public void CollSynapsesToInput()
{
var parameters = GetDefaultParams();
parameters.setInputDimensions(new int[] { 32 });
parameters.setColumnDimensions(new int[] { 128 });
parameters.setNumActiveColumnsPerInhArea(0.02 * 128);
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
sp.Init(mem);
int[] activeArray = new int[128];
int[] inputVector = Helpers.GetRandomVector(32, parameters.Get <Random>(KEY.RANDOM));
for (int i = 0; i < 100; i++)
{
sp.compute(inputVector, activeArray, true);
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
var str = Helpers.StringifyVector(activeCols);
Debug.WriteLine(str);
}
}
public void SPTutorialTest()
{
var parameters = GetDefaultParams();
parameters.setInputDimensions(new int[] { 1000 });
parameters.setColumnDimensions(new int[] { 2048 });
parameters.setNumActiveColumnsPerInhArea(0.02 * 2048);
parameters.setGlobalInhibition(false);
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
sp.Init(mem);
int[] activeArray = new int[2048];
int[] inputVector = Helpers.GetRandomVector(1000, parameters.Get <Random>(KEY.RANDOM));
sp.compute(inputVector, activeArray, true);
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
var str = Helpers.StringifyVector(activeCols);
Debug.WriteLine(str);
}
public void StableOutputWithPersistence()
{
var parameters = GetDefaultParams();
parameters.setInputDimensions(new int[] { 32, 32 });
parameters.setColumnDimensions(new int[] { 64, 64 });
parameters.setNumActiveColumnsPerInhArea(0.02 * 64 * 64);
var mem = new Connections();
parameters.apply(mem);
var sp = new SpatialPooler();
sp.Init(mem);
int[] activeArray = new int[64 * 64];
int[] inputVector = Helpers.GetRandomVector(32 * 32, parameters.Get <Random>(KEY.RANDOM));
string str1 = String.Empty;
for (int i = 0; i < 2; i++)
{
sp.compute(inputVector, activeArray, true);
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
str1 = Helpers.StringifyVector(activeCols);
Debug.WriteLine(str1);
}
var settings = new JsonSerializerSettings {
ContractResolver = new ContractResolver(), Formatting = Formatting.Indented
};
var jsonSp = JsonConvert.SerializeObject(sp, settings);
var sp2 = JsonConvert.DeserializeObject <SpatialPooler>(jsonSp, settings);
activeArray = new int[activeArray.Length];
for (int i = 10; i < 20; i++)
{
sp2.compute(inputVector, activeArray, true);
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
var str2 = Helpers.StringifyVector(activeCols);
Debug.WriteLine(str2);
Assert.IsTrue(str1.SequenceEqual(str2));
}
}
public void ExperimentTest(string inputBinarizedFile)
{
var parameters = SetupParameters(32, 64, 4096, true);
parameters.Set(KEY.DUTY_CYCLE_PERIOD, 100000);
parameters.Set(KEY.MAX_BOOST, 1.0);
parameters.Set(KEY.IS_BUMPUP_WEAKCOLUMNS_DISABLED, true);
var sp = new SpatialPooler();
var mem = new Connections();
int[] inputVector = NeoCortexUtils.ReadCsvFileTest(inputBinarizedFile).ToArray();
int[] activeArray = new int[4096];
parameters.apply(mem);
sp.Init(mem);
for (int i = 0; i < 1000; i++)
{
sp.compute(inputVector, activeArray, true);
var activeCols = activeArray.IndexWhere((el) => el == 1);
var str = Helpers.StringifyVector(activeCols);
Debug.WriteLine(str);
}
}
public void SchemaImageClassificationTest()
{
var imagesFolder = "..\\..\\..\\TestFiles\\SchemaImageClassification\\image";
var csvPath = "..\\..\\..\\TestFiles\\SchemaImageClassification\\csv";
string[] files = Directory.GetFiles(imagesFolder, "*", SearchOption.AllDirectories);
string path, name;
(path, name) = GetPathAndName(files[0]);
int activeColumn = 60;
int inputDimension = 32;
string[] imageNames = new string[files.Length];
SpatialPooler sp = new SpatialPooler();
Connections mem = new Connections();
Parameters config = GetParam(inputDimension, activeColumn);
config.apply(mem);
sp.Init(mem);
List <int[]> activeArray = new List <int[]>();// to store n active sequences for n images
// For each image in this directory
for (int i = 0; i < files.Length; i++)
{
// TODO: activeArray: is a buffer to store active column sequence from spatial pooler
activeArray.Add(new int[activeColumn * activeColumn]);
(path, name) = GetPathAndName(files[i]);
imageNames[i] = name;
string binaryImagePath = BinarizeImage(path, inputDimension, inputDimension, csvPath, name);
int[] inputVector = ReadCsvFileTest(binaryImagePath).ToArray(); // 1D binary of a image
List <int[]> tempArr = new List <int[]>();
tempArr.Add(new int[activeColumn * activeColumn]);
tempArr.Add(new int[activeColumn * activeColumn]);
int iter = -1;
int id = 0;
while (true) // Train spatial pooler with a single image until stable active column sequence is achieve
{
id = (++iter & 1) == 0 ? 0 : 1;
for (int i_x = 0; i_x < tempArr[id].Length; i_x++)
{
tempArr[id][i_x] = 0;
}
sp.compute(inputVector, tempArr[id], true);
if (iter == 1)
{
continue;
}
var d = GetHammingDistance(tempArr[id], tempArr[id ^ 1], false);
if (d != double.NegativeInfinity)
{
Console.WriteLine(d);
}
/*Note: GetHamming distance will return a distance between 0 and 100.
