public Network(params int[] neuronCounts)
{
if (neuronCounts.Length < 2)
{
throw new ArgumentException("Not enough counts, need at least two for Input and Output layers");
}
// Create Layers
inputLayer = new Layer(neuronCounts[0]);
for (var i = 1; i < neuronCounts.Length - 1; i++)
{
hiddenLayers.Add(new Layer(neuronCounts[i]));
}
outputLayer = new Layer(neuronCounts[neuronCounts.Length - 1]);
// Get All Neurons
foreach (var neuron in inputLayer.neurons)
{
neurons.Add(neuron);
}
foreach (var layer in hiddenLayers)
{
foreach (var neuron in layer.neurons)
{
neurons.Add(neuron);
}
}
foreach (var neuron in outputLayer.neurons)
{
neurons.Add(neuron);
}
// Connect Layer Together
if (hiddenLayers.Count > 0)
{
inputLayer.SetNext(hiddenLayers[0]);
for (var i = 1; i < hiddenLayers.Count; i++)
{
hiddenLayers[i - 1].SetNext(hiddenLayers[i]);
}
hiddenLayers[hiddenLayers.Count - 1].SetNext(outputLayer);
}
else
{
inputLayer.SetNext(outputLayer);
}
// Fully Connect Layers
for (var layer = inputLayer.next; layer != null; layer = layer.next)
{
FullyConnect(layer.previous, layer);
}
// Randomize Weights and Biases
foreach (var connection in connections)
{
connection.weight = random.NextDouble();
}
for (var layer = inputLayer; layer != null; layer = layer.next)
{
foreach (var neuron in layer.neurons)
{
neuron.bias = random.NextDouble();
}
}
}