/// <summary>
/// This function train the input image and write result to text files in folder @"/OutputDutyCycle"
/// The result text files include speed comparison between global inhibition and local inhibition,
/// the stable of the out put array (by comparing hamming distance arrays).
/// Finally this method draw an image of active column as .png file.
/// This training method is used for testing speed of training with different value of max boost and duty cycle
/// </summary>
/// <param name="inputBinarizedFile">input image after binarized</param>
/// <param name="hammingFile">Path to hamming distance output file</param>
/// <param name="outputSpeedFile">Path to speed comparison output file</param>
/// <param name="outputImage">Path to active column after training output file (as .png image file)</param>
/// <param name="parameters">Parameter setup</param>
private static void Training(string inputBinarizedFile, string hammingFile, string outputSpeedFile, string outputImage, Parameters parameters)
{
int outputImageSize = 1024;
int topology = parameters.Get <int[]>(KEY.COLUMN_DIMENSIONS)[0];
int activeColumn = topology * topology;
var stopwatch = new Stopwatch();
using (StreamWriter swHamming = new StreamWriter(hammingFile))
{
using (StreamWriter swSpeed = new StreamWriter(outputSpeedFile, true))
{
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
stopwatch.Start();
sp.Init(mem);
stopwatch.Stop();
int actiColumnLength = activeColumn;
int[] activeArray = new int[actiColumnLength];
// Read input csv file into array
int[] inputVector = NeoCortexUtils.ReadCsvFileTest(inputBinarizedFile).ToArray();
stopwatch.Restart();
int iterations = 1000;
int[] oldArray = new int[activeArray.Length];
for (int k = 0; k < iterations; k++)
{
sp.compute(inputVector, activeArray, true);
var activeCols = activeArray.IndexWhere((el) => el == 1);
var distance = MathHelpers.GetHammingDistance(oldArray, activeArray);
var similarity = MathHelpers.CalcArraySimilarity(oldArray, activeArray);
swHamming.WriteLine($"{distance} | {similarity}");
var str = Helpers.StringifyVector(activeCols);
Debug.WriteLine(str);
oldArray = new int[actiColumnLength];
activeArray.CopyTo(oldArray, 0);
}
var activeArrayString = Helpers.StringifyVector(activeArray);
stopwatch.Stop();
Debug.WriteLine("Active Array: " + activeArrayString);
int potentialRadius = parameters.Get <int>(KEY.POTENTIAL_RADIUS);
bool isGlobalInhibition = parameters.Get <bool>(KEY.GLOBAL_INHIBITION);
string inhibition = isGlobalInhibition ? "Global" : "Local";
double milliseconds = stopwatch.ElapsedMilliseconds;
double seconds = milliseconds / 1000;
swSpeed.WriteLine($"Column dimension: {topology.ToString().PadRight(5)} |Potential Radius: {potentialRadius}| Inhibition type: {inhibition.PadRight(7)} | Total time: {milliseconds:N0} milliseconds ({seconds:N2} seconds).");
int[,] twoDimenArray = ArrayUtils.Make2DArray(activeArray, topology, topology);
twoDimenArray = ArrayUtils.Transpose(twoDimenArray);
NeoCortexUtils.DrawBitmap(twoDimenArray, outputImageSize, outputImageSize, outputImage);
}
}
}
/// <summary>
/// This function train the input image and write result to text files in folder @"/Output"
/// The result text files include speed comparison between global inhibition and local inhibition,
/// the stable of the out put array (by comparing hamming distance arrays).
/// Finally this method draw an image of active column as .png file.
