public Network(int inputSize, int layerSize, int layerCount, int outputSize)
{
InputSize = inputSize;
OutputSize = outputSize;
LayerCount = layerCount;
if (layerCount > 0)
{
HiddernLayers.Add(new NeuronLayer(layerSize, InputSize));
for (int x = 1; x < LayerCount; x++)
{
HiddernLayers.Add(new NeuronLayer(layerSize, layerSize));
}
LastLayer = new NeuronLayer(OutputSize, layerSize);
}
else if (layerCount == 0)
{
LastLayer = new NeuronLayer(OutputSize, InputSize);
}
else
{
throw new ArgumentOutOfRangeException("Input size cannot be negative");
}
}
public void ConnectLayer(NeuronLayer next, double[,] weights)
{
for (int i = 0; i < this.neurons.Length; i++)
{
for (int j = 0; j < next.neurons.Length; j++)
{
weights[this.neurons[i].globalIndex, next.neurons[j].globalIndex] = 1;
}
}
}
static void Main(string[] args)
{
var neuron1 = new Neuron();
var neuron2 = new Neuron();
var neuronLayer1 = new NeuronLayer();
var neuronLayer2 = new NeuronLayer();
neuron1.ConnectTo(neuronLayer1);
neuronLayer2.ConnectTo(neuronLayer1);
}
public static void Main(string[] args)
{
// unite Neuron and NeuronLayer in IEnumerable<Neuron> Composite Class
// Add an Extension Method for IEnumerable<Neuron>
// We will be able to connect a single node to the group of nodes (layer)
var neuron1 = new Neuron();
var neuron2 = new Neuron();
neuron1.ConnectTo(neuron2);
var layer1 = new NeuronLayer();
var layer2 = new NeuronLayer();
neuron1.ConnectTo(layer1);
layer1.ConnectTo(neuron2);
}
public NeuralNetwork(int[] _topology)
{
topology = _topology;
layersCount = topology.Length;
layers = new NeuronLayer[layersCount];
int count = 0;
for (int i = 0; i < topology.Length; i++)
{
count += topology[i];
layers[i] = new NeuronLayer(topology[i], i);
}
neurons = new Neuron[count];
weights = new double[count, count];
for (int i = 0; i < count; i++)
{
for (int j = 0; j < count; j++)
{
weights[i, j] = double.NegativeInfinity;
}
}
int index = 0;
for (int i = 0; i < layers.Length; i++)
{
for (int j = 0; j < topology[i]; j++)
{
neurons[index] = new Neuron(index, i, j);
layers[i].AddNeuron(j, neurons[index]);
index++;
}
}
for (int i = 0; i < layers.Length - 1; i++)
{
layers[i].ConnectLayer(layers[i + 1], weights);
}
this.RandomizeWeights();
}
