private INeuron[] CreateNeurons(int neuronsCount, int inputCount, int outputCount, double bias, double excess, IActivation activation)
{
var neurons = new INeuron[neuronsCount];
for (var i = 0; i < _inputCount; i++)
{
neurons[i] = new InputNeuron
{
B = bias,
};
}
for (var i = _inputCount; i < neuronsCount - _outputCount; i++)
{
neurons[i] = new HiddenNeuron
{
Activation = activation,
L = 0.5 + 0.25 * 1,
B = bias,
};
}
for (var i = neuronsCount - _outputCount; i < neuronsCount; i++)
{
neurons[i] = new OutputNeuron
{
Activation = activation,
L = 0.5 + 0.25 * 3,
B = bias,
};
}
return(neurons);
}
private void InitNeuron()
{
_neurons = new INeuron[Architecture.NumberOfNeurons];
for (int i = 0; i < Architecture.NumberOfNeurons; i++)
{
switch (Architecture.Type)
{
case NeuronType.Input:
// Has input size of 1
_neurons[i] = new InputNeuron();
break;
case NeuronType.Multiplication:
_neurons[i] = new MultiplicationNeuron(Architecture.InputSize);
break;
case NeuronType.Normalization:
_neurons[i] = new NormalizationNeuron(Architecture.InputSize);
break;
case NeuronType.Function:
// Has input size of 1
_neurons[i] = new FunctionNeuron(Architecture.InputSize, _numberOfVariables);
break;
case NeuronType.Output:
_neurons[i] = new OutputNeuron(Architecture.InputSize);
break;
default:
throw new ArgumentOutOfRangeException(nameof(Architecture.Type), Architecture.Type, null);
}
}
}
private static void CalculateOutput(OutputNeuron output)
{
var incomingSum = output.Incoming.Sum(i => i.From.Value * i.Weight);
var withBias = incomingSum + (output.BiasSynapse.From.Value * output.BiasSynapse.Weight);
output.Value = output.ActivationFunction.Activate(withBias);
}
public OutputLayer(Activation activation, TrainingInfo trainInfo, int size)
{
var neurons = new OutputNeuron[size];
for (int i = 0; i < size; i++)
neurons[i] = new OutputNeuron(activation, trainInfo);
this.neurons = neurons;
}
private NeuralNetwork.NeuralNetwork CreateNn()
{
NeuralNetwork.NeuralNetwork nn;
nn = new NeuralNetwork.NeuralNetwork();
InputLayer inputLayer = nn.CreateInputLayer();
//inHeading = inputLayer.CreateNeuron("heading");
//inVelocityAngle = inputLayer.CreateNeuron("v_angle");
//inVelocityLength = inputLayer.CreateNeuron("v_length");
inNextCheckpointDistance0 = inputLayer.CreateNeuron("c_dist0");
inNextCheckpointDistance1 = inputLayer.CreateNeuron("c_dist1");
inNextCheckpointDistance2 = inputLayer.CreateNeuron("c_dist2");
inNextCheckpointDistance3 = inputLayer.CreateNeuron("c_dist3");
inNextCheckpointAngle0 = inputLayer.CreateNeuron("c_angle0");
inNextCheckpointAngle1 = inputLayer.CreateNeuron("c_angle1");
inNextCheckpointAngle2 = inputLayer.CreateNeuron("c_angle2");
inNextCheckpointAngle3 = inputLayer.CreateNeuron("c_angle3");
inNextCheckpointAngle4 = inputLayer.CreateNeuron("c_angle4");
inNextCheckpointAngle5 = inputLayer.CreateNeuron("c_angle5");
inNextNextCheckpointAngle0 = inputLayer.CreateNeuron("nnc_angle0");
inNextNextCheckpointAngle1 = inputLayer.CreateNeuron("nnc_angle1");
inNextNextCheckpointAngle2 = inputLayer.CreateNeuron("nnc_angle2");
inNextNextCheckpointAngle3 = inputLayer.CreateNeuron("nnc_angle3");
inNextNextCheckpointAngle4 = inputLayer.CreateNeuron("nnc_angle4");
inNextNextCheckpointAngle5 = inputLayer.CreateNeuron("nnc_angle5");
//inNextCheckpointDistance = inputLayer.CreateNeuron("c_dist");
OutputLayer outputLayer = nn.CreateOutputLayer();
outHeading0 = outputLayer.CreateNeuron("o_heading0");
outHeading1 = outputLayer.CreateNeuron("o_heading1");
