/// <summary>
/// Construct the trainer for a self organizing map.
/// </summary>
/// <param name="som">The self organizing map.</param>
/// <param name="train">The training method.</param>
/// <param name="learnMethod">The learning method.</param>
/// <param name="learnRate">The learning rate.</param>
public TrainSelfOrganizingMap(SelfOrganizingMap som,
double[][] train, LearningMethod learnMethod, double learnRate)
{
this.som = som;
this.train = train;
this.totalError = 1.0;
this.learnMethod = learnMethod;
this.learnRate = learnRate;
this.outputNeuronCount = som.OutputNeuronCount;
this.inputNeuronCount = som.InputNeuronCount;
this.totalError = 1.0;
for (int tset = 0; tset < train.Length; tset++)
{
Matrix.Matrix dptr = Matrix.Matrix.CreateColumnMatrix(train[tset]);
if (MatrixMath.vectorLength(dptr) < VERYSMALL)
{
throw (new System.Exception(
"Multiplicative normalization has null training case"));
}
}
this.bestnet = new SelfOrganizingMap(this.inputNeuronCount,
this.outputNeuronCount, this.som.NormalizationType);
this.won = new int[this.outputNeuronCount];
this.correc = new Matrix.Matrix(this.outputNeuronCount,
this.inputNeuronCount + 1);
if (this.learnMethod == LearningMethod.ADDITIVE)
{
this.work = new Matrix.Matrix(1, this.inputNeuronCount + 1);
}
else
{
this.work = null;
}
Initialize();
this.bestError = Double.MaxValue;
}
/// <summary>
/// Copy the weights from one matrix to another.
/// </summary>
/// <param name="source">The source SOM.</param>
/// <param name="target">The target SOM.</param>
private void CopyWeights(SelfOrganizingMap source,
SelfOrganizingMap target)
{
MatrixMath.Copy(source.OutputWeights, target
.OutputWeights);
}
/// <summary>
/// Construct the trainer for a self organizing map.
/// </summary>
/// <param name="som">The self organizing map.</param>
/// <param name="train">The training method.</param>
/// <param name="learnMethod">The learning method.</param>
/// <param name="learnRate">The learning rate.</param>
public TrainSelfOrganizingMap(SelfOrganizingMap som,
double[][] train, LearningMethod learnMethod, double learnRate)
{
this.som = som;
this.train = train;
this.totalError = 1.0;
this.learnMethod = learnMethod;
this.learnRate = learnRate;
this.outputNeuronCount = som.OutputNeuronCount;
this.inputNeuronCount = som.InputNeuronCount;
this.totalError = 1.0;
for (int tset = 0; tset < train.Length; tset++)
{
Matrix.Matrix dptr = Matrix.Matrix.CreateColumnMatrix(train[tset]);
if (MatrixMath.vectorLength(dptr) < VERYSMALL)
{
throw (new System.Exception(
"Multiplicative normalization has null training case"));
}
}
this.bestnet = new SelfOrganizingMap(this.inputNeuronCount,
this.outputNeuronCount, this.som.NormalizationType);
this.won = new int[this.outputNeuronCount];
this.correc = new Matrix.Matrix(this.outputNeuronCount,
this.inputNeuronCount + 1);
if (this.learnMethod == LearningMethod.ADDITIVE)
{
this.work = new Matrix.Matrix(1, this.inputNeuronCount + 1);
}
else
{
this.work = null;
}
Initialize();
this.bestError = Double.MaxValue;
}
/// <summary>
/// Copy the weights from one matrix to another.
/// </summary>
/// <param name="source">The source SOM.</param>
/// <param name="target">The target SOM.</param>
private void CopyWeights(SelfOrganizingMap source,
SelfOrganizingMap target)
{
MatrixMath.Copy(source.OutputWeights, target
.OutputWeights);
}