* if two sequences are the same the return value will be 100. Therefore, the similarity is (100==distance).
* To give a four digit precision, < is used instead of ==.
*/
if ((100.0 - d) < 0.00001)
{
for (int i_x = 0; i_x < tempArr[id].Length; i_x++)
{
activeArray[i][i_x] = tempArr[id][i_x];
}
break;
}
}
Console.WriteLine("finished image : " + i);
}
int[] parent = new int[files.Length];
bool[] hasFolder = new bool[files.Length];
List <double[]> distance = new List <double[]>();
for (int k = 0; k < files.Length; k++)
{
distance.Add(new double[files.Length]);
for (int kk = 0; kk < files.Length; kk++)
{
distance[k].Append(0);
}
}
for (int k = 0; k < files.Length; k++)
{
parent[k] = k; hasFolder[k] = false;
}
for (int i = 0; i < activeArray.Count; i++)
{
for (int j = i; j < activeArray.Count; j++)
{
double d = Math.Min(GetHammingDistance(activeArray[i], activeArray[j], true), GetHammingDistance(activeArray[j], activeArray[i], true));
//double d = GetHammingDistance(activeArray[i], activeArray[j], false);
distance[i][j] = d;
distance[j][i] = d;
}
}
SaveOutput(activeArray, path, imageNames);
Groupping(distance, imageNames, path);
Report(distance, imageNames);
}
/// <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);
}
}
}
}
public void SerializationTestWithTrainedData()
{
// TODO: see SpatialPooler_Stability_Experiment_3()
// use it here.
var parameters = GetDefaultParams();
parameters.setInputDimensions(new int[] { 16 * 16 });
parameters.setColumnDimensions(new int[] { 32 * 32 });
parameters.setNumActiveColumnsPerInhArea(0.02 * 32 * 32);
parameters.setMinPctOverlapDutyCycles(0.01);
var mem = new Connections();
parameters.apply(mem);
var sp1 = new SpatialPooler();
sp1.Init(mem);
int[] output = new int[32 * 32];
int[] inputVector = Helpers.GetRandomVector(16 * 16, parameters.Get <Random>(KEY.RANDOM));
/* int [] inputVector = {
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,
* 1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,
* 1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
* 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0 };
*
*/
string str1 = String.Empty;
for (int i = 0; i < 5; i++)
{
sp1.compute(inputVector, output, true);
var activeCols1 = ArrayUtils.IndexWhere(output, (el) => el == 1);
// Remember SDR for every input.
// if(i >= 4)
// sdrs.Add(output)
str1 = Helpers.StringifyVector(activeCols1);
Debug.WriteLine(str1);
}
// if stable
// HtmSerializer ser = new HtmSerializer();
// ser.Serialize(sp, "sp.json");
// var sp2 = HtmSerializer.Load("sp.json");
// Implement a code to compare sdrs of sp2 that have been learned with sp1.
