private bool CheckPair(TickDiffPair pair)
{
// Checks whether candidate tick diff pair should be accepted.
// Not allowing pairs that are "too parallell"
TickDiff p0 = pair.TickDiff1;
TickDiff p1 = pair.TickDiff2;
const double thetaThresh = 5;
const double lambda = 0.000001;
// Using dot product and a threshold on the angle theta
// abs = ||a|| * ||b||
// dot = a0*b0 + a1*b1
//theta = arccos(dot/abs)
double abs = Math.Sqrt(Math.Pow(p0.mLatDiff, 2) + Math.Pow(p0.mLongDiff, 2)) * Math.Sqrt(Math.Pow(p1.mLatDiff, 2) + Math.Pow(p1.mLongDiff, 2));
double dot = p0.mLatDiff * p1.mLatDiff + p0.mLongDiff * p1.mLongDiff;
List <double> diffs = new List <double>()
{
p0.mLatDiff,
p1.mLatDiff,
p0.mLongDiff,
p1.mLongDiff
};
if (!diffs.TrueForAll(diff => Math.Abs(diff) > lambda))
{
return(false);
}
double theta = Math.Acos(dot / abs);
double thetaDeg = toDegrees(theta);
return(thetaDeg > thetaThresh && thetaDeg < 180 - thetaThresh);
}
private void RunSingleCalibration(TickDiffPair pair)
{
// Calculates mean and std for calibration factors for x and y
double a0 = Math.Pow(pair.TickDiff1.mLatDiff, 2);
double b0 = Math.Pow(pair.TickDiff1.mLongDiff, 2);
double a1 = Math.Pow(pair.TickDiff2.mLatDiff, 2);
double b1 = Math.Pow(pair.TickDiff2.mLongDiff, 2);
double d0, d1;
if (UseAlt)
{
d0 = Math.Pow(pair.TickDiff1.mDist, 2) - Math.Pow(pair.TickDiff1.mAltDiff, 2);
d1 = Math.Pow(pair.TickDiff2.mDist, 2) - Math.Pow(pair.TickDiff2.mAltDiff, 2);
}
else
{
d0 = Math.Pow(pair.TickDiff1.mDist, 2);
d1 = Math.Pow(pair.TickDiff2.mDist, 2);
}
double y = Math.Sqrt((a0 * d1 - a1 * d0) / (a0 * b1 - a1 * b0));
double x = Math.Sqrt((d0 / a0) - (b0 * (a1 * d0 - a0 * d1)) / (a0 * (a1 * b0 - a0 * b1)));
if (!Double.IsNaN(y) && !Double.IsNaN(x))
{
// This should be controlled for already in FindCandidate()
pair.Prediction = new ConversionFactors(x, y);
}
}
private List <TickDiffPair> FindCandidates(TrainingSession ts, int n, int ticksDiff = 1)
{
// Finds tickDiff pairs suitable for calibration
int ticks = ts.mTicks;
Random rand = new Random();
List <TickDiffPair> tickDiffPairs = new List <TickDiffPair> {
};
HashSet <int> usedPoints = new HashSet <int> {
};
while (tickDiffPairs.Count < n)
{
TickDiff[] candidatePair = new TickDiff[2];
int[] tickNumbers = new int[2];
for (int k = 0; k < 2; k++)
{
bool foundCandidate = false;
while (!foundCandidate)
{
// Comparing the tick with the next tick
tickNumbers[k] = rand.Next(0, ticks - (ticksDiff + 1));
bool isUsed = usedPoints.Contains(tickNumbers[k]);
bool altSuccess = Math.Abs(ts.mAltVector[tickNumbers[k]] - ts.mAltVector[tickNumbers[k] + ticksDiff]) > AltThresh;
if (!isUsed || altSuccess)
{
TickDiff candidate;
try
{
candidate = new TickDiff(ts, tickNumbers[k], ticksDiff);
}
catch (NullReferenceException e)
{
continue;
}
catch (NoDistanceDiffException e)
{
continue;
}
candidatePair[k] = candidate;
foundCandidate = true;
}
}
}
TickDiffPair tickDiffPair = new TickDiffPair(candidatePair[0], candidatePair[1]);
if (CheckPair(tickDiffPair))
{
tickDiffPairs.Add(tickDiffPair);
// Adding the first tick in each tickDiff used tickDiffs
usedPoints.Add(tickNumbers[0]);
usedPoints.Add(tickNumbers[1]);
}
}
return(tickDiffPairs);
}
private void CalculateCalibrationError(TickDiffPair predictionPair, List <TickDiffPair> validationSet)
{
predictionPair.Error = CalculateCalibrationError(predictionPair.Prediction, validationSet);
}