/// <summary>
/// Creates MaxNet network architecture
/// </summary>
/// <param name="neuronsCount">neuron number in network</param>
private void CreateNetwork(int neuronsCount)
{
// set network type
this.NetworkType = NeuralNetworkType.MAXNET;
// createLayer input layer in layer
Layer inputLayer = LayerFactory.createLayer(neuronsCount,
new NeuronProperties());
this.AddLayer(inputLayer);
// createLayer properties for neurons in output layer
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.SetProperty("neuronType", typeof(CompetitiveNeuron));
neuronProperties.SetProperty("transferFunction", TransferFunctionType.RAMP);
// createLayer full connectivity in competitive layer
CompetitiveLayer competitiveLayer = new CompetitiveLayer(neuronsCount, neuronProperties);
// add competitive layer to network
this.AddLayer(competitiveLayer);
double competitiveWeight = -(1 / (double)neuronsCount);
// createLayer full connectivity within competitive layer
ConnectionFactory.FullConnect(competitiveLayer, competitiveWeight, 1);
// createLayer forward connectivity from input to competitive layer
ConnectionFactory.ForwardConnect(inputLayer, competitiveLayer, 1);
// set input and output cells for this network
NeuralNetworkFactory.SetDefaultIO(this);
}
/// <summary>
/// Creates RbfNetwork architecture with specified number of neurons in input
/// layer, output layer and transfer function
/// </summary>
/// <param name="inputNeuronsCount">number of neurons in input layer</param>
/// <param name="rbfNeuronsCount">number of neurons in rbf layer</param>
/// <param name="outputNeuronsCount">number of neurons in output layer</param>
private void CreateNetwork(int inputNeuronsCount, int rbfNeuronsCount,
int outputNeuronsCount)
{
// init neuron settings for this network
NeuronProperties rbfNeuronProperties = new NeuronProperties();
rbfNeuronProperties.SetProperty("weightsFunction", typeof(Diference));
rbfNeuronProperties.SetProperty("summingFunction", typeof(Intensity));
rbfNeuronProperties.SetProperty("transferFunction", typeof(Gaussian));
// set network type code
this.NetworkType = NeuralNetworkType.RBF_NETWORK;
// create input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, TransferFunctionType.LINEAR);
this.AddLayer(inputLayer);
// create rbf layer
Layer rbfLayer = LayerFactory.createLayer(rbfNeuronsCount, rbfNeuronProperties);
this.AddLayer(rbfLayer);
// create output layer
Layer outputLayer = LayerFactory.createLayer(outputNeuronsCount, TransferFunctionType.LINEAR);
this.AddLayer(outputLayer);
// create full conectivity between input and rbf layer
ConnectionFactory.FullConnect(inputLayer, rbfLayer);
// create full conectivity between rbf and output layer
ConnectionFactory.FullConnect(rbfLayer, outputLayer);
// set input and output cells for this network
NeuralNetworkFactory.SetDefaultIO(this);
// set appropriate learning rule for this network
this.LearningRule = new LMS(this);
}
/// <summary>
/// Creates Competitive network architecture
/// </summary>
/// <param name="inputNeuronsCount">input neurons number</param>
/// <param name="outputNeuronsCount">neuron properties</param>
private void CreateNetwork(int inputNeuronsCount, int outputNeuronsCount)
{
// set network type
this.NetworkType = NeuralNetworkType.COMPETITIVE;
// createLayer input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, new NeuronProperties());
this.AddLayer(inputLayer);
// createLayer properties for neurons in output layer
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.SetProperty("neuronType", typeof(CompetitiveNeuron));
neuronProperties.SetProperty("weightsFunction", typeof(WeightedInput));
neuronProperties.SetProperty("summingFunction", typeof(Sum));
neuronProperties.SetProperty("transferFunction", TransferFunctionType.RAMP);
// createLayer full connectivity in competitive layer
CompetitiveLayer competitiveLayer = new CompetitiveLayer(outputNeuronsCount, neuronProperties);
// add competitive layer to network
this.AddLayer(competitiveLayer);
double competitiveWeight = -(1 / (double)outputNeuronsCount);
// createLayer full connectivity within competitive layer
ConnectionFactory.FullConnect(competitiveLayer, competitiveWeight, 1);
// createLayer full connectivity from input to competitive layer
ConnectionFactory.FullConnect(inputLayer, competitiveLayer);
// set input and output cells for this network
NeuralNetworkFactory.SetDefaultIO(this);
this.LearningRule = new CompetitiveLearning(this);
}
/// <summary>
/// Creates Instar 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
