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 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(GaussianNoiseExperiment);
// 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(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(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]}");
}
}
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());
}
}
}
}
/// <summary>
/// Processes the test cases for Noise Test taking the necessary parameters.
/// It's the parent method which calls other utility methods to create the input vectors and the outputs.
/// </summary>
/// <param name="inputSequence">An array of double, consisting the starting indexes for each input vector</param>
/// <param name="inputs">
/// A parameter of the class "InputParameters", which contains all the input parameters needed as properties which can be set in a test case
/// </param>
/// <returns>Returns nothing</returns>
public void ProcessTestCase(List <double[]> inputSequences, InputParameters inputs)
{
string path = Directory.GetCurrentDirectory();
string parentDir = path.Substring(0, path.IndexOf("bin") - 1);
string resultDir = parentDir.Substring(0, parentDir.LastIndexOf(@"\"));
string timeStamp = DateTime.Now.ToString("yyyyMMddHHmmss");
string outFolder = resultDir + @"\SpatialPooler_Results\" + timeStamp + @"\Output\";
string inFolder = resultDir + @"\SpatialPooler_Results\" + timeStamp + @"\InputVectors";
if (!Directory.Exists(outFolder))
{
Directory.CreateDirectory(outFolder);
}
if (!Directory.Exists(inFolder + @"\"))
{
Directory.CreateDirectory(inFolder);
}
//int radius = 0;
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);
Console.WriteLine("Fetched all default parameters\n");
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();
parameters.apply(mem);
Console.WriteLine("\nConfiguring the Inputs...\n");
sp.Init(mem, GetInMemoryDictionary());
int outFolderCount = 0;
int compareIndex = Convert.ToInt32(inputs.getCompareNumber());
double[][] recordOutput = null;
double[] hammingDistance = null;
foreach (double[] inputSequence in inputSequences)
{
outFolderCount++;
double minVal = 0.0;
for (int i = 0; i < inputSequence.Length; i++)
{
if (i == 0)
{
minVal = inputSequence[i];
}
else if (inputSequence[i] < minVal)
{
minVal = inputSequence[i];
}
}
minVal -= 1.0;
Console.WriteLine("\nGetting the Input Vectors...\n");
var inputVectors = GetEncodedSequence(inputSequence, minVal, inputs.getMaxIndex(), inputs, inFolder);
int count = 1;
//string output = String.Empty;
int max = 0;
for (int i = 0; i < inputVectors.Count; i++)
{
hammingDistance = null;
//output = String.Empty;
Console.WriteLine("Computing the Output for the vector no: " + count.ToString() + "...\n");
var activeArray = sp.Compute(inputVectors[i], true) as int[];
for (int j = 0; j < activeArray.Length; j++)
{
if (activeArray[j] > max)
{
max = activeArray[j];
}
}
//var str = Helpers.StringifyVector(activeArray);
int rows = Convert.ToInt32(Math.Ceiling(Math.Sqrt(Convert.ToDouble(max))));
int counter = 0;
int index = 0;
int[,] outTwoDArray = new int[rows, rows];
for (int j = 0; j < rows; j++)
{
for (int k = 0; k < rows; k++)
{
outTwoDArray[j, k] = 0;
}
}
for (int j = 0; j < rows; j++)
{
for (int k = 0; k < rows; k++)
{
counter++;
if (index < activeArray.Length && activeArray[index] == counter)
