public void Learn()
{
Outputs.Clear();
var error = 0.0;
double testError = 0.0;
for (var i = 0; i < hiddenLayerOutputs.Count; i++)
{
_neuron.Inputs = hiddenLayerOutputs[i];
_neuron.CalculateOutput();
Train(hiddenLayerOutputs[i], SampleRepository.TrainSamples[i].ExpectedValues.First());
Outputs.Add(_neuron.Output);
}
for (int i = 0; i < hiddenLayerOutputs.Count; i++)
{
_neuron.Inputs = hiddenLayerOutputs[i];
_neuron.CalculateOutput();
error += _neuron.CalculateError(SampleRepository.TrainSamples[i].ExpectedValues.First());
}
for (int i = 0; i < SampleRepository.TestSamples.Count; i++)
{
CalculateNetworkOutput(new SamplePoint(SampleRepository.TestSamples[i].Inputs));
testError += _neuron.CalculateError(SampleRepository.TestSamples[i].ExpectedValues.First());
}
TotalErrors.Add(error / hiddenLayerOutputs.Count);
TotalTestErrors.Add(testError / SampleRepository.TestSamples.Count);
}
public void SetUpClassificationAlgorithm()
{
_prevOutputWeightError = 0;
Centroid.ResetNextId();
CalculateHiddenLayerOutputs();
InitOutputLayerWeights();
TotalErrors.Clear();
TotalTestErrors.Clear();
}
