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);
}
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;
}