{
index++;
outTwoDArray[j, k] = 1;
}
}
}
double[][] comparingArray = new double[rows][];
for (int j = 0; j < rows; j++)
{
comparingArray[j] = new double[rows];
for (int k = 0; k < rows; k++)
{
comparingArray[j][k] = Convert.ToDouble(outTwoDArray[j, k]);
}
}
int[,] record2Darray = null;
if (inputSequence[i] == compareIndex)
{
if (recordOutput != null)
{
hammingDistance = MathHelpers.GetHammingDistance(recordOutput, comparingArray, false);
record2Darray = new int[recordOutput.Length, recordOutput.Length];
for (int j = 0; j < recordOutput.Length; j++)
{
for (int k = 0; k < recordOutput.Length; k++)
{
record2Darray[j, k] = Convert.ToInt32(recordOutput[j][k]);
}
}
}
recordOutput = new double[rows][];
for (int j = 0; j < rows; j++)
{
recordOutput[j] = new double[rows];
for (int k = 0; k < rows; k++)
{
recordOutput[j][k] = comparingArray[j][k];
}
}
}
if (hammingDistance != null)
{
int rowHam = Convert.ToInt32(Math.Ceiling(Math.Sqrt(hammingDistance.Length)));
int[,] hammingArray = new int[rowHam, rowHam];
int limit = 0;
for (int j = 0; j < rowHam; j++)
{
for (int k = 0; k < rowHam; k++)
{
if (limit < hammingDistance.Length)
{
//hj
hammingArray[j, k] = Convert.ToInt32(hammingDistance[limit]);
limit++;
}
}
}
int compare_no = 1;
if (!File.Exists($"{outFolder}\\Compare_{compareIndex}.png"))
{
DrawBitmapHamming(hammingArray, 1024, 1024, $"{outFolder}\\Compare_{compareIndex}.png", $"Compare_{compareIndex}");
DrawBitmapOverlap(record2Darray, outTwoDArray, 1024, 1024, $"{outFolder}\\Overlap_{compareIndex}.png", $"Overlap_{compareIndex}");
}
else
{
while (File.Exists($"{outFolder}\\Compare_{compareIndex}_{compare_no}.png"))
{
compare_no++;
}
DrawBitmapHamming(hammingArray, 1024, 1024, $"{outFolder}\\Compare_{compareIndex}_{compare_no}.png", $"Compare_{compareIndex}");
compare_no = 1;
while (File.Exists($"{outFolder}\\Overlap_{compareIndex}_{compare_no}.png"))
{
compare_no++;
}
DrawBitmapOverlap(record2Darray, outTwoDArray, 1024, 1024, $"{outFolder}\\Overlap_{compareIndex}_{compare_no}.png", $"Overlap_{compareIndex}");
}
}
if (!Directory.Exists(outFolder + @"\\" + outFolderCount.ToString()))
{
Directory.CreateDirectory(outFolder + @"\\" + outFolderCount.ToString());
}
int[,] out2dimArray = ArrayUtils.Transpose(outTwoDArray);
NeoCortexUtils.DrawBitmap(out2dimArray, 1024, 1024, $"{outFolder}\\{outFolderCount}\\{count}.png", Color.Black, Color.Green, text: inputSequence[i].ToString());
//File.WriteAllLines(outFolder + count.ToString() + ".txt", new string[] { str });
Console.WriteLine("Output is recorded in the path: " + outFolder + count.ToString() + ".txt\n");
count++;
}
}
}
public void NoiseTest()
{
const int colDimSize = 64;
const int noiseStepPercent = 5;
var parameters = GetDefaultParams();
parameters.Set(KEY.POTENTIAL_RADIUS, 32 * 32);
parameters.Set(KEY.POTENTIAL_PCT, 1.0);
parameters.Set(KEY.GLOBAL_INHIBITION, true);
parameters.Set(KEY.STIMULUS_THRESHOLD, 0.5);
parameters.Set(KEY.INHIBITION_RADIUS, (int)0.01 * colDimSize * colDimSize);
parameters.Set(KEY.LOCAL_AREA_DENSITY, -1);
parameters.Set(KEY.NUM_ACTIVE_COLUMNS_PER_INH_AREA, 0.02 * colDimSize * colDimSize);
parameters.Set(KEY.DUTY_CYCLE_PERIOD, 1000);
parameters.Set(KEY.MAX_BOOST, 0.0);
parameters.Set(KEY.SYN_PERM_INACTIVE_DEC, 0.008);
