/// <summary>
/// Multi encoder for day month and segment
/// </summary>
/// <returns></returns>
public static EncoderBase FetchDateTimeEncoder()
{
EncoderBase dayEncoder = FetchDayEncoder();
EncoderBase monthEncoder = FetchMonthEncoder();
EncoderBase segmentEncoder = FetchSegmentEncoder();
EncoderBase dayOfWeek = FetchWeekDayEncoder();
List <EncoderBase> encoder = new List <EncoderBase>();
encoder.Add(dayEncoder);
encoder.Add(monthEncoder);
encoder.Add(segmentEncoder);
encoder.Add(dayOfWeek);
MultiEncoder encoderSetting = new MultiEncoder(encoder);
return(encoderSetting);
}
/// <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>
/// Shows the encoder settings dialog and check overwrite exist file.
/// </summary>
/// <param name="filename">The filename.</param>
/// <param name="encoder">The encoder.</param>
/// <param name="canAddImagesToExistingFile">The value indicating whether encoder can add images to the existing multipage image file.</param>
/// <param name="askIfFileCanBeOverwritten">The value indicating whether we need to ask that the existing file must be overwritten.</param>
/// <returns>
/// <b>True</b> if encoder is initialized; otherwise, <b>false</b>.
/// </returns>
private bool ShowEncoderSettingsDialogAndCheckOverwrite(
string filename,
ref EncoderBase encoder,
bool canAddImagesToExistingFile,
bool askIfFileCanBeOverwritten)
{
//
// show the encoder settings dialog and initialize the encoder
bool result = ShowEncoderSettingsDialog(ref encoder, canAddImagesToExistingFile);
// if encoder is not initialized
if (!result)
{
// exit
return(false);
}
// if file exists
if (File.Exists(filename))
{
// if we need to ask that the existing file must be overwritten
if (askIfFileCanBeOverwritten)
{
// if encoder is multipage encoder
if (encoder is MultipageEncoderBase)
{
// if encoder must create new image file
if (((MultipageEncoderBase)encoder).CreateNewFile)
{
ShowDialogAndAskIfFileMustBeOverwritten(filename);
}
}
// if encoder is NOT multipage encoder
else
{
ShowDialogAndAskIfFileMustBeOverwritten(filename);
}
}
}
return(true);
}
/// <summary>
/// Returns the multipage encoder for the specified <paramref name="filename"/>.
/// </summary>
/// <param name="filename">The filename.</param>
/// <param name="multipageEncoder">The multipage encoder.</param>
/// <returns>
/// <b>True</b> if the multipage encoder is created and initialized; otherwise, <b>false</b>.
/// </returns>
public bool GetMultipageEncoder(string filename, out MultipageEncoderBase multipageEncoder)
{
// create encoder
EncoderBase encoder = AvailableEncoders.CreateEncoder(filename);
// indicates whether the encoder can add images to the existing multipage image file
bool canAddImagesToExistingFile = false;
// if encoder can add images to the existing multipage image file
if (CanAddImagesToExistingFile)
{
// if file exists
if (File.Exists(filename))
{
// specify that encoder can add images to the existing multipage image file
canAddImagesToExistingFile = true;
}
}
// get the multipage encoder
multipageEncoder = encoder as MultipageEncoderBase;
// if encoder is not multipage
if (multipageEncoder == null)
{
return(false);
}
// set encoder settings
bool result = ShowEncoderSettingsDialogAndCheckOverwrite(
filename,
ref encoder,
canAddImagesToExistingFile,
multipageEncoder.CreateNewFile);
multipageEncoder = encoder as MultipageEncoderBase;
return(result);
}
/// <summary>
/// Sets the rendering settings if necessary.
/// </summary>
/// <param name="images">The images.</param>
/// <param name="encoder">The encoder.</param>
/// <param name="defaultRenderingSettings">The default rendering settings.</param>
public static void SetRenderingSettingsIfNeed(
ImageCollection images, EncoderBase encoder, RenderingSettings defaultRenderingSettings)
{
if (encoder == null || !(encoder is IPdfEncoder))
{
for (int i = 0; i < images.Count; i++)
{
if (images[i].IsVectorImage)
{
RenderingSettingsForm settingsForm = new RenderingSettingsForm(defaultRenderingSettings.CreateClone());
if (settingsForm.ShowDialog() == DialogResult.OK)
{
images.SetRenderingSettings(settingsForm.RenderingSettings);
}
else
{
return;
}
break;
}
}
}
}
/// <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>
/// Implements the experiment.
/// </summary>
/// <param name="cfg"></param>
/// <param name="encoder"></param>
/// <param name="inputValues"></param>
private static void RunExperiment(HtmConfig cfg, EncoderBase encoder, List <double> inputValues)
{
// Creates the htm memory.
var mem = new Connections(cfg);
bool isInStableState = false;
//
// HPC extends the default Spatial Pooler algorithm.
// The purpose of HPC is to set the SP in the new-born stage at the begining of the learning process.
// In this stage the boosting is very active, but the SP behaves instable. After this stage is over
// (defined by the second argument) the HPC is controlling the learning process of the SP.
// Once the SDR generated for every input gets stable, the HPC will fire event that notifies your code
// that SP is stable now.
HomeostaticPlasticityController hpa = new HomeostaticPlasticityController(mem, inputValues.Count * 15,
(isStable, numPatterns, actColAvg, seenInputs) =>
{
// Event should only be fired when entering the stable state.
// Ideal SP should never enter unstable state after stable state.
if (isStable == false)
{
Debug.WriteLine($"INSTABLE");
// This should usually not happen.
isInStableState = false;
}
else
{
Debug.WriteLine($"STABILITY");
// Here you can perform any action if required.
isInStableState = true;
}
});
// It creates the instance of Spatial Pooler Multithreaded version.
SpatialPooler sp = new SpatialPoolerMT(hpa);
// Initializes the
sp.Init(mem, new DistributedMemory()
{
ColumnDictionary = new InMemoryDistributedDictionary <int, NeoCortexApi.Entities.Column>(1)
});
// It creates the instance of the neo-cortex layer.
// Algorithm will be performed inside of that layer.
CortexLayer <object, object> cortexLayer = new CortexLayer <object, object>("L1");
// Add encoder as the very first module. This model is connected to the sensory input cells
// that receive the input. Encoder will receive the input and forward the encoded signal
// to the next module.
cortexLayer.HtmModules.Add("encoder", encoder);
// The next module in the layer is Spatial Pooler. This module will receive the output of the
// encoder.
cortexLayer.HtmModules.Add("sp", sp);
double[] inputs = inputValues.ToArray();
// Will hold the SDR of every inputs.
Dictionary <double, int[]> prevActiveCols = new Dictionary <double, int[]>();
// Will hold the similarity of SDKk and SDRk-1 fro every input.
Dictionary <double, double> prevSimilarity = new Dictionary <double, double>();
//
// Initiaize start similarity to zero.
foreach (var input in inputs)
{
prevSimilarity.Add(input, 0.0);
prevActiveCols.Add(input, new int[0]);
}
// Learning process will take 1000 iterations (cycles)
int maxSPLearningCycles = 1000;
for (int cycle = 0; cycle < maxSPLearningCycles; cycle++)
{
Debug.WriteLine($"Cycle ** {cycle} ** Stability: {isInStableState}");
//
// This trains the layer on input pattern.
foreach (var input in inputs)
{
double similarity;
// Learn the input pattern.
// Output lyrOut is the output of the last module in the layer.