string ser1 = File.ReadAllText("spTrain1.json");
//var sp2 = SpatialPooler.Deserializer("spTrain1.json");
//sp2.Serializer("spTrainDes1.json");
//string des1 = File.ReadAllText("spTrainDes1.json");
//Assert.IsTrue(ser1.SequenceEqual(des1));
//for (int i = 5; i < 10; i++)
//{
// sp1.compute(inputVector, sdrArray, false);
// var sdr2 = ArrayUtils.IndexWhere(sdrArray, (el) => el == 1);
// // Compare sdr with sdr1 of the same input
// var str2 = Helpers.StringifyVector(sdr2);
// Debug.WriteLine(str2);
// Assert.IsTrue(str1.SequenceEqual(str2));
//}
}
public void TestMethod2()
{
var parameters = Helpers.GetDefaultParams();;
var imageWidth = 60;
var imageHeight = 60;
int outputWidth = 120;
int outputHeight = 120;
String trainingImageName = "Lamp.png";
String predictionImageNameShift = "Lamp75Shift.PNG";
bool withTraining = true;
StringBuilder reportFile = new StringBuilder();
reportFile.Append($"Given Image Width: {imageWidth}");
reportFile.AppendLine();
reportFile.Append($"Given Image Height: {imageHeight}");
reportFile.AppendLine();
reportFile.Append($"Given Output Column Width: {outputWidth}");
reportFile.AppendLine();
reportFile.Append($"Given Output Column Height: {outputHeight}");
reportFile.AppendLine();
parameters.setInputDimensions(new int[] { imageWidth, imageHeight });
parameters.setColumnDimensions(new int[] { outputWidth, outputHeight });
parameters.setNumActiveColumnsPerInhArea(0.02 * outputWidth * outputHeight);
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
sp.init(mem);
int[] activeArray = new int[outputWidth * outputHeight];
int[] inputVector = ReadImageData(trainingImageName, imageHeight, imageWidth);
int[] newActiveArray = new int[outputWidth * outputHeight];
double[][] newActiveArrayDouble = new double[1][];
newActiveArrayDouble[0] = new double[newActiveArray.Length];
if (withTraining)
{
//Training
sp.compute(mem, inputVector, activeArray, true);
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
double[][] oldActiveArrayDouble = new double[1][];
oldActiveArrayDouble[0] = new double[activeArray.Length];
for (int i = 0; i < activeArray.Length; i++)
{
oldActiveArrayDouble[0][i] = activeArray[i];
}
int isTrained = 0;
while (isTrained == 0)
{
sp.compute(mem, inputVector, newActiveArray, true);
activeCols = ArrayUtils.IndexWhere(newActiveArray, (el) => el == 1);
for (int i = 0; i < newActiveArray.Length; i++)
{
newActiveArrayDouble[0][i] = newActiveArray[i];
}
if (GetHammingDistances(oldActiveArrayDouble, newActiveArrayDouble, true)[0] == 100)
{
isTrained = 1;
}
else
{
isTrained = 0;
oldActiveArrayDouble = newActiveArrayDouble;
}
}
var str = Helpers.StringifyVector(activeCols);
reportFile.AppendLine();
reportFile.Append($"Active Columns of Trained Image({trainingImageName}):");
reportFile.AppendLine();
reportFile.Append(str);
reportFile.AppendLine();
reportFile.Append($"Number of Active Columns of Trained Image({trainingImageName}): {activeCols.Length}");
reportFile.AppendLine();
}
else
{
//Without Training
sp.compute(mem, inputVector, newActiveArray, false);
var activeCols = ArrayUtils.IndexWhere(newActiveArray, (el) => el == 1);
var str = Helpers.StringifyVector(activeCols);
reportFile.AppendLine();
reportFile.Append($"Active Columns of Untrained Image({trainingImageName}):");
reportFile.AppendLine();
reportFile.Append(str);
reportFile.AppendLine();
reportFile.Append($"Number of Active Columns of Untrained Image({trainingImageName}): {activeCols.Length}");
reportFile.AppendLine();
}
//Prediction with Shift
int[] inputVectorShift = ReadImageData(predictionImageNameShift, imageHeight, imageWidth);
int[] activeArrayShift = new int[outputWidth * outputHeight];
sp.compute(mem, inputVectorShift, activeArrayShift, false);
var resActiveColsShift = ArrayUtils.IndexWhere(activeArrayShift, (el) => el == 1);
var resStrShift = Helpers.StringifyVector(resActiveColsShift);
for (int i = 0; i < newActiveArray.Length; i++)
{
newActiveArrayDouble[0][i] = newActiveArray[i];
}
double[][] activeArrayShiftDouble = new double[1][];
