public void ApplyInputs_WithValidNetwork_PropogatesNetworkInputs()
{
// Arrange
var network = new DFFNeuralNetwork(1, 1, 2, 2);
var inputs = new List <INetworkInput>()
{
new NetworkInput()
{
ActivationLevel = .75
}
};
network.RandomizeNetwork();
IInputNeuron input1 = network.Layers.OfType <IInputLayer>().First().Neurons.First() as IInputNeuron;
IOutgoingConnection input1Hidden1Conn = input1.Connections.OfType <IOutgoingConnection>().First();
IOutgoingConnection input1Hidden2Conn = input1.Connections.OfType <IOutgoingConnection>().ToList()[1];
IHiddenNeuron hidden1 = input1Hidden1Conn.ToNeuron as IHiddenNeuron;
IOutgoingConnection hidden1Output1Conn = hidden1.Connections.OfType <IOutgoingConnection>().First();
IOutgoingConnection hidden1Output2Conn = hidden1.Connections.OfType <IOutgoingConnection>().ToList()[1];
IHiddenNeuron hidden2 = input1Hidden2Conn.ToNeuron as IHiddenNeuron;
IOutgoingConnection hidden2Output1Conn = hidden2.Connections.OfType <IOutgoingConnection>().First();
IOutgoingConnection hidden2Output2Conn = hidden2.Connections.OfType <IOutgoingConnection>().ToList()[1];
IOutputNeuron output1 = hidden1Output1Conn.ToNeuron as IOutputNeuron;
IOutputNeuron output2 = hidden1Output2Conn.ToNeuron as IOutputNeuron;
// Act
network.ApplyInputs(inputs);
var outputLayer = network.Layers.OfType <IOutputLayer>().First();
IOutputNeuron output1After = outputLayer.Neurons.First() as IOutputNeuron;
IOutputNeuron output2After = outputLayer.Neurons.ToList()[1] as IOutputNeuron;
// sigmoid function : (1.0 / (1 + Math.Exp(-1.0 * value)));
var h1Activation = ApplyActivationFunction((.75 * input1Hidden1Conn.Weight) + hidden1.Bias);
var h2Activation = ApplyActivationFunction((.75 * input1Hidden2Conn.Weight) + hidden2.Bias);
var o1Activation = ApplyActivationFunction((h1Activation * hidden1Output1Conn.Weight) + (h2Activation * hidden2Output1Conn.Weight) + output1.Bias);
var o2Activation = ApplyActivationFunction((h1Activation * hidden1Output2Conn.Weight) + (h2Activation * hidden2Output2Conn.Weight) + output2.Bias);
// Assert
Assert.IsTrue(o1Activation == output1After.ActivationLevel);
Assert.IsTrue(o2Activation == output2After.ActivationLevel);
}
/// <summary>
/// Creates the incoming and outgoing connections between this neuron and a previous hidden neuron.
/// </summary>
/// <param name="incomingNeuron">The incoming neuron to connect this neuron to.</param>
public void GenerateConnectionsWith(IHiddenNeuron incomingNeuron)
{
if (incomingNeuron == null)
{
throw new ArgumentNullException("incomingNeuron");
}
// Create connection only if this neuron doesn't already have an incoming connection from the incoming hidden neuron.
if (!GetIncomingConnections().Any(c => c.FromNeuron == incomingNeuron))
{
Connections.Add(new IncomingConnection(incomingNeuron));
}
// Create connection only if the incoming hidden neuron doesn't already have an outgoing connection to this neuron.
if (!incomingNeuron.GetOutgoingConnections().Any(c => c.ToNeuron == this))
{
incomingNeuron.Connections.Add(new OutgoingConnection(this));
}
}
public void Initialize()
{
_neuron = new HiddenNeuron();
_connection = new IncomingConnection(_neuron);
}
public void Initialize()
{
_inputNeuron = new InputNeuron();
_hiddenNeuron = new HiddenNeuron();
_outputNeuron = new OutputNeuron();
}
