public NNFeedForwardNetwork(int[] unitsPerLayer)
{
layers = new NNLayer[unitsPerLayer.Length];
weightsPerLayer = new NNMatrix[getLayerCount()];
weightedinputOfLayer = new double[getLayerCount()][];
outputOfLayer = new double[getLayerCount()][];
for (int layer = 0; layer < getLayerCount(); layer++)
{
layers[layer] = new NNLayer(unitsPerLayer[layer], ActivationFunctions.sigmoid);
weightedinputOfLayer[layer] = new double[layers[layer].getUnitCount()];
outputOfLayer[layer] = new double[layers[layer].getUnitCount()];
if (layer == 0)
{
weightsPerLayer[layer] = new NNMatrix(getLayer(layer).getUnitCount(), getLayer(layer).getUnitCount());
for (int unit = 0; unit < getLayer(layer).getUnitCount(); unit++)
{
weightsPerLayer[layer][unit, unit] = 1.0;
}
}
else
{
weightsPerLayer[layer] = new NNMatrix(getLayer(layer).getUnitCount(), getLayer(layer - 1).getUnitCount());
}
}
System.Threading.Thread.Sleep(1);
rnd = new Random(System.DateTime.Now.Millisecond);
callUpdateCallbacks();
}
public NNRestrictedBoltzmannMachine(int visibleUnitCnt, int hiddenUnitCnt)
{
rnd = new Random(System.DateTime.Now.Millisecond);
visible = new NNLayer(visibleUnitCnt, ActivationFunctions.sigmoid);
hidden = new NNLayer(hiddenUnitCnt, ActivationFunctions.sigmoid);
biasVisible = new double[visibleUnitCnt];
biasHidden = new double[hiddenUnitCnt];
weights = new NNMatrix(hiddenUnitCnt, visibleUnitCnt);
}
