public void SpatialSequenceLearningExperiment()
{
var parameters = GetDefaultParams();
parameters.Set(KEY.POTENTIAL_RADIUS, 64 * 64);
parameters.Set(KEY.POTENTIAL_PCT, 1.0);
parameters.Set(KEY.GLOBAL_INHIBITION, false);
parameters.Set(KEY.STIMULUS_THRESHOLD, 0.5);
parameters.Set(KEY.INHIBITION_RADIUS, (int)0.25 * 64 * 64);
parameters.Set(KEY.LOCAL_AREA_DENSITY, -1);
parameters.Set(KEY.NUM_ACTIVE_COLUMNS_PER_INH_AREA, 0.1 * 64 * 64);
parameters.Set(KEY.DUTY_CYCLE_PERIOD, 1000000);
parameters.Set(KEY.MAX_BOOST, 5);
parameters.setInputDimensions(new int[] { 32, 32 });
parameters.setColumnDimensions(new int[] { 64, 64 });
parameters.setNumActiveColumnsPerInhArea(0.02 * 64 * 64);
var sp = new SpatialPoolerMT();
var mem = new Connections();
double[] inputSequence = new double[] { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 };
var inputVectors = GetEncodedSequence(inputSequence, 0.0, 100.0);
parameters.apply(mem);
sp.Init(mem, UnitTestHelpers.GetMemory());
foreach (var inputVector in inputVectors)
{
for (int i = 0; i < 3; i++)
{
var activeIndicies = sp.Compute(inputVector, true, true) as int[];
var activeArray = sp.Compute(inputVector, true, false) as int[];
Debug.WriteLine(Helpers.StringifyVector(activeArray));
Debug.WriteLine(Helpers.StringifyVector(activeIndicies));
}
}
}
public void MyTestMethod()
{
//DIRECTORIES TO STORE INPUT AND OUTPUT FILES OF THE EXPERIMENT
//Encoder
string E_inFolder = "NoiseExperiments/Input"; //Encoder's raw input file directory
//Directory.CreateDirectory(E_inFolder);
//----------------INPUT FILE PATH-----------------
string E_inFile_train = $"{E_inFolder}\\sinusoidal.csv"; //Encoder's input file in "Training mode"
//<Robustness>
string E_inFile_robustness = $"{E_inFolder}/Noisy_N-0-2_sinusoidal.csv"; // Encoder's input file in "Testing mode - Robustness" - All the files with name of the form "Noisy_*.csv"
//</Robustness>
//< Specificity >
string E_inFile_specificity = $"{E_inFolder}/sinusoidal-specificity.csv"; // Encoder's input file in "Testing mode - Specificity"
//</ Specificity >
//------------------------------------------------
string E_outFolder = "NoiseExperiments/MyEncoderOutput"; //Encoder's graphical output (PNG format) during "Testing mode" will be created here
Directory.CreateDirectory(E_outFolder);
string E_outFolder_train = $"{E_outFolder}/train"; // Encoder's graphical output (PNG format) during "Training mode" will be created here
Directory.CreateDirectory(E_outFolder_train);
//Spatial Pooler
string SP_inFolder = "NoiseExperiments/MySPInput"; //Spatial Pooler's input file (Encoder's output (CSV format)) directory
Directory.CreateDirectory(SP_inFolder);
string SP_inFile_train = $"{SP_inFolder}/MyEncoderOut_train.csv"; //Spatial Pooler's input file during "Training mode"
string SP_inFile_robustness = $"{SP_inFolder}/MyEncoderOut_robustness.csv"; //Spatial Pooler's input file during "Testing mode - robustness"
string SP_inFile_specificity = $"{SP_inFolder}/MyEncoderOut_specificity.csv"; //Spatial Pooler's input file during "Testing mode - specificity"
string SP_outFolder = "MySPOutput"; //Spatial Pooler's graphical output (PNG format) will be stored here
Directory.CreateDirectory(SP_outFolder);
