//Generates the response training data
public static void GenerateTrainingData(PhysicallyUncloneableFunction aPUF, sbyte[][] trainingData, sbyte[] trainingResponse, Random randomGenerator)
{
int trainingSize = trainingData.Length;
int bitNum = aPUF.BitNumber;
for (int i = 0; i < trainingSize; i++)
{
trainingData[i] = GenerateRandomPhiVector(bitNum, randomGenerator);
//trainingResponse[i] = new sbyte[1];
trainingResponse[i] = aPUF.ComputeResponse(trainingData[i]);
}
}
//Generates textfiles for loading IPUF training data into Keras
public static void GenerateIPUFDataForKeras(PhysicallyUncloneableFunction iPUF, int trainingSize, string trainDirectory)
{
int indexer = 0;
sbyte[][] trainingData = new sbyte[trainingSize][];
sbyte[][] trainingResponse = new sbyte[1][]; //first index corresponds to the number of different PUFs (in this case 1) second index is sample number
trainingResponse[0] = new sbyte[trainingSize];
Random[] rGen = new Random[AppConstants.CoreNumber];
PhysicallyUncloneableFunction[] iPUFHolder = new PhysicallyUncloneableFunction[] { iPUF }; //only need a single IPUF for this type of data generation
//Data is generated in parallel so we need a random number generator for each core
for (int i = 0; i < AppConstants.CoreNumber; i++)
{
rGen[i] = new Random((int)DateTime.Now.Ticks);
System.Threading.Thread.Sleep(10); //prevent the random number generators from being the same
}
//Generate all the training data and responses in blocks (in parallel)
GenerateTrainingDataParallel(iPUFHolder, trainingData, trainingResponse, rGen);
//Now have the training data and responses filled in, time to write to textfiles
string[] lines = new string[trainingSize];
string[] phiLine = new string[1]; //this is to write phi information into the text file
for (int i = 0; i < trainingSize; i++)
{
//lines[i] = trainingResponse[0][i].ToString();
lines[i] = "";
for (int j = 0; j < iPUF.BitNumber + 1; j++) //length of phi
{
if (trainingData[i][j] == 1)
{
lines[i] = lines[i] + trainingData[i][j];
}
else
{
lines[i] = lines[i] + "m"; //m means -1
}
}
phiLine[0] = lines[i];
string currentFileName = trainingResponse[0][i].ToString() + indexer.ToString();
File.WriteAllLines(trainDirectory + "\\" + currentFileName + ".txt", phiLine);
indexer++;
}
//Write the training text file that contains all the examples
//string[] trainingFileLines = new string[trainingSize];
//for (int i = 0; i < trainingSize; i++)
//{
// trainingFileLines[i] = lines[i] + ".txt" + "\t" + trainingResponse[0][i].ToString();
//}
//File.WriteAllLines(trainDirectory + "\\TrainingClassList.txt", trainingFileLines);
}
//Generates the response training data for a single PUF model in parallel
public static void GenerateTrainingDataParallel(PhysicallyUncloneableFunction aPUF, sbyte[][] trainingData, sbyte[][] trainingResponse, Random[] randomGenerator)
{
int trainingSize = trainingData.Length;
int coreNumberForThisMethodOnly = AppConstants.CoreNumber;
//error checking
double roundCheck = trainingSize / (double)coreNumberForThisMethodOnly;
if (roundCheck != (int)roundCheck)
{
coreNumberForThisMethodOnly = AppConstants.CoreNumber - 3; //try reducing the core number to make the parallelization work
//do another double check just to be safe
roundCheck = trainingSize / (double)coreNumberForThisMethodOnly;
if (roundCheck != (int)roundCheck)
{
throw new Exception("The training size is not integer divisible by the number of CPU cores. Resize training data or avoid parallelization.");
}
}
int trainingBlock = trainingSize / coreNumberForThisMethodOnly;
int bitNum = aPUF.BitNumber;
Parallel.For(0, coreNumberForThisMethodOnly, i =>
//for(int i=0;i<coreNumberForThisMethodOnly;i++)
{
//for (int i = 0; i < AppConstants.CoreNumber; i++)
//{
for (int j = trainingBlock * i; j < (trainingBlock * (i + 1)); j++)
{
trainingData[j] = GenerateRandomPhiVector(bitNum, randomGenerator[i]);
trainingResponse[0][j] = aPUF.ComputeResponse(trainingData[j]);
}
Console.Out.WriteLine(i.ToString());
// }
});
//int l = 0;
}
