public void Train(NetData traindata, NetData testdata)
{
bool stop;
double sr;
double bestsr;
string now;
int totalepoch;
int epoch;
long timeId;
string netfile;
timeId = DateTime.Now.Millisecond + DateTime.Now.Year + DateTime.Now.Minute + DateTime.Now.Hour + DateTime.Now.Day;
//// create training data
IMLDataSet trainingSet = new BasicMLDataSet(traindata.Data, traindata.Targets);
//// create training data
IMLDataSet testingSet = new BasicMLDataSet(testdata.Data, testdata.Targets);
//// train the neural network
ResilientPropagation train = new ResilientPropagation(network, trainingSet);
train.RType = rproptype;
stop = false;
bestsr = 0;
totalepoch = 0;
epoch = 10;
int i = 0;
now = DateTime.Now.ToString().Replace(":", "_").Replace("/", "_");
Console.WriteLine("Begin train. Inputs : " + testdata.ColCount + " Nodes : " + (network.Flat.NeuronCount -testdata.ColCount).ToString() );
do
{
train.Iteration(epoch);
totalepoch=totalepoch + epoch;
Console.WriteLine("total epoch" + totalepoch.ToString());
i = 0;
foreach (IMLDataPair pair in testingSet)
{
IMLData output = network.Compute(pair.Input);
testdata.Targets[i][0] = output[0];
i++;
}
sr = testdata.CalcTestResult();
if (sr > bestsr)
{
bestsr = sr;
netfile = "E:/Users/Brian/netfiles/encog/Date_" + now + "_eph_" + totalepoch.ToString() + "_v" + timeId++.ToString() + ".net";
testdata.epochs = totalepoch;
Save(netfile);
Console.WriteLine(netfile);
}
else if(sr < bestsr - .005)
{
stop = true;
}
//Console.WriteLine("Epoch Error:" + train.Error);
} while (stop == false);
}