string SP_outFolder_compare = $"{SP_outFolder}/compare_445"; //This folder containing CSV files, which show Hamming distance between Spatial Pooler's output in "Training mode" and "Testing Mode"
Directory.CreateDirectory(SP_outFolder_compare);
//--------------------OUTPUT FILE PATH-------------------------
//<Robustness>
string SP_outFile_robustness = $"{SP_outFolder_compare}/compare_N-0-2.csv"; //The final output file in "Robustness test"
//</Robustness>
//<Specificity>
string SP_outFile_specificity = $"{SP_outFolder_compare}/compare_specificity.csv"; //The final output file in "Specificity test"
//</Specificity>
//-------------------------------------------------------------
//-------------------------------------------------------
//| HTM PARAMETERS |
//-------------------------------------------------------
const int E_outBits = 445; //Number of Scalar Encoder's output bits
const int columnsNumber = 2048; //Number of Spatial Pooler's output columns
Parameters p = Parameters.getAllDefaultParameters();
p.Set(KEY.RANDOM, new ThreadSafeRandom(42));
//------------------SPATIAL POOLER PARAMETERS-----------------
p.Set(KEY.INPUT_DIMENSIONS, new int[] { E_outBits });
p.Set(KEY.POTENTIAL_RADIUS, -1);
p.Set(KEY.POTENTIAL_PCT, 1);
p.Set(KEY.GLOBAL_INHIBITION, true);
p.Set(KEY.INHIBITION_RADIUS, 15);
//Leave it
p.Set(KEY.LOCAL_AREA_DENSITY, -1.0);
p.Set(KEY.NUM_ACTIVE_COLUMNS_PER_INH_AREA, 0.02 * columnsNumber);
p.Set(KEY.STIMULUS_THRESHOLD, 0.5);
p.Set(KEY.SYN_PERM_INACTIVE_DEC, 0.008);
p.Set(KEY.SYN_PERM_ACTIVE_INC, 0.01);
p.Set(KEY.SYN_PERM_CONNECTED, 0.10);
//Leave it
p.Set(KEY.SYN_PERM_BELOW_STIMULUS_INC, 0.01);
p.Set(KEY.SYN_PERM_TRIM_THRESHOLD, 0.05);
p.Set(KEY.MIN_PCT_OVERLAP_DUTY_CYCLES, 0.001);
p.Set(KEY.MIN_PCT_ACTIVE_DUTY_CYCLES, 0.001);
p.Set(KEY.DUTY_CYCLE_PERIOD, 100);
p.Set(KEY.MAX_BOOST, 10 /*10.0*/);
p.Set(KEY.WRAP_AROUND, true);
//p.Set(KEY.LEARN, true);
//-------------------TEMPORAL MEMORY PARAMETERS----------------
p.Set(KEY.COLUMN_DIMENSIONS, new int[] { columnsNumber });
p.Set(KEY.CELLS_PER_COLUMN, 32);
p.Set(KEY.ACTIVATION_THRESHOLD, 10);
p.Set(KEY.LEARNING_RADIUS, 10);
p.Set(KEY.MIN_THRESHOLD, 9);
p.Set(KEY.MAX_NEW_SYNAPSE_COUNT, 20);
p.Set(KEY.MAX_SYNAPSES_PER_SEGMENT, 225);
p.Set(KEY.MAX_SEGMENTS_PER_CELL, 225);
p.Set(KEY.INITIAL_PERMANENCE, 0.21);
p.Set(KEY.CONNECTED_PERMANENCE, 0.5);
p.Set(KEY.PERMANENCE_INCREMENT, 0.10);
p.Set(KEY.PERMANENCE_DECREMENT, 0.10);
p.Set(KEY.PREDICTED_SEGMENT_DECREMENT, 0.1);
//p.Set(KEY.LEARN, true);
//---------------------------------------------------
//| UNIT TEST |
//---------------------------------------------------
//Initiating HTM modules
SpatialPoolerMT sp1 = new SpatialPoolerMT();
TemporalMemory tm1 = new TemporalMemory();
var mem = new Connections();
p.apply(mem);
sp1.Init(mem, UnitTestHelpers.GetMemory());
tm1.Init(mem);
HtmClassifier <double, ComputeCycle> cls = new HtmClassifier <double, ComputeCycle>();
//-------------------ENCODING INPUTS----------------------
Encoding(E_inFile_train, SP_inFile_train, E_outFolder_train, E_outBits);
//--------------------TRAINING MODE---------------------
//SP training
for (int j = 0; j < 5; j++)
{
using (StreamReader sr = new StreamReader(SP_inFile_train))
