public PredictorMLP(int aDimension, int aInputLength)
{
dimension = aDimension;
inputLength = aInputLength;
mlp = new MLP(new int[] { 30, dimension }, dimension * inputLength);
r = new Random();
mu = 0.0001;
trainCount = 0;
}
public MLP(MLP copyInstance)
{
mlp = new List <List <INeuron> >();
List <INeuron> inputlayer = new List <INeuron>();
for (int i = 0; i < copyInstance.mlp[0].Count; ++i)
{
InputNeuron n = new InputNeuron();
inputlayer.Add(n);
}
mlp.Add(inputlayer);
int layernum = 0;
for (int i = 1; i < copyInstance.mlp.Count; ++i)
{
List <INeuron> layer = new List <INeuron>();
for (int i2 = 0; i2 < copyInstance.mlp[i].Count; ++i2)
{
Neuron n = new Neuron(((Neuron)copyInstance.mlp[i][i2]).nonlinear);
layer.Add(n);
//elozo reteg osszes neuronja bemenet
foreach (INeuron n2 in mlp[i - 1])
{
n.AddInputNeuron(n2);
}
//sulyok atmasolasa
int i3 = 0;
foreach (NeuronInput ni in n.inputs)
{
ni.w = ((Neuron)copyInstance.mlp[i][i2]).inputs[i3].w;
++i3;
}
}
mlp.Add(layer);
layernum++;
}
}
public MLP(MLP copyInstance)
{
mlp = new List<List<INeuron>>();
List<INeuron> inputlayer = new List<INeuron>();
for (int i = 0; i < copyInstance.mlp[0].Count; ++i)
{
InputNeuron n = new InputNeuron();
inputlayer.Add(n);
}
mlp.Add(inputlayer);
int layernum = 0;
for (int i = 1; i < copyInstance.mlp.Count; ++i)
{
List<INeuron> layer = new List<INeuron>();
for (int i2 = 0; i2 < copyInstance.mlp[i].Count; ++i2)
{
Neuron n = new Neuron(((Neuron)copyInstance.mlp[i][i2]).nonlinear);
layer.Add(n);
//elozo reteg osszes neuronja bemenet
foreach (INeuron n2 in mlp[i - 1])
{
n.AddInputNeuron(n2);
}
//sulyok atmasolasa
int i3=0;
foreach (NeuronInput ni in n.inputs)
{
ni.w = ((Neuron)copyInstance.mlp[i][i2]).inputs[i3].w;
++i3;
}
}
mlp.Add(layer);
layernum++;
}
}