private Fitnessvalues DifferenceMethod()
{
Lvalueavg = Math.Abs(Lcleanvalueavg - Ladvvalueavg);
// Lvalueavg = 1 / Lvalueavg;
double distance = 0.0;
double distancepercentage = 0.0;
if (cleanupperrange > adversupperrange && cleanlowerrange > adversupperrange)
{
distance = cleanlowerrange - adversupperrange;
distancepercentage = distance / cleanlowerrange;
distance = distance * distance;
}
else if (adversupperrange > cleanupperrange && adverselowerrange > cleanupperrange)
{
distance = adverselowerrange - cleanupperrange;
distancepercentage = distance / adverselowerrange;
distance = distance * distance;
}
else
{
distance = Math.Abs(adversupperrange + adverselowerrange - cleanupperrange - cleanlowerrange);
distancepercentage = distance / adversupperrange;
}
// diff = distance;
double lbp_distance = 0.0;
if (lbp_cleanupperrange > lbp_adversupperrange && lbp_cleanlowerrange > lbp_adversupperrange)
{
lbp_distance = lbp_cleanlowerrange - lbp_adversupperrange;
lbp_distance = lbp_distance * lbp_distance;
}
else if (lbp_adversupperrange > lbp_cleanupperrange && lbp_adverselowerrange > lbp_cleanupperrange)
{
lbp_distance = lbp_adverselowerrange - lbp_cleanupperrange;
lbp_distance = lbp_distance * lbp_distance;
}
else
{
lbp_distance = Math.Abs(lbp_adversupperrange + lbp_adverselowerrange - lbp_cleanupperrange - lbp_cleanlowerrange);
}
Accord.Math.Distances.Euclidean euclidean = new Accord.Math.Distances.Euclidean();
var p = Array.ConvertAll <int, double>(adv_histogram.ToArray(), x => (double)x / (double)adv_histogram.Max);
var q = Array.ConvertAll <int, double>(clean_histogram.ToArray(), x => (double)x / (double)clean_histogram.Max);
// foreach(var b in q)
// Console.Write(b + "");
var plist = p.ToList();
var qlist = q.ToList();
double ecu_distance = euclidean.Distance(p, q);
double crossentropyp = CEL.Crossentropy(plist, plist);
double crossentropyq = CEL.Crossentropy(qlist, qlist);
double crossentropy = CEL.Crossentropy(plist, qlist);
// Console.WriteLine(sta + " crossentropy " + crossentropyp + " " + crossentropyq + " " + crossentropy);
// var adv_normal = new NormalDistribution(mean: plist.Average(), stdDev: plist.StdDev);
// var clean_normal = new NormalDistribution(mean: clean_histogram.Mean, stdDev: clean_histogram.StdDev);
double crossentropyppde = CEL.CrossentropyPDE(plist, plist);
double crossentropyqpde = CEL.CrossentropyPDE(qlist, qlist);
double crossentropypde = CEL.CrossentropyPDE(qlist, plist);;
// Console.WriteLine(sta + " crossentropypde " + crossentropyppde + " " + crossentropyqpde + " " + crossentropypde);
double deltax = 0 - lbp_distance;// point1[0];
double deltay = 0 - distance;
double deltaz = 0 - ecu_distance;
diff = (double)Math.Sqrt(
(deltax * deltax) +
(deltay * deltay) +
(deltaz * deltaz));
// Console.WriteLine(sta+ " fitness " + diff+"-"+ ecu_distance + "-"+ lbp_distance);
euc_cleanupperrange = clean_histogram.Mean + clean_histogram.StdDev;
euc_cleanlowerrange = clean_histogram.Mean - clean_histogram.StdDev;
string sa = sta + ", LVALUE, " + Lvalueavg + " fitness ," + diff + "," + ecu_distance + "," + lbp_distance + " ," + crossentropy + " ," + crossentropypde;
// if (!MainGA.recodedfilters.Contains(sta + "," + diff + "," + cleanupperrange + "," + cleanlowerrange + "," + lbp_cleanupperrange + "," + lbp_cleanlowerrange + "," + euc_cleanupperrange + "," + euc_cleanlowerrange));
// MainGA.recodedfilters.Add(sta + "," + diff + "," + cleanupperrange + "," + cleanlowerrange + "," + lbp_cleanupperrange + "," + lbp_cleanlowerrange + "," + euc_cleanupperrange + "," + euc_cleanlowerrange);
