public void CanConnectNeurons()
        {
            var in1 = new Input();
            var in2 = new Input();
            var in3 = new Input();

            var dendrite1 = new Dendrite();
            dendrite1.SetConnection(in1);

            var dendrite2 = new Dendrite();
            dendrite2.SetConnection(in2);

            var dendrite3 = new Dendrite();
            dendrite3.SetConnection(in3);

            var neuron = new StepNeuron();

            neuron.Connect(dendrite1);
            neuron.Connect(dendrite2);
            neuron.Connect(dendrite3);

            var dendrite4 = new Dendrite();

            dendrite4.SetConnection(neuron);

            Assert.AreEqual(0d, neuron.Output());
        }
        public void DendriteMultipliesInputByWeight()
        {
            var input = new Input(1.1);
            var dendrite = new Dendrite(10);

            dendrite.SetConnection(input);

            Assert.AreEqual(11, dendrite.Output());
        }
        public Perceptron BuildPerceptron(int inputs)
        {
            var network = new Perceptron(new StepNeuronFactory());

            for (int i = 0; i < inputs; i++)
            {
                var simpleInput = new Input();

                network.AddInput(simpleInput);
            }

            return network;
        }
        public FTPerceptron(INeuronFactory neuronFactory) : base(neuronFactory)
        {
            // this is the unit input which will handle any bias
            var simpleInput = new Input(1.0);

            // dendrite has weight
            var dendrite = new Dendrite();

            // connect to the input
            dendrite.SetConnection(simpleInput);

            // connect to the neuron
            _neuron.Connect(dendrite);
        }
Esempio n. 5
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        public void AddInput(Input simpleInput)
        {
            // dendrite has weight
            var dendrite = new Dendrite();

            // keep ref for classifying
            _inputs.Add(simpleInput);

            // connect to the input
            dendrite.SetConnection(simpleInput);

            // connect to the neuron
            _neuron.Connect(dendrite);
        }
        public void NeuronFiresRelativeToThreshold(double val, double weight, double threshold, double output)
        {
            var input1 = new Input(val);
            var dendrite1 = new Dendrite(weight);
            dendrite1.SetConnection(input1);

            var input2 = new Input(val);
            var dendrite2 = new Dendrite(weight);
            dendrite2.SetConnection(input2);

            var neuron = new StepNeuron(threshold);
            neuron.Connect(dendrite1);
            neuron.Connect(dendrite2);

            Assert.AreEqual(output, neuron.Output());
        }
        public MultiLayerPerceptron BuildMultiLayerPerceptron()
        {
            // 4 inputs, 3 hidden neurons, 2 outputs, fully connected

            var factory = new SigmoidNeuronFactory();

            var network = new MultiLayerPerceptron(factory);

            // input values
            var i1 = new Input();
            var i2 = new Input();
            var i3 = new Input();
            var i4 = new Input();

            // hidden layer
            var hn1 = factory.Create();
            var hn2 = factory.Create();
            var hn3 = factory.Create();
            //var hiddenLayer = new Layer(new List<INeuron> { hn1, hn2, hn3 });
            
            // output neurons
            var on1 = factory.Create();
            var on2 = factory.Create();
            //var outputLayer = new Layer(new List<INeuron> {on1, on2});

            network.AddInput(i1);
            network.AddInput(i2);
            network.AddInput(i3);
            network.AddInput(i4);

            // hidden layer
            network.AddHiddenNeuron(hn1);
            network.AddHiddenNeuron(hn2);
            network.AddHiddenNeuron(hn3);

            // output layer
            network.AddOutput(on1);
            network.AddOutput(on2);


            // hidden connections
            var dh11 = new Dendrite(learningRate: 1);
            var dh12 = new Dendrite(learningRate: 1);
            var dh21 = new Dendrite(learningRate: 1);
            var dh22 = new Dendrite(learningRate: 1);
            var dh31 = new Dendrite(learningRate: 1);
            var dh32 = new Dendrite(learningRate: 1);

            dh11.SetConnection(hn1);
            dh12.SetConnection(hn1);
            dh21.SetConnection(hn2);
            dh22.SetConnection(hn2);
            dh31.SetConnection(hn3);
            dh32.SetConnection(hn3);

            on1.Connect(dh11);
            on1.Connect(dh21);
            on1.Connect(dh31);
            on2.Connect(dh12);
            on2.Connect(dh22);
            on2.Connect(dh32);


            // input dendrite connections
            var d11 = new Dendrite(learningRate: 1);
            var d12 = new Dendrite(learningRate: 1);
            var d13 = new Dendrite(learningRate: 1);
            var d21 = new Dendrite(learningRate: 1);
            var d22 = new Dendrite(learningRate: 1);
            var d23 = new Dendrite(learningRate: 1);
            var d31 = new Dendrite(learningRate: 1);
            var d32 = new Dendrite(learningRate: 1);
            var d33 = new Dendrite(learningRate: 1);

            d11.SetConnection(i1);
            d12.SetConnection(i1);
            d13.SetConnection(i1);
            d21.SetConnection(i2);
            d22.SetConnection(i2);
            d23.SetConnection(i2);
            d31.SetConnection(i3);
            d32.SetConnection(i3);
            d33.SetConnection(i3);

            hn1.Connect(d11);
            hn1.Connect(d21);
            hn1.Connect(d31);

            hn2.Connect(d12);
            hn2.Connect(d22);
            hn2.Connect(d32);

            hn3.Connect(d13);
            hn3.Connect(d23);
            hn3.Connect(d33);

            return network;
        }