internal double[] Evaluate(DataPoint p)
{
foreach (Neuron n in hiddenNeurons)
{
n.Clear();
}
foreach (Neuron n in outputNeurons)
{
n.Clear();
}
double[] hiddenOutputs = new double[nbHidden];
for (int i = 0; i < nbHidden; i++)
{
hiddenOutputs[i] = hiddenNeurons[i].Evaluate(p);
}
double[] outputs = new double[nbOutput];
for (int outputNb = 0; outputNb < nbOutput; outputNb++)
{
outputs[outputNb] = outputNeurons[outputNb].Evaluate(hiddenOutputs);
}
return outputs;
}
internal void AdjustWeights(DataPoint p, double learningRate)
{
// Delta sortie
double[] outputDelta = new double[nbOutput];
for (int i = 0; i < nbOutput; i++)
{
double output = outputNeurons[i].Output;
double expectedOutput = p.Outputs[i];
outputDelta[i] = output * (1 - output) * (expectedOutput - output);
}
//Delta neuron cachés
double[] hiddenDeltas = new double[nbHidden];
for (int i = 0; i < nbHidden; i++)
{
double hiddenOutput = hiddenNeurons[i].Output;
double sum = 0.0;
for (int j = 0; j < nbOutput; j++)
{
sum += outputDelta[j] * outputNeurons[j].Weight(i);
}
hiddenDeltas[i] = hiddenOutput * (1 - hiddenOutput) * sum;
}
double value;
for (int i = 0; i < nbOutput; i++)
{
Neuron outputNeuron = outputNeurons[i];
for (int j = 0; j < nbHidden; j++)
{
value = outputNeuron.Weight(j) + learningRate * outputDelta[i] * hiddenNeurons[j].Output;
outputNeuron.AdjustWeight(j, value);
}
value = outputNeuron.Weight(nbHidden) + learningRate * outputDelta[i] * 1.0;
outputNeuron.AdjustWeight(nbHidden, value);
}
// ajustement poids
for (int i = 0; i < nbHidden; i++)
{
Neuron hiddenNeuron = hiddenNeurons[i];
for (int j = 0; j < nbInputs; j++)
{
value = hiddenNeuron.Weight(j) + learningRate * hiddenDeltas[i] * p.Inputs[j];
hiddenNeuron.AdjustWeight(j, value);
}
value = hiddenNeuron.Weight(nbInputs) + learningRate * hiddenDeltas[i] * 1.0;
hiddenNeuron.AdjustWeight(nbInputs, value);
}
}
internal double Evaluate(DataPoint point)
{
double[] nbInput = point.Inputs;
return Evaluate(nbInput);
}
