private void btnMuatModelBetaHatt_Click(object sender, EventArgs e)
{
switch (cbMachineLearning.SelectedIndex)
{
case 0:
var openfile = Tmp.OpenFileDialog("Muat Model", "betahatt", "MODEL Files (*.betaHatt)|*.betaHatt");
var dialogResult = openfile.ShowDialog();
var listModelbetaHatt = new List <double[]>();
updateAktivitas("Open File BetaHatt " + openfile.FileNames);
if (dialogResult == DialogResult.OK)
{
foreach (String file in openfile.FileNames)
{
var filecsv = File.ReadAllLines(file);
foreach (var row in filecsv)
{
listModelbetaHatt.Add(Array.ConvertAll(row.Split(';'), double.Parse));
}
}
_modelbetaHatt = new double[listModelbetaHatt.Count()];
Parallel.For(0, listModelbetaHatt.Count(), i => {
Parallel.For(0, listModelbetaHatt[0].Length, j =>
{
_modelbetaHatt[i] = listModelbetaHatt[i][j];
});
});
groupPemakaian.Enabled = true;
btnUji.Enabled = true;
btnUjiDatasets.Enabled = true;
groupOpsiLatih.Enabled = true;
enn = new ExtremeNeuralNetwork();
}
break;
}
}
private void ELM()
{
this.Invoke((MethodInvoker) delegate {
groupLatih.Enabled = false;
groupOpsiLatih.Enabled = false;
groupPemakaian.Enabled = false;
});
Console.WriteLine(_neuronHidden);
Console.WriteLine(_minRand);
Console.WriteLine(_maxRand);
Network net = new Network(_neuronHidden, _minRand, _maxRand);
enn = new ExtremeNeuralNetwork(net, Activation.SigmoidBiner);
_error = enn.teach(_arrayFitur, _arrayKelas);
tbMape.Invoke((MethodInvoker) delegate { tbMape.Text = _error + ""; });
DialogResult dialogres;
string _filename = "";
do
{
dialogres = Tmp.InputBox("Form Nama", "Masukkan nama model Training: ", ref _filename);
if (dialogres == DialogResult.Cancel)
{
break;
}
} while ((_filename == "" && dialogres == DialogResult.OK));
if (dialogres.ToString() == "Cancel")
{
}
else
{
saveFile(enn.Weight, "weight", _filename);
saveFile(enn.BetaHatt, "betaHatt", _filename);
updateAktivitas("Save File Model");
}
this.Invoke((MethodInvoker) delegate {
groupLatih.Enabled = true;
groupOpsiLatih.Enabled = true;
groupPemakaian.Enabled = true;
});
}
static void MainProsesAuto(int iterasi, int i, int j, int k, int l, int m, int n,
int[] PersenData, int[][] KombinasiChannels, Activation[] FungsiAktivasi, int[] JumlahNeuron, double[] NilaiRandomAlpha, double[,] trainingFeature, double[] trainingKelas,
double[,] testingFeature, double[] testingKelas, int total, string date, int[] kelasNonDupe)
{
string name = iterasi + "_" + "ke-" + n + ", percentTraining " + PersenData[i] + ", KombinasiChannels " + string.Join(", ", KombinasiChannels[j]) + ", Neuron-" + JumlahNeuron[l] + ", Alpha-" + NilaiRandomAlpha[m] + ", " + DateTime.Now.ToString("hh_mm_ss");
Console.WriteLine(name);
Network net = new Network(JumlahNeuron[l], -NilaiRandomAlpha[m], NilaiRandomAlpha[m]);
enn = new ExtremeNeuralNetwork(net, FungsiAktivasi[k]);
var kelas2DTraining = enn.ConvertTo2DArrayKelas(kelasNonDupe, trainingKelas);
var kelas2DTesting = enn.ConvertTo2DArrayKelas(kelasNonDupe, testingKelas);
