriteLine("------------ END ------------");
}
public void InputBitsExperiment(int W, int InputB, int loop)
{
string filename = "InputBits" + InputB + ".csv";
using (StreamWriter writer = new StreamWriter(filename))
{
Debug.WriteLine($"Learning Cycles: {460}");
Debug.WriteLine("Cycle;Similarity");
//Parent Loop
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($" ** {input} **");
for (int i = 0; i < 3; i++)
{
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
}
}
// Here we add TM module to the layer.
layer1.HtmModules.Add("tm", tm1);
int cycle = 0;
int matches = 0;
double lastPredictedValue = 0;
//
// Now, training with SP+TM. SP is pretrained on pattern.
//Child loop
for (int i = 0; i < 460; i++)
{
matches = 0;
cycle++;
foreach (var input in inputs)
{
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
cls.Learn(input, lyrOut.ActiveCells.ToArray(), lyrOut.PredictiveCells.ToArray());
Debug.WriteLine($"-------------- {input} ---------------");
if (learn == false)
{
Debug.WriteLine($"Inference mode");
}
Debug.WriteLine($"W: {Helpers.StringifyVector(lyrOut.WinnerCells.Select(c => c.Index).ToArray())}");
Debug.WriteLine($"P: {Helpers.StringifyVector(lyrOut.PredictiveCells.Select(c => c.Index).ToArray())}");
var predictedValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray());
Debug.WriteLine($"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($"Cycle: {cycle}\tMatches={matches} of {inputs.Length}\t {matches / (double)inputs.Length * 100.0}%");
if (matches / (double)inputs.Length == 1)
{
writer.WriteLine($"{cycle}");
break;
}
}
}
Debug.WriteLine("New Iteration");
}
//cls.TraceState();
Debug.WriteLine("------------------------------------------------------------------------\n----------------------------------------------------------------------------");
}
private void RunSerializationExperiment(double maxBoost, double minOverlapCycles, int inputBits, Parameters p, EncoderBase encoder, List <double> inputValues)
{
string path = nameof(RunSerializationExperiment);
if (Directory.Exists(path))
{
Directory.Delete(path, true);
}
Directory.CreateDirectory(path);
Stopwatch sw = new Stopwatch();
sw.Start();
bool learn = true;
CortexNetwork net = new CortexNetwork("my cortex");
List <CortexRegion> regions = new List <CortexRegion>();
CortexRegion region0 = new CortexRegion("1st Region");
regions.Add(region0);
var mem = new Connections();
bool isInStableState = false;
HomeostaticPlasticityController hpa = new HomeostaticPlasticityController(mem, inputValues.Count * 15, (isStable, numPatterns, actColAvg, seenInputs) =>
{
Assert.IsTrue(numPatterns == inputValues.Count);
// Event should only be fired when entering the stable state.
// Ideal SP should never enter unstable state after stable state.
if (isStable == false)
{
isInStableState = false;
Debug.WriteLine($"UNSTABLE!: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
//Assert.IsTrue(isStable);
isInStableState = true;
Debug.WriteLine($"STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
});
SpatialPooler sp1 = new SpatialPooler(hpa);
p.apply(mem);
sp1.Init(mem, UnitTestHelpers.GetMemory());
CortexLayer <object, object> layer1 = new CortexLayer <object, object>("L1");
region0.AddLayer(layer1);
layer1.HtmModules.Add("encoder", encoder);
layer1.HtmModules.Add("sp", sp1);
HtmClassifier <double, ComputeCycle> cls = new HtmClassifier <double, ComputeCycle>();
double[] inputs = inputValues.ToArray();
Dictionary <double, int[]> prevActiveCols = new Dictionary <double, int[]>();
Dictionary <double, double> prevSimilarity = new Dictionary <double, double>();
foreach (var input in inputs)
{
prevSimilarity.Add(input, 0.0);
prevActiveCols.Add(input, new int[0]);
}
int maxSPLearningCycles = 5000;
List <(double Element, (int Cycle, double Similarity)[] Oscilations)> oscilationResult = new List <(double Element, (int Cycle, double Similarity)[] Oscilations)>();
/// <summary>
///
/// </summary>
private async Task RunExperimentNeuroVisualizer(int inputBits, Parameters p, EncoderBase encoder, List <double> inputValues)
{
Stopwatch sw = new Stopwatch();
sw.Start();
int maxMatchCnt = 0;
bool learn = true;
INeuroVisualizer vis = new WSNeuroVisualizer();
GenerateNeuroModel model = new GenerateNeuroModel();
await vis.ConnectToWSServerAsync();
await vis.InitModelAsync(model.CreateNeuroModel(new int[] { 1 }, (long[, ])p[KEY.COLUMN_DIMENSIONS], (int)p[KEY.CELLS_PER_COLUMN]));
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);
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>();
HtmClassifier <string, ComputeCycle> cls = new HtmClassifier <string, ComputeCycle>();
double[] inputs = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int maxSPLearningCycles = 50;
//
// This trains SP on input pattern.