parameters.Set(KEY.SYN_PERM_ACTIVE_INC, 0.01);
parameters.Set(KEY.MIN_PCT_OVERLAP_DUTY_CYCLES, 0.001);
parameters.Set(KEY.SEED, 42);
parameters.setInputDimensions(new int[] { 32, 32 });
parameters.setColumnDimensions(new int[] { colDimSize, colDimSize });
var sp = new SpatialPoolerMT();
var mem = new Connections();
//var rnd = new Random();
parameters.apply(mem);
sp.init(mem);
List <int[]> inputVectors = new List <int[]>();
inputVectors.Add(getInputVector1());
inputVectors.Add(getInputVector2());
int vectorIndex = 0;
int[][] activeArrayWithZeroNoise = new int[inputVectors.Count][];
foreach (var inputVector in inputVectors)
{
var x = getNumBits(inputVector);
Debug.WriteLine("");
Debug.WriteLine($"----- VECTOR {vectorIndex} ----------");
//int[] activeArray = new int[64 * 64];
activeArrayWithZeroNoise[vectorIndex] = new int[colDimSize * colDimSize];
int[] activeArray = null;
for (int j = 0; j < 25; j += noiseStepPercent)
{
Debug.WriteLine($"--- Vector {0} - Noise Iteration {j} ----------");
int[] noisedInput;
if (j > 0)
{
noisedInput = ArrayUtils.flipBit(inputVector, (double)((double)j / 100.00));
}
else
{
noisedInput = inputVector;
}
var d = MathHelpers.GetHammingDistance(inputVector, noisedInput, true);
Debug.WriteLine($"Input with noise {j} - HamDist: {d}");
Debug.WriteLine($"Original: {Helpers.StringifyVector(inputVector)}");
Debug.WriteLine($"Noised: {Helpers.StringifyVector(noisedInput)}");
for (int i = 0; i < 10; i++)
{
//sp.compute( noisedInput, activeArray, true);
activeArray = sp.Compute(noisedInput, true, returnActiveColIndiciesOnly: false) as int[];
if (j > 0)
{
Debug.WriteLine($"{ MathHelpers.GetHammingDistance(activeArrayWithZeroNoise[vectorIndex], activeArray, true)} -> {Helpers.StringifyVector(ArrayUtils.IndexWhere(activeArray, (el) => el == 1))}");
}
}
if (j == 0)
{
Array.Copy(activeArray, activeArrayWithZeroNoise[vectorIndex], activeArrayWithZeroNoise[vectorIndex].Length);
}
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
var d2 = MathHelpers.GetHammingDistance(activeArrayWithZeroNoise[vectorIndex], activeArray, true);
Debug.WriteLine($"Output with noise {j} - Ham Dist: {d2}");
Debug.WriteLine($"Original: {Helpers.StringifyVector(ArrayUtils.IndexWhere(activeArrayWithZeroNoise[vectorIndex], (el) => el == 1))}");
Debug.WriteLine($"Noised: {Helpers.StringifyVector(ArrayUtils.IndexWhere(activeArray, (el) => el == 1))}");
List <int[, ]> arrays = new List <int[, ]>();
int[,] twoDimenArray = ArrayUtils.Make2DArray <int>(activeArray, 64, 64);
twoDimenArray = ArrayUtils.Transpose(twoDimenArray);
arrays.Add(ArrayUtils.Transpose(ArrayUtils.Make2DArray <int>(noisedInput, 32, 32)));
arrays.Add(ArrayUtils.Transpose(ArrayUtils.Make2DArray <int>(activeArray, 64, 64)));
// NeoCortexUtils.DrawHeatmaps(bostArrays, $"{outputImage}_boost.png", 1024, 1024, 150, 50, 5);
NeoCortexUtils.DrawBitmaps(arrays, $"Vector_{vectorIndex}_Noise_{j * 10}.png", Color.Yellow, Color.Gray, OutImgSize, OutImgSize);
}
vectorIndex++;
}
//
// Prediction code.
// This part of code takes a single sample of every input vector and add
// some noise to it. Then it predicts it.
// Calculated hamming distance (percent overlap) between predicted output and output
// trained without noise is final result, which should be higher than 95% (realistic guess).