activeArrayShiftDouble[0] = new double[activeArrayShift.Length];
for (int i = 0; i < activeArrayShift.Length; i++)
{
activeArrayShiftDouble[0][i] = activeArrayShift[i];
}
double hammingDistancePercentage = GetHammingDistances(newActiveArrayDouble, activeArrayShiftDouble, true)[0];
reportFile.AppendLine();
reportFile.Append($"Active Columns of Prediction of Trained Image with Shift({predictionImageNameShift}):");
reportFile.AppendLine();
reportFile.Append(resStrShift);
reportFile.AppendLine();
reportFile.Append($"Number of Active Columns of Prediction of Trained Image with Shift({predictionImageNameShift}): {resActiveColsShift.Length}");
reportFile.AppendLine();
reportFile.Append($"Hamming Distance in % between Trained Image({trainingImageName}) and Prediction of same Image with Shift({predictionImageNameShift}): {100-hammingDistancePercentage}%");
reportFile.AppendLine();
reportFile.Append($"Output Overlap for Trained Image({trainingImageName}) and Prediction of same Image with Shift({predictionImageNameShift}): Output Overlap: {hammingDistancePercentage/100}");
reportFile.AppendLine();
using (StreamWriter writer = File.CreateText(Path.Combine(AppContext.BaseDirectory, $"Output/report-{trainingImageName}.txt")))
{
string text = reportFile.ToString();
writer.Write(text);
}
}
public void TestMethod1()
{
var parameters = Helpers.GetDefaultParams();;
var imageWidth = 30;
var imageHeight = 30;
int outputWidth = 60;
int outputHeight = 60;
String trainingImageName = "Lamp25Shift.PNG";
String predictionImageNameShift = "Lamp25Shift.PNG";
String predictionImageNameRotate = "LampRotate.PNG";
String predictionImageNameDifferent = "Fish.PNG";
bool withTraining = true;
StringBuilder reportFile = new StringBuilder();
reportFile.Append($"Given Image Width: {imageWidth}");
reportFile.AppendLine();
reportFile.Append($"Given Image Height: {imageHeight}");
reportFile.AppendLine();
reportFile.Append($"Given Output Column Width: {outputWidth}");
reportFile.AppendLine();
reportFile.Append($"Given Output Column Height: {outputHeight}");
reportFile.AppendLine();
parameters.setInputDimensions(new int[] { imageWidth, imageHeight });
parameters.setColumnDimensions(new int[] { outputWidth, outputHeight });
parameters.setNumActiveColumnsPerInhArea(0.02 * outputWidth * outputHeight);
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
sp.init(mem);
int[] activeArray = new int[outputWidth * outputHeight];
int[] inputVector = ReadImageData(trainingImageName, imageHeight, imageWidth);
int[] newActiveArray = new int[outputWidth * outputHeight];
if (withTraining)
{
//Training
sp.compute(mem, inputVector, activeArray, true);
var activeCols = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
int[] oldActiveArray = activeArray;
int flag = 0;
while (flag == 0)
{
sp.compute(mem, inputVector, newActiveArray, true);
activeCols = ArrayUtils.IndexWhere(newActiveArray, (el) => el == 1);
if (GetHammingDistance(oldActiveArray, newActiveArray) == 0)
{
flag = 1;
}
else
{
flag = 0;
oldActiveArray = newActiveArray;
}
}
var str = Helpers.StringifyVector(activeCols);
reportFile.AppendLine();
reportFile.Append($"Active Columns of Trained Image({trainingImageName}):");
reportFile.AppendLine();
reportFile.Append(str);
reportFile.AppendLine();
reportFile.Append($"Number of Active Columns of Trained Image({trainingImageName}): {activeCols.Length}");
reportFile.AppendLine();
}
else
{
//Without Training
sp.compute(mem, inputVector, newActiveArray, false);
var activeCols = ArrayUtils.IndexWhere(newActiveArray, (el) => el == 1);
var str = Helpers.StringifyVector(activeCols);
reportFile.AppendLine();
reportFile.Append($"Active Columns of Untrained Image({trainingImageName}):");
reportFile.AppendLine();
reportFile.Append(str);
reportFile.AppendLine();
reportFile.Append($"Number of Active Columns of Untrained Image({trainingImageName}): {activeCols.Length}");
reportFile.AppendLine();
}
//Prediction with Shift
int[] inputVectorShift = ReadImageData(predictionImageNameShift, imageHeight, imageWidth);