{
string line;
while ((line = sr.ReadLine()) != null)
{
string[] tokens = line.Split(",");
int[] SP_input = new int[E_outBits];
for (int i = 0; i < E_outBits; i++)
{
if (tokens[i + 1] == "0")
{
SP_input[i] = 0;
}
else
{
SP_input[i] = 1;
}
}
for (int i = 0; i < 3; i++)
{
sp1.Compute(SP_input, true);
}
}
}
}
Debug.WriteLine("-----------------------------------------------------");
Debug.WriteLine("|-----------------FINISHED TRAINING-----------------|");
Debug.WriteLine("-----------------------------------------------------");
//TM + SP training
double lastPredictedValue = 0.0;
for (int j = 0; j < 20; j++)
{
using (StreamReader sr = new StreamReader(SP_inFile_train))
{
string line;
while ((line = sr.ReadLine()) != null)
{
string[] tokens = line.Split(",");
int[] SP_input = new int[E_outBits];
for (int i = 0; i < E_outBits; i++)
{
if (tokens[i + 1] == "0")
{
SP_input[i] = 0;
}
else
{
SP_input[i] = 1;
}
}
var SP_res = sp1.Compute(SP_input, true);
var TM_res = tm1.Compute(SP_res, true) as ComputeCycle;
double input = Convert.ToDouble(tokens[0], CultureInfo.InvariantCulture);
Debug.WriteLine($"Input: {input} - Predicted: {lastPredictedValue}");
//cls.Learn(input, TM_res.ActiveCells.ToArray(), TM_res.PredictiveCells.ToArray());
lastPredictedValue = cls.GetPredictedInputValue(TM_res.PredictiveCells.ToArray());
}
}
}
}
public void TestCortexLayer()
{
int inputBits = 100;
double max = 20;
int numColumns = 2048;
List <double> inputValues = new List <double>(new double[] { 0.0, 1.0, 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, 12.0, 13.0, 14.0, 11.0, 12.0, 14.0, 5.0, 7.0, 6.0, 9.0, 3.0, 4.0, 3.0, 4.0, 3.0, 4.0 });
int numInputs = inputValues.Distinct().ToList().Count;
var inputs = inputValues.ToArray();
Dictionary <string, object> settings = new Dictionary <string, object>()
{
{ "W", 15 },
{ "N", inputBits },
{ "Radius", -1.0 },
{ "MinVal", 0.0 },
{ "Periodic", false },
{ "Name", "scalar" },
{ "ClipInput", false },
{ "MaxVal", max }
};
EncoderBase encoder = new ScalarEncoder(settings);
HtmConfig htmConfig = new HtmConfig(new int[] { inputBits }, new int[] { numColumns })
{
Random = new ThreadSafeRandom(42),
CellsPerColumn = 25,
GlobalInhibition = true,
LocalAreaDensity = -1,
NumActiveColumnsPerInhArea = 0.02 * numColumns,
PotentialRadius = 50,
InhibitionRadius = 15,
MaxBoost = 10.0,
DutyCyclePeriod = 25,
MinPctOverlapDutyCycles = 0.75,
MaxNewSynapseCount = (int)(0.02 * numColumns),
ActivationThreshold = 15,
ConnectedPermanence = 0.5,
PermanenceDecrement = 0.25,
PermanenceIncrement = 0.15,
PredictedSegmentDecrement = 0.1
};
Connections memory = new Connections(htmConfig);
HomeostaticPlasticityController hpa = new HomeostaticPlasticityController(memory, 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}");
}
}, numOfCyclesToWaitOnChange: 25);
SpatialPoolerMT spatialPooler = new SpatialPoolerMT(hpa);
spatialPooler.Init(memory, UnitTestHelpers.GetMemory());
TemporalMemory temporalMemory = new TemporalMemory();
temporalMemory.Init(memory);
List <CortexRegion> regions = new List <CortexRegion>();
CortexRegion region0 = new CortexRegion("1st Region");
regions.Add(region0);
CortexLayer <object, object> layer1 = new CortexLayer <object, object>("L1");