/// </summary>
/// <param name="imageSize">Size of the image (image has same width and height)</param>
/// <param name="columnDimension">List of sparse space size.(with same width and height)</param>
/// <param name="inputBinarizedFile">input image after binarized</param>
/// <param name="hammingFile">Path to hamming distance output file </param>
/// <param name="outputSpeedFile">Path to speed comparison output file</param>
/// <param name="activeColumnFile">Path to active column after training output file (as array text)</param>
/// <param name="outputImage">Path to active column after training output file (as .png image file)</param>
/// <param name="isGlobalInhibition">is using Global inhibition algorithms or not (if false using local inhibition)</param>
private static void Training(int imageSize, int columnDimension, string inputBinarizedFile, string hammingFile, string outputSpeedFile, string activeColumnFile, string outputImage, bool isGlobalInhibition)
{
int outputImageSize = 1024;
int activeColumn = columnDimension * columnDimension;
var stopwatch = new Stopwatch();
using (StreamWriter swHamming = new StreamWriter(hammingFile))
{
using (StreamWriter swSpeed = new StreamWriter(outputSpeedFile, true))
{
using (StreamWriter swActiveColumn = new StreamWriter(activeColumnFile))
{
var parameters = SetupParameters(imageSize, columnDimension, isGlobalInhibition);
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
stopwatch.Start();
sp.Init(mem);
stopwatch.Stop();
int actiColumnLength = activeColumn;
int[] activeArray = new int[actiColumnLength];
// Read input csv file into array
int[] inputVector = NeoCortexUtils.ReadCsvFileTest(inputBinarizedFile).ToArray();
stopwatch.Restart();
int iterations = 300;
int[] oldArray = new int[activeArray.Length];
for (int k = 0; k < iterations; k++)
{
sp.compute(inputVector, activeArray, true);
var activeCols = activeArray.IndexWhere((el) => el == 1);
var distance = MathHelpers.GetHammingDistance(oldArray, activeArray);
var similarity = MathHelpers.CalcArraySimilarity(oldArray, activeArray);
swHamming.WriteLine($"{distance} | {similarity}");
var str = Helpers.StringifyVector(activeCols);
Debug.WriteLine(str);
oldArray = new int[actiColumnLength];
activeArray.CopyTo(oldArray, 0);
}
stopwatch.Stop();
var activeArrayString = Helpers.StringifyVector(activeArray);
swActiveColumn.WriteLine("Active Array: " + activeArrayString);
string inhibition = isGlobalInhibition ? "Global" : "Local";
double milliseconds = stopwatch.ElapsedMilliseconds;
double seconds = milliseconds / 1000;
swSpeed.WriteLine($"Topology: {columnDimension.ToString().PadRight(5)} | Inhibition type: {inhibition.PadRight(7)} | Total time: {milliseconds:N0} milliseconds ({seconds:N2} seconds).");
int[,] twoDimenArray = ArrayUtils.Make2DArray(activeArray, columnDimension, columnDimension);
twoDimenArray = ArrayUtils.Transpose(twoDimenArray);
NeoCortexUtils.DrawBitmap(twoDimenArray, outputImageSize, outputImageSize, outputImage);
}
}
}
}
//[DataRow("MnistTestImages\\digit7.png", 128, 30)]
public void CalculateSpeedOfLearningTest(string mnistImage, int[] imageSize, int[] topologies)
{
int index1 = mnistImage.IndexOf("\\") + 1;
int index2 = mnistImage.IndexOf(".");
string sub1 = mnistImage.Substring(0, index2);
string sub2 = mnistImage.Substring(0, index1);
string name = mnistImage.Substring(index1, sub1.Length - sub2.Length);
for (int imSizeIndx = 0; imSizeIndx < imageSize.Length; imSizeIndx++)
