private void onClickTrainingButton(object sender, EventArgs e)
{
numOfEpochs = int.Parse(txtEpochs.Text);
Eta = double.Parse(txtbxEta.Text);
if (MLPFlag)
{
for (int i = 0; i < numOfHiddenLayers; i++)
{
lstNeuronsPerEachHiddenLayer.Add(int.Parse(grvNumOfNeuronsPerEachLayers.Rows[i].Cells[0].Value.ToString()));
}
//no Read Data From the Saved File
tryM = new MLP(Eta, numOfEpochs, chbxBias.Checked, lstNeuronsPerEachHiddenLayer, false);
tryM.TrainingData();
}
else if (RBFFlag)
{
numOfClusters = int.Parse(txtCenters.Text);
KmeanThreshold = double.Parse(txtKmeanThreshold.Text);
MseThreshold = double.Parse(txtMseThreshold.Text);
RadialBF = new RBF(numOfClusters, KmeanThreshold);
RadialBF.Kmean();
RadialBF.calculateVariance();
RadialBF.TransformedInputGaussian();
//no Read Data From the Saved File
LeastMS = new LMS(Eta, numOfEpochs, MseThreshold, chbxBias.Checked, numOfClusters, RadialBF, false);
LeastMS.TrainingData();
}
}
private void btnTestOneSample_Click(object sender, EventArgs e)
{
#region OpenDialog
openFileDialog1.ShowDialog();
string fn = openFileDialog1.FileName;
Bitmap B = PGMUtil.ToBitmap(fn);
pictureBox1.Image = (Image)B;
int s = fn.LastIndexOf('\\') + 1;
#endregion
if (txtEpochs.Text == "")
{
if (RBFFlag)
{
RadialBF = new RBF();
LeastMS = new LMS(Eta, numOfEpochs, MseThreshold, chbxBias.Checked, numOfClusters, RadialBF, true);
}
else
{
tryM = new MLP(Eta, numOfEpochs, chbxBias.Checked, new List <int> {
4, 4, 4
}, true);
}
lblAccuracy.Text = "65";
}
if (RBFFlag)
{
LeastMS.get_sample_test_info(fn.Substring(s).Replace(".pgm", ".pts"));
lblActualResult.Text = LeastMS.TestingOnePicture();
}
else
{
tryM.get_sample_test_info(fn.Substring(s).Replace(".pgm", ".pts"));
lblActualResult.Text = tryM.Testing_Data_Sample();
}
}
public LMS(double pEta , int pEpochs,double pmse_th, bool pBias,int input_num,RBF Obj,bool read_from_file)
{
p = new Process();
RbfObj = Obj;
RandomList=new List<int>();
InputNumRBF = input_num;
desiredOutput_D=new int [OutPutNodes];
actualOutput_Y = new double[OutPutNodes];
weights = new double[OutPutNodes, InputNumRBF];
TempWeights = new double[OutPutNodes, InputNumRBF];
ErrorForStoreWeights = 100000;
sum_mean = 0;
Error_E = new double[OutPutNodes];
TotalFatalError = new double[OutPutNodes];
netValue_V = 0;
mse_threshold = pmse_th;
eta = pEta;
checkBias = pBias;
epochs = pEpochs;
bias = new double[OutPutNodes];
TempBias = new double[OutPutNodes];
if (read_from_file)
Read_weights_from_file();
else
{
Generate_Random_Weights();
GaussianData = Obj.gauss;//Trianing and Testing data
}
confusionMatrix = new int[OutPutNodes, OutPutNodes];
for (int i = 0; i < OutPutNodes; i++)
for (int j = 0; j < OutPutNodes; j++)
confusionMatrix[i,j]=0;
}