//
var lyrOut = cortexLayer.Compute((object)input, true) as int[];
// This is a general way to get the SpatialPooler result from the layer.
var activeColumns = cortexLayer.GetResult("sp") as int[];
var actCols = activeColumns.OrderBy(c => c).ToArray();
similarity = MathHelpers.CalcArraySimilarity(activeColumns, prevActiveCols[input]);
Debug.WriteLine($"[cycle={cycle.ToString("D4")}, i={input}, cols=:{actCols.Length} s={similarity}] SDR: {Helpers.StringifyVector(actCols)}");
prevActiveCols[input] = activeColumns;
prevSimilarity[input] = similarity;
}
}
}
/// <summary>
/// Implements the experiment.
/// </summary>
/// <param name="cfg"></param>
/// <param name="encoder"></param>
/// <param name="inputValues"></param>
private static void RunExperiment(HtmConfig cfg, EncoderBase encoder, List <int[]> inputValues)
{
// Creates the htm memory.
var mem = new Connections(cfg);
bool isInStableState = false;
//
// HPC extends the default Spatial Pooler algorithm.
// The purpose of HPC is to set the SP in the new-born stage at the begining of the learning process.
// In this stage the boosting is very active, but the SP behaves instable. After this stage is over
// (defined by the second argument) the HPC is controlling the learning process of the SP.
// Once the SDR generated for every input gets stable, the HPC will fire event that notifies your code
// that SP is stable now.
HomeostaticPlasticityController hpa = new HomeostaticPlasticityController(mem, inputValues.Count * 40,
(isStable, numPatterns, actColAvg, seenInputs) =>
{
// Event should only be fired when entering the stable state.
// Ideal SP should never enter unstable state after stable state.
if (isStable == false)
{
Debug.WriteLine($"INSTABLE STATE");
// This should usually not happen.
isInStableState = false;
}
else
{
Debug.WriteLine($"STABLE STATE");
// Here you can perform any action if required.
isInStableState = true;
}
}, requiredSimilarityThreshold: 0.975);
// It creates the instance of Spatial Pooler Multithreaded version.
SpatialPooler sp = new SpatialPoolerMT(hpa);
// Initializes the
sp.Init(mem);
// Holds the indicies of active columns of the SDR.
Dictionary <string, int[]> prevActiveColIndicies = new Dictionary <string, int[]>();
// Holds the active column SDRs.
Dictionary <string, int[]> prevActiveCols = new Dictionary <string, int[]>();
// Will hold the similarity of SDKk and SDRk-1 fro every input.
Dictionary <string, double> prevSimilarity = new Dictionary <string, double>();
//
// Initiaize start similarity to zero.
for (int i = 0; i < inputValues.Count; i++)
{
string inputKey = GetInputGekFromIndex(i);
prevSimilarity.Add(inputKey, 0.0);
prevActiveColIndicies.Add(inputKey, new int[0]);
}
// Learning process will take 1000 iterations (cycles)
int maxSPLearningCycles = 1000;
for (int cycle = 0; cycle < maxSPLearningCycles; cycle++)
{
//Debug.WriteLine($"Cycle ** {cycle} ** Stability: {isInStableState}");
//
// This trains the layer on input pattern.
for (int inputIndx = 0; inputIndx < inputValues.Count; inputIndx++)
{
string inputKey = GetInputGekFromIndex(inputIndx);
int[] input = inputValues[inputIndx];
double similarity;
int[] activeColumns = new int[(int)cfg.NumColumns];
// Learn the input pattern.
// Output lyrOut is the output of the last module in the layer.
sp.compute(input, activeColumns, true);
// DrawImages(cfg, inputKey, input, activeColumns);
var actColsIndicies = ArrayUtils.IndexWhere(activeColumns, c => c == 1);
similarity = MathHelpers.CalcArraySimilarity(actColsIndicies, prevActiveColIndicies[inputKey]);
Debug.WriteLine($"[i={inputKey}, cols=:{actColsIndicies.Length} s={similarity}] SDR: {Helpers.StringifyVector(actColsIndicies)}");
prevActiveCols[inputKey] = activeColumns;
prevActiveColIndicies[inputKey] = actColsIndicies;
prevSimilarity[inputKey] = similarity;
if (isInStableState)
{
GenerateResult(cfg, inputValues, prevActiveColIndicies, prevActiveCols);
return;
}
}
}
}
/// <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($"------------ {input} ------------");
foreach (var actCols in input.Value)
{
Debug.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
colSw.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
}
}
}
Debug.WriteLine("------------ END ------------");
}
/// <summary>
///
/// </summary>
private void RunExperiment(int inputBits, HtmConfig cfg, EncoderBase encoder, List <double> inputValues)
{
Stopwatch sw = new Stopwatch();
sw.Start();
int maxMatchCnt = 0;
bool learn = true;
var mem = new Connections(cfg);
bool isInStableState = false;
HtmClassifier <string, ComputeCycle> cls = new HtmClassifier <string, ComputeCycle>();
var numInputs = inputValues.Distinct <double>().ToList().Count;
CortexLayer <object, object> layer1 = new CortexLayer <object, object>("L1");
TemporalMemory tm = new TemporalMemory();
HomeostaticPlasticityController hpa = new HomeostaticPlasticityController(mem, numInputs * 150, (isStable, numPatterns, actColAvg, seenInputs) =>
{
if (isStable)
{
// Event should be fired when entering the stable state.
Debug.WriteLine($"STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
// Ideal SP should never enter unstable state after stable state.
Debug.WriteLine($"INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
// We are not learning in instable state.
learn = isInStableState = isStable;
//if (isStable && layer1.HtmModules.ContainsKey("tm") == false)
// layer1.HtmModules.Add("tm", tm);
// Clear all learned patterns in the classifier.
cls.ClearState();
// Clear active and predictive cells.
//tm.Reset(mem);
}, numOfCyclesToWaitOnChange: 50);
SpatialPoolerMT sp = new SpatialPoolerMT(hpa);
sp.Init(mem);
tm.Init(mem);
layer1.HtmModules.Add("encoder", encoder);
layer1.HtmModules.Add("sp", sp);
double[] inputs = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int cycle = 0;
int matches = 0;
string lastPredictedValue = "0";
//Dictionary<double, List<List<int>>> activeColumnsLst = new Dictionary<double, List<List<int>>>();
//foreach (var input in inputs)
//{
// if (activeColumnsLst.ContainsKey(input) == false)
// activeColumnsLst.Add(input, new List<List<int>>());
//}
int maxCycles = 3500;
int maxPrevInputs = inputValues.Count - 1;
List <string> previousInputs = new List <string>();
previousInputs.Add("-1.0");
//
// Training SP to get stable. New-born stage.
//
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle++;
Debug.WriteLine($"-------------- Newborn Cycle {cycle} ---------------");
foreach (var input in inputs)
{
Debug.WriteLine($" -- {input} --");
var lyrOut = layer1.Compute(input, learn);
if (isInStableState)
{
break;
}
}
if (isInStableState)
{
break;
}
}
layer1.HtmModules.Add("tm", tm);
//
// Now training with SP+TM. SP is pretrained on the given input pattern set.
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle++;
Debug.WriteLine($"-------------- Cycle {cycle} ---------------");
foreach (var input in inputs)
{
Debug.WriteLine($"-------------- {input} ---------------");
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
// lyrOut is null when the TM is added to the layer inside of HPC callback by entering of the stable state.