region0.AddLayer(layer1);
layer1.HtmModules.Add("encoder", encoder);
layer1.HtmModules.Add("sp", spatialPooler);
layer1.HtmModules.Add("tm", temporalMemory);
bool learn = true;
int maxCycles = 3500;
for (int i = 0; i < maxCycles; i++)
{
foreach (var input in inputs)
{
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
}
}
}
/// <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($"------------ {input.Key} ------------");
foreach (var actCols in input.Value)
{
Debug.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
colSw.WriteLine(Helpers.StringifyVector(actCols.ToArray()));
}
}
}
Debug.WriteLine("------------ END ------------");
}
[DataRow(81, 450, 1000)] //Sparsity - 0.18
//[DataRow(9, 90, 1000)] //Sparsity - 0.10
//[DataRow(9, 70, 1000)] //Sparsity - 0.128
//[DataRow(9, 60, 1000)] //Sparsity - 0.15
//[DataRow(9, 45, 1000)] //Sparsity - 0.20
//[DataRow(9, 40, 1000)] //Sparsity - 0.225
//[DataRow(9, 36, 1000)] //Sparsity - 0.25
//[DataRow(9, 30, 1000)] //Sparsity - 0.30
public void HtmSparsity(int W, int InputB, int loop)
{
string filename = "Sparsity_" + W + "." + InputB + ".csv";
using (StreamWriter writer = new StreamWriter(filename))
{
Debug.WriteLine($"Learning Cycles: {460}");
Debug.WriteLine("Cycle;Similarity");
//Parent loop / Outer loop
//This loop defines the number of times the experiment will run for the given data
for (int j = 0; j < loop; j++)
{
int inputBits = InputB;
bool learn = true;
Parameters p = Parameters.getAllDefaultParameters();
p.Set(KEY.RANDOM, new ThreadSafeRandom(42));
p.Set(KEY.INPUT_DIMENSIONS, new int[] { inputBits });
p.Set(KEY.CELLS_PER_COLUMN, 5);
p.Set(KEY.COLUMN_DIMENSIONS, new int[] { 500 });
CortexNetwork net = new CortexNetwork("my cortex");
List <CortexRegion> regions = new List <CortexRegion>();
CortexRegion region0 = new CortexRegion("1st Region");
regions.Add(region0);
SpatialPoolerMT sp1 = new SpatialPoolerMT();
TemporalMemory tm1 = new TemporalMemory();
var mem = new Connections();
p.apply(mem);
sp1.Init(mem, UnitTestHelpers.GetMemory());
tm1.Init(mem);
Dictionary <string, object> settings = new Dictionary <string, object>()
{
{ "W", W },
{ "N", inputBits },
{ "Radius", -1.0 },
{ "MinVal", 0.0 },
// { "MaxVal", 20.0 },
{ "Periodic", false },
{ "Name", "scalar" },
{ "ClipInput", false },
};
double max = 10;
List <double> lst = new List <double>();
for (double i = max - 1; i >= 0; i--)
{
lst.Add(i);
}
settings["MaxVal"] = max;
EncoderBase encoder = new ScalarEncoder(settings);
CortexLayer <object, object> layer1 = new CortexLayer <object, object>("L1");
//
// NewBorn learning stage.
region0.AddLayer(layer1);
layer1.HtmModules.Add("encoder", encoder);
layer1.HtmModules.Add("sp", sp1);
HtmClassifier <double, ComputeCycle> cls = new HtmClassifier <double, ComputeCycle>();
double[] inputs = lst.ToArray();
//
// This trains SP.
foreach (var input in inputs)
{
Debug.WriteLine($" ** {input} **");
for (int i = 0; i < 3; i++)
{
var lyrOut = layer1.Compute((object)input, learn) as ComputeCycle;
}
}
// Here we add TM module to the layer.
layer1.HtmModules.Add("tm", tm1);
int cycle = 0;
int matches = 0;
double lastPredictedValue = 0;
//
// Now, training with SP+TM. SP is pretrained on pattern.
