public void Train()
{
// Read all training data and transforming them.
trainingData = new DataSetReader(Parameter.TrainingFile,
Parameter.InputSize, Parameter.TargetSize);
double[][] data, target;
trainingData.ReadAll(out data, out target);
for (int i = 0; i < trainingData.Count; ++i)
{
// transform input level 1
for (int j = 0; j < Parameter.InputSize; ++j)
{
data[i][j] = inputTransformers1[j].TransformToValue(data[i][j]);
}
// transform input level 2
data[i] = inputTransformers2.TransformToDimension(data[i]);
// transform target
target[i] = targetTransform.TransformToDimension(target[i]);
}
oldArtAWeights = new List <double[]>();
bool isWeightsChanged = true, completedLimited = false;
// Order input and target pattern before.
if (param.Mode == ProcessModifiedHybridFuzzyARTMapMode.EXTERNAL ||
param.Mode == ProcessModifiedHybridFuzzyARTMapMode.DUAL)
{
mcoa.OrderSequence(data, target);
}
do
{
SaveOldWeights();
// Training ordered fuzzy art map
if (param.Mode == ProcessModifiedHybridFuzzyARTMapMode.EXTERNAL)
{
fartmap.Run(data, target);
}
else
{
mfartmap.Run(data, target);
}
double mseValue = CalculateMse();
if (mseValue < Parameter.LimitedMseValue)
{
isWeightsChanged = false;
}
performanceCounter.Increment();
if (param.LimitedEpochs != 0 &&
param.LimitedEpochs <= performanceCounter.Count)
{
completedLimited = true;
}
}while (!completedLimited && isWeightsChanged);
}
public void Run(double[][] data, double[][] target)
{
// Order input sequence with min-max clustering algorithms.
if (dataSequence != data || targetSequence != target)
{
orderingAlgorithms.OrderSequence(data, target);
dataSequence = data;
targetSequence = target;
}
// Run simplified FuzzyARTMap neural network.
fuzzyARTMap.Run(dataSequence, targetSequence);
}
