void ShowBtnClick(object sender, EventArgs e)
{
object selectedItem = patternsComboBox.SelectedItem;
if (selectedItem != null && selectedItem.ToString().Trim().Length != 0)
{
string patternName = selectedItem.ToString();
NetworkTeachingPair dictPair = PatternDictionary.GetInstance().GetTeachingPairForPattern(patternName);
patternInput.LoadPattern(dictPair.Input);
}
}
public void Teach(NetworkTeachingPair pair, double ratio)
{
if (NeuronInputDimension != pair.Input.Length)
{
throw new ArgumentException(string.Format(MISALIGNED_TEACHING_PAIR_MSG, NeuronInputDimension, pair.Input.Length));
}
IList <TeachingPair> teachingPairs = pair.DecomposeToTeachingPairs();
for (int i = 0; i < neurons.Count; i++)
{
neurons[i].Teach(teachingPairs[i], ratio);
}
}
void CorrectBtnClick(object sender, EventArgs e)
{
object selectedItem = patternsComboBox.SelectedItem;
if (selectedItem != null && selectedItem.ToString().Trim().Length != 0)
{
string patternName = selectedItem.ToString();
NetworkTeachingPair dictPair = PatternDictionary.GetInstance().GetTeachingPairForPattern(patternName);
double[] currentPattern = patternInput.GetNetworkInput();
oneLayerNetwork.Teach(new NetworkTeachingPair(currentPattern, dictPair.Output), Commons.TEACHING_RATIO);
MessageBox.Show("Taught successfully.", "Correction Completed");
}
}
/// <summary>
/// Back-propagation teaching method
/// </summary>
public void Teach(NetworkTeachingPair pair, double speedRatio, double ratio)
{
double[] actualSignal = FireSignal(pair.Input);
double[] expectedSignal = pair.Output;
int len = expectedSignal.Length;
double[] errors = new double[len];
for (int i = 0; i < len; i++)
{
double oi = actualSignal[i], ti = expectedSignal[i];
errors[i] = -oi * (1 - oi) * (ti - oi);
}
for (int i = Size - 2; i >= 0; i--)
{
//double[] signal = FireSignal(
}
}
