private void rekam(string uji)
{
_ujikan = true;
do
{
var epoching = new List <float[]>();
float[,] buffer = new float[_chunks, _kanal];
double[] timestamps = new double[_chunks];
updateAktivitas("Rekam " + arah);
inlet = new liblsl.StreamInlet(results[0]);
do
{
int num = inlet.pull_chunk(buffer, timestamps);
for (int s = 0; s < num; s++)
{
var d = new float[_kanal];
//Parallel.For(0, _kanal, i =>
//{
for (int i = 0; i < _kanal; i++)
{
d[i] = buffer[s, i];
}
//});
epoching.Add(d);
if (epoching.Count == _epocSizeTesting)
{
break;
}
}
} while (epoching.Count < _epocSizeTesting);
var pecah = pecahSinyal(epoching);
var preprocess = preprocessSignal(pecah);
var dft = featureExtraction(preprocess);
var feature = dftToOneRowFeature(dft);
var f = new double[1, feature.Length];
for (int j = 0; j < 1; j++)
{
for (int k = 0; k < f.GetLength(1); k++)
{
f[j, k] = feature[k];
}
}
int kelas = (int)Math.Round(enn.testing(f, _modelWeight, _modelbetaHatt, Activation.SigmoidBiner)[0]);
Console.WriteLine(kelas);
Console.Beep();
updateArah(kelas);
} while (_ujikan);
}
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)
{
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);
}
