//Este metodo lo q hace es llenar la lista de indices con los indices q se van a usar en este caso (AHORA TENGO DESHABILITADO TODO LO Q TIENE Q VER CON LOS INDICES).
//private void FillSegmentationIndexList()
//{
// //indexes = new List<SegmentationIndex>();
// //indexes.Add(new CompactnessIndex());
// //indexes.Add(new CircularityIndex());
// //indexes.Add(new ConnectivityIndex());
//}
//private double[] ComputeWeights()
//{
// double[] weights = new double[this.prob.Segmentations.Count];
// foreach (SegmentationIndex sidx in indexes)
// sidx.EvaluateSegmentations(this.prob);
// double min = double.MaxValue; double max = 0;
// for (int i = 0; i < this.prob.Segmentations.Count; i++)
// {
// foreach (SegmentationIndex sidx in indexes)
// weights[i] += sidx.Weights[i];
// if (weights[i] < min)
// min = weights[i];
// if (weights[i] > max)
// max = weights[i];
// }
// for (int i = 0; i < weights.Length; i++)
// {
// //weights[i] = weights[i] / max;
// //Otra variante
// weights[i] = (weights[i] - min) / (max - min);
// }
// return weights;
//}
private double Compute3thTerm(double[] weights)
{
double result = 0;
for (int i = 0; i < prob.Segmentations.Count; i++)
{
for (int j = 0; j < prob.Segmentations.Count; j++)
{
result += weights[i] * weights[j] * rand.Process(prob.Segmentations[i], prob.Segmentations[j]);
}
}
return(result);
}
//Evaluo la segmentacion con las medidas RandIndex, VI and NMI y dejo los resultados en el prob. Evaluo contra el mejor ground-truth y el promedio contra todos.
private void EvaluateMatrixSegmentation(Segmentation segm)
{
RandIndexSegSim rand = new RandIndexSegSim();
NMISegSim nmi = new NMISegSim();
VISegSim vi = new VISegSim();
//Calcular el rand
double min = double.MaxValue;
double ave = 0;
double aux = 0;
for (int i = 0; i < this.prob.GroundTruths.Count; i++)
{
aux = 1 - rand.Process(segm, this.prob.GroundTruths[i]);
if (aux < min)
{
min = aux;
}
ave += aux;
}
ave = ave / this.prob.GroundTruths.Count;
this.prob.Best_RandEvaluation = min;
this.prob.Ave_RandEvaluation = ave;
//Calcular NMI
min = double.MaxValue;
ave = 0;
aux = 0;
for (int i = 0; i < this.prob.GroundTruths.Count; i++)
{
aux = 1 - nmi.Process(segm, this.prob.GroundTruths[i]);
if (aux < min)
{
min = aux;
}
ave += aux;
}
ave = ave / this.prob.GroundTruths.Count;
this.prob.Best_NMIEvaluation = min;
this.prob.Ave_NMIEvaluation = ave;
//Calcular VI
min = double.MaxValue;
ave = 0;
aux = 0;
for (int i = 0; i < this.prob.GroundTruths.Count; i++)
{
aux = vi.Process(segm, this.prob.GroundTruths[i]);
if (aux < min)
{
min = aux;
}
ave += aux;
}
ave = ave / this.prob.GroundTruths.Count;
this.prob.Best_VIEvaluation = min;
this.prob.Ave_VIEvaluation = ave;
}
//Evaluo la segmentacion con las medidas RandIndex, VI and NMI y dejo los resultados en el prob. Evaluo contra el mejor ground-truth y el promedio contra todos.
private void EvaluateMatrixSegmentation(Segmentation segm)
{
RandIndexSegSim rand = new RandIndexSegSim();
NMISegSim nmi = new NMISegSim();
VISegSim vi = new VISegSim();
//Calcular el rand
double min = double.MaxValue;
double ave = 0;
double aux = 0;
for (int i = 0; i < this.prob.GroundTruths.Count; i++)
{
aux = 1 - rand.Process(segm, this.prob.GroundTruths[i]);
if (aux < min)
min = aux;
ave += aux;
}
ave = ave / this.prob.GroundTruths.Count;
this.prob.Best_RandEvaluation = min;
this.prob.Ave_RandEvaluation = ave;
//Calcular NMI
min = double.MaxValue;
ave = 0;
aux = 0;
for (int i = 0; i < this.prob.GroundTruths.Count; i++)
{
aux = 1 - nmi.Process(segm, this.prob.GroundTruths[i]);
if (aux < min)
min = aux;
ave += aux;
}
ave = ave / this.prob.GroundTruths.Count;
this.prob.Best_NMIEvaluation = min;
this.prob.Ave_NMIEvaluation = ave;
//Calcular VI
min = double.MaxValue;
ave = 0;
aux = 0;
for (int i = 0; i < this.prob.GroundTruths.Count; i++)
{
aux = vi.Process(segm, this.prob.GroundTruths[i]);
if (aux < min)
min = aux;
ave += aux;
}
ave = ave / this.prob.GroundTruths.Count;
this.prob.Best_VIEvaluation = min;
this.prob.Ave_VIEvaluation = ave;
}
