public void TestManhattan()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
IMLTrain bprop = new ManhattanPropagation(network, trainingData, 0.01);
NetworkUtil.TestTraining(bprop, 0.01);
}
private void Train(BasicNetwork network, IMLDataSet trainingSet)
{
if (mTrainingMethod == TrainingMethod.ResilientPropagation)
{
ITrain train = new ResilientPropagation(network, trainingSet);
int epoch = 1;
do
{
train.Iteration();
epoch++;
} while (train.Error > mMaxError && epoch < mMaxEpoch);
}
else if (mTrainingMethod == TrainingMethod.LevenbergMarquardt)
{
LevenbergMarquardtTraining train = new LevenbergMarquardtTraining(network, trainingSet);
int epoch = 1;
do
{
train.Iteration();
epoch++;
} while (train.Error > mMaxError && epoch < mMaxEpoch);
}
else if (mTrainingMethod == TrainingMethod.Backpropagation)
{
Backpropagation train = new Backpropagation(network, trainingSet);
int epoch = 1;
do
{
train.Iteration();
epoch++;
} while (train.Error > mMaxError && epoch < mMaxEpoch);
}
else if (mTrainingMethod == TrainingMethod.ManhattanPropagation)
{
ManhattanPropagation train = new ManhattanPropagation(network, trainingSet, 0.9);
int epoch = 1;
do
{
train.Iteration();
epoch++;
} while (train.Error > mMaxError && epoch < mMaxEpoch);
}
}