var accTrainingData = enn.teach(trainingFeature, kelas2DTraining);
var YHattTraining = enn.YHattTraining2D;
var forecast = enn.testing(testingFeature, enn.Weight, enn.BetaHatt2D, FungsiAktivasi[k]);
var accTesting = enn.Accuration(forecast, kelas2DTesting);
//var errorTrainingData = enn.teach(trainingFeature, trainingKelas);
//var accurationTrainingData = enn.Acc;
//var YHattTraining = enn.YHattTraining;
saveFile(enn.Weight, "weight", name);
saveFile(enn.BetaHatt2D, "betaHatt", name);
saveFile(YHattTraining, kelas2DTraining, "comparasionTraining", name);
saveFile(forecast, kelas2DTesting, "comparasionTesting", name);
//var forecast = enn.testing(testingFeature, FungsiAktivasi[k]);
//var errorTestingData = enn.MAPEEval(forecast, testingKelas);
//var accurationTestingData = enn.Accuration(forecast, testingKelas);
//saveFile(YHattTraining, trainingKelas, "comparasionTraining", name);
//saveFile(forecast, testingKelas, "comparasionTesting", name);
var rows = new string[] { iterasi + ";" + n + ";" + PersenData[i] + ";" + string.Join(", ", KombinasiChannels[j]) + ";" + JumlahNeuron[l] + ";" + NilaiRandomAlpha[m] + ";" + accTrainingData + ";" + accTesting + "\n" };
Console.WriteLine("Iterasi Ke-" + iterasi + "; Percobaan Ke-" + n + "; PersenDataTraining : " + PersenData[i] + "; Kombinasi Channels" + string.Join(", ", KombinasiChannels[j]) + "; Jumlah Neuron " + JumlahNeuron[l] + ";" + NilaiRandomAlpha[m] + "; Akurasi Kepada Training Data " + accTrainingData + "; Akurasi Kepada Testing Data " + accTesting);
File.AppendAllText(date + ".csv", rows[0]);
}
static void Main(string[] args)
{
double[,] input = new double[, ] {
{ 5.1, 3.5, 1.4, 0.2 }, {
4.9, 3.0, 1.4, 0.2
}, {
4.7, 3.2, 1.3, 0.2
}, {
4.6, 3.1, 1.5, 0.2
}, {
5.0, 3.6, 1.4, 0.2
}, {
5.4, 3.9, 1.7, 0.4
}, {
4.6, 3.4, 1.4, 0.3
}, {
5.0, 3.4, 1.5, 0.2
}, {
4.4, 2.9, 1.4, 0.2
}, {
4.9, 3.1, 1.5, 0.1
}, {
5.4, 3.7, 1.5, 0.2
}, {
4.8, 3.4, 1.6, 0.2
}, {
4.8, 3.0, 1.4, 0.1
}, {
4.3, 3.0, 1.1, 0.1
}, {
5.8, 4.0, 1.2, 0.2
}, {
5.7, 4.4, 1.5, 0.4
}, {
5.4, 3.9, 1.3, 0.4
}, {
5.1, 3.5, 1.4, 0.3
}, {
5.7, 3.8, 1.7, 0.3
}, {
5.1, 3.8, 1.5, 0.3
}, {
5.4, 3.4, 1.7, 0.2
}, {
5.1, 3.7, 1.5, 0.4
}, {
4.6, 3.6, 1.0, 0.2
}, {
5.1, 3.3, 1.7, 0.5
}, {
4.8, 3.4, 1.9, 0.2
}, {
5.0, 3.0, 1.6, 0.2
}, {
5.0, 3.4, 1.6, 0.4
}, {
5.2, 3.5, 1.5, 0.2
}, {
5.2, 3.4, 1.4, 0.2
}, {
4.7, 3.2, 1.6, 0.2
}, {
4.8, 3.1, 1.6, 0.2
}, {
5.4, 3.4, 1.5, 0.4
}, {
5.2, 4.1, 1.5, 0.1
}, {
5.5, 4.2, 1.4, 0.2
}, {
4.9, 3.1, 1.5, 0.1
}, {
5.0, 3.2, 1.2, 0.2
}, {
5.5, 3.5, 1.3, 0.2
}, {
4.9, 3.1, 1.5, 0.1
}, {
4.4, 3.0, 1.3, 0.2
}, {
5.1, 3.4, 1.5, 0.2
}, {
5.0, 3.5, 1.3, 0.3
}, {
4.5, 2.3, 1.3, 0.3
}, {
4.4, 3.2, 1.3, 0.2
}, {
5.0, 3.5, 1.6, 0.6
}, {
5.1, 3.8, 1.9, 0.4
}, {
4.8, 3.0, 1.4, 0.3
}, {
5.1, 3.8, 1.6, 0.2
}, {
4.6, 3.2, 1.4, 0.2
}, {
5.3, 3.7, 1.5, 0.2
}, {
5.0, 3.3, 1.4, 0.2
}, {
7.0, 3.2, 4.7, 1.4
}, {
6.4, 3.2, 4.5, 1.5
}, {
