public void Initialize(int randomSeed, int inputNeuronCount,
int hiddenNeuronCount, int outputNeuronCount)
{
int i, j;
Random rand;
// initializations
rand = new Random(randomSeed);
m_inputLayer = new NeuralLayer();
m_outputLayer = new NeuralLayer();
m_hiddenLayer = new NeuralLayer();
for (i = 0; i < inputNeuronCount; i++)
{
m_inputLayer.Add(new Neuron(0));
}
for (i = 0; i < outputNeuronCount; i++)
{
m_outputLayer.Add(new Neuron(rand.NextDouble()));
}
for (i = 0; i < hiddenNeuronCount; i++)
{
m_hiddenLayer.Add(new Neuron(rand.NextDouble()));
}
// wire-up input layer to hidden layer
for (i = 0; i < m_hiddenLayer.Count; i++)
{
for (j = 0; j < m_inputLayer.Count; j++)
{
m_hiddenLayer[i].Input.Add(m_inputLayer[j],
new NeuralFactor(rand.NextDouble()));
}
}
// wire-up output layer to hidden layer
for (i = 0; i < m_outputLayer.Count; i++)
{
for (j = 0; j < m_hiddenLayer.Count; j++)
{
m_outputLayer[i].Input.Add(m_hiddenLayer[j],
new NeuralFactor(rand.NextDouble()));
}
}
}
public void Initialize(int seed, int inputNeuronCount, int hiddenNeuronCount, int outputNeuronCount)
{
int i, layerCount;
Random rnd;
INeuralLayer layer;
rnd = new Random(seed);
m_inputLayer = new NeuralLayer();
m_outputLayer = new NeuralLayer();
m_hiddenLayer = new NeuralLayer();
for (i = 0; i < inputNeuronCount; i++)
{
m_inputLayer.Add(new Neuron());
}
for (i = 0; i < outputNeuronCount; i++)
{
m_outputLayer.Add(new Neuron());
}
for (i = 0; i < hiddenNeuronCount; i++)
{
m_hiddenLayer.Add(new Neuron());
}
// input layer to hidden layer
foreach (INeuron hiddenNeuron in m_hiddenLayer)
{
foreach (INeuron inputNeuron in m_inputLayer)
{
hiddenNeuron.Input.Add(inputNeuron, new NeuralFactor(rnd.NextDouble()));
}
}
// output neuron to hidden layer
foreach (INeuron outputNeuron in m_outputLayer)
{
foreach (INeuron hiddenNeuron in m_hiddenLayer)
{
outputNeuron.Input.Add(hiddenNeuron, new NeuralFactor(rnd.NextDouble()));
}
}
}
public void Initialize(int randomSeed)
{
var random = new Random(randomSeed);
_inputLayer = new NeuralLayer {
Name = "InputLayer"
};
_outputLayer = new NeuralLayer {
Name = "OutputLayer"
};
_hiddenLayers = new List <INeuralLayer>();
foreach (var hiddenLayer in NetConfiguration.HiddenLayerNodes)
{
_hiddenLayers.Add(new NeuralLayer {
Name = $"HiddenLayer{hiddenLayer.Key}"
});
}
for (var i = 0; i < NetConfiguration.NrOfInputNeurons; i++)
{
_inputLayer.Add(new Neuron(0));
}
for (var i = 0; i < NetConfiguration.NumberOfOutputNeurons; i++)
{
_outputLayer.Add(new Neuron(random.NextDouble()));
}
_inputLayer.OutputLayer = _hiddenLayers.First();
for (var i = 0; i < NetConfiguration.HiddenLayerNodes.Count; i++)
{
var nrOfNodesInLayer = NetConfiguration.HiddenLayerNodes.Values.ToArray()[i];
for (var j = 0; j < nrOfNodesInLayer; j++)
{
_hiddenLayers[i].Add(new Neuron(random.NextDouble()));
}
}
// wire-up the hidden layers
foreach (var hiddenLayer in _hiddenLayers)
{
var index = _hiddenLayers.IndexOf(hiddenLayer);
hiddenLayer.InputLayer = index == 0 ? _inputLayer : _hiddenLayers[index - 1];
hiddenLayer.OutputLayer = index < _hiddenLayers.Count - 1 ? _hiddenLayers[index + 1] : _outputLayer;
foreach (var hiddenNode in hiddenLayer)
{
foreach (var inputNode in hiddenLayer.InputLayer)
{
hiddenNode.Input.Add(inputNode, new NeuralFactor(random.NextDouble()));
}
}
}
_outputLayer.InputLayer = _hiddenLayers.Last();
// wire-up output layer to hidden layer
foreach (var outputNode in _outputLayer)
{
foreach (var hiddenNode in _outputLayer.InputLayer)
{
outputNode.Input.Add(hiddenNode, new NeuralFactor(random.NextDouble()));
}
}
}