int[] activeArrayShift = new int[outputWidth * outputHeight];
sp.compute(mem, inputVectorShift, activeArrayShift, false);
var resActiveColsShift = ArrayUtils.IndexWhere(activeArrayShift, (el) => el == 1);
var resStrShift = Helpers.StringifyVector(resActiveColsShift);
int hamDistShift = GetHammingDistance(newActiveArray, activeArrayShift);
reportFile.AppendLine();
reportFile.Append($"Active Columns of Prediction of Trained Image with Shift({predictionImageNameShift}):");
reportFile.AppendLine();
reportFile.Append(resStrShift);
reportFile.AppendLine();
reportFile.Append($"Number of Active Columns of Prediction of Trained Image with Shift({predictionImageNameShift}): {resActiveColsShift.Length}");
reportFile.AppendLine();
reportFile.Append($"Hamming Distance between Trained Image({trainingImageName}) and Prediction of same Image with Shift({predictionImageNameShift}): {hamDistShift}");
reportFile.AppendLine();
reportFile.Append($"Hamming Distance in % between Trained Image({trainingImageName}) and Prediction of same Image with Shift({predictionImageNameShift}): {Math.Round((double)(hamDistShift * 100) / (outputWidth * outputHeight), 4)}%");
reportFile.AppendLine();
//Prediction with 90 degree Rotate
int[] inputVectorR90 = ReadImageData(predictionImageNameRotate, imageHeight, imageWidth);
int[] activeArrayR90 = new int[outputWidth * outputHeight];
sp.compute(mem, inputVectorR90, activeArrayR90, false);
var resActiveColsR90 = ArrayUtils.IndexWhere(activeArrayR90, (el) => el == 1);
var resStrR90 = Helpers.StringifyVector(resActiveColsR90);
int hamDistR90 = GetHammingDistance(newActiveArray, activeArrayR90);
reportFile.AppendLine();
reportFile.Append($"Active Columns of Prediction of Trained Image with 90 degrees rotate({predictionImageNameRotate}):");
reportFile.AppendLine();
reportFile.Append(resStrR90);
reportFile.AppendLine();
reportFile.Append($"Number of Active Columns of Prediction of Trained Image with 90 degrees rotate({predictionImageNameRotate}): {resActiveColsR90.Length}");
reportFile.AppendLine();
reportFile.Append($"Hamming Distance between Trained Image({trainingImageName}) and Prediction of same Image with 90 degrees rotate({predictionImageNameRotate}): {hamDistR90}");
reportFile.AppendLine();
reportFile.Append($"Hamming Distance in % between Trained Image({trainingImageName}) and Prediction of same Image with 90 degrees rotate({predictionImageNameRotate}): {Math.Round((double)(hamDistR90 * 100) / (outputWidth * outputHeight), 4)}%");
reportFile.AppendLine();
//Prediction with Different Image
int[] inputVectorDifferent = ReadImageData(predictionImageNameDifferent, imageHeight, imageWidth);
int[] activeArrayDifferent = new int[outputWidth * outputHeight];
sp.compute(mem, inputVectorDifferent, activeArrayDifferent, false);
var resActiveColsDifferent = ArrayUtils.IndexWhere(activeArrayDifferent, (el) => el == 1);
var resStrDifferent = Helpers.StringifyVector(resActiveColsDifferent);
int hamDistDifferent = GetHammingDistance(newActiveArray, activeArrayDifferent);
reportFile.AppendLine();
reportFile.Append($"Active Columns of Prediction of Different Image than Trained Image({predictionImageNameDifferent}):");
reportFile.AppendLine();
reportFile.Append(resStrDifferent);
reportFile.AppendLine();
reportFile.Append($"Number of Active Columns of Prediction of Different Image than Trained Image({predictionImageNameDifferent}): {resActiveColsDifferent.Length}");
reportFile.AppendLine();
reportFile.Append($"Hamming Distance between Trained Image({trainingImageName}) and Prediction of Different Image than Trained Image({predictionImageNameDifferent}): {hamDistDifferent}");
reportFile.AppendLine();
reportFile.Append($"Hamming Distance in % between Trained Image({trainingImageName}) and Prediction of Different Image than Trained Image({predictionImageNameDifferent}): {Math.Round((double)(hamDistDifferent * 100) / (outputWidth * outputHeight), 4)}%");
reportFile.AppendLine();
//Prediction with Noise of given percentages