this.NetworkType = NeuralNetworkType.INSTAR;
// init neuron settings for this type of network
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.SetProperty("transferFunction", TransferFunctionType.STEP);
// create input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, neuronProperties);
this.AddLayer(inputLayer);
// createLayer output layer
neuronProperties.SetProperty("transferFunction", TransferFunctionType.STEP);
Layer outputLayer = LayerFactory.createLayer(1, neuronProperties);
this.AddLayer(outputLayer);
// create full conectivity between input and output layer
ConnectionFactory.FullConnect(inputLayer, outputLayer);
// set input and output cells for this network
NeuralNetworkFactory.SetDefaultIO(this);
// set appropriate learning rule for this network
this.LearningRule = new InstarLearning(this);
}
/// <summary>
/// Creates Kohonen network architecture with specified number of neurons in
/// input and map layer
/// </summary>
/// <param name="inputNeuronsCount">number of neurons in input layer</param>
/// <param name="outputNeuronsCount">number of neurons in output layer</param>
private void CreateNetwork(int inputNeuronsCount, int outputNeuronsCount)
{
// specify input neuron properties (use default: weighted sum input with
// linear transfer)
NeuronProperties inputNeuronProperties = new NeuronProperties();
// specify map neuron properties
NeuronProperties outputNeuronProperties = new NeuronProperties();
outputNeuronProperties.SetProperty("weightsFunction", typeof(Diference));
outputNeuronProperties.SetProperty("summingFunction", typeof(Intensity));
outputNeuronProperties.SetProperty("transferFunction", typeof(Linear));
// set network type
this.NetworkType = NeuralNetworkType.KOHONEN;
// createLayer input layer
Layer inLayer = LayerFactory.createLayer(inputNeuronsCount,
inputNeuronProperties);
this.AddLayer(inLayer);
// createLayer map layer
Layer mapLayer = LayerFactory.createLayer(outputNeuronsCount,
outputNeuronProperties);
this.AddLayer(mapLayer);
// createLayer full connectivity between input and output layer
ConnectionFactory.FullConnect(inLayer, mapLayer);
// set network input and output cells
NeuralNetworkFactory.SetDefaultIO(this);
this.LearningRule = new KohonenLearning(this);
}
/// <summary>
/// Creates new MultiLayerPerceptron with specified number of neurons in layers
/// </summary>
/// <param name="neuronsInLayers">collection of neuron number in layers</param>
public MultiLayerPerceptron(IList<int> neuronsInLayers)
{
// init neuron settings
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.SetProperty("useBias", true);
neuronProperties.SetProperty("transferFunction", TransferFunctionType.SIGMOID);
this.CreateNetwork(neuronsInLayers, neuronProperties);
}
/// <summary>
/// Creates new Hopfield network with specified neuron number
/// </summary>
/// <param name="neuronsCount">neurons number in Hopfied network</param>
public Hopfield(int neuronsCount)
{
// init neuron settings for hopfield network
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.SetProperty("neuronType", typeof(InputOutputNeuron));
neuronProperties.SetProperty("bias", 0);
neuronProperties.SetProperty("transferFunction", TransferFunctionType.STEP);
neuronProperties.SetProperty("transferFunction.yHigh", 1);
neuronProperties.SetProperty("transferFunction.yLow", 0);
this.CreateNetwork(neuronsCount, neuronProperties);
}
public MultiLayerPerceptron(TransferFunctionType transferFunctionType, params int[] neuronsInLayers)
{
// init neuron settings
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.SetProperty("useBias", true);
neuronProperties.SetProperty("transferFunction", transferFunctionType);
neuronProperties.SetProperty("inputFunction", typeof(WeightedSum));
IList<int> neuronsInLayersVector = new List<int>();
for (int i = 0; i < neuronsInLayers.Length; i++)
neuronsInLayersVector.Add(neuronsInLayers[i]);
this.CreateNetwork(neuronsInLayersVector, neuronProperties);
}
/// <summary>
/// Creates an instance of Supervised Hebbian Network with specified number
/// of neurons in input layer, output layer and transfer function
/// </summary>
/// <param name="inputNeuronsNum">number of neurons in input layer</param>
/// <param name="outputNeuronsNum">number of neurons in output layer</param>
/// <param name="transferFunctionType">transfer function type</param>
private void CreateNetwork(int inputNeuronsNum, int outputNeuronsNum,
TransferFunctionType transferFunctionType)
{
// init neuron properties
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.SetProperty("transferFunction", transferFunctionType);