{
string testName = $"{name}_{imageSize[imSizeIndx]}";
string outputSpeedFile = $"Output\\{testName}_speed.txt";
string inputBinaryImageFile = BinarizeImage("Output\\" + mnistImage, imageSize[imSizeIndx], testName);
for (int topologyIndx = 0; topologyIndx < topologies.Length; topologyIndx++)
{
string finalName = $"{testName}_{topologies[topologyIndx]}";
string outputHamDistFile = $"Output\\{finalName}_hamming.txt";
string outputActColFile = $"Output\\{finalName}_activeCol.txt";
string outputImage = $"Output\\{finalName}.png";
int numOfActCols = 0;
var sw = new Stopwatch();
using (StreamWriter swHam = new StreamWriter(outputHamDistFile))
{
using (StreamWriter swSpeed = new StreamWriter(outputSpeedFile, true))
{
using (StreamWriter swActCol = new StreamWriter(outputActColFile))
{
numOfActCols = topologies[topologyIndx] * topologies[topologyIndx];
var parameters = GetDefaultParams();
parameters.setInputDimensions(new int[] { imageSize[imSizeIndx], imageSize[imSizeIndx] });
parameters.setColumnDimensions(new int[] { topologies[topologyIndx], topologies[topologyIndx] });
parameters.setNumActiveColumnsPerInhArea(0.02 * numOfActCols);
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
sw.Start();
sp.init(mem);
sw.Stop();
swSpeed.WriteLine($"{topologies[topologyIndx]}|{(double)sw.ElapsedMilliseconds / (double)1000}");
int actiColLen = numOfActCols;
int[] activeArray = new int[actiColLen];
//Read input csv file into array
int[] inputVector = NeoCortexUtils.ReadCsvFileTest(inputBinaryImageFile).ToArray();
sw.Restart();
int iterations = 2;
int[] oldArray = new int[activeArray.Length];
for (int k = 0; k < iterations; k++)
{
sp.compute(inputVector, activeArray, true);
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
var distance = MathHelpers.GetHammingDistance(oldArray, activeArray);
swHam.WriteLine(distance + "\n");
var str = Helpers.StringifyVector(activeCols);
oldArray = new int[actiColLen];
activeArray.CopyTo(oldArray, 0);
}
var activeStr = Helpers.StringifyVector(activeArray);
swActCol.WriteLine("Active Array: " + activeStr);
sw.Stop();
int[,] twoDimenArray = ArrayUtils.Make2DArray <int>(activeArray, topologies[topologyIndx], topologies[topologyIndx]);
twoDimenArray = ArrayUtils.Transpose(twoDimenArray);
NeoCortexUtils.DrawBitmap(twoDimenArray, OutImgSize, OutImgSize, outputImage);
}
}
}
}
}
}
// [DataRow("MnistTestImages\\digit7.png", 128, 30)]
public void LearningimageDoubleShiftStble(string mnistImage, string shiftedImage, int[] imageSize, int[] topologies)
{
var path = Path.Combine(Directory.GetParent(Environment.CurrentDirectory).Parent.Parent.FullName, mnistImage);
var pathShifted = Path.Combine(Directory.GetParent(Environment.CurrentDirectory).Parent.Parent.FullName, shiftedImage);
Console.WriteLine("Test started");
Console.WriteLine(mnistImage);
Console.WriteLine(shiftedImage);
const int OutImgSize = 1024;
int index1 = mnistImage.IndexOf("\\") + 1;
int index2 = mnistImage.IndexOf(".");
string sub1 = mnistImage.Substring(0, index2);
string sub2 = mnistImage.Substring(0, index1);
string name = mnistImage.Substring(index1, sub1.Length - sub2.Length);
int index1Shift = shiftedImage.IndexOf("\\") + 1;
int index2Shift = shiftedImage.IndexOf(".");
string sub1Shift = shiftedImage.Substring(0, index2Shift);
string sub2Shift = shiftedImage.Substring(0, index1Shift);
string nameShift = shiftedImage.Substring(index1Shift, sub1Shift.Length - sub2Shift.Length);