outHeading2 = outputLayer.CreateNeuron("o_heading2");
outHeading3 = outputLayer.CreateNeuron("o_heading3");
outHeading4 = outputLayer.CreateNeuron("o_heading4");
outHeading5 = outputLayer.CreateNeuron("o_heading5");
outThrust0 = outputLayer.CreateNeuron("o_thrust0");
outThrust1 = outputLayer.CreateNeuron("o_thrust1");
outThrust2 = outputLayer.CreateNeuron("o_thrust2");
outThrust3 = outputLayer.CreateNeuron("o_thrust3");
outThrust4 = outputLayer.CreateNeuron("o_thrust4");
outThrust5 = outputLayer.CreateNeuron("o_thrust5");
outThrust6 = outputLayer.CreateNeuron("o_thrust6");
for (int i = 0; i < 3; i++)
{
HiddenLayer hiddenLayer = nn.CreateHiddenLayer();
for (int j = 0; j < 32; j++)
{
HiddenNeuron hiddenNeuron = hiddenLayer.CreateNeuron(string.Format("hidden[{0}][{1}]", i, j));
}
}
nn.CreateFullConnections();
nn.InitWithRandomValues();
return(nn);
}
public void NeuralNetwork_Neuron_OutputNeuronIOTest()
{
OutputNeuron ON = new OutputNeuron(1, 1, 3);
ON.InValue = new double[] { 0.20202, 0.30303, 0.40404 };
ON.Propogate();
Assert.AreEqual(ON.OutValue, 0.71281391251731641);
}
public void OutputNeuron_DoesNothing()
{
// Arrange
var outputNeuron = new OutputNeuron();
// Assert
Assert.IsNotNull(outputNeuron.Connections);
Assert.IsTrue(outputNeuron.Connections.Count == 0);
}
/// <summary>
///
/// </summary>
/// <param name="_inSize"></param>
/// <param name="_numHLay"></param>
/// <param name="_hidSize"></param>
/// <param name="_outSize"></param>
public MultilayerNeuralNet(int _inSize, int _numHLay, int _hidSize, int _outSize)
{
InputLayerSize = _inSize;
NumHiddenLayers = _numHLay;
HiddenLayerSize = _hidSize;
OutputLayerSize = _outSize;
//Instantiate Jagged Arrays of Neurons
int TotalLayers = NumHiddenLayers + 2;
Neurons = new Neuron[TotalLayers][];
Neurons[0] = new InputNeuron[InputLayerSize];
for (int i = 0; i < NumHiddenLayers; i++)
{
Neurons[i + 1] = new HiddenNeuron[HiddenLayerSize];
}
Neurons[TotalLayers - 1] = new OutputNeuron[OutputLayerSize];
//Instantiate the layers based on the inputs
for (int i = 0; i < InputLayerSize; i++)
{
Neurons[0][i] = new InputNeuron(HiddenLayerSize);
}
for (int i = 0; i < NumHiddenLayers; i++)
{
for (int j = 0; j < HiddenLayerSize; j++)
{
if (i == 0)
{
Neurons[i + 1][j] = new HiddenNeuron(InputLayerSize, HiddenLayerSize);
}
if (i == (NumHiddenLayers - 1))
{
Neurons[i + 1][j] = new HiddenNeuron(HiddenLayerSize, OutputLayerSize);
}
else
{
Neurons[i + 1][j] = new HiddenNeuron(HiddenLayerSize, HiddenLayerSize);
}
}
}
for (int i = 0; i < OutputLayerSize; i++)
{
Neurons[NumHiddenLayers + 1][i] = new OutputNeuron(HiddenLayerSize);
}
//Get input values
}
public static void Reset()
{
First = new InputNeuron();
Second = new InputNeuron();
A = new HiddenNeuron(First, Second);
B = new HiddenNeuron(First, Second);
Output = new OutputNeuron(A, B);
}
public static void Reset()
{
First = new InputNeuron();
Second = new InputNeuron();
A = new HiddenNeuron(First, Second);
B = new HiddenNeuron(First, Second);
Output = new OutputNeuron(A, B);
}
public OutputLayer(Activation activation, TrainingInfo trainInfo, int size)
{
var neurons = new OutputNeuron[size];
for (int i = 0; i < size; i++)
{
neurons[i] = new OutputNeuron(activation, trainInfo);
}
this.neurons = neurons;
}
public OutputLayer(HiddenLayer h, NeuralNetwork neuralNetwork)
{
network = neuralNetwork;
hiddenLayer = h;
neurons = new OutputNeuron[outputNeurons];
for (int i = 0; i < outputNeurons; i++)
{
neurons[i] = new OutputNeuron(this);
}
}
public override Neuron[] CreateNeurons()