//if (isInStableState && lyrOut != null)
{
var activeColumns = layer1.GetResult("sp") as int[];
//layer2.Compute(lyrOut.WinnerCells, true);
//activeColumnsLst[input].Add(activeColumns.ToList());
previousInputs.Add(input.ToString());
if (previousInputs.Count > (maxPrevInputs + 1))
{
previousInputs.RemoveAt(0);
}
// In the pretrained SP with HPC, the TM will quickly learn cells for patterns
// In that case the starting sequence 4-5-6 might have the sam SDR as 1-2-3-4-5-6,
// Which will result in returning of 4-5-6 instead of 1-2-3-4-5-6.
// HtmClassifier allways return the first matching sequence. Because 4-5-6 will be as first
// memorized, it will match as the first one.
if (previousInputs.Count < maxPrevInputs)
{
continue;
}
string key = GetKey(previousInputs, input);
List <Cell> actCells;
if (lyrOut.ActiveCells.Count == lyrOut.WinnerCells.Count)
{
actCells = lyrOut.ActiveCells;
}
else
{
actCells = lyrOut.WinnerCells;
}
cls.Learn(key, actCells.ToArray());
if (learn == false)
{
Debug.WriteLine($"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 = (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.");
//
// Experiment is completed if we are 30 cycles long at the 100% accuracy.
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("------------ END ------------");
}
/// <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($"------------ {input.Key} ------------");
foreach (var actCols in input.Value)
{
Debug.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
colSw.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
}
}
}
Debug.WriteLine("------------ END ------------");
Console.WriteLine("\n Please enter a number that has been learnt");
int inputNumber = Convert.ToInt16(Console.ReadLine());
Inference(inputNumber, false, layer1, cls);
}
private void RunExperiment(int inputBits, HtmConfig cfgL4, EncoderBase encoder, List <double> inputValues, HtmConfig cfgL2)
{
Stopwatch swL2 = new Stopwatch();
int maxMatchCnt = 0;
bool learn = true;
bool isSP4Stable = false;
bool isSP2STable = false;
Connections memL4 = new Connections(cfgL4);
Connections memL2 = new Connections(cfgL2);
int numInputs = inputValues.Distinct <double>().ToList().Count;
HtmClassifier <string, ComputeCycle> cls = new HtmClassifier <string, ComputeCycle>();
layerL4 = new CortexLayer <object, object>("L4");
layerL2 = new CortexLayer <object, object>("L2");
//tm4 = new TemporalMemoryMT();
//tm2 = new TemporalMemoryMT();
tm4 = new TemporalMemory();
tm2 = new TemporalMemory();
//
// HPC for Layer 4 SP
HomeostaticPlasticityController hpa_sp_L4 = new HomeostaticPlasticityController(memL4, numInputs * 50, (isStable, numPatterns, actColAvg, seenInputs) =>
{
if (isStable)
{
Debug.WriteLine($"SP L4 STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
Debug.WriteLine($"SP L4 INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
learn = isSP4Stable = isStable;
cls.ClearState();
}, numOfCyclesToWaitOnChange: 50);
//
// HPC for Layer 2 SP
HomeostaticPlasticityController hpa_sp_L2 = new HomeostaticPlasticityController(memL2, numInputs * 50, (isStable, numPatterns, actColAvg, seenInputs) =>
{
if (isStable)
{
Debug.WriteLine($"SP L2 STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
Debug.WriteLine($"SP L2 INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
learn = isSP2STable = isStable;
cls.ClearState();
}, numOfCyclesToWaitOnChange: 50);
SpatialPooler sp4 = new SpatialPooler(hpa_sp_L4);
SpatialPooler sp2 = new SpatialPooler(hpa_sp_L2);
sp4.Init(memL4);
sp2.Init(memL2);
// memL2.TraceInputPotential();
tm4.Init(memL4);
tm2.Init(memL2);
layerL4.HtmModules.Add("encoder", encoder);
layerL4.HtmModules.Add("sp", sp4);
layerL4.HtmModules.Add("tm", tm4);
layerL2.HtmModules.Add("sp", sp2);
layerL2.HtmModules.Add("tm", tm2);
int[] inpCellsL4ToL2 = new int[cfgL4.CellsPerColumn * cfgL4.NumColumns];
double[] inputs = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int cycle = 0;
int matches = 0;
string lastPredictedValue = "0";
int maxCycles = 3500;
int maxPrevInputs = inputValues.Count - 1;
List <string> previousInputs = new List <string>();
//
// Training SP at Layer 4 to get stable. New-born stage.
//
using (StreamWriter swL4Sdrs = new StreamWriter($"L4-SDRs-in_{cfgL2.NumInputs}-col_{cfgL2.NumColumns}-r_{cfgL2.PotentialRadius}.txt"))
{
using (StreamWriter sw = new StreamWriter($"in_{cfgL2.NumInputs}-col_{cfgL2.NumColumns}-r_{cfgL2.PotentialRadius}.txt"))
{
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle = i;
Debug.WriteLine($"-------------- Newborn Cycle {cycle} at L4 SP region ---------------");
foreach (double input in inputs)
{
Debug.WriteLine($" INPUT: '{input}'\t Cycle:{cycle}");
Debug.Write("L4: ");
object lyrOut = layerL4.Compute(input, learn);
int[] activeColumns = layerL4.GetResult("sp") as int[];
int[] cellSdrL4Indexes = memL4.ActiveCells.Select(c => c.Index).ToArray();
Debug.WriteLine($"L4out Active Coloumn for input: {input}: {Helpers.StringifyVector(activeColumns)}");
Debug.WriteLine($"L4out SDR for input: {input}: {Helpers.StringifyVector(cellSdrL4Indexes)}");
if (isSP4Stable)
{
/// <summary>
/// Checking Layer4 SP is giving similar or different
/// Active Cell Sdr Indexes After it reaches to stable state.
/// This portion is actuallly to hold all acive cell sdr
/// indexes after Layer 4 SP reaches at stable state via HPC.
///
/// But why we have done this?
///
/// Actually, In Necortex api we have obeserved during severel
/// experiments that whenvever Layer4 SP reaches to STABLE sate
/// it doesnt give us smilar pattern of Active Cell SDR Indexes
/// for each particular input data.
///
/// Instead active cell sdr patern of L4 varried though it has reached
/// to stable sate!
///
/// So we want to obeserve whether Layer 4 SP is giving similar active cell sdr indexes
/// or different acrive cell sdr indexes after it reaches to stable state.
///
/// If we receive similar acrive cell sdr indexes from Layer 4 sp after it reaches
/// to stable state then do train Layer 2 sp by as usual process in NeocortexApi by
/// calling Layer4.Compute().
///
/// But if we receive different active cell sdr indexes then we will train Layer 2 sp
/// from L4_ActiveCell_sdr_log so that during training
/// Layer 2 SP gets similar stable active cell sdr indexes of layer4
/// from that dictionary. As a result, L2 SP will get similar sequence
/// of active cell sdr indexes during SP Tarining and reach to STABLE state
/// </summary>
Array.Sort(cellSdrL4Indexes);
if (!L4_ActiveCell_sdr_log.ContainsKey(input))
{
L4_ActiveCell_sdr_log.Add(input, cellSdrL4Indexes);
}
else
{
if (L4_ActiveCell_sdr_log[input].SequenceEqual(cellSdrL4Indexes))
{
Debug.WriteLine($"Layer4.Compute() is giving similar cell sdr indexes for input : {input} after reaching to stable state");
isSimilar_L4_active_cell_sdr = true;
}
else
{
isSimilar_L4_active_cell_sdr = false;
Debug.WriteLine($"Layer4.Compute() is giving different cell sdr indexes for input : {input} after reaching to stable state");
Debug.WriteLine($"Sdr Mismatch with L4_ActiveCell_sdr_log after reaching to stable state");
Debug.WriteLine($" L4_ActiveCell_sdr_log output for input {input}: { Helpers.StringifyVector(L4_ActiveCell_sdr_log[input])}");
Debug.WriteLine($"L4 out sdr input:{input} {Helpers.StringifyVector(cellSdrL4Indexes)}");
//Debug.WriteLine($"L4 out ac input: {input}: {Helpers.StringifyVector(activeColumns)}");
}
}
if (!isSimilar_L4_active_cell_sdr)
{
cellSdrL4Indexes = L4_ActiveCell_sdr_log[input];
}
//
// Training SP at Layer 2 to get stable. New-born stage.