for (int imSizeIndx = 0; imSizeIndx < imageSize.Length; imSizeIndx++)
{
Console.WriteLine(String.Format("Image Size: \t{0}", imageSize[imSizeIndx]));
string testName = $"{name}_{imageSize[imSizeIndx]}";
string outputSpeedFile = $"Output\\{testName}_speed.txt";
string testNameShifted = $"{nameShift}_{imageSize[imSizeIndx]}";
string outputSpeedFileShifted = $"Output\\{testNameShifted}_speed.txt";
string inputBinaryImageFile = BinarizeImage(path, imageSize[imSizeIndx], testName);
string inputBinaryImageShiftedFile = BinarizeImage(pathShifted, imageSize[imSizeIndx], testNameShifted);
//string inputBinaryImageFile = BinarizeImage("Output\\" + mnistImage, imageSize[imSizeIndx], testName);
for (int topologyIndx = 0; topologyIndx < topologies.Length; topologyIndx++)
{
Console.WriteLine(String.Format("Topology: \t{0}", topologies[topologyIndx]));
string finalName = $"{testName}_{topologies[topologyIndx]}";
string outputHamDistFile = $"Output\\{finalName}_hamming.txt";
string outputActColFile = $"Output\\{finalName}_activeCol.txt";
string outputImage = $"Output\\{finalName}.png";
string finalNameShifted = $"{testNameShifted}_{topologies[topologyIndx]}";
string outputHamDistFileShifted = $"Output\\{finalNameShifted}_hamming.txt";
string outputActColFileShifted = $"Output\\{finalNameShifted}_activeCol.txt";
string outputImageShifted = $"Output\\{finalNameShifted}.png";
//File.Create(finalName);
//Directory.CreateDirectory("Output");
//File.Create(outputHamDistFile);
//File.Create(outputActColFile);
int numOfActCols = 0;
var sw = new Stopwatch();
using (StreamWriter swHam = new StreamWriter(outputHamDistFile))
{
using (StreamWriter swSpeed = new StreamWriter(outputSpeedFile, true))
{
using (StreamWriter swActCol = new StreamWriter(outputActColFile))
{
numOfActCols = topologies[topologyIndx] * topologies[topologyIndx];
var parameters = GetDefaultParams();
parameters.setInputDimensions(new int[] { imageSize[imSizeIndx], imageSize[imSizeIndx] });
parameters.setColumnDimensions(new int[] { topologies[topologyIndx], topologies[topologyIndx] });
parameters.setNumActiveColumnsPerInhArea(0.02 * numOfActCols);
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
sw.Start();
sp.init(mem);
sw.Stop();
swSpeed.WriteLine($"{topologies[topologyIndx]}|{(double)sw.ElapsedMilliseconds / (double)1000}");
int actiColLen = numOfActCols;
int[] activeArray = new int[actiColLen];
//Read input csv file into array
int[] inputVector = NeoCortexUtils.ReadCsvFileTest(inputBinaryImageFile).ToArray();
var inputOverlap = new List <double>();
var outputOverlap = new List <double>();
int[] newActiveArray = new int[topologies[topologyIndx] * topologies[topologyIndx]];
double[][] newActiveArrayDouble = new double[1][];
newActiveArrayDouble[0] = new double[newActiveArray.Length];
//Training
sp.compute(inputVector, activeArray, true);
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
double[][] oldActiveArray = new double[1][];
oldActiveArray[0] = new double[activeArray.Length];
for (int a = 0; a < activeArray.Length; a++)
{
oldActiveArray[0][a] = activeArray[a];
}
int isTrained = 0;
while (isTrained == 0)
{
sp.compute(inputVector, newActiveArray, true);
activeCols = ArrayUtils.IndexWhere(newActiveArray, (el) => el == 1);
for (int a = 0; a < newActiveArray.Length; a++)
{
newActiveArrayDouble[0][a] = newActiveArray[a];
}
if (MathHelpers.GetHammingDistance(oldActiveArray, newActiveArrayDouble, true)[0] == 100)
{
isTrained = 1;
