public NeuralNet(int inputLength, int hiddenLayersCount, int hiddenCount, int outputCount, double skew = -4d, int?seed = null)
{
_inputLength = inputLength;
_hiddenLayers = new INeuralLayer[hiddenLayersCount];
INeuralLayer clearHLayer = (INeuralLayer)Activator.CreateInstance(typeof(HLayer)) ?? throw new NeuralTypeException(typeof(INeuralLayer), typeof(HLayer));
INeuralLayer clearOLayer = (INeuralLayer)Activator.CreateInstance(typeof(OLayer)) ?? throw new NeuralTypeException(typeof(INeuralLayer), typeof(OLayer));
for (var i = 0; i < hiddenLayersCount; i++)
{
var z = i == 0 ? InputLength : _hiddenLayers[i - 1].NeuronsCount + 1;
_hiddenLayers[i] = clearHLayer.Create(this, typeof(HCell), typeof(HBias), typeof(HFunc), hiddenCount, z, skew);
}
_outputLayer = clearOLayer.Create(this, typeof(OCell), typeof(HBias), typeof(OFunc), outputCount, _hiddenLayers.Last().NeuronsCount + 1, skew);
if (seed.HasValue)
{
_entropy = new Random(seed.Value);
}
else
{
_entropy = new Random();
}
}