//Child loop / Inner loop
for (int i = 0; i < 460; i++)
{
matches = 0;
cycle++;
foreach (var input in inputs)
{
var lyrOut = layer1.Compute(input, learn) as ComputeCycle;
cls.Learn(input, lyrOut.ActiveCells.ToArray());
Debug.WriteLine($"-------------- {input} ---------------");
if (learn == false)
{
Debug.WriteLine($"Inference mode");
}
Debug.WriteLine($"W: {Helpers.StringifyVector(lyrOut.WinnerCells.Select(c => c.Index).ToArray())}");
Debug.WriteLine($"P: {Helpers.StringifyVector(lyrOut.PredictiveCells.Select(c => c.Index).ToArray())}");
var predictedValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray());
Debug.WriteLine($"Current Input: {input} \t| - Predicted value in previous cycle: {lastPredictedValue} \t| Predicted Input for the next cycle: {predictedValue}");
if (input == lastPredictedValue)
{
matches++;
Debug.WriteLine($"Match {input}");
}
else
{
Debug.WriteLine($"Missmatch Actual value: {input} - Predicted value: {lastPredictedValue}");
}
lastPredictedValue = predictedValue;
}
if (i == 500)
{
Debug.WriteLine("Stop Learning From Here. Entering inference mode.");
learn = false;
}
//tm1.reset(mem);
Debug.WriteLine($"Cycle: {cycle}\tMatches={matches} of {inputs.Length}\t {(double)matches / (double)inputs.Length * 100.0}%");
if ((double)matches / (double)inputs.Length == 1)
{
writer.WriteLine($"{cycle}");
break;
}
}
}
Debug.WriteLine("New Iteration");
}
//cls.TraceState();
Debug.WriteLine("------------------------------------------------------------------------\n----------------------------------------------------------------------------");
}
public void RunGaussianNoiseExperiment()
{
const int E_outBits = 423;
const int columnsNumber = 2048;
int[] SP_Result = null;
int[] SP_NoisyResult = null;
List <int[]> SP_Result_List = new List <int[]>();
List <int[]> SP_NoisyResult_List = new List <int[]>();
double[] ham_total = { 0, 0, 0, 0, 0, 0, 0, 0, 0 };
double[] ham_avg = { 0, 0, 0, 0, 0, 0, 0, 0, 0 };
double ham_upper;
double ham_lower;
int number_training_set = 41; // Integer numbers range from -20 to 20 with step of 1.
int number_testing_set = 401; // Decimal numbers range from -20 to 20 with step of 0.1.
