// Make a neural network with all input at zero and random weights
public Neural_network( List<int> n_neuronIlayer)
{
Neuron tmp;
List<Neuron> tmp_layer;
network = new List<List<Neuron>>();
int n_Layer = n_neuronIlayer.Count;
rand = new System.Random();
for (int i = 0; i < n_Layer; i++)
{
tmp_layer = new List<Neuron>();
for (int j = 0; j < n_neuronIlayer[i]; j++)
{
if (i == 0) // special treatment for first layer, that take input of the network
{ // for this case the weight is
List<double> n = new List<double>(); n.Add(1); // a List of one element valued at 1
tmp = new Neuron(n, rand, false);
//tmp = new Neuron(n,0); // and the biais is set to 0
}
else
{
tmp = new Neuron(n_neuronIlayer[i - 1], rand); // fill Input with n_neuronIlayer[i - 1] random values
}
tmp_layer.Add(tmp);
}
this.network.Add(tmp_layer);
}
output = new List<bool>();
}
// If specific weight are needed use this constructor
public Neural_network(List<List<List<double>>> network_weight)
{
Neuron tmp;
List<Neuron> tmp_layer;
List<double> tmp_weight;
network = new List<List<Neuron>>();
rand = new System.Random();
for (int i = 0; i < network_weight.Count; i++)
{
tmp_layer = new List<Neuron>();
for (int j = 0; j < network_weight[i].Count; j++)
{
tmp_weight = new List<double>(network_weight[i][j]);
tmp = new Neuron(tmp_weight, rand);
tmp_layer.Add(tmp);
}
this.network.Add(tmp_layer);
}
output = new List<bool>();
}
public void addNeuron(int layer_num, Neuron neur)
{
network[layer_num].Add(neur);
}
public Synapse(Neuron destination, double weight)
{
Destination = destination;
Weight = weight;
}
public void Activate(double value, Neuron from)
{
InputValues[from] = value;
Outputs.ForEach(o => o.Destination.Activate(ActivatedValue * o.Weight, this));
}
public Neuron Connect(Neuron next, double weight)
{
Outputs.Add(new Synapse(next, weight));
return(this);
}
public Neuron(Neuron N)
{
inputs = new List<Input>(N.inputs);
fire_val = N.fire_val;
}
