// https://www.tutorialspoint.com/artificial_neural_network/artificial_neural_network_hopfield.htm
public string GetResult(string input)
{
if (_curentWeightsMatrix.Size != input.Length)
{
throw new ArgumentException("Wrong image size");
}
OnItemProcessed?.Invoke(this,
new ProcessEventArgs(int.MaxValue)
{
CurrentItem = ++_iteration
});
_inputVector = input;
var savedEnergyState = CurrentEnergyState;
var output = new StringBuilder(input);
var nodeToUpdate = NodeToUpdate;
output[nodeToUpdate] = ApplyActivationFunction(input, nodeToUpdate);
_outputVector = output.ToString();
if (CurrentEnergyState < _localMinimum.Key)
{
_localMinimum = new KeyValuePair <double, string>(CurrentEnergyState, _outputVector);
}
if (CurrentEnergyState > savedEnergyState ||
_nodesToUpdate.Count > 0 &&
!FinalCheck(_outputVector))
{
GetResult(_outputVector);
}
return(_localMinimum.Value);
}
private void HebbianLearning(List <string> inputVectors)
{
var trainEventArgs = new ProcessEventArgs(_curentWeightsMatrix.Size);
// Hebbian Rule
for (int i = 0; i < _curentWeightsMatrix.Size; i++)
{
for (int j = 0; j < _curentWeightsMatrix.Size; j++)
{
var weights = 0.0;
if (i != j)
{
foreach (string vector in inputVectors)
{
weights += (2 * double.Parse(vector[i].ToString()) - 1) *
(2 * double.Parse(vector[j].ToString()) - 1);
}
}
_curentWeightsMatrix[i, j] = weights / _curentWeightsMatrix.Size;
}
trainEventArgs.CurrentItem = i + 1;
OnItemProcessed?.Invoke(this, trainEventArgs);
}
}
