public void TestBias()
{
double input = 0.3;
double inpToOutWeight = 0.4;
double biasToOutWeight = 0.5;
var sigmoid = new SigmoidFunction();
var nw = new Network(sigmoid, false);
var inputNode = new Input(input);
var bias = new Bias();
var outputNode = new Perceptron(sigmoid, "Output");
Connection.Create(inpToOutWeight, inputNode, outputNode);
Connection.Create(biasToOutWeight, bias, outputNode);
nw.Nodes = new Node[][] {
new Node[] { inputNode },
new Node[] { outputNode }
};
var nwOut = nw.Nodes[1][0].Output;
//Output
// = Sig(inpToOut.Output + biasToOut.Output)
// = Sig((inpToOut.Weight * inpToOut.GetInput()) + (biasToOutWeight * 1))
// = Sig((inpToOut.Weight * inputNode.Output) + (biasToOutWeight))
// = Sig((this.weight * this.input) + (biasToOutWeight))
var expOut = sigmoid.Calculate((inpToOutWeight * input) + biasToOutWeight);
Assert.AreEqual(nwOut, expOut);
}
public Network(TransferFunction tf, bool createBiasNode)
{
TransferFunction = tf;
random = new Random();
if(createBiasNode) {
Bias = new Bias("Bias");
}
}