//
// Write SDR as output of L4 and input of L2
// swL4Sdrs.WriteLine($"{input} - {Helpers.StringifyVector(cellSdrL4Indexes)}");
// Set the output active cell array
InitArray(inpCellsL4ToL2, 0);
ArrayUtils.SetIndexesTo(inpCellsL4ToL2, cellSdrL4Indexes, 1);
Debug.WriteLine($"L4 cell sdr to L2 SP Train for Input {input}: ");
layerL2.Compute(inpCellsL4ToL2, true);
int[] overlaps = ArrayUtils.IndexWhere(memL2.Overlaps, o => o > 0);
string strOverlaps = Helpers.StringifyVector(overlaps);
Debug.WriteLine($"Potential columns: {overlaps.Length}, overlaps: {strOverlaps}");
}
}
if (isSP4Stable && isSP2STable)
{
break;
}
}
}
}
//
// SP+TM at L2
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle = i;
Debug.WriteLine($"-------------- L2 TM Train region Cycle {cycle} ---------------");
foreach (double input in inputs)
{
Debug.WriteLine($"-------------- {input} ---------------");
// Reset tha array
//var cellSdrL4Indexes = L4_ActiveCell_sdr_log[input];
object layerL4Out = layerL4.Compute(input, learn);
previousInputs.Add(input.ToString());
if (previousInputs.Count > (maxPrevInputs + 1))
{
previousInputs.RemoveAt(0);
}
if (previousInputs.Count < maxPrevInputs)
{
continue;
}
key = GetKey(previousInputs, input);
List <Cell> actCells;
InitArray(inpCellsL4ToL2, 0);
int[] cellSdrL4Indexes;
if (!isSimilar_L4_active_cell_sdr)
{
cellSdrL4Indexes = L4_ActiveCell_sdr_log[input];
}
else
{
cellSdrL4Indexes = memL4.ActiveCells.Select(c => c.Index).ToArray();
}
// Set the output active cell array
ArrayUtils.SetIndexesTo(inpCellsL4ToL2, cellSdrL4Indexes, 1);
ComputeCycle layerL2Out = layerL2.Compute(inpCellsL4ToL2, true) as ComputeCycle;
if (layerL2Out.ActiveCells.Count == layerL2Out.WinnerCells.Count)
{
actCells = layerL2Out.ActiveCells;
}
else
{
actCells = layerL2Out.WinnerCells;
}
/// <summary>
/// HTM Classifier has added for Layer 2
/// </summary>
cls.Learn(key, actCells.ToArray());
if (key == lastPredictedValue)
{
matches++;
Debug.WriteLine($"Match. Actual Sequence: {key} - Last Predicted Sequence: {lastPredictedValue}");
}
else
{
Debug.WriteLine($"Missmatch! Actual Sequence: {key} - Last Predicted Sequence: {lastPredictedValue}");
}
/// <summary>
/// Classifier is taking Predictive Cells from Layer 2
/// </summary>
if (layerL2Out.PredictiveCells.Count > 0)
{
string predictedInputValue = cls.GetPredictedInputValue(layerL2Out.PredictiveCells.ToArray());
Debug.WriteLine($"Current Input: {input} \t| New Predicted Input Sequence: {predictedInputValue}");
lastPredictedValue = predictedInputValue;
}
else
{
Debug.WriteLine($"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.");
//
// Experiment is completed if we are 20 cycles long at the 100% accuracy.
if (maxMatchCnt >= 20)
{
Debug.WriteLine($"Exit experiment in the stable state after 20 repeats with 100% of accuracy.");
learn = false;
break;
}
}
else if (maxMatchCnt > 0)
{
Debug.WriteLine($"At 100% accuracy after {maxMatchCnt} repeats we get a drop of accuracy with {accuracy}. This indicates instable state. Learning will be continued.");
}
}
}
public Encoder(EncoderBase encoder)
{
Name = encoder?.Name;
}
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)>();
public StreamEngine(Socket fd, Options options, String endpoint)
{
m_handle = fd;
// inbuf = null;
m_insize = 0;
m_inputError = false;
// outbuf = null;
m_outsize = 0;
m_handshaking = true;
m_session = null;
m_options = options;
m_plugged = false;
m_endpoint = endpoint;
m_socket = null;
m_encoder = null;
m_decoder = null;
// Put the socket into non-blocking mode.
Utils.UnblockSocket(m_handle);
// Set the socket buffer limits for the underlying socket.
if (m_options.SendBuffer != 0)
{
m_handle.SendBufferSize = m_options.SendBuffer;
}
if (m_options.ReceiveBuffer != 0)
{
m_handle.ReceiveBufferSize = m_options.ReceiveBuffer;
}
}
private void RunExperiment(int inputBits, HtmConfig cfgL4, EncoderBase encoder, List <double> inputValues, HtmConfig cfgL2)
{
Stopwatch swL2 = new Stopwatch();
int maxMatchCnt = 0;
bool learn = true;
bool isSP4Stable = false;
bool isSP2STable = false;
var memL4 = new Connections(cfgL4);
var memL2 = new Connections(cfgL2);
var numInputs = inputValues.Distinct <double>().ToList().Count;
HtmClassifier <string, ComputeCycle> cls = new HtmClassifier <string, ComputeCycle>();
layerL4 = new CortexLayer <object, object>("L4");
layerL2 = new CortexLayer <object, object>("L2");
tm4 = new TemporalMemoryMT();
tm2 = new TemporalMemoryMT();
// HPC for Layer 4 SP
HomeostaticPlasticityController hpa_sp_L4 = new HomeostaticPlasticityController(memL4, numInputs * 50, (isStable, numPatterns, actColAvg, seenInputs) =>
{
if (isStable)
{
Debug.WriteLine($"SP L4 STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
Debug.WriteLine($"SP L4 INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
learn = isSP4Stable = isStable;
}, numOfCyclesToWaitOnChange: 50);
// HPC for Layer 2 SP
HomeostaticPlasticityController hpa_sp_L2 = new HomeostaticPlasticityController(memL2, numInputs * 50, (isStable, numPatterns, actColAvg, seenInputs) =>
{
if (isStable)
{
Debug.WriteLine($"SP L2 STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
Debug.WriteLine($"SP L2 INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
learn = isSP2STable = isStable;
cls.ClearState();
}, numOfCyclesToWaitOnChange: 50);
SpatialPooler sp4 = new SpatialPoolerMT(hpa_sp_L4);
SpatialPooler sp2 = new SpatialPoolerMT(hpa_sp_L2);
sp4.Init(memL4);
sp2.Init(memL2);
// memL2.TraceInputPotential();
tm4.Init(memL4);
tm2.Init(memL2);
layerL4.HtmModules.Add("encoder", encoder);
layerL4.HtmModules.Add("sp", sp4);
layerL4.HtmModules.Add("tm", tm4);
layerL2.HtmModules.Add("sp", sp2);
layerL2.HtmModules.Add("tm", tm2);
int[] inpCellsL4ToL2 = new int[cfgL4.CellsPerColumn * cfgL4.NumColumns];
double[] inputs = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int cycle = 0;
int matches = 0;
string lastPredictedValue = "0";
int maxCycles = 3500;
int maxPrevInputs = inputValues.Count - 1;
List <string> previousInputs = new List <string>();
//
// Training SP at Layer 4 to get stable. New-born stage.
