//return the gradient of -log{P(y*|x,w)} as follows: E_{P(y|x)}(F(x,y)) - F(x,y*)
virtual public double getGrad(List <double> vecGrad, model m, dataSeq x, baseHashSet <int> idSet)
{
if (idSet != null)
{
idSet.Clear();
}
int nTag = m.NTag;
//compute beliefs
belief bel = new belief(x.Count, nTag);
belief belMasked = new belief(x.Count, nTag);
_inf.getBeliefs(bel, m, x, false);
_inf.getBeliefs(belMasked, m, x, true);
double ZGold = belMasked.Z;
double Z = bel.Z;
List <featureTemp> fList;
for (int i = 0; i < x.Count; i++)
{
fList = _fGene.getFeatureTemp(x, i);
for (int j = 0; j < fList.Count; j++)
{
featureTemp im = fList[j];
int id = im.id;
double v = im.val;
for (int s = 0; s < nTag; s++)
{
int f = _fGene.getNodeFeatID(id, s);
if (idSet != null)
{
idSet.Add(f);
}
vecGrad[f] += bel.belState[i][s] * v;
vecGrad[f] -= belMasked.belState[i][s] * v;
}
}
}
for (int i = 1; i < x.Count; i++)
{
for (int s = 0; s < nTag; s++)
{
for (int sPre = 0; sPre < nTag; sPre++)
{
int f = _fGene.getEdgeFeatID(sPre, s);
if (idSet != null)
{
idSet.Add(f);
}
vecGrad[f] += bel.belEdge[i][sPre, s];
vecGrad[f] -= belMasked.belEdge[i][sPre, s];
}
}
}
return(Z - ZGold);
}
public double getGrad_SGD(List <double> g, model m, dataSeq x, baseHashSet <int> idset)
{
if (idset != null)
{
idset.Clear();
}
if (x == null)
{
return(0);
}
return(getGradCRF(g, m, x, idset));
}
//the scalar version
virtual public double getGradCRF(List <double> vecGrad, double scalar, model m, dataSeq x, baseHashSet <int> idSet)
{
idSet.Clear();
int nTag = m.NTag;
//compute beliefs
belief bel = new belief(x.Count, nTag);
belief belMasked = new belief(x.Count, nTag);
_inf.getBeliefs(bel, m, x, scalar, false);
_inf.getBeliefs(belMasked, m, x, scalar, true);
double ZGold = belMasked.Z;
double Z = bel.Z;
List <featureTemp> fList;
//Loop over nodes to compute features and update the gradient
for (int i = 0; i < x.Count; i++)
{
fList = _fGene.getFeatureTemp(x, i);
foreach (featureTemp im in fList)
{
for (int s = 0; s < nTag; s++)
{
int f = _fGene.getNodeFeatID(im.id, s);
idSet.Add(f);
vecGrad[f] += bel.belState[i][s] * im.val;
vecGrad[f] -= belMasked.belState[i][s] * im.val;
}
}
}
//Loop over edges to compute features and update the gradient
for (int i = 1; i < x.Count; i++)
{
for (int s = 0; s < nTag; s++)
{
for (int sPre = 0; sPre < nTag; sPre++)
{
int f = _fGene.getEdgeFeatID(sPre, s);
idSet.Add(f);
vecGrad[f] += bel.belEdge[i][sPre, s];
vecGrad[f] -= belMasked.belEdge[i][sPre, s];
}
}
}
return(Z - ZGold);//-log{P(y*|x,w)}
}
//the mini-batch version
public double getGrad_SGD_miniBatch(List <double> g, model m, List <dataSeq> X, baseHashSet <int> idset)
{
if (idset != null)
{
idset.Clear();
}
double error = 0;
foreach (dataSeq x in X)
{
baseHashSet <int> idset2 = new baseHashSet <int>();
error += getGradCRF(g, m, x, idset2);
if (idset != null)
{
foreach (int i in idset2)
{
idset.Add(i);
}
}
}
return(error);
}
override public double getGradCRF(List <double> gradList, model m, dataSeq x, baseHashSet <int> idSet)
{
if (idSet != null)
{
idSet.Clear();
}
int nTag = m.NTag;
//compute beliefs
belief bel = new belief(x.Count, nTag);
belief belMasked = new belief(x.Count, nTag);
//store the YY and Y
List <dMatrix> YYlist = new List <dMatrix>(), maskYYlist = new List <dMatrix>();
List <List <double> > Ylist = new List <List <double> >(), maskYlist = new List <List <double> >();
_inf.getYYandY(m, x, YYlist, Ylist, maskYYlist, maskYlist);
_inf.getBeliefs(bel, m, x, YYlist, Ylist);
_inf.getBeliefs(belMasked, m, x, maskYYlist, maskYlist);
double ZGold = belMasked.Z;
double Z = bel.Z;
List <featureTemp> fList;
//Loop over nodes to compute features and update the gradient
for (int i = 0; i < x.Count; i++)
{
fList = _fGene.getFeatureTemp(x, i);
foreach (featureTemp im in fList)
{
for (int s = 0; s < nTag; s++)
{
int f = _fGene.getNodeFeatID(im.id, s);
if (idSet != null)
{
idSet.Add(f);
}
gradList[f] += bel.belState[i][s] * im.val;
gradList[f] -= belMasked.belState[i][s] * im.val;
}
}
}
//Loop over edges to compute features and update the gradient
for (int i = 1; i < x.Count; i++)
{
//non-rich
if (Global.useTraditionalEdge)
{
for (int s = 0; s < nTag; s++)
{
for (int sPre = 0; sPre < nTag; sPre++)
{
int f = _fGene.getEdgeFeatID(sPre, s);
if (idSet != null)
{
idSet.Add(f);
}
gradList[f] += bel.belEdge[i][sPre, s];
gradList[f] -= belMasked.belEdge[i][sPre, s];
}
}
}
//rich
fList = _fGene.getFeatureTemp(x, i);
foreach (featureTemp im in fList)
{
int id = im.id;
if (id < _fGene.getNRichFeatTemp())
{
for (int s = 0; s < nTag; s++)
{
for (int sPre = 0; sPre < nTag; sPre++)
{
int f = _fGene.getEdgeFeatID(id, sPre, s);
if (idSet != null)
{
idSet.Add(f);
}
gradList[f] += bel.belEdge[i][sPre, s] * im.val;
gradList[f] -= belMasked.belEdge[i][sPre, s] * im.val;
}
}
}
}
}
return(Z - ZGold);//-log{P(y*|x,w)}
}