// It performs some kind of unsupervised new-born learning.
foreach (var input in inputs)
{
List <(int Cycle, double Similarity)> elementOscilationResult = new List <(int Cycle, double Similarity)>();
Debug.WriteLine($"Learning ** {input} **");
for (int i = 0; i < maxSPLearningCycles; i++)
{
var lyrOut = layer1.Compute((object)input, learn) as ComputeCycle;
var activeColumns = layer1.GetResult("sp") as int[];
var actCols = activeColumns.OrderBy(c => c).ToArray();
var similarity = MathHelpers.CalcArraySimilarity(prevActiveCols, actCols);
await vis.UpdateColumnAsync(GetColumns(actCols));
Debug.WriteLine($" {i.ToString("D4")} SP-OUT: [{actCols.Length}/{similarity.ToString("0.##")}] - {Helpers.StringifyVector(actCols)}");
prevActiveCols = activeColumns;
}
}
// Here we add TM module to the layer.
layer1.HtmModules.Add("tm", tm1);
int cycle = 0;
int matches = 0;
string lastPredictedValue = "0";
Dictionary <double, List <List <int> > > activeColumnsLst = new Dictionary <double, List <List <int> > >();
foreach (var input in inputs)
{
if (activeColumnsLst.ContainsKey(input) == false)
{
activeColumnsLst.Add(input, new List <List <int> >());
}
}
int maxCycles = 3500;
//
// Now training with SP+TM. SP is pretrained on the given input pattern.
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle++;
Debug.WriteLine($"-------------- Cycle {cycle} ---------------");
string prevInput = "-1.0";
//
// Activate the 'New - Born' effect.
//if (i == 300)
//{
// mem.setMaxBoost(0.0);
// mem.updateMinPctOverlapDutyCycles(0.0);
// cls.ClearState();
//}
foreach (var input in inputs)
{
Debug.WriteLine($"-------------- {input} ---------------");
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
var activeColumns = layer1.GetResult("sp") as int[];
activeColumnsLst[input].Add(activeColumns.ToList());
//cls.Learn(input, lyrOut.ActiveCells.ToArray());
cls.Learn(GetKey(prevInput, input), lyrOut.ActiveCells.ToArray());
List <Synapse> synapses = new List <Synapse>();
Cell cell = new Cell(0, 1, 6, 0, CellActivity.ActiveCell); // where to get all these values
Synapse synap = new Synapse(cell, 1, 1, 0.78); // here is just supposed to update the permanence, all other values remains same; where do we get all other values
synapses.Add(synap);
await vis.UpdateSynapsesAsync(synapses); //update Synapse or add new ones
await vis.UpdateCellsAsync(GetCells(lyrOut.ActiveCells));
if (learn == false)
{
Debug.WriteLine($"Inference mode");
}
if (GetKey(prevInput, input) == lastPredictedValue)
{
matches++;
Debug.WriteLine($"Match {input}");
}
else
{
Debug.WriteLine($"Missmatch Actual value: {GetKey(prevInput, input)} - Predicted value: {lastPredictedValue}");
}
if (lyrOut.PredictiveCells.Count > 0)
{
var predictedInputValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray());
Debug.WriteLine($"Current Input: {input} \t| Predicted Input: {predictedInputValue}");
lastPredictedValue = predictedInputValue;
}
else
{
Debug.WriteLine($"NO CELLS PREDICTED for next cycle.");
}
prevInput = input.ToString();
}
//tm1.reset(mem);
double accuracy = (double)matches / (double)inputs.Length * 100.0;
Debug.WriteLine($"Cycle: {cycle}\tMatches={matches} of {inputs.Length}\t {accuracy}%");
if (accuracy == 100.0)
{
maxMatchCnt++;
Debug.WriteLine($"100% accuracy reched {maxMatchCnt} times.");
if (maxMatchCnt >= 20)
{
sw.Stop();
Debug.WriteLine($"Exit experiment in the stable state after 10 repeats with 100% of accuracy. Elapsed time: {sw.ElapsedMilliseconds / 1000 / 60} min.");
learn = false;
//var testInputs = new double[] { 0.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 7.0, 1.0, 9.0, 12.0, 11.0, 0.0, 1.0 };
// C-0, D-1, E-2, F-3, G-4, H-5
//var testInputs = new double[] { 0.0, 0.0, 4.0, 4.0, 5.0, 5.0, 4.0, 3.0, 3.0, 2.0, 2.0, 1.0, 1.0, 0.0 };
//// Traverse the sequence and check prediction.