double[] givenNoisePercentages = { 0, 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 };
for (int i = 0; i < givenNoisePercentages.Length; i++)
{
int[] oldInputVector = ReadImageData(trainingImageName, imageHeight, imageWidth);
int[] inputVectorNoisePercent = MakeSomeNoise(oldInputVector, givenNoisePercentages[i] / 100);
int[] activeArrayNoisePercent = new int[outputWidth * outputHeight];
MakeBinaryFile(inputVectorNoisePercent, imageHeight, imageWidth, $"{trainingImageName}-Noise{givenNoisePercentages[i]}p");
sp.compute(mem, inputVectorNoisePercent, activeArrayNoisePercent, false);
var resActiveColsPercent = ArrayUtils.IndexWhere(activeArrayNoisePercent, (el) => el == 1);
var resStrPercent = Helpers.StringifyVector(resActiveColsPercent);
int hamDistNoisePercent = GetHammingDistance(newActiveArray, activeArrayNoisePercent);
reportFile.AppendLine();
reportFile.Append($"Active Columns of Prediction of Trained Image({trainingImageName}) with Noise of {givenNoisePercentages[i]} percent:");
reportFile.AppendLine();
reportFile.Append(resStrPercent);
reportFile.AppendLine();
reportFile.Append($"Number of Active Columns of Prediction of Trained Image({trainingImageName}) with Noise of {givenNoisePercentages[i]} percent: {resActiveColsPercent.Length}");
reportFile.AppendLine();
reportFile.Append($"Hamming Distance between Trained Image({trainingImageName}) and Prediction of same Image with Noise of {givenNoisePercentages[i]} percent: {hamDistNoisePercent}");
reportFile.AppendLine();
reportFile.Append($"Hamming Distance in % between Trained Image({trainingImageName}) and Prediction of same Image with Noise of {givenNoisePercentages[i]} percent: {Math.Round((double)(hamDistNoisePercent*100) / (outputWidth * outputHeight), 4)}%");
reportFile.AppendLine();
reportFile.Append($"Output Overlap versus Input Overlap for Trained Image({trainingImageName}) and Prediction of same Image with Noise of {givenNoisePercentages[i]} percent: Output Overlap: {1 - Math.Round((double)(hamDistNoisePercent * 100) / (outputWidth * outputHeight), 4) / 100}, Input Overlap: {1 - givenNoisePercentages[i] / 100}");
reportFile.AppendLine();
}
using (StreamWriter writer = File.CreateText(Path.Combine(AppContext.BaseDirectory, $"Output/report-{trainingImageName}.txt")))
{
string text = reportFile.ToString();
writer.Write(text);
}
}
// [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.ReadCsvIntegers(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.ReadCsvIntegers(inputBinaryImageFile).ToArray();
int[] inputVectorShifted = NeoCortexUtils.ReadCsvIntegers(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()));
}
}
}
}
}
}
//[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.ReadCsvIntegers(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);
}
}
}
}
}
}
public void TestMethod1()
{
//number of binary vectors that will be used to train the SP
int numExamples = 10;
//2 dimensional input vector. Each row will be used as one-dimensional vector
int[,] inputVectors = new int[numExamples, 1000];
//2 dimensional output binary vector
int[,] outputColumns = new int[numExamples, 2048];
//Feeding here the input vector with random binary numbers
for (int i = 0; i < numExamples; i++)
{
for (int k = 0; k < 1000; k++)
{
var random = new Random();
inputVectors[i, k] = random.Next(0, 2);
}
}
//Spatial Pooler initialization and configuration
var parameters = Helpers.GetDefaultParams();
parameters.setInputDimensions(new int[] { 1000 });
parameters.setColumnDimensions(new int[] { 2048 });
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
sp.init(mem);
//One dimensional input vector initialized with binary numbers
int[] inputVector = Helpers.GetRandomVector(1000,
parameters.Get <Random>(KEY.RANDOM));
//Array that will be expose to SP to active some of its columns
int[] activeCols = new int[2048];
//List for active columns scores
List <int> activeColScores = new List <int>();
//Learning is turned off
sp.compute(mem, inputVector, activeCols, false);
//overlaps score of the active columns