6.9, 3.1, 4.9, 1.5
}, {
5.5, 2.3, 4.0, 1.3
}, {
6.5, 2.8, 4.6, 1.5
}, {
5.7, 2.8, 4.5, 1.3
}, {
6.3, 3.3, 4.7, 1.6
}, {
4.9, 2.4, 3.3, 1.0
}, {
6.6, 2.9, 4.6, 1.3
}, {
5.2, 2.7, 3.9, 1.4
}, {
5.0, 2.0, 3.5, 1.0
}, {
5.9, 3.0, 4.2, 1.5
}, {
6.0, 2.2, 4.0, 1.0
}, {
6.1, 2.9, 4.7, 1.4
}, {
5.6, 2.9, 3.6, 1.3
}, {
6.7, 3.1, 4.4, 1.4
}, {
5.6, 3.0, 4.5, 1.5
}, {
5.8, 2.7, 4.1, 1.0
}, {
6.2, 2.2, 4.5, 1.5
}, {
5.6, 2.5, 3.9, 1.1
}, {
5.9, 3.2, 4.8, 1.8
}, {
6.1, 2.8, 4.0, 1.3
}, {
6.3, 2.5, 4.9, 1.5
}, {
6.1, 2.8, 4.7, 1.2
}, {
6.4, 2.9, 4.3, 1.3
}, {
6.6, 3.0, 4.4, 1.4
}, {
6.8, 2.8, 4.8, 1.4
}, {
6.7, 3.0, 5.0, 1.7
}, {
6.0, 2.9, 4.5, 1.5
}, {
5.7, 2.6, 3.5, 1.0
}, {
5.5, 2.4, 3.8, 1.1
}, {
5.5, 2.4, 3.7, 1.0
}, {
5.8, 2.7, 3.9, 1.2
}, {
6.0, 2.7, 5.1, 1.6
}, {
5.4, 3.0, 4.5, 1.5
}, {
6.0, 3.4, 4.5, 1.6
}, {
6.7, 3.1, 4.7, 1.5
}, {
6.3, 2.3, 4.4, 1.3
}, {
5.6, 3.0, 4.1, 1.3
}, {
5.5, 2.5, 4.0, 1.3
}, {
5.5, 2.6, 4.4, 1.2
}, {
6.1, 3.0, 4.6, 1.4
}, {
5.8, 2.6, 4.0, 1.2
}, {
5.0, 2.3, 3.3, 1.0
}, {
5.6, 2.7, 4.2, 1.3
}, {
5.7, 3.0, 4.2, 1.2
}, {
5.7, 2.9, 4.2, 1.3
}, {
6.2, 2.9, 4.3, 1.3
}, {
5.1, 2.5, 3.0, 1.1
}, {
5.7, 2.8, 4.1, 1.3
}, {
6.3, 3.3, 6.0, 2.5
}, {
5.8, 2.7, 5.1, 1.9
}, {
7.1, 3.0, 5.9, 2.1
}, {
6.3, 2.9, 5.6, 1.8
}, {
6.5, 3.0, 5.8, 2.2
}, {
7.6, 3.0, 6.6, 2.1
}, {
4.9, 2.5, 4.5, 1.7
}, {
7.3, 2.9, 6.3, 1.8
}, {
6.7, 2.5, 5.8, 1.8
}, {
7.2, 3.6, 6.1, 2.5
}, {
6.5, 3.2, 5.1, 2.0
}, {
6.4, 2.7, 5.3, 1.9
}, {
6.8, 3.0, 5.5, 2.1
}, {
5.7, 2.5, 5.0, 2.0
}, {
5.8, 2.8, 5.1, 2.4
}, {
6.4, 3.2, 5.3, 2.3
}, {
6.5, 3.0, 5.5, 1.8
}, {
7.7, 3.8, 6.7, 2.2
}, {
7.7, 2.6, 6.9, 2.3
}, {
6.0, 2.2, 5.0, 1.5
}, {
6.9, 3.2, 5.7, 2.3
}, {
5.6, 2.8, 4.9, 2.0
}, {
7.7, 2.8, 6.7, 2.0
}, {
6.3, 2.7, 4.9, 1.8
}, {
6.7, 3.3, 5.7, 2.1
}, {
7.2, 3.2, 6.0, 1.8
}, {
6.2, 2.8, 4.8, 1.8
}, {
6.1, 3.0, 4.9, 1.8
}, {
6.4, 2.8, 5.6, 2.1
}, {
7.2, 3.0, 5.8, 1.6
}, {
7.4, 2.8, 6.1, 1.9
}, {
7.9, 3.8, 6.4, 2.0
}, {
6.4, 2.8, 5.6, 2.2
}, {
6.3, 2.8, 5.1, 1.5
}, {
6.1, 2.6, 5.6, 1.4
}, {
7.7, 3.0, 6.1, 2.3
}, {
6.3, 3.4, 5.6, 2.4
}, {
6.4, 3.1, 5.5, 1.8
}, {
6.0, 3.0, 4.8, 1.8
}, {
6.9, 3.1, 5.4, 2.1
}, {
6.7, 3.1, 5.6, 2.4
}, {
6.9, 3.1, 5.1, 2.3
}, {
5.8, 2.7, 5.1, 1.9
}, {
6.8, 3.2, 5.9, 2.3
}, {
6.7, 3.3, 5.7, 2.5
}, {
6.7, 3.0, 5.2, 2.3
}, {
6.3, 2.5, 5.0, 1.9
}, {
6.5, 3.0, 5.2, 2.0
}, {
6.2, 3.4, 5.4, 2.3
}, {
5.9, 3.0, 5.1, 1.8
}
};
double[] output = new double[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3 };
Network net = new Network(10, -1.0, 1.0);
ExtremeNeuralNetwork enn = new ExtremeNeuralNetwork(net, Activation.SigmoidBiner);
var error = enn.teach(input, output);
Console.WriteLine(error);
Console.WriteLine(enn.Accuration(enn.YHattTraining, output) + "\n");
//Console.WriteLine(string.Join("\n", enn.YHattTraining));
Console.ReadKey();
}
static void Main(string[] args)
{
var Min = Array.ConvertAll(File.ReadAllLines("Min.csv")[0].Split(';'), double.Parse);