//foreach (var input in inputValues)
//{
// var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
// predictedInputValue = cls.GetPredictedInputValue(lyrOut.predictiveCells.ToArray());
// Debug.WriteLine($"I={input} - P={predictedInputValue}");
//}
//
// Here we let the HTM predict seuence five times on its own.
// We start with last predicted value.
int cnt = 5 * inputValues.Count;
Debug.WriteLine("---- Start Predicting the Sequence -----");
// We take a random value to start somwhere in the sequence.
var predictedInputValue = inputValues[new Random().Next(0, inputValues.Count - 1)].ToString();
List <string> predictedValues = new List <string>();
while (--cnt > 0)
{
//var lyrOut = layer1.Compute(predictedInputValue, learn) as ComputeCycle;
var lyrOut = layer1.Compute(double.Parse(predictedInputValue[predictedInputValue.Length - 1].ToString()), learn) as ComputeCycle;
predictedInputValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray());
predictedValues.Add(predictedInputValue);
}
;
foreach (var item in predictedValues)
{
Debug.Write(item);
Debug.Write(" ,");
}
break;
}
}
else if (maxMatchCnt > 0)
{
Debug.WriteLine($"At 100% accuracy after {maxMatchCnt} repeats we get a drop of accuracy with {accuracy}. This indicates instable state. Learning will be continued.");
maxMatchCnt = 0;
}
}
Debug.WriteLine("---- cell state trace ----");
cls.TraceState($"cellState_MinPctOverlDuty-{p[KEY.MIN_PCT_OVERLAP_DUTY_CYCLES]}_MaxBoost-{p[KEY.MAX_BOOST]}.csv");
Debug.WriteLine("---- column state trace ----");
foreach (var input in activeColumnsLst)
{
using (StreamWriter colSw = new StreamWriter($"ColumState_MinPctOverlDuty-{p[KEY.MIN_PCT_OVERLAP_DUTY_CYCLES]}_MaxBoost-{p[KEY.MAX_BOOST]}_input-{input.Key}.csv"))
{
Debug.WriteLine($"------------ {input} ------------");
foreach (var actCols in input.Value)
{
Debug.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
colSw.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
}
}
}
Debug.WriteLine("------------ END ------------");
}
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);
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>();
//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;
}
}
// 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($"Current Input: {input}");
cls.Learn(input, lyrOut.ActiveCells.ToArray());
Debug.WriteLine($"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);
* }
* }
*/
}
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($" ** {input} **");
for (int i = 0; i < 3; i++)
{
var lyrOut = layer1.Compute((object)input, learn) as ComputeCycle;
}
}
// Here we add TM module to the layer.
layer1.HtmModules.Add("tm", tm1);
//
// Now, training with SP+TM. SP is pretrained on pattern.
for (int i = 0; i < 200; i++)
{
foreach (var input in inputs)
{
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
cls.Learn(input, lyrOut.ActiveCells.ToArray());
Debug.WriteLine($"-------------- {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----------------------------------------------------------------------------");
}
/// <summary>
///
/// </summary>
private static void RunExperiment(int inputBits, HtmConfig cfg, EncoderBase encoder, List <double> inputValues)
{
Stopwatch sw = new Stopwatch();
sw.Start();
int maxMatchCnt = 0;
bool learn = true;
CortexNetwork net = new CortexNetwork("my cortex");
List <CortexRegion> regions = new List <CortexRegion>();
CortexRegion region0 = new CortexRegion("1st Region");
regions.Add(region0);
var mem = new Connections(cfg);
bool isInStableState;
HtmClassifier <string, ComputeCycle> cls = new HtmClassifier <string, ComputeCycle>();
var numInputs = inputValues.Distinct <double>().ToList().Count;
TemporalMemory tm1 = new TemporalMemory();
HomeostaticPlasticityController hpa = new HomeostaticPlasticityController(mem, numInputs * 55, (isStable, numPatterns, actColAvg, seenInputs) =>
{
if (isStable)
{
// Event should be fired when entering the stable state.
Debug.WriteLine($"STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
// Ideal SP should never enter unstable state after stable state.
