Пример #1
0
        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);
        }
Пример #2
0
        public Network(TransferFunction tf, bool createBiasNode)
        {
            TransferFunction = tf;
            random = new Random();

            if(createBiasNode) {
                Bias = new Bias("Bias");
            }
        }