private void Btn_GA_Click(object sender, RoutedEventArgs e)
{
FileReader fr = new FileReader();
ListDataSet dsl = fr.ReadFile("DataTest", cc);
dsl.Normalized();
for (int i = 0; i < dsl.Count; i++)
{
int popCount = 0;
for (int j = 0; j < fitChrom.Length; j++)
{
if (fitChrom[j] == 0)
{
dsl[i].RemoveBit(j - popCount);
popCount++;
}
}
}
FeedForward ff = new FeedForward();
ff.Initialise(nn, dsl);
int jumlahBenar = 0;
for (int i = 0; i < dsl.Count; i++)
{
ff.Run(i);
bool cekData = true;
for (int j = 0; j < cc.TargetCount; j++)
{
//Console.Write(ff.GetActualClass()[j] + "--> " + cc.GetTarget(dsl[i].ClassName)[j]+" ");
if (ff.GetActualClass()[j] != cc.GetTarget(dsl[i].ClassName)[j])
{
cekData = false;
break;
}
}
if (cekData)
{
jumlahBenar++;
}
//Console.WriteLine(ce();kData);
}
Console.WriteLine("Dari total " + dsl.Count + " datatest, yang benar adalah " + jumlahBenar);
}
private float[] GetErrorOutput(int index)
{
float[] outputError = new float[classificationClass.TargetCount];
for (int i = 0; i < classificationClass.TargetCount; i++)
{
outputError[i] = classificationClass.GetTarget(DataSetList[index].ClassName)[i] - Network.OutputLayer[i].Input;
// Console.Write(classificationClass.GetTarget(DataSetList[index].ClassName)[i] + " ");
//outputError[i] = Network.OutputLayer[i].Input *
// (1 - Network.OutputLayer[i].Input) *
// (classificationClass.GetTarget(DataSetList[index].ClassName)[i] - Network.OutputLayer[i].Input);
//Console.WriteLine(Network.OutputLayer[i].GetOutput(0) + "*(" + "1-" + Network.OutputLayer[i].GetOutput(0) + ")*" + "(" + classificationClass.GetTarget(DataSetList[index].ClassName)[i] + "-" + Network.OutputLayer[i].GetOutput(0)+")");
}
UpdateWeightHidden(outputError, index);
return(outputError);
}
private void DoBackPropagation()
{
for (int i = 0; i < chromosoms.Count; i++)
{
ListDataSet lds = new ListDataSet(listDataSet);
for (int j = 0; j < lds.Count; j++)
{
int popCount = 0;
for (int k = 0; k < chromosoms[i].Length; k++)
{
if (chromosoms[i][k] == 0)
{
lds[j].RemoveBit(k - popCount);
popCount++;
}
}
}
NeuralNetwork nn = new NeuralNetwork();
nn.InitialiseNetwork(lds[0].AttributeCount, lds[0].AttributeCount / 2, classificationClass.TargetCount);
nn.InitialiseWeight();
BackPropagation bp = new BackPropagation();
bp.Initialise(nn, lds, classificationClass);
bp.Run();
FeedForward ff = new FeedForward();
ff.Initialise(nn, lds);
int totalCorrect = 0;
for (int j = 0; j < lds.Count; j++)
{
ff.Run(j);
//nn.Print();
//foreach (int ac in ff.GetActualClass())
// Console.Write(ac + " ");
//Console.Write(" --> ");
//foreach (int ac in classificationClass.GetTarget(lds[j].ClassName))
// Console.Write(ac + " ");
//Console.WriteLine();
bool correct = true;
int[] targetClass = classificationClass.GetTarget(lds[j].ClassName);
for (int k = 0; k < ff.GetActualClass().Length; k++)
{
if (targetClass[k] != ff.GetActualClass()[k])
{
correct = false;
}
}
if (correct)
{
totalCorrect++;
}
}
//Console.WriteLine("total: " + totalCorrect + "/" + lds.Count);
chromosoms[i].FitnessValue = totalCorrect / (float)lds.Count;
//chromosoms[i].Print();
}
}
