public void Init(double[] stateAv, double[] stateStddev, double[] actionMin, int vsize)
{
int stateDim = stateAv.GetLength(0);
int actionDim = actionMin.GetLength(0);
#region budowa sieci neuronowej
V = new MLPerceptron2();
V.Build(stateDim, CellType.Arcustangent, new int[] { vsize, 1 });
V.SetInputDescription(new Vector(stateAv), new Vector(stateStddev));
V.InitWeights(1.0 / Math.Sqrt(vsize + 1));
Vval = new Vector(1);
Vgrad = new Vector(1);
#endregion koniec budowy sieci neuronowej
Actions = new Vector[HORIZON_SIZE];
NextStates = new Vector[HORIZON_SIZE];
for (int i = 0; i < HORIZON_SIZE; i++)
{
Actions[i] = new Vector(actionDim, _model.GenerateControlVariables()[0]);
NextStates[i] = new Vector(stateDim, 0.0);
}
}
// umozliwia dzialanie kilku sieci (np. w roznych watkach) z tymi samymi wagami
public void Connect2Weights(MLPerceptron2 network)
{
if (Layer.Length != network.Layer.Length)
throw new Exception(this.GetType().ToString() + ".Weights2");
for (int l = 0; l < Layer.Length; l++)
if (Layer[l].Weights.Height != network.Layer[l].Weights.Height
|| Layer[l].Weights.Width != network.Layer[l].Weights.Width)
throw new Exception(this.GetType().ToString() + ".Connect2Weights");
for (int l = 0; l < Layer.Length; l++)
Layer[l].Weights = network.Layer[l].Weights;
}