protected static double GetPatternError(INet net, ILearningPattern pattern)
{
var actual = net.Propagate(pattern.Input);
var expected = pattern.Output;
var errors = new List<IFuzzyNumber>();
var i = 0;
foreach (var actualNumber in actual)
{
errors.Add(actualNumber.Sub(expected.ElementAt(i)));
i++;
}
var patternError = 0.0;
foreach (var errorNumber in errors)
{
var leftError = 0.0;
var rightError = 0.0;
errorNumber.ForeachLevel((alpha, level) =>
{
leftError += alpha * (level.X * level.X);
rightError += alpha * (level.Y * level.Y);
});
var currentOutputError = leftError + rightError;
patternError += currentOutputError;
}
return patternError / 2.0;
}
protected override void LearnPattern(INet net, ILearningPattern learningPattern, double currentPatternError)
{
//call only after net.propagation()
PropagateErrorOnLayers(net.Layers, learningPattern.Output);
CalculateWeightDelta(net);
//ChangeAndSetWeights(_deltas, net);
//_deltas = null;
}
//here we summarize gradient of each pattern
protected override void LearnPattern(INet net, ILearningPattern learningPattern, double currentPatternError)
{
PropagateErrorOnLayers(net.Layers, learningPattern.Output); //nablaF(xk)
var currentGradient = CreateWeightsGradient(net.Layers);
_gradient = _gradient == null ? currentGradient : _gradient.Sum(currentGradient);
}
protected abstract void LearnPattern(INet net, ILearningPattern learningPattern, double currentPatternError);
protected virtual double CalculatePatternError(INet net, ILearningPattern pattern)
{
return GetPatternError(net, pattern);
}