{
int len = info.neurons.Count;
Neuron[] neurons = new Neuron[len];
for (int i = 0; i < len; i++)
{
NeuronInfo n = info.neurons[i];
neurons[i] = new OutputNeuron(n.activation, n.cost);
}
return(neurons);
}
public IList <Neuron> AddOutputNeurons(
int count,
ActivationFunction activationFunction,
IEnumerable <NeuronValueModifier> neuronValueModifiers = null)
{
var sampleNeuron = new OutputNeuron(-1, activationFunction)
{
ValueModifiers = neuronValueModifiers?.ToArray()
};
return(AddNeurons(sampleNeuron, count));
}
public void OutputLayer_InstantiatedWithOutputLabels_MakesCorrectLayer()
{
OutputNeuron n1 = new OutputNeuron(1);
OutputNeuron n2 = new OutputNeuron(2);
List <Neuron> expectedList = new List <Neuron> {
n1, n2
};
List <Neuron> actualList = outputLayer.Neurons;
CollectionAssert.AreEqual(expectedList, actualList, new OutputNeuronListComparer());
}
public void Both_inputs_high()
{
var neuronA = new InputNeuron(1, 1);
var neuronB = new InputNeuron(1, 1);
var neuronC = new OutputNeuron(.9, 1, 0);
var network = new Network();
network.Connect(neuronA, neuronC);
network.Connect(neuronB, neuronC);
Assert.IsTrue(network.Execute());
}
public void Both_inputs_low()
{
var neuronA = new InputNeuron(0, 1);
var neuronB = new InputNeuron(0, 1);
var neuronC = new OutputNeuron(1.5, 1, 0);
var network = new Network();
network.Connect(neuronA, neuronC);
network.Connect(neuronB, neuronC);
Assert.IsFalse(network.Execute());
}
public void Both_inputs_different()
{
var neuronA = new InputNeuron(0, 1);
var neuronB = new InputNeuron(1, 1);
var neuronC = new OutputNeuron(.9, 1, 0);
var network = new Network();
network.Connect(neuronA, neuronC);
network.Connect(neuronB, neuronC);
Assert.IsTrue(network.Execute());
}
public void Both_inputs_low()
{
var neuronA = new InputNeuron(0, 1);
var neuronB = new InputNeuron(0, 1);
var neuronC = new OutputNeuron(1.5, 1, 0);
var network = new Network();
network.Connect(neuronA, neuronC);
network.Connect(neuronB, neuronC);
Assert.IsFalse(network.Execute());
}
public void NeuralNetwork_Neuron_SoftmaxTest1()
{
double[] input = { 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0 };
double[] expectedOutput = { 0.024, 0.064, 0.175, 0.475, 0.024, 0.064, 0.175 };
double[] output = OutputNeuron.Softmax(input);
for (int i = 0; i < output.Length; i++)
{
output[i] = Math.Round(output[i], 3);
Assert.AreEqual(output[i], expectedOutput[i]);
}
}
public void OutputLayer_WithSortOrderAndNeurons_InitializesProperty()
{
// Arrange
var outputNeuron = new OutputNeuron();
_layer = new OutputLayer(5, new List <IOutputNeuron>()
{
outputNeuron
});
// Assert
Assert.IsTrue(_layer.Neurons.Count() == 1);
Assert.IsTrue(_layer.Neurons.First() == outputNeuron);
}
public void GetOutput(out float[] output)
{
var outputs = neurons.FindAll(x => x is OutputNeuron);
float[] result = new float[outputs.Count];
for (int i = 0; i < outputs.Count; i++)
{
OutputNeuron output_neuron = (OutputNeuron)outputs[i];
result[i] = output_neuron.Value;
}
output = result;
}
public void GetOutput_Condition_ReturnsCorrectOutputValue()
{
double[] inputValues = { 5, 2 };
NetworkStructure networkStructure = new NetworkStructure(networkSettings, weights);
networkStructure.UpdateInput(inputValues);
networkStructure.MakeOutput();
OutputNeuron outputNeuron = networkStructure.GetOutput();
double expectedResult = 37.5;
double actualResult = outputNeuron.Output;
Assert.AreEqual(expectedResult, actualResult);
}
/// <summary>
///Evaluates all of the outputs of the neural network.
