/// <summary>
/// Initializes weights of all spatial synapses in a Kohonen connector.
/// </summary>
/// <param name="connector">
/// The Kohonen connector to initialize.
/// </param>
/// <exception cref="ArgumentNullException">
/// If <c>connector</c> is <c>null</c>
/// </exception>
public void Initialize(KohonenConnectorND connector)
{
Helper.ValidateNotNull(connector, "connector");
foreach (KohonenSynapseND synapse in connector.Synapses)
{
synapse.Weight = constant;
}
}
/// <summary>
/// Initializes weights of all spatial synapses in a Kohonen connector 3D.
/// </summary>
/// <param name="connector">
/// The Kohonen connector to initialize.
/// </param>
/// <exception cref="ArgumentNullException">
/// If <c>connector</c> is <c>null</c>
/// </exception>
public void Initialize(KohonenConnectorND connector)
{
Helper.ValidateNotNull(connector, "connector");
foreach (KohonenSynapseND synapse in connector.Synapses)
{
synapse.Weight = Helper.GetRandom(minLimit, maxLimit);
}
}
/// <summary>
/// Initializes weights of all spatial synapses in a Kohonen connector 3D.
/// </summary>
/// <param name="connector">
/// The Kohonen connector to initialize.
/// </param>
/// <exception cref="ArgumentNullException">
/// If <c>connector</c> is <c>null</c>
/// </exception>
public void Initialize(KohonenConnectorND connector)
{
Helper.ValidateNotNull(connector, "connector");
int i = 0;
foreach (KohonenSynapseND synapse in connector.Synapses)
{
synapse.Weight = Inputs[i];
i++;
}
}
/// <summary>
/// Initializes weights of all spatial synapses in a Kohonen connector 3D.
/// </summary>
/// <param name="connector">
/// The Kohonen connector to initialize.
/// </param>
/// <exception cref="ArgumentNullException">
/// If <c>connector</c> is <c>null</c>
/// </exception>
public void Initialize(KohonenConnectorND connector)
{
Helper.ValidateNotNull(connector, "connector");
int i = 0;
double[] normalized = Helper.GetRandomVector(connector.SynapseCount, 1d);
foreach (KohonenSynapseND synapse in connector.Synapses)
{
synapse.Weight = normalized[i++];
}
}
/// <summary>
/// Creates a new Kohonen Synapse connecting the given neurons
/// </summary>
/// <param name="sourceNeuron">
/// The source neuron
/// </param>
/// <param name="targetNeuron">
/// The target neuron
/// </param>
/// <param name="parent">
/// Parent connector containing this synapse
/// </param>
/// <exception cref="System.ArgumentNullException">
/// If any of the arguments is <c>null</c>
/// </exception>
public KohonenSynapseND(INeuron sourceNeuron, PositionNeuron targetNeuron, KohonenConnectorND parent)
{
Helper.ValidateNotNull(sourceNeuron, "sourceNeuron");
Helper.ValidateNotNull(targetNeuron, "targetNeuron");
Helper.ValidateNotNull(parent, "parent");
this.weight = 1d;
sourceNeuron.TargetSynapses.Add(this);
targetNeuron.SourceSynapses.Add(this);
this.sourceNeuron = sourceNeuron;
this.targetNeuron = targetNeuron;
this.parent = parent;
}
/// <summary>
/// Initializes weights of all spatial synapses in a Kohonen connector.
/// </summary>
/// <param name="connector">
/// The Kohonen connector to initialize.
/// </param>
/// <exception cref="ArgumentNullException">
/// If <c>connector</c> is <c>null</c>
/// </exception>
public void Initialize(KohonenConnectorND connector)
{
Helper.ValidateNotNull(connector, "connector");
double nGuyenWidrowFactor = NGuyenWidrowFactor(
connector.SourceLayer.NeuronCount, connector.TargetLayer.NeuronCount);
int synapsesPerNeuron = connector.SynapseCount / connector.TargetLayer.NeuronCount;
foreach (INeuron neuron in connector.TargetLayer.Neurons)
{
int i = 0;
double[] normalizedVector = Helper.GetRandomVector(synapsesPerNeuron, nGuyenWidrowFactor);
foreach (KohonenSynapseND synapse in connector.GetSourceSynapses(neuron))
{
synapse.Weight = normalizedVector[i++];
}
}
}
void Solve()
{
#region prepare and assign
trainingSet.Clear();
for (int i = 0; i < trainVectorCount; i++)
{
List <double> dl = new List <double>();
for (int j = 0; j < trainVectorDimension; j++)
{
dl.Add(trainVectors[i][j]);
}
trainingSet.Add(new TrainingSample(dl.ToArray()));
}
/// process
/// start learning
/// get learning radius for neighborhood function
int learningRadius = 0;
for (int i = 0; i < dimension; i++)
{
if (size[i] > learningRadius)
{
learningRadius = size[i];
}
}
learningRadius /= 2;
INeighborhoodFunction neighborhoodFunction = new GaussianFunction(learningRadius, netUP.neighborDistance) as INeighborhoodFunction;
if (neighborhood)
{
neighborhoodFunction = new MexicanHatFunction(learningRadius) as INeighborhoodFunction;
}
LatticeTopology topology = LatticeTopology.Rectangular;
if (latticeTopology)
{
topology = LatticeTopology.Hexagonal;
}
/// instantiate relevant network layers
KohonenLayer inputLayer = new KohonenLayer(trainVectorDimension);
KohonenLayerND outputLayer = new KohonenLayerND(size, neighborhoodFunction, topology);
KohonenConnectorND connector = new KohonenConnectorND(inputLayer, outputLayer, netUP.initialNodes);
if (netUP.initialNodes.Length != 0)
{
connector.Initializer = new GivenInput(netUP.initialNodes);
}
else
{
connector.Initializer = new RandomFunction(0.0, 1.0);
}
outputLayer.SetLearningRate(learningRate, 0.05d);
outputLayer.IsDimensionCircular = isDimensionCircular;
network = new KohonenNetworkND(inputLayer, outputLayer);
network.useRandomTrainingOrder = randomTrainingOrder;
inputLayer.ParallelComputation = false;
outputLayer.ParallelComputation = parallelComputing;
#endregion
#region delegates
network.BeginEpochEvent += new TrainingEpochEventHandler(
delegate(object senderNetwork, TrainingEpochEventArgs args)
{
#region trainingCylce
if (network == null || !GO)
{
return;
}
trainedVectors = new double[outputLayer.neuronCount, trainVectorDimension];
for (int i = 0; i < outputLayer.neuronCount; i++)
{
IList <ISynapse> synapses = (network.OutputLayer as KohonenLayerND)[outputLayer.adressBook[i]].SourceSynapses;
for (int j = 0; j < trainVectorDimension; j++)
{
trainedVectors[i, j] = synapses[j].Weight;
}
}
//make new net here
netP = new CrowNetSOMNDP(size, isDimensionCircular, latticeTopology, neighborhood, trainedVectors, outputLayer.adressBook);
counter++;
#endregion
});
network.EndSampleEvent += new TrainingSampleEventHandler(
delegate(object senderNetwork, TrainingSampleEventArgs args)
{
netP.winner = outputLayer.WinnerND.CoordinateND;
});
#endregion
network.Learn(trainingSet, cycles);
}