//
using (StreamWriter swL4Sdrs = new StreamWriter($"L4-SDRs-in_{cfgL2.NumInputs}-col_{cfgL2.NumColumns}-r_{cfgL2.PotentialRadius}.txt"))
{
using (StreamWriter sw = new StreamWriter($"in_{cfgL2.NumInputs}-col_{cfgL2.NumColumns}-r_{cfgL2.PotentialRadius}.txt"))
{
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle = i;
Debug.WriteLine($"-------------- Newborn Cycle {cycle} at L4 SP region ---------------");
foreach (var input in inputs)
{
Debug.WriteLine($" INPUT: '{input}'\tCycle:{cycle}");
Debug.Write("L4: ");
//L4 SP pre train
layerL4.Compute(input, learn);
InitArray(inpCellsL4ToL2, 0);
if (isSP4Stable)
{
var cellSdrL4Indexes = memL4.ActiveCells.Select(c => c.Index).ToArray();
// Write SDR as output of L4 and input of L2
swL4Sdrs.WriteLine($"{input} - {Helpers.StringifyVector(cellSdrL4Indexes)}");
// Set the output active cell array
ArrayUtils.SetIndexesTo(inpCellsL4ToL2, cellSdrL4Indexes, 1);
Debug.WriteLine($"L4 out sdr: {Helpers.StringifyVector(cellSdrL4Indexes)}");
Debug.WriteLine("L2: ");
swL2.Restart();
/// <summary>
/// This part is for to make SP of Layer2 stable thourgh help
/// of HPC Boosting Algo from new born stage.
/// </summary>
layerL2.Compute(inpCellsL4ToL2, true);
swL2.Stop();
Debug.WriteLine($"{swL2.ElapsedMilliseconds / 1000}");
sw.WriteLine($"{swL2.ElapsedMilliseconds / 1000}");
sw.Flush();
var overlaps = ArrayUtils.IndexWhere(memL2.Overlaps, o => o > 0);
var strOverlaps = Helpers.StringifyVector(overlaps);
Debug.WriteLine($"Potential columns: {overlaps.Length}, overlaps: {strOverlaps}");
}
}
if (isSP4Stable && isSP2STable)
{
break;
}
}
}
}
// SP+TM at L4
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle = i;
Debug.WriteLine($"-------------- L4 TM Train region Cycle {cycle} ---------------");
foreach (var input in inputs)
{
Debug.WriteLine($"-------------- {input} ---------------");
var layerL4Out = layerL4.Compute(input, learn) as ComputeCycle;
previousInputs.Add(input.ToString());
if (previousInputs.Count > (maxPrevInputs + 1))
{
previousInputs.RemoveAt(0);
}
if (previousInputs.Count < maxPrevInputs)
{
continue;
}
string key = GetKey(previousInputs, input);
List <Cell> actCells;
if (layerL4Out.ActiveCells.Count == layerL4Out.WinnerCells.Count)
{
// SP+TM at L2
Debug.WriteLine($"-------------- L2 TM Train region Cycle {cycle} ---------------");
// Reset tha array
InitArray(inpCellsL4ToL2, 0);
var cellSdrL4Indexes = memL4.ActiveCells.Select(c => c.Index).ToArray();
// Set the output active cell array
ArrayUtils.SetIndexesTo(inpCellsL4ToL2, cellSdrL4Indexes, 1);
var layerL2Out = layerL2.Compute(inpCellsL4ToL2, true) as ComputeCycle;
int[] overlaps = ArrayUtils.IndexWhere(memL2.Overlaps, o => o > 0);
var strOverlaps = Helpers.StringifyVector(overlaps);
Debug.WriteLine($"Potential columns: {overlaps.Length}, overlaps: {strOverlaps}");
if (layerL2Out.ActiveCells.Count == layerL2Out.WinnerCells.Count)
{
actCells = layerL2Out.ActiveCells;
}
else
{
actCells = layerL2Out.WinnerCells;
}
cls.Learn(key, actCells.ToArray());
if (key == lastPredictedValue)
{
matches++;
Debug.WriteLine($"Match. Actual value: {key} - Predicted value: {lastPredictedValue}");
}
else
{
Debug.WriteLine($"Missmatch! Actual value: {key} - Predicted value: {lastPredictedValue}");
}
if (layerL2Out.PredictiveCells.Count > 0)
{
var predictedInputValue = cls.GetPredictedInputValue(layerL2Out.PredictiveCells.ToArray());
Debug.WriteLine($"Current Input: {input} \t| Predicted Input: {predictedInputValue}");
lastPredictedValue = predictedInputValue;
}
else
{
Debug.WriteLine($"NO CELLS PREDICTED for next cycle.");
lastPredictedValue = String.Empty;
}
}
}
double accuracy = (double)matches / (double)inputs.Length * 100.0;
Debug.WriteLine($"Cycle: {cycle}\tMatches={matches} of {inputs.Length}\t {accuracy}%");
if (accuracy >= 100.0)
{
maxMatchCnt++;
Debug.WriteLine($"100% accuracy reched {maxMatchCnt} times.");
//
// Experiment is completed if we are 20 cycles long at the 100% accuracy.
if (maxMatchCnt >= 20)
{
Debug.WriteLine($"Exit experiment in the stable state after 20 repeats with 100% of accuracy.");
learn = false;
break;
}
}
else if (maxMatchCnt > 0)
{
Debug.WriteLine($"At 100% accuracy after {maxMatchCnt} repeats we get a drop of accuracy with {accuracy}. This indicates instable state. Learning will be continued.");
maxMatchCnt = 0;
}
}
}
private void RunExperiment(int inputBits, HtmConfig cfgL4, EncoderBase encoder, List <double> inputValues, HtmConfig cfgL2)
{
Stopwatch swL2 = new Stopwatch();
//int maxMatchCnt = 0;
bool learn = true;
bool isSP1Stable = false;
bool isSP2STable = false;
var memL4 = new Connections(cfgL4);
var memL2 = new Connections(cfgL2);
var numInputs = inputValues.Distinct <double>().ToList().Count;
HtmClassifier <string, ComputeCycle> cls = new HtmClassifier <string, ComputeCycle>();
layerL4 = new CortexLayer <object, object>("L4");
layerL2 = new CortexLayer <object, object>("L2");
tm4 = new TemporalMemoryMT();
tm2 = new TemporalMemoryMT();
//
// HPC for Layer 4 SP
HomeostaticPlasticityController hpa_sp_L4 = new HomeostaticPlasticityController(memL4, numInputs * 50, (isStable, numPatterns, actColAvg, seenInputs) =>
{
if (isStable)
{
Debug.WriteLine($"SP L4 STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
Debug.WriteLine($"SP L4 INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
learn = isSP1Stable = isStable;
//cls.ClearState();
}, numOfCyclesToWaitOnChange: 50);
//
// HPC for Layer 2 SP
HomeostaticPlasticityController hpa_sp_L2 = new HomeostaticPlasticityController(memL2, numInputs * 50, (isStable, numPatterns, actColAvg, seenInputs) =>
{
if (isStable)
{
Debug.WriteLine($"SP L2 STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
else
{
Debug.WriteLine($"SP L2 INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
learn = isSP2STable = isStable;
//cls.ClearState();
}, numOfCyclesToWaitOnChange: 50);
SpatialPooler sp4 = new SpatialPoolerMT(hpa_sp_L4);
SpatialPooler sp2 = new SpatialPoolerMT(hpa_sp_L2);
sp4.Init(memL4);
sp2.Init(memL2);
// memL2.TraceInputPotential();
tm4.Init(memL4);
tm2.Init(memL2);
layerL4.HtmModules.Add("encoder", encoder);
layerL4.HtmModules.Add("sp", sp4);
layerL4.HtmModules.Add("tm", tm4);
layerL2.HtmModules.Add("sp", sp2);
layerL2.HtmModules.Add("tm", tm2);
int[] inpCellsL4ToL2 = new int[cfgL4.CellsPerColumn * cfgL4.NumColumns];
double[] inputs = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int cycle = 0;
int matches = 0;
// string lastPredictedValue = "0";
int maxCycles = 3500;
int maxPrevInputs = inputValues.Count - 1;
List <string> previousInputs = new List <string>();
//
// Training SP at Layer 4 to get stable. New-born stage.