/// </summary>
/// <param name="inputs">Enumerable of all the input values</param>
/// <returns>dictionary with names of outputs mapping to their values.</returns>
public Dictionary <string, double> Evaluate(IEnumerable <double> inputs)
{
for (int i = 1; i < NumInputs; i++)
{
Neurons.TryGetValue(i, out Neuron inputNeuron);
inputNeuron.Value = inputs.ElementAt(i - 1);
}
Dictionary <string, double> outputs = new Dictionary <string, double>();
for (int i = NumInputs; i < NumInputs + NumOutputs; i++)
{
Neurons.TryGetValue(i, out Neuron OutputNeuron);
outputs.Add(outputNames[i - NumInputs], OutputNeuron.GetValue());
}
return(outputs);
}
private void LearningTest(bool signal, double expected)
{
var neuron = Substitute.For <ISender>();
neuron.Signal.Returns(signal);
neuron.Index.Returns(0);
var outputNeuron = new OutputNeuron(
0,
new[] { 0.5d },
new[] { neuron });
outputNeuron.Learn(0.01d);
outputNeuron.WeightingFactors[0].Should().Be(expected);
}
public void Init(int[] layers)
{
var neurons = new INeuron[layers.Length][];
_inputValues = new double[layers[0]];
for (var i = 0; i < layers.Length; i++)
{
var layerSize = layers[i];
neurons[i] = new INeuron[layerSize];
for (var j = 0; j < layerSize; j++)
{
INeuron neuron;
if (i == 0)
{
neuron = new InputNeuron();
_inputNeurons.Add(neuron);
}
else if (i < layers.Length - 1)
{
neuron = new FunctionNeuronProcess(_layerFunc);
}
else
{
neuron = new OutputNeuron(_outputFunc);
_outputNeurons.Add(neuron);
}
neurons[i][j] = neuron;
_network.AddNeuron(neuron);
if (i > 0)
{
for (var k = 0; k < layers[i - 1]; k++)
{
var synapse = new Synapse(neurons[i - 1][k], neuron);
synapse.Weight = 1;
_synapses.Add(synapse);
_network.AddSynapse(synapse);
}
}
}
}
}
public PilotC(NeuralNetwork.NeuralNetwork nn)
{
if (nn == null)
{
this.nn = CreateNn();
}
else
{
this.nn = nn;
inNextCheckpointDistance0 = (InputNeuron)nn.InputLayer.Neurons[0];
inNextCheckpointDistance1 = (InputNeuron)nn.InputLayer.Neurons[1];
inNextCheckpointDistance2 = (InputNeuron)nn.InputLayer.Neurons[2];
inNextCheckpointDistance3 = (InputNeuron)nn.InputLayer.Neurons[3];
inNextCheckpointAngle0 = (InputNeuron)nn.InputLayer.Neurons[4];
inNextCheckpointAngle1 = (InputNeuron)nn.InputLayer.Neurons[5];
inNextCheckpointAngle2 = (InputNeuron)nn.InputLayer.Neurons[6];
inNextCheckpointAngle3 = (InputNeuron)nn.InputLayer.Neurons[7];
inNextCheckpointAngle4 = (InputNeuron)nn.InputLayer.Neurons[8];
inNextCheckpointAngle5 = (InputNeuron)nn.InputLayer.Neurons[9];
inNextNextCheckpointAngle0 = (InputNeuron)nn.InputLayer.Neurons[10];
inNextNextCheckpointAngle1 = (InputNeuron)nn.InputLayer.Neurons[11];
inNextNextCheckpointAngle2 = (InputNeuron)nn.InputLayer.Neurons[12];
inNextNextCheckpointAngle3 = (InputNeuron)nn.InputLayer.Neurons[13];
inNextNextCheckpointAngle4 = (InputNeuron)nn.InputLayer.Neurons[14];
inNextNextCheckpointAngle5 = (InputNeuron)nn.InputLayer.Neurons[15];
//inNextCheckpointDistance = (InputNeuron)nn.InputLayer.Neurons[1];
outHeading0 = (OutputNeuron)nn.OutputLayer.Neurons[0];
outHeading1 = (OutputNeuron)nn.OutputLayer.Neurons[1];
outHeading2 = (OutputNeuron)nn.OutputLayer.Neurons[2];