neuronProperties.SetProperty("transferFunction.slope", 1);
neuronProperties.SetProperty("transferFunction.yHigh", 1);
neuronProperties.SetProperty("transferFunction.xHigh", 1);
neuronProperties.SetProperty("transferFunction.yLow", -1);
neuronProperties.SetProperty("transferFunction.xLow", -1);
// set network type code
this.NetworkType = NeuralNetworkType.SUPERVISED_HEBBIAN_NET;
// createLayer input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsNum,
neuronProperties);
this.AddLayer(inputLayer);
// createLayer output layer
Layer outputLayer = LayerFactory.createLayer(outputNeuronsNum,
neuronProperties);
this.AddLayer(outputLayer);
// createLayer full conectivity between input and output layer
ConnectionFactory.FullConnect(inputLayer, outputLayer);
// set input and output cells for this network
NeuralNetworkFactory.SetDefaultIO(this);
// set appropriate learning rule for this network
this.LearningRule = new SupervisedHebbianLearning(this);
}
/// <summary>
/// Creates perceptron architecture with specified number of neurons in input
/// and output layer, specified transfer function
/// </summary>
/// <param name="inputNeuronsCount">number of neurons in input layer</param>
/// <param name="outputNeuronsCount">number of neurons in output layer</param>
/// <param name="transferFunctionType">neuron transfer function type</param>
private void CreateNetwork(int inputNeuronsCount, int outputNeuronsCount, TransferFunctionType transferFunctionType)
{
// set network type
this.NetworkType = NeuralNetworkType.PERCEPTRON;
// init neuron settings for input layer
NeuronProperties inputNeuronProperties = new NeuronProperties();
inputNeuronProperties.SetProperty("transferFunction", TransferFunctionType.LINEAR);
// create input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, inputNeuronProperties);
this.AddLayer(inputLayer);
NeuronProperties outputNeuronProperties = new NeuronProperties();
outputNeuronProperties.SetProperty("neuronType", typeof(ThresholdNeuron));
outputNeuronProperties.SetProperty("thresh", Math.Abs(ThreadSafeRandom.NextDouble()));
outputNeuronProperties.SetProperty("transferFunction", transferFunctionType);
// for sigmoid and tanh transfer functions set slope propery
outputNeuronProperties.SetProperty("transferFunction.slope", 1);
// createLayer output layer
Layer outputLayer = LayerFactory.createLayer(outputNeuronsCount, outputNeuronProperties);
this.AddLayer(outputLayer);
// create full conectivity between input and output layer
ConnectionFactory.FullConnect(inputLayer, outputLayer);
// set input and output cells for this network
NeuralNetworkFactory.SetDefaultIO(this);
this.LearningRule = new BinaryDeltaRule();
}
public static Layer createLayer(int neuronsCount, TransferFunctionType transferFunctionType)
{
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.SetProperty("transferFunction", transferFunctionType);
Layer layer = createLayer(neuronsCount, neuronProperties);
return layer;
}
/// <summary>
/// Creates an instance of Unsuervised Hebian net with specified number
/// of neurons in input layer and output layer, and transfer function
/// </summary>
/// <param name="inputNeuronsNum">number of neurons in input layer</param>
/// <param name="outputNeuronsNum">number of neurons in output layer</param>
/// <param name="transferFunctionType">transfer function type</param>
private void CreateNetwork(int inputNeuronsNum, int outputNeuronsNum,
TransferFunctionType transferFunctionType)
{
// init neuron properties
NeuronProperties neuronProperties = new NeuronProperties();
// neuronProperties.setProperty("bias", new Double(-Math
// .abs(Math.random() - 0.5))); // Hebbian network cann not work
// without bias
neuronProperties.SetProperty("transferFunction", transferFunctionType);
neuronProperties.SetProperty("transferFunction.slope", 1);
// set network type code
this.NetworkType = NeuralNetworkType.UNSUPERVISED_HEBBIAN_NET;
// createLayer input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsNum,
neuronProperties);
this.AddLayer(inputLayer);
// createLayer output layer
Layer outputLayer = LayerFactory.createLayer(outputNeuronsNum,
neuronProperties);
this.AddLayer(outputLayer);
// createLayer full conectivity between input and output layer
ConnectionFactory.FullConnect(inputLayer, outputLayer);
// set input and output cells for this network
NeuralNetworkFactory.SetDefaultIO(this);
// set appropriate learning rule for this network
this.LearningRule = new UnsupervisedHebbianLearning(this);
//this.setLearningRule(new OjaLearning(this));
}