//this function can be called by train(), cv(), & richEdge.train()
public static double basicTrain(dataSet XTest, toolbox tb)
{
Global.reinitGlobal();
double score = 0;
if (Global.modelOptimizer.EndsWith("bfgs"))
{
Global.tb = tb;
Global.XX = XTest;
tb.train();
score = Global.scoreListList[Global.scoreListList.Count - 1][0];
}
else
{
for (int i = 0; i < Global.ttlIter; i++)
{
Global.glbIter++;
Stopwatch timer = new Stopwatch();
timer.Start();
double err = tb.train();
timer.Stop();
double time = timer.ElapsedMilliseconds / 1000.0;
Global.timeList.Add(time);
Global.errList.Add(err);
Global.diffList.Add(Global.diff);
List <double> scoreList = tb.test(XTest, i);
score = scoreList[0];
Global.scoreListList.Add(scoreList);
Global.swLog.WriteLine("iter{0} diff={1} train-time(sec)={2} {3}={4}%", Global.glbIter, Global.diff.ToString("e2"), time.ToString("f2"), Global.metric, score.ToString("f2"));
Global.swLog.WriteLine("------------------------------------------------");
Global.swLog.Flush();
Console.WriteLine("iter{0} diff={1} train-time(sec)={2} {3}={4}%", Global.glbIter, Global.diff.ToString("e2"), time.ToString("f2"), Global.metric, score.ToString("f2"));
//if (Global.diff < Global.convergeTol)
//break;
}
}
return(score);
}
