public model(dataSet X, featureGenerator fGen)
{
_nTag = X.NTag;
_nHiddenState = Global.nHiddenStatePerTag * _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();
if (Global.tuneWeightInit)
{
if (Global.tmpW == null)
{
Global.tmpW = new float[_w.Length];
}
_w.CopyTo(Global.tmpW, 0);
}
}
else if (Global.random == 2)
{
_w = new float[fGen.NCompleteFeature];
Global.optimW.CopyTo(_w, 0);
}
else
{
throw new Exception("error");
}
}
//for multi-task
public toolbox(dataSet X, List <dataSet> XList, bool train = true)
{
if (train)//to train
{
_X = X;
_XList = XList;
_fGene = new featureGenerator(X);
_model = null;
_modelList = new List <model>();
for (int i = 0; i < Global.nTask; i++)
{
model m = new model(XList[i], _fGene);
_modelList.Add(m);
}
_inf = new inference(this);
_grad = new gradient(this);
initOptimizer();
}
else//to test
{
_X = X;
_XList = XList;
_model = null;
_modelList = new List <model>();
for (int i = 0; i < Global.nTask; i++)
{
model m = new model(Global.modelDir + i.ToString() + Global.fModel);
_modelList.Add(m);
}
_fGene = new featureGenerator(X);
_inf = new inference(this);
_grad = new gradient(this);
}
}
public toolbox(dataSet X, bool train = true)
{
if (train)//for training
{
_X = X;
_fGene = new featureGenerator(X);
_model = new model(X, _fGene);
_inf = new inference(this);
initOptimizer();
}
else//for test
{
_X = X;
_model = new model(Global.fModel);
_fGene = new featureGenerator(X);
_inf = new inference(this);
}
}
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");
}
}
//for train
public model(dataSet X, featureGenerator fGen)
{
_nTag = X.NTag;
//default value is 0
if (Global.random == 0)
{
double[] dAry = new double[fGen.NCompleteFeature];
List <double> w = new List <double>(dAry);
W = w;
}
else if (Global.random == 1)
{
List <double> randList = randomDoubleTool.getRandomList(fGen.NCompleteFeature);
W = randList;
}
else
{
throw new Exception("error");
}
}
//for single-task
public toolbox(dataSet X, bool train = true)
{
if (train)//to train
{
_XList = null;
_modelList = null;
_X = X;
_fGene = new featureGenerator(X);
_model = new model(X, _fGene);
_inf = new inference(this);
_grad = new gradient(this);
initOptimizer();
}
else//to test
{
_XList = null;
_modelList = null;
_X = X;
_model = new model(Global.modelDir + Global.fModel);
_fGene = new featureGenerator(X);
_inf = new inference(this);
_grad = new gradient(this);
}
}
public inference(toolbox tb)
{
_optim = tb.Optim;
_fGene = tb.FGene;
}
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;
}