}
else
{
isTrained = 0;
oldActiveArray = newActiveArrayDouble;
}
}
var str = Helpers.StringifyVector(activeCols);
int[] oldInputVector = NeoCortexUtils.ReadCsvFileTest(inputBinaryImageFile).ToArray();
int[] inputVectorShifted = NeoCortexUtils.ReadCsvFileTest(inputBinaryImageShiftedFile).ToArray();
int[] activeArrayShifted = new int[topologies[topologyIndx] * topologies[topologyIndx]];
double[][] activeArrayShiftedDouble = new double[1][];
activeArrayShiftedDouble[0] = new double[activeArrayShifted.Length];
sw.Restart();
//Prediction
sp.compute(inputVectorShifted, activeArrayShifted, false);
var resActiveColsPercent = ArrayUtils.IndexWhere(activeArrayShifted, (el) => el == 1);
var resStrPercent = Helpers.StringifyVector(activeArrayShifted);
for (int a = 0; a < activeArrayShifted.Length; a++)
{
activeArrayShiftedDouble[0][a] = activeArrayShifted[a];
}
var distance = MathHelpers.GetHammingDistance(newActiveArrayDouble, activeArrayShiftedDouble, false)[0];
var distPercent = ((100 - distance) * 100) / (topologies[topologyIndx] * topologies[topologyIndx]);
swHam.WriteLine(distance + "\t" + (100 - distance) + "\t" + distPercent + "\t" + (1 - (distPercent / 100.0)) + "\n");
outputOverlap.Add(1 - (distPercent / 100.0));
swActCol.WriteLine(String.Format(@"Active Cols: {0}", Helpers.StringifyVector(resActiveColsPercent)));
Console.WriteLine(resStrPercent);
swActCol.WriteLine("Active Array: " + resStrPercent);
sw.Stop();
int[,] twoDimenArray = ArrayUtils.Make2DArray <int>(activeArrayShifted, topologies[topologyIndx], topologies[topologyIndx]);
twoDimenArray = ArrayUtils.Transpose(twoDimenArray);
NeoCortexUtils.DrawBitmap(twoDimenArray, OutImgSize, OutImgSize, outputImage);
//NeoCortexUtils.DrawBitmap(twoDimenArray, OutImgSize, OutImgSize, "D:\\Project_Latest\\FromGit\\se-dystsys-2018-2019-softwareengineering\\outputs\\eight");
swActCol.WriteLine("inputOverlaps: " + Helpers.StringifyVector(inputOverlap.ToArray()));
swActCol.WriteLine("outputOverlaps: " + Helpers.StringifyVector(outputOverlap.ToArray()));
}
}
}
}
}
}
public void SparseSingleMnistImageTest(string trainingFolder, string digit, int imageSize, int columnTopology)
{
//Thread.Sleep(3000);
string TestOutputFolder = $"Output-{nameof(SparseSingleMnistImageTest)}";
var trainingImages = Directory.GetFiles(Path.Combine(trainingFolder, digit));
//if (Directory.Exists(TestOutputFolder))
// Directory.Delete(TestOutputFolder, true);
Directory.CreateDirectory(TestOutputFolder);
Directory.CreateDirectory($"{TestOutputFolder}\\{digit}");
int counter = 0;
var numOfActCols = columnTopology * columnTopology;
ThreadSafeRandom rnd = new ThreadSafeRandom(42);
var parameters = Parameters.getAllDefaultParameters();
parameters.Set(KEY.POTENTIAL_RADIUS, imageSize * imageSize);
parameters.Set(KEY.POTENTIAL_PCT, 1.0);
parameters.Set(KEY.GLOBAL_INHIBITION, true);
parameters.Set(KEY.RANDOM, rnd);
parameters.Set(KEY.NUM_ACTIVE_COLUMNS_PER_INH_AREA, 0.02 * 64 * 64);
parameters.Set(KEY.LOCAL_AREA_DENSITY, -1);
parameters.Set(KEY.POTENTIAL_RADIUS, imageSize * imageSize);
parameters.Set(KEY.POTENTIAL_PCT, 1.0);
parameters.Set(KEY.STIMULUS_THRESHOLD, 50.0); //***
parameters.Set(KEY.SYN_PERM_INACTIVE_DEC, 0.008); //***
parameters.Set(KEY.SYN_PERM_ACTIVE_INC, 0.05); //***
//parameters.Set(KEY.STIMULUS_THRESHOLD, 0.0); //***
//parameters.Set(KEY.SYN_PERM_INACTIVE_DEC, 0.0); //***
//parameters.Set(KEY.SYN_PERM_ACTIVE_INC, 0.0); //***
