public ListDataSet ReadFile(string path, ClassificationClass cc)
{
path = path.TrimEnd('\\').TrimEnd('/') + "\\";
DirectoryInfo dirInfo = new DirectoryInfo(path);
FileInfo[] fileInfo = dirInfo.GetFiles("*.txt");
ListDataSet dataSetList = new ListDataSet();
foreach (FileInfo fi in fileInfo)
{
string className = fi.Name.Split('.')[0];
cc.Add(className);
StreamReader reader = new StreamReader(path + fi.Name);
List <float> attr = new List <float>();
while (!reader.EndOfStream)
{
attr.Add(float.Parse(reader.ReadLine()));
}
DataSet ds = new DataSet(attr.Count);
ds.ClassName = className;
for (int i = 0; i < attr.Count; i++)
{
ds[i] = attr[i];
}
dataSetList.Add(ds);
}
return(dataSetList);
}
public ListDataSet(ListDataSet lds)
{
dataSetList = new List <DataSet>();
for (int i = 0; i < lds.Count; i++)
{
dataSetList.Add(new DataSet(lds[i]));
}
}
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 void Btn_Read_Click(object sender, RoutedEventArgs e)
{
FileReader fr = new FileReader();
ListDataSet dsl = fr.ReadFile("DataSetBaru", cc);
dsl.Normalized();
GeneticAlgorithm ga = new GeneticAlgorithm();
ga.Initialize(dsl, cc);
fitChrom = ga.Run();
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++;
}
}
}
Stopwatch sw = Stopwatch.StartNew();
nn.InitialiseNetwork(dsl[0].AttributeCount, dsl[0].AttributeCount / 2, cc.TargetCount);
nn.Seed = 0;
nn.InitialiseWeight();
BackPropagation bp = new BackPropagation();
bp.Initialise(nn, dsl, cc);
bp.Run(500);
sw.Stop();
Console.WriteLine(sw.ElapsedMilliseconds / 1000.0f);
}
public void Initialise(NeuralNetwork nn, ListDataSet dsl, ClassificationClass cc)
{
feedForward = new FeedForward();
feedForward.Initialise(nn, dsl);
classificationClass = cc;
}
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();
}
}
public void Initialize(ListDataSet lds, ClassificationClass cc)
{
listDataSet = lds;
classificationClass = cc;
}
public void Initialise(NeuralNetwork nn, ListDataSet dsl)
{
neuralNetwork = nn;
dataSetList = dsl;
}