//
using (StreamWriter swL4Sdrs = new StreamWriter($"L4-SDRs-in_{cfgL2.NumInputs}-col_{cfgL2.NumColumns}-r_{cfgL2.PotentialRadius}.txt"))
{
using (StreamWriter sw = new StreamWriter($"in_{cfgL2.NumInputs}-col_{cfgL2.NumColumns}-r_{cfgL2.PotentialRadius}.txt"))
{
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle++;
Debug.WriteLine($"-------------- Newborn Cycle {cycle} at L4 SP region ---------------");
foreach (var input in inputs)
{
Debug.WriteLine($" INPUT: '{input}'\tCycle:{cycle}");
Debug.Write("L4: ");
var lyrOut = layerL4.Compute(input, learn);
InitArray(inpCellsL4ToL2, 0);
if (isSP1Stable)
{
var cellSdrL4Indexes = memL4.ActiveCells.Select(c => c.Index).ToArray();
// Write SDR as output of L4 and input of L2
swL4Sdrs.WriteLine($"{input} - {Helpers.StringifyVector(cellSdrL4Indexes)}");
// Set the output active cell array
ArrayUtils.SetIndexesTo(inpCellsL4ToL2, cellSdrL4Indexes, 1);
Debug.Write("L2: ");
swL2.Restart();
layerL2.Compute(inpCellsL4ToL2, true);
swL2.Stop();
Debug.WriteLine($"{swL2.ElapsedMilliseconds / 1000}");
sw.WriteLine($"{swL2.ElapsedMilliseconds / 1000}");
sw.Flush();
Debug.WriteLine($"L4 out sdr: {Helpers.StringifyVector(cellSdrL4Indexes)}");
var overlaps = ArrayUtils.IndexWhere(memL2.Overlaps, o => o > 0);
var strOverlaps = Helpers.StringifyVector(overlaps);
Debug.WriteLine($"Potential columns: {overlaps.Length}, overlaps: {strOverlaps}");
}
if (isSP1Stable && isSP2STable)
{
break;
}
}
}
}
}
Debug.WriteLine($"-------------- L4 SP region is {isSP1Stable} ---------------");
//layerL4.HtmModules.Add("tm", tm4);
// SP+TM at L4
for (int i = 0; i < maxCycles; i++)
{
matches -= 0;
cycle++;
Debug.WriteLine($"-------------- L4 TM Train region Cycle {cycle} ---------------");
foreach (var input in inputs)
{
Debug.WriteLine($"-------------- {input} ---------------");
var lyrOut = layerL4.Compute(input, learn) as ComputeCycle;
previousInputs.Add(input.ToString());
if (previousInputs.Count > (maxPrevInputs + 1))
{
previousInputs.RemoveAt(0);
}
if (previousInputs.Count < maxPrevInputs)
{
continue;
}
if (lyrOut.ActiveCells.Count == lyrOut.WinnerCells.Count)
{
/*var activeColumns = L4.GetResult("sp") as int[];
* foreach (var item in activeColumns)
* {
* L2.Compute(item, true);
* }*/
// Reset tha array
InitArray(inpCellsL4ToL2, 0);
// Set the output active cell array
ArrayUtils.SetIndexesTo(inpCellsL4ToL2, memL4.ActiveCells.Select(c => c.Index).ToArray(), 1);
// 4102,25072, 25363, 25539, 25738, 25961, 26009, 26269, 26491, 26585, 26668, 26920, 26934, 27040, 27107, 27262, 27392, 27826, 27948, 28174, 28243, 28270, 28294, 28308, 28429, 28577, 28671, 29139, 29618, 29637, 29809, 29857, 29897, 29900, 29969, 30057, 30727, 31111, 49805, 49972,
layerL2.Compute(inpCellsL4ToL2, true);
/*foreach (var item in lyrOut.ActiveCells)
* {
* L2.Compute(item, true);
* }*/
//var activeCell = Helpers.StringifyVector(lyrOut.ActiveCells.Select(c => c.Index).ToArray());
//L2.Compute(activeCell, true);
}
}
}
}
/// <summary>
///
/// </summary>
private HtmPredictionEngine RunExperiment(int inputBits, HtmConfig cfg, EncoderBase encoder, Dictionary <string, List <double> > sequences)
{
Stopwatch sw = new Stopwatch();
sw.Start();
int maxMatchCnt = 0;
var mem = new Connections(cfg);
bool isInStableState = false;
HtmClassifier <string, ComputeCycle> cls = new HtmClassifier <string, ComputeCycle>();
var numUniqueInputs = GetNumberOfInputs(sequences);
CortexLayer <object, object> layer1 = new CortexLayer <object, object>("L1");
TemporalMemory tm = new TemporalMemory();
// For more information see following paper: https://www.scitepress.org/Papers/2021/103142/103142.pdf
HomeostaticPlasticityController hpc = new HomeostaticPlasticityController(mem, numUniqueInputs * 150, (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($"INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}");
}
// We are not learning in instable state.
isInStableState = isStable;
// Clear active and predictive cells.
//tm.Reset(mem);
}, numOfCyclesToWaitOnChange: 50);
SpatialPoolerMT sp = new SpatialPoolerMT(hpc);
sp.Init(mem);
tm.Init(mem);
// Please note that we do not add here TM in the layer.
// This is omitted for practical reasons, because we first eneter the newborn-stage of the algorithm
// In this stage we want that SP get boosted and see all elements before we start learning with TM.
// All would also work fine with TM in layer, but it would work much slower.
// So, to improve the speed of experiment, we first ommit the TM and then after the newborn-stage we add it to the layer.
layer1.HtmModules.Add("encoder", encoder);
layer1.HtmModules.Add("sp", sp);
//double[] inputs = inputValues.ToArray();
int[] prevActiveCols = new int[0];
int cycle = 0;
int matches = 0;
var lastPredictedValues = new List <string>(new string[] { "0" });
int maxCycles = 3500;
//
// Training SP to get stable. New-born stage.