string experimentFolder = nameof(GoussianNoiseExperiment);
// string SP_noisyinFolder = nameof(GoussianNoiseExperiment);
Directory.CreateDirectory(experimentFolder);
// Directory.CreateDirectory(SP_noisyinFolder);
string SP_inFile = $"{experimentFolder}\\MyEncoderOut.csv";
string SP_noisyinFile = $"{experimentFolder}\\MyNoisyEncoderOut.csv";
//string SP_outFolder = "MySPOutput";
//string SP_noisyoutFolder = "MyNoisySPOutput";
//Directory.CreateDirectory(SP_outFolder);
//Directory.CreateDirectory(SP_noisyoutFolder);
string SP_outFile = $"{experimentFolder}\\MySPOut.csv";
string SP_noisyoutFile = $"{experimentFolder}\\MyNoisySPOut.csv";
//string testFolder = "MyDraftFoler";
//Directory.CreateDirectory(testFolder);
//-------------------------------------------------------
//| PARAMETERS |
//-------------------------------------------------------
Parameters p = Parameters.getAllDefaultParameters();
p.Set(KEY.RANDOM, new ThreadSafeRandom(42));
//------------------SPATIAL POOLER PARAMETERS-----------------
p.Set(KEY.INPUT_DIMENSIONS, new int[] { E_outBits });
p.Set(KEY.POTENTIAL_RADIUS, -1);
p.Set(KEY.POTENTIAL_PCT, 0.75);
p.Set(KEY.GLOBAL_INHIBITION, true);
p.Set(KEY.INHIBITION_RADIUS, 15);
p.Set(KEY.LOCAL_AREA_DENSITY, -1.0);
p.Set(KEY.NUM_ACTIVE_COLUMNS_PER_INH_AREA, 0.02 * columnsNumber);
p.Set(KEY.STIMULUS_THRESHOLD, 5);
p.Set(KEY.SYN_PERM_INACTIVE_DEC, 0.008);
p.Set(KEY.SYN_PERM_ACTIVE_INC, 0.05);
p.Set(KEY.SYN_PERM_CONNECTED, 0.10);
p.Set(KEY.SYN_PERM_BELOW_STIMULUS_INC, 0.01);
p.Set(KEY.SYN_PERM_TRIM_THRESHOLD, 0.05);
p.Set(KEY.MIN_PCT_OVERLAP_DUTY_CYCLES, 1);
p.Set(KEY.MIN_PCT_ACTIVE_DUTY_CYCLES, 0.001);
p.Set(KEY.DUTY_CYCLE_PERIOD, 100);
// These values activate powerfull boosting.
p.Set(KEY.MAX_BOOST, 5);
p.Set(KEY.DUTY_CYCLE_PERIOD, 100);
p.Set(KEY.MIN_PCT_OVERLAP_DUTY_CYCLES, 1);
p.Set(KEY.MAX_BOOST, 10);
p.Set(KEY.WRAP_AROUND, true);
p.Set(KEY.LEARN, true);
//-------------------TEMPORAL MEMORY PARAMETERS----------------
p.Set(KEY.COLUMN_DIMENSIONS, new int[] { columnsNumber });
p.Set(KEY.CELLS_PER_COLUMN, 32);
p.Set(KEY.ACTIVATION_THRESHOLD, 10);
p.Set(KEY.LEARNING_RADIUS, 10);
p.Set(KEY.MIN_THRESHOLD, 9);
p.Set(KEY.MAX_NEW_SYNAPSE_COUNT, 20);
p.Set(KEY.MAX_SYNAPSES_PER_SEGMENT, 225);
p.Set(KEY.MAX_SEGMENTS_PER_CELL, 225);
p.Set(KEY.INITIAL_PERMANENCE, 0.21);
p.Set(KEY.CONNECTED_PERMANENCE, 0.1);
p.Set(KEY.PERMANENCE_INCREMENT, 0.10);
p.Set(KEY.PERMANENCE_DECREMENT, 0.10);
p.Set(KEY.PREDICTED_SEGMENT_DECREMENT, 0.1);
p.Set(KEY.LEARN, true);
//Initiating components of a Cortex Layer
SpatialPoolerMT sp1 = new SpatialPoolerMT();
TemporalMemory tm1 = new TemporalMemory();
var mem = new Connections();
p.apply(mem);
sp1.Init(mem, UnitTestHelpers.GetMemory());
tm1.Init(mem);
HtmClassifier <double, ComputeCycle> cls = new HtmClassifier <double, ComputeCycle>();
Encoding(E_outBits);
// Can adjust the number of SP learning cycles below
for (int cycle = 0; cycle < 320; cycle++)
{
if (cycle >= 300)
{
// These activates ew-born effect which switch offs the boosting.
