public NeuroNetSolvingWindow(NeuroNet net)
{
InitializeComponent();
currentNet = net;
inputs = new List<Tuple<Label, TextBox>>();
outputs = new List<Tuple<Label, TextBox>>();
for (int i = 0; i < net.InputNeuronsCount; i++)
{
Label lb = new Label();
lb.Text = "x[" + i + "]=";
lb.Location = new Point(15, 17 + i * 25);
lb.Size = new Size(lb.Text.Length * 8, 20);
gbInputs.Controls.Add(lb);
TextBox tb = new TextBox();
tb.Text = "0,0";
tb.Location = new Point(lb.Text.Length * 10 + 5, 15 + i * 25);
tb.Size = new Size(100, 20);
gbInputs.Controls.Add(tb);
inputs.Add(new Tuple<Label, TextBox>(lb, tb));
}
for (int i = 0; i < net.OutputNeuronsCount; i++)
{
Label lb = new Label();
lb.Text = "y[" + i + "]=";
lb.Location = new Point(15, 17 + i * 25);
lb.Size = new Size(lb.Text.Length * 8, 20);
gbOutputs.Controls.Add(lb);
TextBox tb = new TextBox();
tb.Text = "0,0";
tb.Location = new Point(lb.Text.Length * 10 + 5, 15 + i * 25);
tb.Size = new Size(100, 20);
gbOutputs.Controls.Add(tb);
outputs.Add(new Tuple<Label, TextBox>(lb, tb));
}
}
private void btnUse_Click(object sender, EventArgs e)
{
int countInputNeurons = dbHandler.SelectCountInputParametersInTask(lbTaskSelected.Text);
int countOutputNeurons = 1;
ActivateFunction af = LibraryOfActivateFunctions.
GetActivateFunction(dbHandler.SelectActivateFunctionTypeByNeuroNet(lbNetSelected.Text),
LibraryOfActivateFunctions.GetterParameter.TypeOfActivateFunctionName);
List<double> valuesOfParametersAF = dbHandler.SelectValuesOfParametersOfAF(lbNetSelected.Text);
int k = 0;
foreach (double item in valuesOfParametersAF)
{
af.SetValueOfParameter(k, item);
k++;
}
int countNeurons = dbHandler.SelectCountNeuronsInNet(lbNetSelected.Text);
bool[,] connections = new bool[countNeurons, countNeurons];
double[,] weights = new double[countNeurons, countNeurons];
List<Tuple<int, int, double>> ls = dbHandler.SelectLearnedTopology(lbNetSelected.Text,
lbSelSelected.Text, LearningAlgorithmsLibrary.GetNameOfTypeOfAlgoritm(lbLASelected.Text));
for (int i = 0; i < countNeurons; i++)
{
for (int j = 0; j < countNeurons; j++)
{
connections[i, j] = false;
weights[i, j] = 0.0;
}
}
foreach (Tuple<int, int, double> item in ls)
{
connections[item.Item2, item.Item1] = true;
weights[item.Item2, item.Item1] = item.Item3;
}
int[] neuronsInLayers = dbHandler.SelectNeuronsInLayers(lbNetSelected.Text);
NeuroNet net = new NeuroNet(countInputNeurons, countOutputNeurons, neuronsInLayers, connections, weights, af);
NeuroNetSolvingWindow solvingWnd = new NeuroNetSolvingWindow(net);
solvingWnd.Show();
}
public NeuroNetLearningInterface(NeuroNet net, string _neuroNetName, string _selectionName)
{
learned_net = net;
netName = _neuroNetName;
selectionName = _selectionName;
}
public NeuroNetLearningInterface(NeuroNet net, string _neuroNetName, string _selectionName)
{
learned_net = net;
netName = _neuroNetName;
selectionName = _selectionName;
}
