public static double runtestIteration(List <DataSeq> X, bool train)
{
double _total = 0, _prTotal = 0, _correct = 0;
double total = 0, accuracy = 0, recall = 0, f_score = 0;
double _total4 = 0, _prTotal4 = 0, _correct4 = 0, accuracy4 = 0, f_score4 = 0, recall4 = 0;
Global.threadList = new List <TrainThread>();
TrainThread runThread = new TrainThread(train);
List <ManualResetEvent> manualEvents = new List <ManualResetEvent>();
List <DataStep> temp = new List <DataStep>();
StreamWriter sw = new StreamWriter("data/temp/answer.txt");
string[] write_strings = new string[X.Count];
Parallel.ForEach(X, (ex) =>
{
param pa = new param(ex);
runThread.runtest(pa);
});
for (int i = 0; i < X.Count; i++)
{
sw.Write(X[i].write_string);
}
sw.Close();
accuracy = runThread._correct / runThread._prTotal;
recall = runThread._correct / runThread._total;
accuracy4 = runThread._correct4 / runThread._prTotal4;
recall4 = runThread._correct4 / runThread._total4;
f_score = 2 * accuracy * recall / (accuracy + recall);
f_score4 = 2 * accuracy4 * recall4 / (accuracy4 + recall4);
//Console.WriteLine("total windows: " + _total);
//Console.WriteLine("acc: " + (accuracy4 * 100).ToString("f3"));
//Console.WriteLine("recall: " + (recall4 * 100).ToString("f3"));
//Console.WriteLine("fscore: " + f_score4);
//Global.swLog.WriteLine("total windows: " + total);
//Global.swLog.WriteLine("acc: " + (accuracy4 * 100).ToString("f3"));
//Global.swLog.WriteLine("recall: " + (recall4 * 100).ToString("f3"));
//Global.swLog.WriteLine("fscore: " + f_score4);
//Global.swLog.Flush();
return(f_score4);
}
public static double runtrainIteration(List <DataSeq> X, List <DataSeq> Xtest, bool train, int iter)
{
List <DataSeq> x = new List <DataSeq>();
if (train)
{
x = shuffle(X);//shuffle every window (point)
}
TrainThread runThread = new TrainThread(train);
List <ManualResetEvent> manualEvents = new List <ManualResetEvent>();
List <DataStep> temp = new List <DataStep>();
int i = 0, j = 0;
int length = x.Count();
while (i < length)
{
if (i != 0 && i % 16 == 0)
{
testAccuracy1 = runtestIteration(Xtest, false);
if (testAccuracy <= testAccuracy1)
{
LSTMLayer.SerializeWordembedding("model//deepNetwork//embedding");
//LSTMLayer.SerializeBigramWordembedding();
Global.upLSTMLayer.saveLSTM("model//deepNetwork//lstmmodel.txt");
Global.upLSTMLayerr.saveLSTM("model//deepNetwork//lstmmodelr.txt");
Global.GRNNLayer1.saveGRNN("model//deepNetwork//grnnmodel1.txt");
Global.GRNNLayer2.saveGRNN("model//deepNetwork//grnnmodel1.txt");
Global.GRNNLayer3.saveGRNN("model//deepNetwork//grnnmodel1.txt");
Global.GRNNLayer4.saveGRNN("model//deepNetwork//grnnmodel1.txt");
Global.feedForwardLayer.saveFFmodel("model//deepNetwork//feedforwardmodel.txt");
testAccuracy = testAccuracy1;
}
Console.WriteLine("test f-score: {0}", (testAccuracy * 100).ToString("f3"));
Console.WriteLine("test1 f-score: {0}", (testAccuracy1 * 100).ToString("f3"));
Global.swLog.WriteLine("test f-score: {0}", (testAccuracy * 100).ToString("f3"));
Global.swLog.WriteLine("test f-score: {0}", (testAccuracy1 * 100).ToString("f3"));
}
for (int k = 0; k < Global.nThread && i < length; k++, i++)
{
ManualResetEvent mre = new ManualResetEvent(false);
param pa = new param(x[i]);
pa.mre = mre;
pa.datastep = x[i].datasteps;
manualEvents.Add(mre);
for (int m = 0; m < x[i].datasteps.Count; m++)
{
temp.Add(x[i].datasteps[m]);
}
ThreadPool.QueueUserWorkItem(new WaitCallback(runThread.run), pa);
}
WaitHandle.WaitAll(manualEvents.ToArray());
if (train)
{
UpdateWeight_rmProp(temp);
}
manualEvents.Clear();
temp.Clear();
}
return(runThread.accword / runThread.totalword);
}