//mem.setMaxBoost(0.0);
mem.HtmConfig.MaxBoost = 0.0;
//mem.updateMinPctOverlapDutyCycles(0.0);
mem.HtmConfig.MinPctOverlapDutyCycles = 0.0;
}
using (StreamReader sr = new StreamReader(SP_inFile))
{
string line;
while ((line = sr.ReadLine()) != null)
{
string[] tokens = line.Split(",");
int[] SP_input = new int[E_outBits];
for (int i = 0; i < E_outBits; i++)
{
if (tokens[i + 1] == "0")
{
SP_input[i] = 0;
}
else
{
SP_input[i] = 1;
}
}
SP_Result = sp1.Compute(SP_input, true);
}
}
}
using (StreamReader sr = new StreamReader(SP_inFile))
{
string line;
int lineNumber = 0;
while ((line = sr.ReadLine()) != null)
{
string[] tokens = line.Split(",");
int[] SP_input = new int[E_outBits];
for (int i = 0; i < E_outBits; i++)
{
if (tokens[i + 1] == "0")
{
SP_input[i] = 0;
}
else
{
SP_input[i] = 1;
}
}
SP_Result = sp1.Compute(SP_input, false, false);
SP_Result_List.Add(SP_Result);
int[,] SP_twoDimenArray = ArrayUtils.Make2DArray <int>(SP_Result, 32, 64);
var SP_twoDimArray = ArrayUtils.Transpose(SP_twoDimenArray);
NeoCortexUtils.DrawBitmap(SP_twoDimArray, 1024, 1024, $"{experimentFolder}\\{lineNumber}.png", Color.DimGray, Color.LawnGreen, text: tokens[0]);
lineNumber++;
}
}
using (StreamReader sr = new StreamReader(SP_noisyinFile))
{
string line;
int lineNumber = 0;
while ((line = sr.ReadLine()) != null)
{
string[] tokens = line.Split(",");
int[] SP_input = new int[E_outBits];
for (int i = 0; i < E_outBits; i++)
{
if (tokens[i + 1] == "0")
{
SP_input[i] = 0;
}
else
{
SP_input[i] = 1;
}
}
SP_NoisyResult = sp1.Compute(SP_input, false, false);
SP_NoisyResult_List.Add(SP_NoisyResult);
var ham = MathHelpers.GetHammingDistance(SP_Result_List[lineNumber], SP_NoisyResult_List[lineNumber], true);
Debug.WriteLine($"Noisy input: {tokens[0]} - Hamming NonZ: {ham}");
ham = MathHelpers.GetHammingDistance(SP_Result_List[lineNumber], SP_NoisyResult_List[lineNumber], false);
Debug.WriteLine($"Noisy input: {tokens[0]} - Hamming All: {ham}");
int[,] SP_twoDimenArray = ArrayUtils.Make2DArray <int>(SP_NoisyResult, 32, 64);
var SP_twoDimArray = ArrayUtils.Transpose(SP_twoDimenArray);
NeoCortexUtils.DrawBitmap(SP_twoDimArray, 1024, 1024, $"{experimentFolder}\\{lineNumber}.png", Color.DimGray, Color.LawnGreen, text: tokens[0]);
lineNumber++;
}
}
for (int i = 0; i < number_testing_set - 1; i += 10)
{
int count = 1;
for (int j = i + 1; j < i + 1 + 9; j++)
{
if (i != 0 && i != number_testing_set - 1)
{
ham_upper = MathHelpers.GetHammingDistance(SP_NoisyResult_List[i], SP_NoisyResult_List[j], true);
ham_lower = MathHelpers.GetHammingDistance(SP_NoisyResult_List[i], SP_NoisyResult_List[i - count], true);
ham_total[count - 1] += ham_upper + ham_lower;
count++;
}
else if (i == 0)
{
ham_upper = MathHelpers.GetHammingDistance(SP_NoisyResult_List[i], SP_NoisyResult_List[j], true);
ham_total[count - 1] += ham_upper;
count++;
}
else
{
ham_lower = MathHelpers.GetHammingDistance(SP_NoisyResult_List[i], SP_NoisyResult_List[i - count], true);
ham_total[count - 1] += ham_lower;
count++;
}
}
}
for (int i = 0; i < 9; i++)
{
ham_avg[i] = (ham_total[i] / (number_training_set * 2 - 2));
Debug.WriteLine($"0.{i + 1} step avg hamming distance: {ham_avg[i]}");
}
}