public void Setup(NeuralNetwork nn, NeuralNetworkContext context, NeuralNetworkFullContext fullCtx, NeuralNetworkDerivativeMemory derivMem)
{
//initialize memory buffers
state = new float[nn.hiddenLayers.Length][];
weights = new float[nn.hiddenLayers.Length + 1][];
biases = new float[nn.hiddenLayers.Length + 1][];
buf = new float[nn.hiddenLayers.Length + 1][];
recurrBuf = new float[nn.hiddenLayers.Length][];
biasMems = derivMem.biasMems;
weightMems = derivMem.weightMems;
recurrWeightMems = derivMem.recurrWeightMems;
recurrWeights = new float[nn.hiddenLayers.Length][];
derivativeMemory = derivMem;
for (int i = 0; i < nn.hiddenLayers.Length; i++)
{
state[i] = new float[nn.hiddenLayers[i].numberOfNeurons];
weights[i] = nn.hiddenConnections[i].weights;
biases[i] = nn.hiddenLayers[i].biases;
if (i == 0)
{
buf[i] = context.inputData;
}
else
{
buf[i] = fullCtx.hiddenBuffer[i - 1];
}
if (nn.hiddenLayers[i].recurring)
{
recurrWeights[i] = nn.hiddenRecurringConnections[i].weights;
recurrBuf[i] = fullCtx.hiddenRecurringBuffer[i];
}
}
int lid = nn.hiddenLayers.Length;
weights[lid] = nn.outputConnection.weights;
biases[lid] = nn.outputLayer.biases;
if (lid > 0)
{
buf[lid] = fullCtx.hiddenBuffer[lid - 1];
}
else
{
buf[lid] = context.inputData;
}
}
