static void weightRun()
{
DinamicTimeWrapping DTW = new DinamicTimeWrapping();
KolmogorovSmirnovTest KST = new KolmogorovSmirnovTest();
Duration D = new Duration();
double[,,] dtwRes = new double[3, 1600, 40];
double[,,] kstRes = new double[3, 1600, 40];
double[,] durRes = new double[1600, 40];
for (int user = 1; user <= 40; user++)
{
for (int signId = 1; signId <= 20; signId++)
{
List <List <double> > sign = InPut.readSample.GetDataFromFile(user, signId);
for (int signId2 = 1; signId2 <= 40; signId2++)
{
List <List <double> > sign2 = InPut.readSample.GetDataFromFile(user, signId2);
durRes[user * 40 + signId, signId2] = D.calculate(sign, sign2);
for (int column = 0; column < 2; column++)
{
kstRes[user * 40 + signId, signId2, column] = KST.Calculate(sign[column], sign2[column]);
dtwRes[user * 40 + signId, signId2, column] = DTW.Calculate(sign[column], sign2[column]);
}
}
}
}
//TODO: for lm in user, sign
// for i in user, sign
//count value for KST, DTW, Dur, PenUp
//TODO: PLOT
}
static void MainRun(string[] args)
{
double FINAL_maxbad = 0;
double FINAL_mingood = 1;
int userCount = 40;
double falsePozitive = 0;
double falseNegative = 0;
double kstFP = 0;
double kstFN = 0;
double dtwFP = 0;
double dtwFN = 0;
double durFP = 0;
double durFN = 0;
List <List <List <double> > > teacherMatrix;
List <List <double> > testedMatrix;
DinamicTimeWrapping DTW = new DinamicTimeWrapping();
KolmogorovSmirnovTest KST = new KolmogorovSmirnovTest();
Duration D = new Duration();
for (int i = 1; i <= userCount; i++)
{
teacherMatrix = InPut.readSample.GetTeachers(userId);
KST.Teach(teacherMatrix, 2);
DTW.Teach(teacherMatrix, 2);
D.Teach(teacherMatrix);
for (int j = 1; j < 31; j++)
{
double kstRET = 0.5;
double dtwRET = 0.5;
double durRET = 0.5;
double bayes = 0.5;
testedMatrix = InPut.readSample.GetDataFromFile(userId, 10 + j);
kstRET = KST.Test(teacherMatrix, testedMatrix[2], 2);
dtwRET = DTW.Test(teacherMatrix, testedMatrix[2], 2);
durRET = D.TestMethod(testedMatrix);
//KST.Teach(kstTMatrix2);
//DTW.Teach(dtwTMatrix2);
//kstRET = StatMathLib.LikelihoodFusion.BayesTrap(KST.Test(teacherMatrix, testedMatrix[2], 2), kstRET);
//dtwRET = StatMathLib.LikelihoodFusion.BayesTrap(DTW.Test(teacherMatrix, testedMatrix[2], 2), dtwRET);
bayes = LikelihoodFusion.BayesTrap(kstRET, LikelihoodFusion.BayesTrap(dtwRET, durRET));
if (j <= 10)
{
if (FINAL_mingood > bayes)
{
FINAL_mingood = bayes;
}
if (kstRET < 0.5)
{
kstFN++;
}
if (dtwRET < 0.5)
{
dtwFN++;
}
if (durRET < 0.5)
{
durFN++;
}
if (bayes < 0.5)
{
falseNegative++;
}
}
else
{
if (FINAL_maxbad < bayes)
{
FINAL_maxbad = bayes;
}
if (kstRET >= 0.5)
{
kstFP++;
}
if (dtwRET >= 0.5)
{
dtwFP++;
}
if (durRET >= 0.5)
{
durFP++;
}
if (bayes >= 0.5)
{
falsePozitive++;
}
}
Console.WriteLine("U:" + i + " S:" + j + " KST: " + kstRET + " DTW: " + dtwRET + " Dur: " + durRET + " FINAL: " + bayes);
if (j == 10)
{
Console.WriteLine();
}
}
Console.WriteLine("\n");
}
Console.WriteLine("KST FN: " + kstFN / (10 * userCount));
Console.WriteLine("KST FP: " + kstFP / (20 * userCount));
Console.WriteLine("KST sum: " + (2 * kstFN + kstFP) / (40 * userCount) + "\n");
Console.WriteLine("DTW FN: " + dtwFN / (10 * userCount));
Console.WriteLine("DTW FP: " + dtwFP / (20 * userCount));
Console.WriteLine("DTW sum: " + (2 * dtwFN + dtwFP) / (40 * userCount) + "\n");
Console.WriteLine("KST FN: " + durFN / (10 * userCount));
Console.WriteLine("KST FP: " + durFP / (20 * userCount));
Console.WriteLine("KST sum: " + (2 * durFN + durFP) / (40 * userCount) + "\n");
Console.WriteLine("____BAYES_____");
Console.WriteLine("FN: " + falseNegative / (10 * userCount));
Console.WriteLine("FP: " + falsePozitive / (20 * userCount));
Console.WriteLine("sum: " + (2 * falseNegative + falsePozitive) / (40 * userCount) + "\n");
Console.WriteLine("________________________________________________");
Console.WriteLine("FINAL_mingood" + FINAL_mingood);
Console.WriteLine("FINAL_maxbad" + FINAL_maxbad);
Console.ReadKey();
}
