static void Main(string[] args)
{
ado ado = new ado();
ado.ConnectToDB();
//ado.SQLToFile("select * from courses","C:/Users/Brian/Documents/datasets/courses.txt");
NetData train = new NetData();
NetData test = new NetData();
NNet nnet = new NNet();
string sqltrain;
string sqltest;
sqltrain = SQLNetData.TrainHeader() + SQLNetData.MainInput() + SQLNetData.TrainWhere();
sqltest = SQLNetData.TestHeader() + SQLNetData.MainInput() + SQLNetData.TestWhere();
train.SQLtoTrainData(sqltrain,ado.Conn);
test.SQLtoTestData(sqltest, ado.Conn);
//Utils.DataTableToFile(train.DataTable,"E:/Users/Brian/datasets/encogtrain.txt");
////Utils.DataTableToFile(test.DataTable, "E:/Users/Brian/datasets/encogtest.txt");
//train.DataTable = Utils.DataTableFromFile("E:/Users/Brian/datasets/encogtrain.txt");
//Utils.DataTableToFile(train.DataTable, "E:/Users/Brian/datasets/encogtrain2.txt");
//train.DataTable = Utils.DataTableFromFile("E:/Users/Brian/datasets/encogtrain2.txt");
//Utils.DataTableToFile(train.DataTable, "E:/Users/Brian/datasets/encogtrain3.txt");
while (true)
{
for (int nodes = 16; nodes < 42; nodes = nodes + 2)
{
nnet.Create(train.ColCount - 2, nodes);
nnet.Train(train, test);
}
}
Console.Write("\n\nPress ENTER to continue.");
Console.ReadLine();
}
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);
}
private void Save(string filename, NetData resultnet)
{
Save(filename);
}
