public static MLPNetwork CreateMLPNetwork(int[] layerSetup, double[] weights = null, Func <double, double> activationFunction = null)
{
var network = new MLPNetwork {
Layers = new Node[layerSetup.Length][]
};
for (var i = 0; i < layerSetup.Length; i++)
{
var actualActivationFunc = i == layerSetup.Length - 1 ? Identity : (activationFunction ?? TanH);
network.Layers[i] = Enumerable.Range(0, layerSetup[i]).Select(l => new Node(actualActivationFunc)).ToArray();
}
var weightCounter = 0;
var rnd = new Random();
for (var i = 1; i < network.Layers.Length; i++)
{
foreach (var n in network.Layers[i - 1])
{
foreach (var nextNode in network.Layers[i])
{
nextNode.InputLinks.Add(new Link(weights == null ? rnd.NextDouble() : weights[weightCounter++])
{
SourceNode = n, DestinationNode = nextNode
});
}
}
}
return(network);
}
public static double[] ExtractWeights(this MLPNetwork network)
{
var weights = new List <double>();
foreach (var n in network.Layers)
{
foreach (var n2 in n)
{
foreach (var l in n2.InputLinks)
{
weights.Add(l.Weight);
}
}
}
return(weights.ToArray());
}
