//build model for multi class problems
public Problem buildModelMultiClass(ObjectInstanceSelection firefly, Problem prob)
{
int tNI = firefly.Attribute_Values.Count(); //size of each Instance Mask
List <double> y = new List <double>();
List <Node[]> x = new List <Node[]>();
bool pos = false, neg = false;
List <double> classes = getClassLabels(prob.Y); //get the class labels
int nClass = classes.Count; //count the number of classes
int[] classCount = new int[nClass];
//building model for each instance in instance mask in each firefly object
for (int j = 0; j < tNI; j++)
{
if (firefly.__Attribute_Values[j] == 1) //if instance is selected, use for classification
{
int p = firefly.__Pointers[j];
x.Add(prob.X[p]);
y.Add(prob.Y[p]);
for (int i = 0; i < nClass; i++)
{
if (prob.Y[p] == classes[i])
{
classCount[i]++; //count the total number of instances in each class
}
}
}
else
{
continue;
}
}
Node[][] X = new Node[x.Count][];
double[] Y = new double[y.Count];
//ensuring that the subproblem consist of both positive and negative instance
int k = 0;
if (classCount.Sum() == 0) //if the sum is zero, then no instance was selected
{
return(null);
}
else //ensure that instance mask contains at least, one of each class instance
{
for (int a = 0; a < nClass; a++)
{
if (classCount[a] == 0)
{
int m = 0;
for (int i = 0; i < prob.Count; i++) //if no instance in this class, search the whole subproblem and insert one instance in the kth position of subproblem
{
if (prob.Y[i] == classes[a])
{
x[k] = prob.X[i]; //insert negative instance in the first and second position
y[k] = prob.Y[i]; //insert label
k++; m++;
}
if (m == 2)
{
break;
}
}
}
}
}
x.CopyTo(X); //convert from list to double[] array
y.CopyTo(Y);
Problem subProb = new Problem(X.Count(), Y, X, X[0].GetLength(0));
return(subProb);
}
//build model for binary problems
public Problem buildModel(ObjectInstanceSelection firefly, Problem prob)
{
int tNI = firefly.Attribute_Values.Count(); //size of each Instance Mask
List <double> y = new List <double>();
List <Node[]> x = new List <Node[]>();
bool pos = false, neg = false;
//building model for each instance in instance mask in each firefly object
for (int j = 0; j < tNI; j++)
{
if (firefly.__Attribute_Values[j] == 1) //if instance is selected, use for classification
{
int p = firefly.__Pointers[j];
x.Add(prob.X[p]);
y.Add(prob.Y[p]);
if (prob.Y[p] == 1)
{
pos = true;
}
else if (prob.Y[p] == -1)
{
neg = true;
}
}
else
{
continue;
}
}
Node[][] X = new Node[x.Count][];
double[] Y = new double[y.Count];
//ensuring that the subproblem consist of both positive and negative instance
int k = 0;
int countP = y.Count(r => r == 1); //counting the total number of positive instance in the subpeoblem
int countN = y.Count(r => r == -1); //counting the total number of negative instance in the subproble
if (pos == false && neg == false) //if no instance (positive and negative) was selected, return null. Don't perform any computation
{
return(null);
}
else if (pos == false || countP <= 1) //if pos == false, then no positive instance is in the subproblem
{
for (int i = 0; i < prob.Count; i++) //if no positive instance, search the whole subproblem and insert two postive instance in the first and second position of subproblem
{
if (prob.Y[i] == 1)
{
x[k] = prob.X[i]; //insert negative instance in the first and second position
y[k] = prob.Y[i]; //insert label
k++;
}
if (k == 2)
{
break;
}
}
}
else if (neg == false || countN <= 1) //if neg == false, then no negative instance is in the subproblem
{
k = 0;
for (int i = 0; i < prob.Count; i++) //if no negative instance, search the whole subproblem and insert two negative instances in the first and second position of subproblem
{
if (prob.Y[i] == -1)
{
x[k] = prob.X[i]; //insert negative instance in the first and second position
y[k] = prob.Y[i]; //insert label
k++;
}
if (k == 2)
{
break;
}
}
}
x.CopyTo(X); //convert from list to double[] array
y.CopyTo(Y);
Problem subProb = new Problem(X.Count(), Y, X, X[0].GetLength(0));
return(subProb);
}