// Training algorithm, backpropagation
public override bool Train(ClassificationData trainingData)
{
// Initialize the network, set random weights for the hidden layer
for (int i = 0; i < hiddenLayer.Count; i++)
{
hiddenLayer[i].LoadRandomWeights( );
}
// Set random weights for the second layer
secondLayer.LoadRandomWeights( );
// Go through all the samples, until the the max number of repetitions
int repetitions = 0;
while (repetitions < maxRepetitions)
{
// Go through all samples
for (int i = 0; i < trainingData.Samples.Count; i++)
{
// Compute the predicted result and the error
double error;
string result = Classify(trainingData.Samples[i]);
string expected = trainingData.Samples[i].Category;
if ((result == "good") && (expected == "bad"))
{
error = -1.0;
}
else if ((result == "bad") && (expected == "good"))
{
error = 1.0;
}
else
{
// No error, continue to the next sample
error = 0.0;
continue;
}
// Backpropagate the second layer
secondLayer.BackPropagate(learningRate, error);
// Backpropagate the first layer
for (int j = 0; j < hiddenLayer.Count; j++)
{
FirstLayerNeuron neuron = (FirstLayerNeuron)hiddenLayer[j];
neuron.BackPropagate(learningRate, error, secondLayer);
}
}
// Next repetition
repetitions++;
}
// Set as trained and return true if there are no errors
isTrained = true;
return(true);
}
public FirstLayer(int n, Dictionary <double, List <double[]> > xSet)
{
Neurons = new FirstLayerNeuron[3];
var initParams = NeuronsInitTools.GetInitRBFParams(xSet, n);
for (int i = 0; i < Neurons.Length; i++)
{
Neurons[i] = new FirstLayerNeuron(n);
Neurons[i].CList = initParams.Item1[i];
Neurons[i].SList = initParams.Item2;
}
}
public static double CalculateL(List <double> x, FirstLayerNeuron neuron, int withoutIndex = -1) =>
x
.Select((t1, t2) =>
withoutIndex == t2 ? 1 : neuron.Calculate(t2, t1))
.Aggregate((t3, t4) => t3 *= t4);