var Max = Array.ConvertAll(File.ReadAllLines("Max.csv")[0].Split(';'), double.Parse);
var fileWeight = File.ReadAllLines("model.weight");
var betaHatt = Array.ConvertAll(File.ReadAllLines("model.betaHatt"), double.Parse);
var weight = new double[fileWeight.Length, fileWeight[0].Split(';').Count()];
for (int i = 0; i < fileWeight.Length; i++)
{
var temp = Array.ConvertAll(fileWeight[i].Split(';'), double.Parse);
for (int j = 0; j < temp.Length; j++)
{
weight[i, j] = temp[j];
}
}
do
{
results = liblsl.resolve_stream("type", "EEG", 0, 5);
if (results.Length != 0)
{
Console.WriteLine("Menghubungkan Oke");
inlet = new liblsl.StreamInlet(results[0]);
Console.WriteLine("Nama Perangkat\t\t: " + inlet.info().name() + "\n" +
"Nomor UID Perangkat\t: " + inlet.info().uid() + "\n" +
"Versi Perangkat\t\t: " + inlet.info().version() + "\n" +
"Sumber Arus EEG\t\t: " + inlet.info().source_id() + "\n" +
"Jumlah Kanal\t\t: " + inlet.info().channel_count() + " Kanal\n" +
"Sampling rate\t\t: " + inlet.info().nominal_srate() + "\n" +
"Bentuk nilai Kanal\t\t: " + inlet.info().channel_format() + "\n");
_kanal = inlet.info().channel_count();
}
else
{
Console.WriteLine("Tidak ditemukan arus EEG dari aplikasi BlueMuse atau perangkat EEG tidak terhubung ke komputer! Check kembali");
}
} while (results.Length == 0);
Console.WriteLine("==============================================================");
Console.WriteLine("=========================Mulai Stream=========================");
do
{
enn = new ExtremeNeuralNetwork();
var epoching = new List <float[]>();
float[,] buffer = new float[_chunks, _kanal];
double[] timestamps = new double[_chunks];
inlet = new liblsl.StreamInlet(results[0]);
Stopwatch watch = new Stopwatch();
watch.Start();
do
{
int num = inlet.pull_chunk(buffer, timestamps);
for (int s = 0; s < num; s++)
{
var d = new float[_kanal];
for (int i = 0; i < _kanal; i++)
{
d[i] = buffer[s, i];
}
epoching.Add(d);
if (epoching.Count == _epocSizeTesting)
{
break;
}
}
} while (epoching.Count < _epocSizeTesting);
Console.WriteLine(watch.ElapsedMilliseconds + "ms");
watch.Stop();
var pecah = pecahSinyal(epoching);
var preprocess = preprocessSignal(pecah);
var dft = featureExtraction(preprocess).ToArray();
var feature = dftToOneRowFeature(dft);
var norm = new double[1, feature.Length];
for (int s = 0; s < feature.Length; s++)
{
norm[0, s] = Normalisasi(feature[s], Min[s], Max[s]);
}
var kelas = enn.testing(norm, weight, betaHatt, Activation.SigmoidBiner);
var keyboard = new Keyboard();
switch (Convert.ToInt16(Math.Round(kelas[0])))
{
case 1:
Console.WriteLine("Kiri");
for (int loop = 0; loop < 2; loop++)
{
keyboard.Send(Keyboard.ScanCodeShort.KEY_A);
Thread.Sleep(500);
}
break;
case 2:
Console.WriteLine("Kanan");
for (int loop = 0; loop < 2; loop++)
{
keyboard.Send(Keyboard.ScanCodeShort.KEY_D);
Thread.Sleep(500);
}
break;
default:
Console.WriteLine("Bukan Keduanya");
keyboard.Send(Keyboard.ScanCodeShort.KEY_W); break;
}
} while (true);
}
