public Lattice(model m, inference inf, dataSeq x)
{
_w = x.Count;
_h = m.NTag;
_logBel = new belief(_w, _h);
List <dMatrix> YYlist = new List <dMatrix>();
List <List <double> > Ylist = new List <List <double> >();
inf.getYYandY(m, x, YYlist, Ylist);
for (int i = 0; i < _w; i++)
{
_logBel.belState[i] = new List <double>(Ylist[i]);
if (i > 0)
{
_logBel.belEdge[i] = new dMatrix(YYlist[i]);
}
}
_heuListList = new List <List <double> >();
for (int i = 0; i < _w; i++)
{
_heuListList.Add(new List <double>(new double[_h]));
}
Viterbi _bwdViterbi = new Viterbi(_w, _h);
for (int i = 0; i < _w; i++)
{
_bwdViterbi.setScores(i, Ylist[i], YYlist[i]);
}
List <int> tags = new List <int>();
_bwdViterbi.runViterbi(ref tags);
//update the viterbiHeuristicMap
for (int i = 0; i < _w; i++)
{
for (int j = 0; j < _h; j++)
{
double h = _bwdViterbi.getPathScore(i, j);
setHeuMap(i, j, h);
}
}
//get zGold
ZGold = 0;
for (int i = 0; i < x.Count; i++)
{
int s = x.getTags(i);
ZGold += Ylist[i][s];
if (i > 0)
{
int sPre = x.getTags(i - 1);
ZGold += YYlist[i][sPre, s];
}
}
}
//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 double getNBest(model m, inference inf, dataSeq x, int N, ref List <List <int> > nBestTaggings, ref List <double> scores)
{
nBestTaggings.Clear();
_w = x.Count;
_h = m.NTag;
_lattice = new Lattice(m, inf, x);
setStartAndGoal(-1, 0, _w, 0);//a virtual begin node & a virtual end node
for (int n = 0; n < N; n++)
{
List <int> tagging = new List <int>();
double logNumer = searchForPath(ref tagging);
if (logNumer == -2)//search fail
{
break;
}
nBestTaggings.Add(tagging);
scores.Add(logNumer);//log numerator
double check = Math.Exp((scores[0] - scores[n]));
if (check >= Global.stopSearchFactor)//20 times bigger then break
{
break;
}
}
double Z = logSum(scores);
listTool.listAdd(ref scores, -Z);
listTool.listExp(ref scores);//prob
//error
double error = Z - _lattice.ZGold;
//update the profiler
Global.nbestCount += scores.Count;
Global.nbestNorm++;
int small = scores.Count < 10 ? scores.Count : 10;
for (int i = 0; i < small; i++)
{
Global.nbestProbList[i] += scores[i];
}
return(error);
}
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);
}
}
//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 gradient(toolbox tb)
{
_optim = tb.Optim;
_inf = tb.Inf;
_fGene = tb.FGene;
}
