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);
        }
Esempio n. 5
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 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;
 }
Esempio n. 8
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 public void Initialise(NeuralNetwork nn, ListDataSet dsl)
 {
     neuralNetwork = nn;
     dataSetList   = dsl;
 }