static void crossValidation()
{
//load data
Console.WriteLine("reading cross validation data...");
Global.swLog.WriteLine("reading cross validation data...");
List <dataSet> XList = new List <dataSet>();
List <dataSet> XXList = new List <dataSet>();
loadDataForCV(XList, XXList);
//start cross validation
foreach (double r in Global.regList)//do CV for each different regularizer r (sigma)
{
Global.swLog.WriteLine("\ncross validation. r={0}", r);
Console.WriteLine("\ncross validation. r={0}", r);
if (Global.rawResWrite)
{
Global.swResRaw.WriteLine("% cross validation. r={0}", r);
}
for (int i = 0; i < Global.nCV; i++)
{
Global.swLog.WriteLine("\n#validation={0}", i + 1);
Console.WriteLine("\n#validation={0}", i + 1);
if (Global.rawResWrite)
{
Global.swResRaw.WriteLine("% #validation={0}", i + 1);
}
Global.reg = r;
dataSet Xi = XList[i];
if (Global.runMode.Contains("rich"))
{
toolboxRich tb = new toolboxRich(Xi);
basicTrain(XXList[i], tb);
}
else
{
toolbox tb = new toolbox(Xi);
basicTrain(XXList[i], tb);
}
resSummarize.write();
if (Global.rawResWrite)
{
Global.swResRaw.WriteLine();
}
}
if (Global.rawResWrite)
{
Global.swResRaw.WriteLine();
}
}
}
public static double test()
{
dataSet X = new dataSet(Global.fFeatureTrain, Global.fGoldTrain);
dataSet XX = new dataSet(Global.fFeatureTest, Global.fGoldTest);
Global.swLog.WriteLine("data size (test): {0}", XX.Count);
//load model for testing
toolboxRich tb = new toolboxRich(X, false);
List <double> scoreList = tb.test(XX, 0);
double score = scoreList[0];
Global.scoreListList.Add(scoreList);
resSummarize.write();
return(score);
}
public static double train()
{
//load data
Console.WriteLine("\nreading training & test data...");
Global.swLog.WriteLine("\nreading training & test data...");
dataSet X, XX;
if (Global.runMode.Contains("tune"))
{
dataSet origX = new dataSet(Global.fFeatureTrain, Global.fGoldTrain);
X = new dataSet();
XX = new dataSet();
MainClass.dataSplit(origX, Global.tuneSplit, X, XX);
}
else
{
X = new dataSet(Global.fFeatureTrain, Global.fGoldTrain);
XX = new dataSet(Global.fFeatureTest, Global.fGoldTest);
MainClass.dataSizeScale(X);
}
Global.swLog.WriteLine("data sizes (train, test): {0} {1}", X.Count, XX.Count);
double score = 0;
foreach (double r in Global.regList)
{
Global.reg = r;
Global.swLog.WriteLine("\nr: " + r.ToString());
Console.WriteLine("\nr: " + r.ToString());
if (Global.rawResWrite)
{
Global.swResRaw.WriteLine("\n%r: " + r.ToString());
}
toolboxRich tb = new toolboxRich(X);
score = MainClass.basicTrain(XX, tb);
resSummarize.write();
//save model
if (Global.save == 1)
{
tb.Model.save(Global.fModel);
}
}
return(score);
}