//
for (int i = 0; i < maxCycles && isInStableState == false; i++)
{
matches = 0;
cycle++;
Debug.WriteLine($"-------------- Newborn Cycle {cycle} ---------------");
foreach (var inputs in sequences)
{
foreach (var input in inputs.Value)
{
Debug.WriteLine($" -- {inputs.Key} - {input} --");
var lyrOut = layer1.Compute(input, true);
if (isInStableState)
{
break;
}
}
if (isInStableState)
{
break;
}
}
}
// Clear all learned patterns in the classifier.
cls.ClearState();
// We activate here the Temporal Memory algorithm.
layer1.HtmModules.Add("tm", tm);
//
// Loop over all sequences.
foreach (var sequenceKeyPair in sequences)
{
Debug.WriteLine($"-------------- Sequences {sequenceKeyPair.Key} ---------------");
int maxPrevInputs = sequenceKeyPair.Value.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 set.
for (int i = 0; i < maxCycles; i++)
{
matches = 0;
cycle++;
Debug.WriteLine("");
Debug.WriteLine($"-------------- Cycle {cycle} ---------------");
Debug.WriteLine("");
foreach (var input in sequenceKeyPair.Value)
{
Debug.WriteLine($"-------------- {input} ---------------");
var lyrOut = layer1.Compute(input, true) as ComputeCycle;
var activeColumns = layer1.GetResult("sp") as int[];
previousInputs.Add(input.ToString());
if (previousInputs.Count > (maxPrevInputs + 1))
{
previousInputs.RemoveAt(0);
}
// In the pretrained SP with HPC, the TM will quickly learn cells for patterns
// In that case the starting sequence 4-5-6 might have the sam SDR as 1-2-3-4-5-6,
// Which will result in returning of 4-5-6 instead of 1-2-3-4-5-6.
// HtmClassifier allways return the first matching sequence. Because 4-5-6 will be as first
// memorized, it will match as the first one.
if (previousInputs.Count < maxPrevInputs)
{
continue;
}
string key = GetKey(previousInputs, input, sequenceKeyPair.Key);
List <Cell> actCells;
if (lyrOut.ActiveCells.Count == lyrOut.WinnerCells.Count)
{
actCells = lyrOut.ActiveCells;
}
else
{
actCells = lyrOut.WinnerCells;
}
cls.Learn(key, actCells.ToArray());
Debug.WriteLine($"Col SDR: {Helpers.StringifyVector(lyrOut.ActivColumnIndicies)}");
Debug.WriteLine($"Cell SDR: {Helpers.StringifyVector(actCells.Select(c => c.Index).ToArray())}");
//
// If the list of predicted values from the previous step contains the currently presenting value,
// we have a match.
if (lastPredictedValues.Contains(key))
{
matches++;
Debug.WriteLine($"Match. Actual value: {key} - Predicted value: {lastPredictedValues.FirstOrDefault(key)}.");
}
else
{
Debug.WriteLine($"Missmatch! Actual value: {key} - Predicted values: {String.Join(',', lastPredictedValues)}");
}
if (lyrOut.PredictiveCells.Count > 0)
{
//var predictedInputValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray());
var predictedInputValues = cls.GetPredictedInputValues(lyrOut.PredictiveCells.ToArray(), 3);
foreach (var item in predictedInputValues)
{
Debug.WriteLine($"Current Input: {input} \t| Predicted Input: {item.PredictedInput} - {item.Similarity}");
}
lastPredictedValues = predictedInputValues.Select(v => v.PredictedInput).ToList();
}
else
{
Debug.WriteLine($"NO CELLS PREDICTED for next cycle.");
lastPredictedValues = new List <string> ();
}
}
// The first element (a single element) in the sequence cannot be predicted
double maxPossibleAccuraccy = (double)((double)sequenceKeyPair.Value.Count - 1) / (double)sequenceKeyPair.Value.Count * 100.0;
double accuracy = (double)matches / (double)sequenceKeyPair.Value.Count * 100.0;
Debug.WriteLine($"Cycle: {cycle}\tMatches={matches} of {sequenceKeyPair.Value.Count}\t {accuracy}%");
if (accuracy >= maxPossibleAccuraccy)
{
maxMatchCnt++;
Debug.WriteLine($"100% accuracy reched {maxMatchCnt} times.");
//
// Experiment is completed if we are 30 cycles long at the 100% accuracy.
if (maxMatchCnt >= 30)
{
sw.Stop();
Debug.WriteLine($"Sequence learned. The algorithm is in the stable state after 30 repeats with with accuracy {accuracy} of maximum possible {maxMatchCnt}. Elapsed sequence {sequenceKeyPair.Key} learning time: {sw.Elapsed}.");
break;
}
}
else if (maxMatchCnt > 0)
{
Debug.WriteLine($"At 100% accuracy after {maxMatchCnt} repeats we get a drop of accuracy with accuracy {accuracy}. This indicates instable state. Learning will be continued.");
maxMatchCnt = 0;
}
// This resets the learned state, so the first element starts allways from the beginning.
tm.Reset(mem);
}
}
Debug.WriteLine("------------ END ------------");
return(new HtmPredictionEngine {
Layer = layer1, Classifier = cls, Connections = mem
});
}
/// <summary>
/// Shows the encoder settings dialog.
/// </summary>
/// <param name="encoder">The encoder.</param>
/// <param name="canAddImagesToExistingFile">The value indicating whether encoder can add images to the existing multipage image file.</param>
/// <returns>
/// <b>True</b> if settings are applied to the encoder; otherwise, <b>false</b>.
/// </returns>
protected override bool ShowEncoderSettingsDialog(
ref EncoderBase encoder,
bool canAddImagesToExistingFile)
{
if (base.ShowEncoderSettingsDialog(ref encoder, canAddImagesToExistingFile))
{
return(true);
}
// set encoder settings
switch (encoder.Name)
{
case "Jpeg2000":
using (Jpeg2000EncoderSettingsForm jpeg2000EncoderSettingsForm = new Jpeg2000EncoderSettingsForm())
{
SetEncoderSettingsDialogProperties(jpeg2000EncoderSettingsForm);
if (jpeg2000EncoderSettingsForm.ShowDialog() != DialogResult.OK)
{
return(false);
}
((IJpeg2000Encoder)encoder).Settings = jpeg2000EncoderSettingsForm.EncoderSettings;
return(true);
}
#if !REMOVE_PDF_PLUGIN && !REMOVE_DOCCLEANUP_PLUGIN
case "Pdf":
using (PdfEncoderSettingsForm pdfEncoderSettingsForm = new PdfEncoderSettingsForm())
{
SetEncoderSettingsDialogProperties(pdfEncoderSettingsForm);
pdfEncoderSettingsForm.AppendExistingDocumentEnabled = canAddImagesToExistingFile;
if (pdfEncoderSettingsForm.ShowDialog() != DialogResult.OK)
{
return(false);
}
if (pdfEncoderSettingsForm.MrcCompressionSettings != null &&
pdfEncoderSettingsForm.MrcCompressionSettings.EnableMrcCompression)
{
encoder.Dispose();
encoder = new PdfMrcEncoder();
((PdfMrcEncoder)encoder).MrcCompressionSettings = pdfEncoderSettingsForm.MrcCompressionSettings;
}
((IPdfEncoder)encoder).Settings = pdfEncoderSettingsForm.EncoderSettings;
((MultipageEncoderBase)encoder).CreateNewFile = !pdfEncoderSettingsForm.AppendExistingDocument;
return(true);
}
#endif
case "Jbig2":
using (Jbig2EncoderSettingsForm jbig2EncoderSettingsForm = new Jbig2EncoderSettingsForm())
{
SetEncoderSettingsDialogProperties(jbig2EncoderSettingsForm);
jbig2EncoderSettingsForm.AppendExistingDocumentEnabled = canAddImagesToExistingFile;
if (jbig2EncoderSettingsForm.ShowDialog() != DialogResult.OK)
{
return(false);
}
((IJbig2Encoder)encoder).Settings = jbig2EncoderSettingsForm.EncoderSettings;
((MultipageEncoderBase)encoder).CreateNewFile = !jbig2EncoderSettingsForm.AppendExistingDocument;
return(true);
}
}
return(false);
}
/// <summary>
/// Shows the encoder settings dialog and initializes the encoder.
