private void buttonTrain_Click(object sender, EventArgs e)
{
bool canTrain = true;
for (int i = 0; i < model.classes.Count; i++)
{
if (model.classes[classesCommand[i]] && !classesDataLoaded[classesCommand[i]])
{
canTrain = false;
}
}
if (!canTrain)
{
MessageBox.Show("Masih ada data set kelas yang belum dicari");
}
else
{
buttonPhase2.Enabled = false;
groupBoxLoadDataSet.Enabled = false;
buttonTrain.Enabled = false;
model.SetChanelsIndex();
for (int i = 0; i < allData.Count; i++)
{
if (model.classes[classesCommand[i]])
{
model.dataTraining.AddRange(allData[i]);
}
}
fft.Init(model.GetTotalSelectedChannels());
CalculateFeature();
labelDetailModel.Text = model.ToString();
//MessageBox.Show(model.dataTraining[0].Count().ToString());
progressBarModel.Value = 0;
timerProgress.Enabled = true;
}
}
private void timer_Tick(object sender, EventArgs e)
{
int totalSensor = models[comboBoxModel.SelectedIndex].GetTotalSelectedChannels();
dataTesting = new double[totalSensor];
if (checkShowChart.Checked)
{
for (int i = 0; i < totalChannels; i++)
{
DataPoint dp = new DataPoint();
dp.SetValueY(eegData[channelsEeg[i]][counter]);
chartEeg.Series[i].Points.Add(dp);
chartEeg.Update();
if (counter > LIMIT_WINDOW_CHART)
{
chartEeg.Series[i].Points.RemoveAt(0);
}
}
}
for (int i = 0; i < totalSensor; i++)
{
string id = models[comboBoxModel.SelectedIndex].selectedChannelsIndex[i];
double value = eegData[id][counter];
dataTesting[i] = value;
}
fft.Init(totalSensor);
double[] dataEegFreq = fft.DoWork(dataTesting);
List <double[]> result = dsp.Decomposes(dataEegFreq);
for (int i = 0; i < totalSensor; i++)
{
if (checkShowChart.Checked)
{
chartDeltaTheta.Series[i].Points.AddY(result[0][i]);
chartAlpha.Series[i].Points.AddY(result[1][i]);
chartBeta.Series[i].Points.AddY(result[2][i]);
chartGamma.Series[i].Points.AddY(result[3][i]);
if (counter > LIMIT_WINDOW_CHART)
{
chartDeltaTheta.Series[i].Points.RemoveAt(0);
chartAlpha.Series[i].Points.RemoveAt(0);
chartBeta.Series[i].Points.RemoveAt(0);
chartGamma.Series[i].Points.RemoveAt(0);
}
}
fitur.Add(result[0][i]);
fitur.Add(result[1][i]);
fitur.Add(result[2][i]);
fitur.Add(result[3][i]);
}
if (checkShowChart.Checked)
{
chartEeg.ChartAreas[0].RecalculateAxesScale();
chartDeltaTheta.ChartAreas[0].RecalculateAxesScale();
chartAlpha.ChartAreas[0].RecalculateAxesScale();
chartBeta.ChartAreas[0].RecalculateAxesScale();
chartGamma.ChartAreas[0].RecalculateAxesScale();
}
if (totalSensor > 1)
{
for (int i = 0; i < totalSensor; i++)
{
for (int j = i + 1; j < totalSensor; j++)
{
double val1 = result[0][i];
double val2 = result[0][j];
double powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
val1 = result[1][i];
val2 = result[1][j];
powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
val1 = result[2][i];
val2 = result[2][j];
powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
val1 = result[3][i];
val2 = result[3][j];
powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
}
}
}
if ((buffer + 1) == bufferDecision)
{
int classifyResult = knn.Classify(fitur, 1);
if (compareClass == classifyResult)
{
totalCorrectOutput++;
}
labelMovement.Text = classesCommand[classifyResult];
if (counterOutput == 20)
{
double akurasi = (double)totalCorrectOutput / counterOutput * 100;
timer.Enabled = false;
MessageBox.Show("akurasi: " + akurasi + "%");
}
fitur = new List <double>();
buffer = -1;
tickSecond++;
counterOutput++;
labelTickSecond.Text = tickSecond.ToString();
}
++buffer;
++counter;
}