var overlaps = sp.calculateOverlap(mem, inputVector);
//Sorting reversely overlaps arrays then writing it to the text file overlapBeforeTraining.txt
overlaps = reverseSort(overlaps);
WriteIntArrayToFile("overlapBeforeTraining.txt", overlaps);
//we put the overlap score of each active column in a list called activeColScores
for (int i = 0; i < 2048; i++)
{
if (activeCols[i] != 0)
{
activeColScores.Add(overlaps[i]);
}
}
int numberOfActivCols = 0;
//counting the number of active columns
foreach (int i in activeColScores)
{
numberOfActivCols = numberOfActivCols + 1;
}
// Array to take the number of active columns to write it to file for plotting purpose
int[] numberOfActiveColumns = new int[] { 1 };
numberOfActiveColumns[0] = numberOfActivCols;
int[] inputVectorsRowk = new int[] { };
int[] outputColumnsRowk = new int[] { };
//number of times the vectors are exposed to the SP
int epochs = 1;
//the "numExamples" input binary vectors are exposed to the SP "epochs" times to train it
for (int i = 0; i < epochs; i++)
{
for (int k = 0; k < numExamples; k++)
{
inputVectorsRowk = GetRow(inputVectors, k);
outputColumnsRowk = GetRow(outputColumns, k);
sp.compute(mem, inputVectorsRowk, outputColumnsRowk, true);
}
}
overlaps = sp.calculateOverlap(mem, inputVectorsRowk);
overlaps = reverseSort(overlaps);
WriteIntArrayToFile("overlapAfterTraining.txt", overlaps);
WriteIntArrayToFile("numberOfActCols.txt", numberOfActiveColumns);
//overlap before and after training , numberOfActCols needed for graphs
runPythonCode2("overlapBeforeTraining.txt", "overlapAfterTraining.txt", "numberOfActCols.txt");
int[,] inputVectorsCorrupted = new int[numExamples, 1000];
int[,] outputColumnsCorrupted = new int[numExamples, 2048];
float[] Noise = new float[] { 0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100 };
double[] percentOverlapInputs = new double[Noise.Length];
double[] percentOverlapOutputs = new double[Noise.Length];
int[] inputVectorsCorruptedRow0 = GetRow(inputVectorsCorrupted, 0);
int[] inputVectorsRow0 = GetRow(inputVectors, 0);
int[] outputColumnsRow0 = GetRow(outputColumns, 0);
int[] outputColumnsCorruptedRow0 = GetRow(outputColumnsCorrupted, 0);
resetVector(inputVectorsRow0, inputVectorsCorruptedRow0);
for (int i = 0; i < Noise.Length; i++)
{
inputVectorsCorruptedRow0 = corruptVector(ref inputVectorsRow0, Noise[i]);
sp.compute(mem, inputVectorsRow0, outputColumnsRow0, false);
sp.compute(mem, inputVectorsCorruptedRow0, outputColumnsCorruptedRow0, false);
percentOverlapInputs[i] = percentOverlap(inputVectorsRow0, inputVectorsCorruptedRow0);
percentOverlapOutputs[i] = percentOverlap(outputColumnsRow0, outputColumnsCorruptedRow0);
}
WriteDoubleArrayToFile("percentOverlapInputs.txt", percentOverlapInputs);
WriteDoubleArrayToFile("percentOverlapOutputs.txt", percentOverlapOutputs);
runPythonCode1("percentOverlapInputs.txt", "percentOverlapOutputs.txt");
}
public void SerializationTestWithTrainedData()
{
var parameters = GetDefaultParams();
parameters.setInputDimensions(new int[] { 16 * 16 });
parameters.setColumnDimensions(new int[] { 32 * 32 });
parameters.setNumActiveColumnsPerInhArea(0.02 * 32 * 32);
parameters.setMinPctOverlapDutyCycles(0.01);
var mem = new Connections();
parameters.apply(mem);
var sp1 = new SpatialPooler();
sp1.init(mem);
int[] activeArray = new int[32 * 32];
int[] inputVector = Helpers.GetRandomVector(16 * 16, parameters.Get <Random>(KEY.RANDOM));
/* int [] inputVector = {
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,
* 1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,
* 1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
* 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
* 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
* 1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0 };
*
*/
string str1 = String.Empty;
for (int i = 0; i < 5; i++)
{
sp1.compute(inputVector, activeArray, true);
var activeCols1 = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
str1 = Helpers.StringifyVector(activeCols1);