outHeading3 = (OutputNeuron)nn.OutputLayer.Neurons[3];
outHeading4 = (OutputNeuron)nn.OutputLayer.Neurons[4];
outHeading5 = (OutputNeuron)nn.OutputLayer.Neurons[5];
outThrust0 = (OutputNeuron)nn.OutputLayer.Neurons[6];
outThrust1 = (OutputNeuron)nn.OutputLayer.Neurons[7];
outThrust2 = (OutputNeuron)nn.OutputLayer.Neurons[8];
outThrust3 = (OutputNeuron)nn.OutputLayer.Neurons[9];
outThrust4 = (OutputNeuron)nn.OutputLayer.Neurons[10];
outThrust5 = (OutputNeuron)nn.OutputLayer.Neurons[11];
outThrust6 = (OutputNeuron)nn.OutputLayer.Neurons[12];
}
}
/// <summary>
///
/// </summary>
/// <param name="_inSize"></param>
/// <param name="_hidSize"></param>
/// <param name="_outSize"></param>
public SingleHiddenLayerNN(int _inSize, int _hidSize, int _outSize)
{
InputLayerSize = _inSize;
HiddenLayerSize = _hidSize;
OutputLayerSize = _outSize;
for (int i = 0; i < InputLayerSize; i++)
{
int NumSynapse = HiddenLayerSize;
InputNeuron InpN = new InputNeuron(NumSynapse);
inputNeurons.Add(InpN);
}
for (int i = 0; i < HiddenLayerSize; i++)
{
int NumSynapse = OutputLayerSize;
int NumInputSynapse = InputLayerSize;
HiddenNeuron HidN = new HiddenNeuron(NumInputSynapse, NumSynapse);
hiddenNeurons.Add(HidN);
}
for (int i = 0; i < OutputLayerSize; i++)
{
int NumInputSynapse = HiddenLayerSize;
OutputNeuron OutN = new OutputNeuron(NumInputSynapse);
outputNeurons.Add(OutN);
}
for (int i = 0; i < InputLayerSize; i++)
{
Console.WriteLine("Please input Value " + (i + 1));
inputNeurons[i]._inputValue = double.Parse(Console.ReadLine());
}
NetworkForwardInput();
NetworkForwardHidden();
double[] FinalOutPut = NetworkForwardOutput();
FinalOutPutDisplay();
}
static void BasicTest()
{
var inputNeuron = new InputNeuron();
var calcNeuron1 = new FunctionNeuronProcess(ds => ds * 2);
var calcNeuron2 = new FunctionNeuronProcess(ds => ds * 2);
var outputNeuron = new OutputNeuron(ds => ds * 2);
var network = new NeuronalNetwork();
network.AddNeuron(inputNeuron);
network.AddNeuron(calcNeuron1);
network.AddNeuron(calcNeuron2);
network.AddNeuron(outputNeuron);
var synapse11 = network.CreateSynapse(inputNeuron, calcNeuron1);
var synapse12 = network.CreateSynapse(inputNeuron, calcNeuron2);
var synapse21 = network.CreateSynapse(calcNeuron1, outputNeuron);
var synapse22 = network.CreateSynapse(calcNeuron2, outputNeuron);
synapse11.Weight = 2;
synapse12.Weight = 2;
synapse21.Weight = 2;
synapse22.Weight = 2;
Console.WriteLine("Write any Value");
var input = string.Empty;
do
{
network.Reset();
Console.Write("> ");
input = Console.ReadLine();
if (double.TryParse(input, out double value))
{
inputNeuron.Value = value;
Console.Write("Calculating..");
network.FeedForward();
Console.WriteLine("Finished. Result: " + outputNeuron.Value);
}
} while (input != string.Empty);
}
private void NeuronTest(bool signal, double expected)
{
// Arrange
var n1 = Substitute.For <ISender>();
n1.Signal.Returns(signal);
n1.Index.Returns(0);
var n2 = Substitute.For <ISender>();
n2.Signal.Returns(false);
n2.Index.Returns(0);
var outputNeuron = new OutputNeuron(0, new[] { FACTOR }, new[] { n1, n2 });
// Act
outputNeuron.AcceptSignal();