/// </summary>
/// <param name="encoder">The encoder.</param>
/// <param name="canAddImagesToExistingFile">The value indicating whether encoder can add images to the existing multipage image file.</param>
/// <returns>
/// <b>True</b> if settings are applied to the encoder; otherwise, <b>false</b>.
/// </returns>
protected virtual bool ShowEncoderSettingsDialog(
ref EncoderBase encoder,
bool canAddImagesToExistingFile)
{
// set encoder settings
switch (encoder.Name)
{
case "Bmp":
using (BmpEncoderSettingsForm bmpEncoderSettingsForm = new BmpEncoderSettingsForm())
{
SetEncoderSettingsDialogProperties(bmpEncoderSettingsForm);
if (bmpEncoderSettingsForm.ShowDialog() != DialogResult.OK)
{
return(false);
}
BmpEncoder bmpEncoder = (BmpEncoder)encoder;
bmpEncoder.Settings = bmpEncoderSettingsForm.EncoderSettings;
return(true);
}
case "Png":
using (PngEncoderSettingsForm pngEncoderSettingsForm = new PngEncoderSettingsForm())
{
SetEncoderSettingsDialogProperties(pngEncoderSettingsForm);
if (pngEncoderSettingsForm.ShowDialog() != DialogResult.OK)
{
return(false);
}
PngEncoder pngEncoder = (PngEncoder)encoder;
pngEncoder.Settings = pngEncoderSettingsForm.EncoderSettings;
return(true);
}
case "Gif":
using (GifEncoderSettingsForm gifEncoderSettingsForm = new GifEncoderSettingsForm())
{
SetEncoderSettingsDialogProperties(gifEncoderSettingsForm);
gifEncoderSettingsForm.CanAddImagesToExistingFile = canAddImagesToExistingFile;
if (gifEncoderSettingsForm.ShowDialog() != DialogResult.OK)
{
return(false);
}
GifEncoder gifEncoder = (GifEncoder)encoder;
gifEncoder.Settings = gifEncoderSettingsForm.EncoderSettings;
gifEncoder.CreateNewFile = !gifEncoderSettingsForm.AddImagesToExistingFile;
return(true);
}
case "Jpeg":
using (JpegEncoderSettingsForm jpegEncoderSettingsForm = new JpegEncoderSettingsForm())
{
SetEncoderSettingsDialogProperties(jpegEncoderSettingsForm);
if (jpegEncoderSettingsForm.ShowDialog() != DialogResult.OK)
{
return(false);
}
JpegEncoder jpegEncoder = (JpegEncoder)encoder;
jpegEncoder.Settings = jpegEncoderSettingsForm.EncoderSettings;
return(true);
}
case "Tiff":
using (TiffEncoderSettingsForm tiffEncoderSettingsForm = new TiffEncoderSettingsForm())
{
SetEncoderSettingsDialogProperties(tiffEncoderSettingsForm);
tiffEncoderSettingsForm.CanAddImagesToExistingFile = canAddImagesToExistingFile;
if (tiffEncoderSettingsForm.ShowDialog() != DialogResult.OK)
{
return(false);
}
TiffEncoder tiffEncoder = (TiffEncoder)encoder;
tiffEncoder.Settings = tiffEncoderSettingsForm.EncoderSettings;
tiffEncoder.CreateNewFile = !tiffEncoderSettingsForm.AddImagesToExistingFile;
return(true);
}
case "Svg":
using (SvgEncoderSettingsForm svgEncoderSettingsForm = new SvgEncoderSettingsForm())
{
SetEncoderSettingsDialogProperties(svgEncoderSettingsForm);
if (svgEncoderSettingsForm.ShowDialog() != DialogResult.OK)
{
return(false);
}
SvgEncoder svgEncoder = (SvgEncoder)encoder;
svgEncoder.Settings = svgEncoderSettingsForm.EncoderSettings;
return(true);
}
}
return(false);
}
/// <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 ------------");
}
private bool Handshake()
{
Debug.Assert(m_handshaking);
// Receive the greeting.
while (m_greetingBytesRead < GreetingSize)
{
ByteArraySegment greetingSegment = new ByteArraySegment(m_greeting, m_greetingBytesRead);
int n = Read(greetingSegment, GreetingSize - m_greetingBytesRead);
if (n == -1)
{
Error();
return false;
}
if (n == 0)
return false;
m_greetingBytesRead += n;
// We have received at least one byte from the peer.
// If the first byte is not 0xff, we know that the
// peer is using unversioned protocol.
if (m_greeting[0] != 0xff)
break;
if (m_greetingBytesRead < 10)
continue;
// Inspect the right-most bit of the 10th byte (which coincides
// with the 'flags' field if a regular message was sent).
// Zero indicates this is a header of identity message
// (i.e. the peer is using the unversioned protocol).
if ((m_greeting[9] & 0x01) == 0)
break;
// The peer is using versioned protocol.
// Send the rest of the greeting, if necessary.
if (!(((byte[]) m_outpos) == ((byte[]) m_greetingOutputBuffer) &&
m_outpos.Offset + m_outsize == GreetingSize))
{
if (m_outsize == 0)
m_ioObject.SetPollout(m_handle);
m_outpos[m_outsize++] = 1; // Protocol version
m_outpos[m_outsize++] = (byte) m_options.SocketType;
}
}
// Is the peer using the unversioned protocol?
// If so, we send and receive rests of identity
// messages.
if (m_greeting[0] != 0xff || (m_greeting[9] & 0x01) == 0)
{
m_encoder = new Encoder(Config.OutBatchSize);
m_encoder.SetMsgSource(m_session);
m_decoder = new Decoder(Config.InBatchSize, m_options.Maxmsgsize);
m_decoder.SetMsgSink(m_session);
// We have already sent the message header.
// Since there is no way to tell the encoder to
// skip the message header, we simply throw that
// header data away.
int headerSize = m_options.IdentitySize + 1 >= 255 ? 10 : 2;
byte[] tmp = new byte[10];
ByteArraySegment bufferp = new ByteArraySegment(tmp);
int bufferSize = headerSize;
m_encoder.GetData(ref bufferp, ref bufferSize);
Debug.Assert(bufferSize == headerSize);
// Make sure the decoder sees the data we have already received.
m_inpos = new ByteArraySegment(m_greeting);
m_insize = m_greetingBytesRead;
// To allow for interoperability with peers that do not forward
// their subscriptions, we inject a phony subsription
// message into the incomming message stream. To put this
// message right after the identity message, we temporarily
// divert the message stream from session to ourselves.
if (m_options.SocketType == ZmqSocketType.Pub || m_options.SocketType == ZmqSocketType.Xpub)
m_decoder.SetMsgSink(this);
}
else if (m_greeting[VersionPos] == 0)
{
// ZMTP/1.0 framing.
m_encoder = new Encoder(Config.OutBatchSize);
m_encoder.SetMsgSource(m_session);
m_decoder = new Decoder(Config.InBatchSize, m_options.Maxmsgsize);
m_decoder.SetMsgSink(m_session);
}
else
{
// v1 framing protocol.
m_encoder = new V1Encoder(Config.OutBatchSize, m_session);
m_decoder = new V1Decoder(Config.InBatchSize, m_options.Maxmsgsize, m_session);
}
// Start polling for output if necessary.
if (m_outsize == 0)
m_ioObject.SetPollout(m_handle);
// Handshaking was successful.
// Switch into the normal message flow.
m_handshaking = false;
return true;
}
