private void Conv()
{
Random random = new Random();
Vector dat = GetSigXY(random).Item1;
Vector nnwOut = networkMeneger.Forward(dat);
GraphCPU graph = new GraphCPU(false);
// Нейросеть для формирования эмбедингов
NNW net = new NNW();
net.Layers.Add(nnw.Layers[0]);
net.Layers.Add(nnw.Layers[1]);
net.Layers.Add(nnw.Layers[2]);
net.Layers.Add(nnw.Layers[3]);
net.Layers.Add(nnw.Layers[4]);
net.Layers.Add(nnw.Layers[5]);
net.Layers.Add(nnw.Layers[6]);
net.Layers.Add(nnw.Layers[7]);
Vector hl = net.Activate(new NNValue(dat), graph).ToVector();
sigChart.PlotBlack(t, dat);
hChart.BarBlack(hl);
outChart.PlotBlack(t, nnwOut);
}
private void ShDat()
{
Vector dat = random.NextDouble() > 0.5 ? GetSig(2) : GetSig(4);
GraphCPU graph = new GraphCPU(false);
Vector nnwOut = NNW.Activate(new NNValue(dat), graph).ToVector();
chartVisual1.PlotBlack(t, dat);
chartVisual2.BarBlack(nnwOut);
}
/// <summary>
/// Сгенерировать действие на воздействие(состояние среды)
/// </summary>
/// <param name="state"></param>
/// <param name="isRnd">вероятностный подход</param>
/// <returns></returns>
public Action GetAction(State state, bool isRnd = true)
{
if (!(model.Layers.Last() is FeedForwardLayer))
{
throw new NotImplementedException();
}
else
{
var input = state.Input;
var output = model.Activate(input, graphForward);
var vector = new Vector(output.DataInTensor);
return(new Action(vector, random, isRnd));
}
}