Debug.WriteLine(str1);
}
/* JsonSerializerSettings settings = new JsonSerializerSettings
* {
*
* DefaultValueHandling = DefaultValueHandling.Include,
* ObjectCreationHandling = ObjectCreationHandling.Auto,
* ReferenceLoopHandling = ReferenceLoopHandling.Serialize,
* ConstructorHandling = ConstructorHandling.AllowNonPublicDefaultConstructor,
* TypeNameHandling = TypeNameHandling.Auto
*
* };
*/
// var jsConverted = JsonConvert.SerializeObject(sp1, Formatting.Indented, settings);
// string file2 = "spSerializeTrain-newtonsoft.json";
// File.WriteAllText(file2, jsConverted);
sp1.Serializer("spTrain1.json");
string ser1 = File.ReadAllText("spTrain1.json");
var sp2 = SpatialPooler.Deserializer("spTrain1.json");
sp2.Serializer("spTrainDes1.json");
string des1 = File.ReadAllText("spTrainDes1.json");
Assert.IsTrue(ser1.SequenceEqual(des1));
// SpatialPooler sp2 = JsonConvert.DeserializeObject<SpatialPooler>(File.ReadAllText(file2), settings);
for (int i = 5; i < 10; i++)
{
sp2.compute(inputVector, activeArray, false);
var activeCols2 = ArrayUtils.IndexWhere(activeArray, (el) => el == 1);
var str2 = Helpers.StringifyVector(activeCols2);
Debug.WriteLine(str2);
Assert.IsTrue(str1.SequenceEqual(str2));
}
sp2.Serializer("spTrain2.json");
string ser2 = File.ReadAllText("spTrain2.json");
// Assert.IsTrue(ser2.SequenceEqual(des1));
/* string serializedSecondPooler = JsonConvert.SerializeObject(sp2, Formatting.Indented, settings);
* string fileSecondPooler = "spSerializeTrain-secondpooler-newtonsoft.json";
* File.WriteAllText(fileSecondPooler, serializedSecondPooler);
*
* SpatialPooler sp3 = JsonConvert.DeserializeObject<SpatialPooler>(File.ReadAllText(fileSecondPooler), settings);
* string serializedThirdPooler = JsonConvert.SerializeObject(sp3, Formatting.Indented, settings);
*
*
* Assert.IsTrue(serializedThirdPooler.SequenceEqual(serializedSecondPooler), "Third and second poolers are not equal");
* Assert.IsTrue(jsConverted.SequenceEqual(serializedSecondPooler), "First and second poolers are not equal");
*/
}
public void TestMethod1()
{
//Arrays initialization
int[] inputX1 = new int[1000];
int[] inputX2 = new int[1000];
int[] outputX1 = new int[2048];
int[] outputX2 = new int[2048];
//feeding inputX1 with random binary numbers
for (int j = 0; j < 1000; j++)
{
var random = new Random();
inputX1[j] = random.Next(0, 2);
}
//Copying inputX1 to inputX2 to get 2 identical vectors
for (int i = 0; i < 1000; i++)
{
inputX2[i] = inputX1[i];
}
// Array containing noise levels (in percentage)
float[] Noise = new float[] { 0, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100 };
// Arrays where input and output overlap results will be stored
double[] percentOverlapArrayInput = new double[Noise.Length];
double[] percentOverlapArrayOutput = new double[Noise.Length];
//Initialization and configuration of the spatial pooler
var parameters = Helpers.GetDefaultParams();
parameters.setInputDimensions(new int[] { 1000 });
parameters.setColumnDimensions(new int[] { 2048 });
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
sp.init(mem);
for (int j = 0; j < Noise.Length; j++)
{
// inputX1 is equivalent to inputX2 before the loop and can be then used as the reference one to which noise will be added
inputX2 = corruptVector(ref inputX1, Noise[j]);
//Feeding outputX1 and outputX2 to the spatial pooler
sp.compute(mem, inputX1, outputX1, false);
sp.compute(mem, inputX2, outputX2, false);
//computing the input and output overlap and putting results in arrays
percentOverlapArrayInput[j] = percentOverlap(inputX1, inputX2);
percentOverlapArrayOutput[j] = percentOverlap(outputX1, outputX2);
}
// Writing the input overlap and output overlap arrays to text files
WriteArrayToFile("OverlapInput.txt", percentOverlapArrayInput);
WriteArrayToFile("OverlapOutput.txt", percentOverlapArrayOutput);
//calling this method here will launch create-plots-Part2b script that will plot graphs with data contained in the files
runPythonCode("OverlapInput.txt", "OverlapOutput.txt");
}