public List <double> UpdateOne(List <double> inputs)
{
List <double> outPuts = new List <double>();
for (int i = 0; i < mNeuralLayerDataList.Count; i++)
{
if (i > 0)
{
inputs = new List <double>(outPuts);
}
outPuts.Clear();
NeuralLayerData neuralLayer = mNeuralLayerDataList[i];
for (int j = 0; j < neuralLayer.mNeuralDataList.Count; j++)
{
double netInput = 0;
NeuralData ndata = neuralLayer.mNeuralDataList[j];
for (int n = 0; n < ndata._Code.Count - 1; n++)
{
netInput += ndata._Code[n] * inputs[n];
}
netInput += ndata._Code[ndata._Code.Count - 1] * BISDP;
ndata.mActivation = GetSigmoid(netInput);
outPuts.Add(ndata.mActivation);
}
}
return(outPuts);
}
public void CreateNet(int intputNum, int outputNum, int layerNum = 1, int neuralNum = 10)
{
mIntputNum = intputNum;
mOutputNum = outputNum;
mLayerNum = layerNum;
mNeuralNum = neuralNum;
if (layerNum > 0)
{
NeuralLayerData nld = new NeuralLayerData(neuralNum, intputNum);
mNeuralLayerDataList.Add(nld);
for (int i = 0; i < layerNum - 1; i++)
{
nld = new NeuralLayerData(neuralNum, neuralNum);
mNeuralLayerDataList.Add(nld);
}
nld = new NeuralLayerData(outputNum, neuralNum);
mNeuralLayerDataList.Add(nld);
}
else
{
NeuralLayerData nld = new NeuralLayerData(outputNum, intputNum);
mNeuralLayerDataList.Add(nld);
}
}
