public frmHome()
{
InitializeComponent();
listIrisItem = new List<IrisItem>();
jumlahCluster = 3;
cmbJumlahCluster.SelectedIndex = 2;
cmbRBFEpoch.SelectedIndex = 3;
cmbFitness.SelectedIndex = 0;
cmbLearningRate.SelectedIndex = 0;
ucresult = null;
}
private void btnProcessRBF_Click(object sender, EventArgs e)
{
if (listIrisItem.Count <= 0)
{
General.ShowError("Please load data from file");
return;
}
if (listCluster == null)
{
General.ShowError("Please execute K-Means process!");
return;
}
if (!Int32.TryParse(txtMaxEpoch.Text, out jumlahEpoch))
{
General.ShowError("Please enter valid number for max GA epoch!");
return;
}
if (cmbRBFEpoch.SelectedItem.ToString() == "")
{
General.ShowError("Please select max epoch of RBF!");
return;
}
if (cmbLearningRate.SelectedItem.ToString() == "")
{
General.ShowError("Please select learning rate!");
return;
}
if (cmbFitness.SelectedItem.ToString() == "")
{
General.ShowError("Please select max fitness!");
return;
}
jumlahRBFEpoch = Int32.Parse(cmbRBFEpoch.SelectedItem.ToString());
learningRate = double.Parse(cmbLearningRate.SelectedItem.ToString());
maxFitness = double.Parse(cmbFitness.SelectedItem.ToString());
Matrix<double> matrixTarget = DenseMatrix.OfArray(new double[,] { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } });
RBFNN rbfnn = new RBFNN();
outputRBFNN = new double[listIrisItem.Count][];
outputActivationRBFNN = new int[listIrisItem.Count][];
for (int i = 0; i < listIrisItem.Count; i++)
{
Matrix bobotMatrix = rbfnn.getBobot(listIrisItem[i], listCluster);
double[] hiddenValue = rbfnn.countHiddenValue(listIrisItem[i], bobotMatrix, listCluster.Count);
Matrix<double> weightToOutput = (bobotMatrix * bobotMatrix.Transpose()).Inverse() * bobotMatrix * matrixTarget;
double[][] weight = weightToOutput.ToColumnArrays();
outputRBFNN[i] = new double[2] { 0d, 0d };
outputActivationRBFNN[i] = new int[2];
outputActivationRBFNN[i][0] = rbfnn.TrainingRBF(ref outputRBFNN[i][0], ref weight[0], hiddenValue, learningRate, listIrisItem[i].YOut1, jumlahRBFEpoch);
outputActivationRBFNN[i][1] = rbfnn.TrainingRBF(ref outputRBFNN[i][1], ref weight[1], hiddenValue, learningRate, listIrisItem[i].YOut2, jumlahRBFEpoch);
}
int loop = 0;
outputGA = new double[listIrisItem.Count][];
outputActivationGA = new int[listIrisItem.Count][];
StringBuilder sb = new StringBuilder();
for (int i = 0; i < listIrisItem.Count; i++)
{
Matrix bobotMatrix = rbfnn.getBobot(listIrisItem[i], listCluster);
double[] hiddenValue = rbfnn.countHiddenValue(listIrisItem[i], bobotMatrix, listCluster.Count);
Matrix<double> weightToOutput = (bobotMatrix * bobotMatrix.Transpose()).Inverse() * bobotMatrix * matrixTarget;
double[][] weight = weightToOutput.ToColumnArrays();
outputGA[i] = new double[2] { 0d, 0d };
outputActivationGA[i] = new int[2];
outputActivationGA[i][0] = rbfnn.TrainingRBF(ref outputGA[i][0], ref weight[0], hiddenValue, learningRate, listIrisItem[i].YOut1, jumlahRBFEpoch);
outputActivationGA[i][1] = rbfnn.TrainingRBF(ref outputGA[i][1], ref weight[1], hiddenValue, learningRate, listIrisItem[i].YOut2, jumlahRBFEpoch);
if (outputActivationGA[i][0] == listIrisItem[i].YOut1 && outputActivationGA[i][1] == listIrisItem[i].YOut2)
continue;
List<Kromosom> kromosom = new List<Kromosom>();
kromosom.Add(new Kromosom() { Gen = weight[0] });
kromosom.Add(new Kromosom() { Gen = weight[1] });
sb.Append("Iris item " + (i + 1) + "\r\n");
loop = 0;
double lastFitness = 0d;
double tempFitness = 0d;
do
{
lastFitness = tempFitness;
tempFitness = 0d;
sb.Append("Generasi ke-" + (loop + 1) + "\r\n");
for (int j = 0; j < kromosom.Count; j++)
{
kromosom[j].Fitness = Genetika.CountFitness(kromosom[j]);
tempFitness += kromosom[j].Fitness;
sb.Append(String.Format("Chromosome {0} = {1}\r\n", j + 1, kromosom[j].print()));
}
kromosom = Genetika.CountFitnessRelative(kromosom, ref sb);
kromosom = Genetika.CrossOver(kromosom);
sb.Append("Cross Over \r\n");
sb.Append(Kromosom.printList(kromosom));
kromosom = Genetika.Mutasi(kromosom);
sb.Append("Mutasi \r\n");
sb.Append(Kromosom.printList(kromosom));
sb.Append(String.Format("Current fitness {0}, last fitness {1}", tempFitness, lastFitness));
sb.Append("\r\n\r\n");
loop++;
} while (loop < jumlahEpoch && (loop == 1 || tempFitness < 0));
sb.Append("\r\n");
weight[0] = kromosom[0].Gen;
weight[1] = kromosom[1].Gen;
outputActivationGA[i][0] = rbfnn.CountActivation(ref outputGA[i][0], ref weight[0], hiddenValue);
outputActivationGA[i][1] = rbfnn.CountActivation(ref outputGA[i][1], ref weight[1], hiddenValue);
}
ucresult = new ucResult();
ucresult.setSB(sb);
this.Controls.Add(ucresult);
ucresult.Location = new Point(0, 22);
panelControl1.Visible = false;
ucresult.Anchor = ((System.Windows.Forms.AnchorStyles)((((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom)
| System.Windows.Forms.AnchorStyles.Left)
| System.Windows.Forms.AnchorStyles.Right)));
ucresult.Dock = DockStyle.Fill;
}