public void Compute()
{
if (!CanCompute)
{
return;
}
double[] input = UserInput;
DeepBeliefNetwork network = Main.Network;
IDatabase database = Main.Database;
database.Normalize(input);
{
double[] output = network.GenerateOutput(input);
double[] reconstruction = network.Reconstruct(output);
NetworkOutput = (database.ToBitmap(reconstruction).ToBitmapImage());
}
if (Main.CanClassify)
{
double[] output = network.Compute(input);
int imax; output.Max(out imax);
Classification = imax;
}
}
/// <summary>
/// Implementation of the divergence operation.
/// </summary>
protected override void diverge()
{
//convert all the modalities in a single vector
double[] realSignal; double[] predictedSignal;
getConcatenatedModalities(out realSignal, out predictedSignal);
//Generate the prediction
double[] output = network.GenerateOutput(realSignal, 0);
double[] networkPrediction = network.Reconstruct(output);
//Distribute it over the Signal
setConcatenatedModalities(null, networkPrediction);
//Proceed to learning
if (!learningLocked && learningRate > 0)
{
//teacher.Run(realSignal);
//network.UpdateVisibleWeights();
}
}