// three layers: input, hidden, output
// as mlp add context layer
// jordan connect output of output layer to input of context layer
// output of context to input of hidden layer
private void createNetwork(int inputNeuronsCount, int hiddenNeuronsCount, int contextNeuronsCount, int outputNeuronsCount)
{
// create input layer
InputLayer inputLayer = new InputLayer(inputNeuronsCount);
inputLayer.addNeuron(new BiasNeuron());
addLayer(inputLayer);
NeuronProperties neuronProperties = new NeuronProperties();
// neuronProperties.setProperty("useBias", true);
neuronProperties.setProperty("transferFunction", TransferFunctionType.Sigmoid.ToString()); // use linear or logitic function! (TR-8604.pdf)
Layer hiddenLayer = new Layer(hiddenNeuronsCount, neuronProperties);
hiddenLayer.addNeuron(new BiasNeuron());
addLayer(hiddenLayer);
ConnectionFactory.fullConnect(inputLayer, hiddenLayer);
Layer contextLayer = new Layer(contextNeuronsCount, neuronProperties);
addLayer(contextLayer); // we might also need bias for context neurons?
Layer outputLayer = new Layer(outputNeuronsCount, neuronProperties);
addLayer(outputLayer);
ConnectionFactory.fullConnect(hiddenLayer, outputLayer);
ConnectionFactory.fullConnect(outputLayer, contextLayer);
ConnectionFactory.fullConnect(contextLayer, hiddenLayer);
// set input and output cells for network
NeuralNetworkFactory.DefaultIO = this;
// set learnng rule
this.LearningRule = new BackPropagation();
}
/// <summary>
/// Creates adaline network architecture with specified number of input neurons
/// </summary>
/// <param name="inputNeuronsCount">
/// number of neurons in input layer </param>
private void createNetwork(int inputNeuronsCount)
{
// set network type code
this.NetworkType = NeuralNetworkType.ADALINE;
// create input layer neuron settings for this network
NeuronProperties inNeuronProperties = new NeuronProperties();
inNeuronProperties.setProperty("transferFunction", TransferFunctionType.Linear.ToString());
// createLayer input layer with specified number of neurons
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, inNeuronProperties);
inputLayer.addNeuron(new BiasNeuron()); // add bias neuron (always 1, and it will act as bias input for output neuron)
this.addLayer(inputLayer);
// create output layer neuron settings for this network
NeuronProperties outNeuronProperties = new NeuronProperties();
outNeuronProperties.setProperty("transferFunction", TransferFunctionType.Ramp.ToString());
outNeuronProperties.setProperty("transferFunction.slope", 1);
outNeuronProperties.setProperty("transferFunction.yHigh", 1);
outNeuronProperties.setProperty("transferFunction.xHigh", 1);
outNeuronProperties.setProperty("transferFunction.yLow", -1);
outNeuronProperties.setProperty("transferFunction.xLow", -1);
// createLayer output layer (only one neuron)
Layer outputLayer = LayerFactory.createLayer(1, outNeuronProperties);
this.addLayer(outputLayer);
// createLayer full conectivity between input and output layer
ConnectionFactory.fullConnect(inputLayer, outputLayer);
// set input and output cells for network
NeuralNetworkFactory.DefaultIO = this;
// set LMS learning rule for this network
this.LearningRule = new LMS();
}