// Assert
outputNeuron.Value.Should().Be(expected);
}
/*
* Forward pass
* Parameter - data for input
*/
public double forwardPass(double[] data)
{
for (int j = 0; j < inputLayer.Count; j++)
{
InputNeuron inNeuron = (InputNeuron)inputLayer[j];
inNeuron.Input = data[j];
inputLayer[j] = inNeuron;
}
for (int j = 0; j < hiddenLayer.Count; j++)
{
HiddenNeuron hidNeuron = (HiddenNeuron)hiddenLayer[j];
hidNeuron.Soma = 0.0;
hiddenLayer[j] = hidNeuron;
}
for (int j = 0; j < outputLayer.Count; j++)
{
OutputNeuron outNeuron = (OutputNeuron)outputLayer[j];
outNeuron.Soma = 0.0;
outputLayer[j] = outNeuron;
}
return(forwardPass());
}
public void Both_inputs_low()
{
var neuronA = new InputNeuron(0, 1);
var neuronB = new InputNeuron(0, 1);
var hiddenLayerA = new Neuron(1.5, -1);
var hiddenLayerB = new Neuron(.5, 1);
var result = new OutputNeuron(.5, 1, 0);
var network = new Network();
network.Connect(neuronA, hiddenLayerA);
network.Connect(neuronA, hiddenLayerB);
network.Connect(neuronB, hiddenLayerA);
network.Connect(neuronB, hiddenLayerB);
network.Connect(hiddenLayerA, result);
network.Connect(hiddenLayerB, result);
Assert.IsFalse(network.Execute());
}
public void Both_inputs_different()
{
var neuronA = new InputNeuron(0, 1);
var neuronB = new InputNeuron(1, 1);
var hiddenLayerA = new Neuron(1.5, -1);
var hiddenLayerB = new Neuron(.5, 1);
var result = new OutputNeuron(.5, 1, 0);
var network = new Network();
network.Connect(neuronA, hiddenLayerA);
network.Connect(neuronA, hiddenLayerB);
network.Connect(neuronB, hiddenLayerA);
network.Connect(neuronB, hiddenLayerB);
network.Connect(hiddenLayerA, result);
network.Connect(hiddenLayerB, result);
Assert.IsTrue(network.Execute());
}
public Layer(string layerType, short TransferFunction, int neuronNum)
{
neurons = new ArrayList(neuronNum);
for (int i = 0; i < neuronNum; i++)
{
if (layerType == "input")
{
neurons.Add(new InputNeuron());
}
else if (layerType == "output")
{
OutputNeuron n = new OutputNeuron();
n.setTransfetFuntion(TransferFunction);
neurons.Add(n);
}
else if (layerType == "hidden")
{
Neuron n = new Neuron();
n.setTransfetFuntion(TransferFunction);
neurons.Add(n);
}
}
}
private void InitNetwork()
{
inputLayer = new List <Neuron>();
if (NeuralDefines.USE_HIDDEN_LAYER)
{
hiddenLayer = new List <Neuron>();
}
outputLayer = new List <Neuron>();
inputLayer.Add(new InputNeuron(() => this.agentHealthInput / 100.0f));
inputLayer.Add(new InputNeuron(() => this.enemyHealthInput / 100.0f));
inputLayer.Add(new InputNeuron(() => this.enemyVisibilityInput));
if (NeuralDefines.USE_HIDDEN_LAYER)
{
for (int i = 0; i < NeuralDefines.HIDDEN_LAYER_SIZE; ++i)
{
Neuron neuron = new HiddenNeuron(NeuralDefines.SIGMOID_FUNCTION);
foreach (Neuron input in inputLayer)
{
neuron.AddInput(input);
}
hiddenLayer.Add(neuron);
}
}
foreach (OutputVariable var in Enum.GetValues(typeof(OutputVariable)))
{
Neuron neuron = new OutputNeuron(NeuralDefines.SIGMOID_FUNCTION);
List <Neuron> previousLayer = NeuralDefines.USE_HIDDEN_LAYER ? hiddenLayer : inputLayer;
foreach (Neuron input in previousLayer)
{
neuron.AddInput(input);
}
outputLayer.Add(neuron);
}
}