parameters.Set(KEY.INHIBITION_RADIUS, (int)0.025 * imageSize * imageSize);
parameters.Set(KEY.SYN_PERM_CONNECTED, 0.2);
parameters.Set(KEY.MIN_PCT_OVERLAP_DUTY_CYCLES, 0.001);
parameters.Set(KEY.MIN_PCT_ACTIVE_DUTY_CYCLES, 0.001);
parameters.Set(KEY.DUTY_CYCLE_PERIOD, 1000);
parameters.Set(KEY.MAX_BOOST, 100);
parameters.Set(KEY.WRAP_AROUND, true);
parameters.Set(KEY.SEED, -1);
parameters.setInputDimensions(new int[] { imageSize, imageSize });
parameters.setColumnDimensions(new int[] { columnTopology, columnTopology });
var sp = new SpatialPoolerParallel();
var mem = new Connections();
parameters.apply(mem);
Stopwatch sw = new Stopwatch();
sw.Start();
sp.init(mem, UnitTestHelpers.GetMemory(new HtmConfig()));
sw.Stop();
Debug.WriteLine($"Init time: {sw.ElapsedMilliseconds}");
int actiColLen = numOfActCols;
int[] activeArray = new int[actiColLen];
string outFolder = $"{TestOutputFolder}\\{digit}\\{columnTopology}x{columnTopology}";
Directory.CreateDirectory(outFolder);
string outputHamDistFile = $"{outFolder}\\digit{digit}_{columnTopology}_hamming.txt";
string outputActColFile = $"{outFolder}\\digit{digit}_{columnTopology}_activeCol.txt";
using (StreamWriter swHam = new StreamWriter(outputHamDistFile))
{
using (StreamWriter swActCol = new StreamWriter(outputActColFile))
{
foreach (var mnistImage in trainingImages)
{
FileInfo fI = new FileInfo(mnistImage);
string outputImage = $"{outFolder}\\digit_{digit}_cycle_{counter}_{columnTopology}_{fI.Name}";
string testName = $"{outFolder}\\digit_{digit}_{fI.Name}_{columnTopology}";
string inputBinaryImageFile = Helpers.BinarizeImage($"{mnistImage}", imageSize, testName);
//Read input csv file into array
int[] inputVector = NeoCortexUtils.ReadCsvFileTest(inputBinaryImageFile).ToArray();
int numIterationsPerImage = 5;
int[] oldArray = new int[activeArray.Length];
List <double[, ]> overlapArrays = new List <double[, ]>();
List <double[, ]> bostArrays = new List <double[, ]>();
for (int k = 0; k < numIterationsPerImage; k++)
{
sw.Reset();
sw.Start();
sp.compute(inputVector, activeArray, true);
sw.Stop();
Debug.WriteLine($"Compute time: {sw.ElapsedMilliseconds}");
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
var distance = MathHelpers.GetHammingDistance(oldArray, activeArray);
swHam.WriteLine($"{counter++}|{distance} ");
oldArray = new int[actiColLen];
activeArray.CopyTo(oldArray, 0);
//var mem = sp.GetMemory(layer);
overlapArrays.Add(ArrayUtils.Make2DArray <double>(ArrayUtils.toDoubleArray(mem.Overlaps), columnTopology, columnTopology));
bostArrays.Add(ArrayUtils.Make2DArray <double>(mem.BoostedOverlaps, columnTopology, columnTopology));
}
var activeStr = Helpers.StringifyVector(activeArray);
swActCol.WriteLine("Active Array: " + activeStr);
int[,] twoDimenArray = ArrayUtils.Make2DArray <int>(activeArray, columnTopology, columnTopology);
twoDimenArray = ArrayUtils.Transpose(twoDimenArray);
List <int[, ]> arrays = new List <int[, ]>();
arrays.Add(twoDimenArray);
arrays.Add(ArrayUtils.Transpose(ArrayUtils.Make2DArray <int>(inputVector, (int)Math.Sqrt(inputVector.Length), (int)Math.Sqrt(inputVector.Length))));
const int OutImgSize = 1024;
NeoCortexUtils.DrawBitmaps(arrays, outputImage, Color.Yellow, Color.Gray, OutImgSize, OutImgSize);
//NeoCortexUtils.DrawHeatmaps(overlapArrays, $"{outputImage}_overlap.png", OutImgSize, OutImgSize, 150, 50, 5);
//NeoCortexUtils.DrawHeatmaps(bostArrays, $"{outputImage}_boost.png", OutImgSize, OutImgSize, 150, 50, 5);
}
}
}
}