Fitnessvalues fitnessvalues = new Fitnessvalues();
fitnessvalues.Individual = sta;
fitnessvalues.Histogramdistance = diff;
fitnessvalues.Cleanupperrange = cleanupperrange;
fitnessvalues.Lowerupperrange = cleanlowerrange;
fitnessvalues.Lbp_distance = lbp_distance;
fitnessvalues.Lbp_cleanupperrange = lbp_cleanupperrange;
fitnessvalues.Lbp_lowerupperrange = lbp_cleanlowerrange;
fitnessvalues.Ecu_distance = Math.Abs(ecu_distance);
fitnessvalues.Ecu_histogram_clean = clean_histogram;
fitnessvalues.Ecu_histogram_adv = adv_histogram;
fitnessvalues.Lvalue = Lvalueavg;
fitnessvalues.Crossentropy = crossentropy;
fitnessvalues.Crossentropy_clean = crossentropyq;
fitnessvalues.Crossentropy_adv = crossentropyp;
fitnessvalues.Crossentropy_PDE = crossentropypde;
fitnessvalues.Crossentropy_PDE_clean = crossentropyqpde;
fitnessvalues.Crossentropy_PDE_adv = Math.Abs(crossentropyppde);
MainGA.fitness.Add(fitnessvalues);
return(fitnessvalues);
}
private void runonadverseonthread()
{
ObservableCollection <double> means = new ObservableCollection <double>();
ObservableCollection <double> lbpmeans = new ObservableCollection <double>();
ObservableCollection <double> L = new ObservableCollection <double>();
Accord.Statistics.Visualizations.Histogram histogram = null;
//List<double> stddeva = new List<double>();
count = 0;
string[] file = Directory.GetFiles(Form1.adverse);
if (imagetotal > file.Length)
{
imagetotal = file.Length;
}
var T1 = Task.Run(() => {
for (int i = 0; i < threadcount * 1; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T2 = Task.Run(() => {
for (int i = threadcount * 1; i < threadcount * 2; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T3 = Task.Run(() => {
for (int i = threadcount * 2; i < threadcount * 3; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T4 = Task.Run(() => {
for (int i = threadcount * 3; i <= threadcount * 4; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T5 = Task.Run(() => {
for (int i = threadcount * 4; i < threadcount * 5; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T6 = Task.Run(() => {
for (int i = threadcount * 5; i < threadcount * 6; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T7 = Task.Run(() => {
for (int i = threadcount * 6; i < threadcount * 7; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
var T8 = Task.Run(() => {
for (int i = threadcount * 7; i < file.Length; i++)
{
// Form1.textlist.Add("adverse count " + i);
if (i >= imagetotal)
{
break;
}
rettype rt = runalgo(file[i]);
means.Add(rt.histoavg);
lbpmeans.Add(rt.lbpavg);
if (histogram == null)
{
histogram = rt.histogram;
}
else
{
histogram.Add(rt.histogram);
}
L.Add(CEL.Lcalculate(0, rt.histogram));
}
});
T1.Wait();
T2.Wait();
T3.Wait();
T4.Wait();
T5.Wait();
T6.Wait();
T7.Wait();
T8.Wait();
// Form1.textlist.Add("total sample processed " + means.Count());
double std = StandardDeviation(means.ToList(), 0, means.Count());
double avg = means.Average();
adversupperrange = std + avg;
adverselowerrange = Math.Abs(avg - std);
std = StandardDeviation(lbpmeans.ToList(), 0, lbpmeans.Count());
avg = means.Average();
lbp_adversupperrange = std + avg;
lbp_adverselowerrange = Math.Abs(avg - std);
// Form1.textlist.Add(sta+" adversupperrange " + adversupperrange + " adverselowerrange " + adverselowerrange);
adv_histogram = histogram;
Ladvvalueavg = L.Average();
Lvaluesum = Lvaluesum + L.Sum();
}
