public int GetIndexNeuron(Neuron neuron)
{
int index = -1;
for (int i = 0; i < NeuronsCount; i++)
{
if (neurons[i] == neuron)
return i;
}
return index;
}
public NeuroNet(int numb_input_neurons, int numb_output_neurons, int[] numbNeuronsInLayers,
bool[,] _connections, double[,] _weights, ActivateFunction af)
{
if(_connections.GetLength(0) != _connections.GetLength(1) ||
_weights.GetLength(0) != _weights.GetLength(1) ||
_connections.GetLength(0) != _weights.GetLength(0))
{
throw new Exception("Invalid dimensions of connection or weight matrices");
}
int countNeurons = _connections.GetLength(0);
if (numb_input_neurons <= 0 || numb_input_neurons >= countNeurons)
throw new Exception("Number of input neurons is out of range");
if (numb_output_neurons <= 0 || numb_output_neurons >= countNeurons)
throw new Exception("Number of output neurons is out of range");
if(af == null)
{
throw new Exception("Not initialized activate function");
}
if (numbNeuronsInLayers.GetLength(0) < 2 || numbNeuronsInLayers.GetLength(0) > countNeurons)
throw new Exception("Invalid count of layers");
int sum = 0;
foreach (int item in numbNeuronsInLayers)
{
if (item <= 0)
throw new Exception("Must be at least one neuron in each layer");
sum += item;
}
if(sum != countNeurons)
throw new Exception("Invalid count of neurons in layers");
neuronsInLayers = numbNeuronsInLayers;
input_neurons = new InputNeuron[numb_input_neurons];
neurons = new Neuron[countNeurons];
output_neurons = new OutputNeuron[numb_output_neurons];
topology = new bool[countNeurons, countNeurons];
weights = new double[countNeurons, countNeurons];
evaluation_machine = new Queue<Queue<Neuron>>();
for (int i = 0; i < numb_input_neurons; i++)
{
input_neurons[i] = new InputNeuron(af);
neurons[i] = input_neurons[i];
}
for (int i = numb_input_neurons; i < countNeurons - numb_output_neurons; i++)
{
neurons[i] = new Neuron(af);
}
for (int i = 0; i < numb_output_neurons; i++)
{
output_neurons[i] = new OutputNeuron(af);
neurons[i + countNeurons - numb_output_neurons] = output_neurons[i];
}
for (int i = 0; i < countNeurons; i++)
{
Neuron cur = neurons[i];
for (int j = 0; j < countNeurons; j++)
{
topology[j, i] = false;
if (_connections[j, i] == true)
{
topology[j, i] = true;
weights[j, i] = _weights[j, i];
}
}
}
setInputConnections();
isIterationsFinished = true;
isWaveCameToOutputNeuron = false;
}
private NeuronLocation getNeuronLocation(Neuron neuron)
{
int index = learned_net.GetIndexNeuron(neuron);
if (index >= 0)
{
int layer = 1;
int number = 1;
int curInd = 0;
for (int i = 0; i < learned_net.NeuronsInLayers.GetLength(0); i++)
{
int nextInd = curInd + learned_net.NeuronsInLayers[i];
if (index < nextInd && index >= curInd)
{
number = index - curInd + 1;
break;
}
curInd = nextInd;
layer++;
}
return new NeuronLocation(layer, number);
}
else
{
return null;
}
}