public RBM(RBM PA)
{
visibles = (RBMLayer)PA.visibles.Clone();
hiddens = (RBMLayer)PA.hiddens.Clone();
weights = (RBMWeightSet)PA.weights.Clone();
learnrate = PA.learnrate;
trainingdata = PA.trainingdata;
numvisibles = PA.numvisibles;
numhiddens = PA.numhiddens;
}
private void InitTrainingData()
{
trainingdata = new TrainingData();
trainingdata.posvis = new double[numvisibles];
Utility.ZeroArray(trainingdata.posvis);
trainingdata.poshid = new double[numhiddens];
Utility.ZeroArray(trainingdata.poshid);
trainingdata.negvis = new double[numvisibles];
Utility.ZeroArray(trainingdata.negvis);
trainingdata.neghid = new double[numhiddens];
Utility.ZeroArray(trainingdata.neghid);
}
public static void Train(RBMLayer PLayerVis, RBMLayer PLayerHid, TrainingData PData
, RBMLearningRate PLearnRate, RBMWeightSet PWeightSet)
{
weightset = PWeightSet;
learnrate = PLearnRate;
for (int i = 0; i < PLayerVis.Count; i++)
{
for (int j = 0; j < PLayerHid.Count; j++)
{
TrainWeight(i, j, CalculateTrainAmount(PData.posvis[i], PData.poshid[j]
, PData.negvis[i], PData.neghid[j]));
}
TrainBias(PLayerVis, i, PData.posvis[i], PData.negvis[i]);
}
for (int j = 0; j < PLayerHid.Count; j++)
{
TrainBias(PLayerHid, j, PData.poshid[j], PData.neghid[j]);
}
}
