internal void PerformTeaching(double teachingRatio)
{
/* We assume that we have non-zero amount of elements in the teaching set,
* so we don't check if the 0 index is correct. This condition is
* established by LoadTeachingSet(). */
if (++_currentElementIndex >= _teachingSet.Count)
{
_currentElementIndex = 0;
}
_examinedNetwork.LearnSimple(CurrentNormalizedElement, teachingRatio,
ref _previousResponse, ref _previousError);
_currentResponse =
_examinedNetwork.Response(CurrentNormalizedElement.Inputs);
_currentError = new double[_currentResponse.Length];
for (int i = 0; i < _currentResponse.Length; i++)
{
_currentError[i] =
CurrentElement.ExpectedOutputs[i] - _currentResponse[i];
}
/*double avg = 0.0;
* foreach (double err in _currentError)
* avg += Math.Abs(err);
* avg /= _currentError.Length;
* _progressHistory.Buffer.Add((float)avg);*/
double avg = 0.0;
foreach (TeachingSet.Element elem in _normalizedTeachingSet)
{
double[] resp = _examinedNetwork.Response(elem.Inputs);
for (int i = 0; i < resp.Length; i++)
{
avg += Math.Abs(elem.ExpectedOutputs[i] - resp[i]);
}
}
avg /= _examinedNetwork.OutputCount * _normalizedTeachingSet.Count;
_progressHistory.Buffer.Add((float)avg);
_teachingStep++;
}
private void PerformTeachingCycle()
{
MlpNetwork.LearningMethod method =
uiMethodWidrowHoff.Checked ? MlpNetwork.LearningMethod.WidrowHoff
: MlpNetwork.LearningMethod.Perceptron;
int stepCount = (int)uiCycleLength.Value;
for (int i = 0; i < stepCount; i++)
{
int teachingElementIndex = _randomGenerator.Next(_teachingSet.Count);
TeachingSet.Element teachingElement =
_teachingSet[teachingElementIndex];
double[] prevResp = null;
double[] prevErr = null;
_examinedNetwork.LearnSimple(teachingElement, 0.1,
ref prevResp, ref prevErr, method);
}
StepNumber += stepCount;
uiChartPlotter.Invalidate();
}