vectorIndex = 0;
foreach (var inputVector in inputVectors)
{
double noise = 7;
var noisedInput = ArrayUtils.flipBit(inputVector, noise / 100.00);
int[] activeArray = new int[64 * 64];
sp.compute(noisedInput, activeArray, false);
var dist = MathHelpers.GetHammingDistance(activeArrayWithZeroNoise[vectorIndex], activeArray, true);
Debug.WriteLine($"Result for vector {vectorIndex++} with noise {noise} - Ham Dist: {dist}");
Assert.IsTrue(dist >= 95);
}
}
public void NoiseExperimentTest()
{
const int colDimSize = 64;
const int noiseStepPercent = 5;
var parameters = GetDefaultParams();
parameters.Set(KEY.POTENTIAL_RADIUS, 32 * 32);
parameters.Set(KEY.POTENTIAL_PCT, 1.0);
parameters.Set(KEY.GLOBAL_INHIBITION, true);
parameters.Set(KEY.STIMULUS_THRESHOLD, 0.5);
parameters.Set(KEY.INHIBITION_RADIUS, (int)0.01 * colDimSize * colDimSize);
parameters.Set(KEY.LOCAL_AREA_DENSITY, -1);
parameters.Set(KEY.NUM_ACTIVE_COLUMNS_PER_INH_AREA, 0.02 * colDimSize * colDimSize);
parameters.Set(KEY.DUTY_CYCLE_PERIOD, 1000);
parameters.Set(KEY.MAX_BOOST, 0.0);
parameters.Set(KEY.SYN_PERM_INACTIVE_DEC, 0.008);
parameters.Set(KEY.SYN_PERM_ACTIVE_INC, 0.01);
parameters.Set(KEY.MIN_PCT_OVERLAP_DUTY_CYCLES, 0.0);
parameters.Set(KEY.SEED, 42);
parameters.setInputDimensions(new int[] { 32, 32 });
parameters.setColumnDimensions(new int[] { colDimSize, colDimSize });
var sp = new SpatialPoolerMT();
var mem = new Connections();
parameters.apply(mem);
sp.Init(mem);
List <int[]> inputVectors = new List <int[]>();
inputVectors.Add(getInputVector1());
inputVectors.Add(getInputVector2());
int vectorIndex = 0;
int[][] activeColumnsWithZeroNoise = new int[inputVectors.Count][];
foreach (var inputVector in inputVectors)
{
var x = getNumBits(inputVector);
Debug.WriteLine("");
Debug.WriteLine($"----- VECTOR {vectorIndex} ----------");
// Array of active columns with zero noise. The reference (ideal) output.
activeColumnsWithZeroNoise[vectorIndex] = new int[colDimSize * colDimSize];
int[] activeArray = null;
for (int j = 0; j < 25; j += noiseStepPercent)
{
Debug.WriteLine($"--- Vector {0} - Noise Iteration {j} ----------");
int[] noisedInput;
if (j > 0)
{
noisedInput = ArrayUtils.FlipBit(inputVector, (double)((double)j / 100.00));
}
else
{
noisedInput = inputVector;
}
// TODO: Try CalcArraySimilarity
var d = MathHelpers.GetHammingDistance(inputVector, noisedInput, true);
Debug.WriteLine($"Input with noise {j} - HamDist: {d}");
Debug.WriteLine($"Original: {Helpers.StringifyVector(inputVector)}");
Debug.WriteLine($"Noised: {Helpers.StringifyVector(noisedInput)}");
for (int i = 0; i < 10; i++)
{
activeArray = sp.Compute(noisedInput, true, returnActiveColIndiciesOnly: false) as int[];
if (j > 0)
{
Debug.WriteLine($"{ MathHelpers.GetHammingDistance(activeColumnsWithZeroNoise[vectorIndex], activeArray, true)} -> {Helpers.StringifyVector(ArrayUtils.IndexWhere(activeArray, (el) => el == 1))}");
}
}
if (j == 0)
{
Array.Copy(activeArray, activeColumnsWithZeroNoise[vectorIndex], activeColumnsWithZeroNoise[vectorIndex].Length);
}
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
var d2 = MathHelpers.GetHammingDistance(activeColumnsWithZeroNoise[vectorIndex], activeArray, true);
Debug.WriteLine($"Output with noise {j} - Ham Dist: {d2}");
Debug.WriteLine($"Original: {Helpers.StringifyVector(ArrayUtils.IndexWhere(activeColumnsWithZeroNoise[vectorIndex], (el) => el == 1))}");
Debug.WriteLine($"Noised: {Helpers.StringifyVector(ArrayUtils.IndexWhere(activeArray, (el) => el == 1))}");
List <int[, ]> arrays = new List <int[, ]>();
int[,] twoDimenArray = ArrayUtils.Make2DArray <int>(activeArray, 64, 64);
twoDimenArray = ArrayUtils.Transpose(twoDimenArray);
arrays.Add(ArrayUtils.Transpose(ArrayUtils.Make2DArray <int>(noisedInput, 32, 32)));
arrays.Add(ArrayUtils.Transpose(ArrayUtils.Make2DArray <int>(activeArray, 64, 64)));
// NeoCortexUtils.DrawHeatmaps(bostArrays, $"{outputImage}_boost.png", 1024, 1024, 150, 50, 5);
NeoCortexUtils.DrawBitmaps(arrays, $"Vector_{vectorIndex}_Noise_{j * 10}.png", Color.Yellow, Color.Gray, OutImgSize, OutImgSize);
}
vectorIndex++;
}
vectorIndex = OutputPredictionResult(sp, inputVectors, activeColumnsWithZeroNoise);
}
