//Takes in weights, phi challenges and target response bits, gives average error of APUF model
public override double ObjFunValue(double[] weightVector, sbyte[][] phiChallenges, sbyte[] targets)
{
double error = 0;
if (AppConstants.UseParallelismOnSingleCMAES == true)
{
int bitNum = phiChallenges[0].Length - 1;
double[] errorArray = new double[AppConstants.CoreNumber]; //Array for storing errors in parallel
XORArbiterPUF xModel = new XORArbiterPUF(bitNum, weightVector);
int sampleNumber = phiChallenges.Length; //Number of challenge-response pairs (number of training samples)
int blockSize = AppConstants.TrainingSize / AppConstants.CoreNumber;
Parallel.For(0, AppConstants.CoreNumber, coreIndex =>
//for (int coreIndex = 0; coreIndex < AppConstants.CoreNumber; coreIndex++)
{
for (int sampleIndex = coreIndex * blockSize; sampleIndex < (coreIndex + 1) * blockSize; sampleIndex++)
{
sbyte currentTarget = targets[sampleIndex];
sbyte modelOutput = xModel.ComputeResponse(phiChallenges[sampleIndex]);
if (modelOutput != currentTarget)
{
errorArray[coreIndex]++;
}
//Console.Out.WriteLine(sampleIndex.ToString());
}
});
//Time to sum the errors together
for (int i = 0; i < AppConstants.CoreNumber; i++)
{
error = error + errorArray[i];
}
error = error / (double)sampleNumber; //Give the average error
}
else if (AppConstants.UseParallelismOnSingleCMAES == false)
{
int bitNum = phiChallenges[0].Length - 1;
XORArbiterPUF xModel = new XORArbiterPUF(bitNum, weightVector);
int sampleNumber = phiChallenges.Length; //Number of challenge-response pairs (number of training samples)
for (int currentSample = 0; currentSample < sampleNumber; currentSample++)
{
sbyte currentTarget = targets[currentSample];
sbyte modelOutput = xModel.ComputeResponse(phiChallenges[currentSample]);
if (modelOutput != currentTarget)
{
error++;
}
}
error = error / (double)sampleNumber; //Give the average error
}
return(error);
}
