private void RefreshDgwParametersAF()
{
dgwEditParametersAF.Rows.Clear();
int countParameters = LibraryOfActivateFunctions.GetCountParametersOfAF(cbActivateFunction.SelectedItem.ToString(),
LibraryOfActivateFunctions.GetterParameter.ActivateFunctionName);
ActivateFunction af = LibraryOfActivateFunctions.GetActivateFunction(cbActivateFunction.SelectedItem.ToString(),
LibraryOfActivateFunctions.GetterParameter.ActivateFunctionName);
for (int i = 0; i < countParameters; i++)
{
string[] row = new string[2];
row[0] = Convert.ToString(af.GetNameOfParameter(i));
row[1] = Convert.ToString(af.GetDefaultValueOfParameter(i));
dgwEditParametersAF.Rows.Add(row);
dgwEditParametersAF.Rows[i].Cells[0].ReadOnly = true;
}
}
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 List <string[]> SelectParametersOfAF(string NeuroNetName, int countNeurons, string NameAF)
{
int IDAF = LibraryOfActivateFunctions.GetCountParametersOfAF(NameAF,
LibraryOfActivateFunctions.GetterParameter.ActivateFunctionName);
ActivateFunction af = LibraryOfActivateFunctions.GetActivateFunction(NameAF,
LibraryOfActivateFunctions.GetterParameter.ActivateFunctionName);
if (IDAF != -1)
{
List <string[]> ls = new List <string[]>();
connector.ConnectToDB();
SQLiteCommand cmd = new SQLiteCommand(connector.connection);
int[] neurons = new int[countNeurons];
double[] valuesPars = new double[IDAF];
cmd.CommandText = "SELECT AFParameters " +
"FROM NeuroNet,TASK WHERE NeuroNet.TaskID = TASK.ID AND NeuroNet.Name ='" +
NeuroNetName + "'";
try
{
string line = Convert.ToString(cmd.ExecuteScalar());
int k = 0;
int j = 0;
string buf = "";
string[] row;
while (j < line.Length)
{
row = new string[2];
if (line[j] != ' ')
{
buf += line[j];
}
else
{
row[0] = af.GetNameOfParameter(k);
row[1] = buf;
ls.Add(row);
k++;
buf = "";
}
j++;
}
row = new string[2];
row[0] = af.GetNameOfParameter(k);
row[1] = buf;
ls.Add(row);
}
catch (SQLiteException ex)
{
MessageBox.Show(ex.Message);
}
connector.DisconnectFromDB();
return(ls);
}
else
{
throw new Exception("No such name of Activate Function!");
}
}