public model(dataSet X, featureGenerator fGen)
{
_nTag = X.NTag;
//default value is 0
if (Global.random == 0)
{
_w = new float[fGen.NCompleteFeature];
}
else if (Global.random == 1)
{
List <float> randList = randomDoubleTool.getRandomList_float(fGen.NCompleteFeature);
_w = randList.ToArray();
}
else
{
throw new Exception("error");
}
}
public toolbox(dataSet X, bool train = true)
{
if (train)//to train
{
_X = X;
_fGene = new featureGenerator(X);
_model = new model(X, _fGene);
_inf = new inference(this);
_grad = new gradient(this);
initOptimizer();
}
else//to test
{
_X = X;
_model = new model(Global.fModel);
_fGene = new featureGenerator(X);
_inf = new inference(this);
_grad = new gradient(this);
}
}
public inference(toolbox tb)
{
_optim = tb.Optim;
_fGene = tb.FGene;
_grad = tb.Grad;
}
public gradient(toolbox tb)
{
_optim = tb.Optim;
_inf = tb.Inf;
_fGene = tb.FGene;
}