private void UpdateResults()
{
_resultsAvailable = true;
_outputVector = _examinedNetwork.Response(_inputVector);
int lastRow = uiSignalGrid.RowCount - 1;
for (int i = 1; i < uiSignalGrid.ColumnCount - 1; i++)
{
uiSignalGrid.Rows[lastRow].Cells[i].Style = null;
uiSignalGrid.UpdateCellValue(i, lastRow);
}
if (uiShowWinner.Checked)
{
int winner =
LinearNetwork.Winner(_outputVector, (double)uiThreshold.Value);
for (int i = 1; i < uiSignalGrid.ColumnCount - 1; i++)
{
uiSignalGrid.Rows[lastRow].Cells[i].Style = null;
}
if (winner != -1)
{
DataGridViewCellStyle winnerStyle = new DataGridViewCellStyle();
winnerStyle.BackColor = Color.Red;
uiSignalGrid.Rows[lastRow].Cells[winner + 1].Style = winnerStyle;
uiWinnerNumber.Text = String.Format("Neuron {0}", winner + 1);
uiResult.Text =
String.Format("This is a {0}.", _classNames[winner]);
}
else
{
uiWinnerNumber.Text = "(There is no winner.)";
uiResult.Text = "This is something strange!";
}
}
}
internal void LoadTeachingSet(int netSizeX, int netSizeY,
double initialWeights)
{
_netSizeX = netSizeX;
_netSizeY = netSizeY;
_initialWeights = initialWeights;
_min = _netSizeX;
if (_netSizeY < _netSizeX)
{
_min = _netSizeY;
}
_max = _netSizeX;
if (_netSizeY > _netSizeX)
{
_max = _netSizeY;
}
_alpha0 = 0.1;
_alpha1 = 0.02;
_neighbour = Convert.ToInt16(_min + _max) / 4.0 + 1;
_espAlpha = 0.9997;
_espNeighbour = 0.999;
_distribution = 1;
_iteractions = 0;
_numberOfNeurons = _netSizeX * _netSizeY;
_examinedNetwork = new LinearNetwork(_numberOfInputs, _netSizeX * _netSizeY);
InitializeTeaching();
}
private void BuildGrids()
{
LinearNetwork network = _programLogic.ExaminedNetwork;
BuildGridColumns(uiInputData, network.InputCount + 1);
uiInputData.Rows.Add(2);
uiInputData.Rows[0].Cells[0].Value = "Input number (i)";
uiInputData.Rows[1].Cells[0].Value = "Original inputs (u(i))";
uiInputData.Rows[2].Cells[0].Value = "Normalized inputs (x(i))";
BuildGridColumns(uiTeachingProgress, network.Neurons.Length + 1);
uiTeachingProgress.Rows.Add(5);
uiTeachingProgress.Rows[0].Cells[0].Value = "Output number (j)";
uiTeachingProgress.Rows[1].Cells[0].Value = "Value before teaching (y(j))";
uiTeachingProgress.Rows[2].Cells[0].Value = "Value expected (z(j))";
uiTeachingProgress.Rows[3].Cells[0].Value = "Error before teaching (e(j))";
uiTeachingProgress.Rows[4].Cells[0].Value = "Value after teaching (y'(j))";
uiTeachingProgress.Rows[5].Cells[0].Value = "Error after teaching (e'(j))";
for (int i = 1; i < uiTeachingProgress.ColumnCount; i++)
{
DataGridViewCellStyle style =
uiTeachingProgress.Rows[2].Cells[i].Style;
style.BackColor = style.SelectionBackColor = Color.LightGreen;
style = uiTeachingProgress.Rows[3].Cells[i].Style;
style.BackColor = style.SelectionBackColor = Color.Pink;
style = uiTeachingProgress.Rows[5].Cells[i].Style;
style.BackColor = style.SelectionBackColor = Color.Pink;
}
}
internal void LoadTeachingSet(int numberOfNeurons, double ethaFactor)
{
_ethaFactor = ethaFactor;
_numberOfNeurons = numberOfNeurons;
InitializeRandom();
_randomGenerator.RewindTable();
_randomGenerator.Randomize(_randomize);
_examinedNetwork = new LinearNetwork(_numberOfInputs, numberOfNeurons);
InitializeTeaching();
}
public MainForm()
{
InitializeComponent();
double[,] weights =
{
{ 4, 0.01, 0.01, -1, -1.5 },
{ 2, -1, 2, 2.5, 2 },
{ -1, 3.5, 0.01, -2, 1.5 }
};
_examinedNetwork = new LinearNetwork(weights);
_inputVector = new double[weights.GetLength(1)];
BuildSignalGrid();
DiscardResults();
}
internal void LoadTeachingSet(string fileName)
{
TeachingSet tset = NeuralNetworks.TeachingSet.FromFile(fileName);
if (tset == null || tset.Count == 0)
{
throw new ApplicationException("The file does not contain any data.");
}
_teachingSetFileName = fileName;
_teachingSet = tset;
_normalizedTeachingSet = new TeachingSet(tset);
_normalizedTeachingSet.Normalize();
_examinedNetwork = new LinearNetwork(tset.InputCount, tset.OutputCount);
InitializeTeaching();
}