Debug.WriteLine($"INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
if (numPatterns != numInputs)
{
throw new InvalidOperationException("Stable state must observe all input patterns");
}
isInStableState = true;
cls.ClearState();
tm1.Reset(mem);
}, numOfCyclesToWaitOnChange: 25);
SpatialPoolerMT sp1 = new SpatialPoolerMT(hpa);
sp1.Init(mem, new DistributedMemory()
{
ColumnDictionary = new InMemoryDistributedDictionary <int, NeoCortexApi.Entities.Column>(1),
});
tm1.Init(mem);
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("tm", tm1);
double[] inputs = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int cycle = 0;
int matches = 0;
string lastPredictedValue = "0";
String prediction = null;
Dictionary <double, List <List <int> > > activeColumnsLst = new Dictionary <double, List <List <int> > >();
foreach (var input in inputs)
{
if (activeColumnsLst.ContainsKey(input) == false)
{
activeColumnsLst.Add(input, new List <List <int> >());
}
}
int maxCycles = 3500;
int maxPrevInputs = inputValues.Count - 1;
List <string> previousInputs = new List <string>();
previousInputs.Add("-1.0");
//
// Now training with SP+TM. SP is pretrained on the given input pattern.
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle++;
Debug.WriteLine($"-------------- Cycle {cycle} ---------------");
foreach (var input in inputs)
{
Debug.WriteLine($"-------------- {input} ---------------");
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
var activeColumns = layer1.GetResult("sp") as int[];
activeColumnsLst[input].Add(activeColumns.ToList());
previousInputs.Add(input.ToString());
if (previousInputs.Count > (maxPrevInputs + 1))
{
previousInputs.RemoveAt(0);
}
string key = GetKey(previousInputs, input);
cls.Learn(key, lyrOut.ActiveCells.ToArray());
if (learn == false)
{
Debug.WriteLine($"Inference mode");
}
Debug.WriteLine($"Col SDR: {Helpers.StringifyVector(lyrOut.ActivColumnIndicies)}");
Debug.WriteLine($"Cell SDR: {Helpers.StringifyVector(lyrOut.ActiveCells.Select(c => c.Index).ToArray())}");
if (key == lastPredictedValue)
{
matches++;
Debug.WriteLine($"Match. Actual value: {key} - Predicted value: {lastPredictedValue}");
}
else
{
Debug.WriteLine($"Missmatch! Actual value: {key} - Predicted value: {lastPredictedValue}");
}
if (lyrOut.PredictiveCells.Count > 0)
{
var predictedInputValue = cls.GetPredictedInputValues(lyrOut.PredictiveCells.ToArray(), 3);
Debug.WriteLine($"Current Input: {input}");
Debug.WriteLine("The predictions with similarity greater than 50% are");
foreach (var t in predictedInputValue)
{
if (t.Similarity >= (double)50.00)
{
Debug.WriteLine($"Predicted Input: {string.Join(", ", t.PredictedInput)},\tSimilarity Percentage: {string.Join(", ", t.Similarity)}, \tNumber of Same Bits: {string.Join(", ", t.NumOfSameBits)}");
}
}
lastPredictedValue = predictedInputValue.First().PredictedInput;
}
else
{
Debug.WriteLine($"NO CELLS PREDICTED for next cycle.");
lastPredictedValue = String.Empty;
}
}
double accuracy = (double)matches / (double)inputs.Length * 100.0;
Debug.WriteLine($"Cycle: {cycle}\tMatches={matches} of {inputs.Length}\t {accuracy}%");
if (accuracy == 100.0)
{
maxMatchCnt++;
Debug.WriteLine($"100% accuracy reched {maxMatchCnt} times.");
if (maxMatchCnt >= 30)
{
sw.Stop();
Debug.WriteLine($"Exit experiment in the stable state after 30 repeats with 100% of accuracy. Elapsed time: {sw.ElapsedMilliseconds / 1000 / 60} min.");
learn = false;
break;
}
}
else if (maxMatchCnt > 0)
{
Debug.WriteLine($"At 100% accuracy after {maxMatchCnt} repeats we get a drop of accuracy with {accuracy}. This indicates instable state. Learning will be continued.");
maxMatchCnt = 0;
}
}
Debug.WriteLine("---- cell state trace ----");
cls.TraceState($"cellState_MinPctOverlDuty-{cfg.MinPctOverlapDutyCycles}_MaxBoost-{cfg.MaxBoost}.csv");
Debug.WriteLine("---- Spatial Pooler column state ----");
foreach (var input in activeColumnsLst)
{
using (StreamWriter colSw = new StreamWriter($"ColumState_MinPctOverlDuty-{cfg.MinPctOverlapDutyCycles}_MaxBoost-{cfg.MaxBoost}_input-{input.Key}.csv"))
{
Debug.WriteLine($"------------ {input.Key} ------------");
foreach (var actCols in input.Value)
{
Debug.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
colSw.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
}
}
}
Debug.WriteLine("------------ END ------------");
Console.WriteLine("\n Please enter a number that has been learnt");
int inputNumber = Convert.ToInt16(Console.ReadLine());
Inference(inputNumber, false, layer1, cls);
}
