コード例 #1
0
        virtual public void getLogYY(model m, dataSeq x, int i, ref dMatrix YY, ref List <double> Y, bool takeExp, bool mask)
        {
            YY.set(0);
            listTool.listSet(ref Y, 0);

            float[]            w     = m.W;
            List <featureTemp> fList = _fGene.getFeatureTemp(x, i);
            int nTag = m.NTag;

            foreach (featureTemp ft in fList)
            {
                for (int s = 0; s < nTag; s++)
                {
                    int f = _fGene.getNodeFeatID(ft.id, s);
                    Y[s] += w[f] * ft.val;
                }
            }
            if (i > 0)
            {
                for (int s = 0; s < nTag; s++)
                {
                    for (int sPre = 0; sPre < nTag; sPre++)
                    {
                        int f = _fGene.getEdgeFeatID(sPre, s);
                        YY[sPre, s] += w[f];
                    }
                }
            }
            double maskValue = double.MinValue;

            if (takeExp)
            {
                listTool.listExp(ref Y);
                YY.eltExp();
                maskValue = 0;
            }
            if (mask)
            {
                List <int> tagList = x.getTags();
                for (int s = 0; s < Y.Count; s++)
                {
                    if (tagList[i] != s)
                    {
                        Y[s] = maskValue;
                    }
                }
            }
        }
コード例 #2
0
ファイル: CRF.Gradient.cs プロジェクト: zhangxt/LancoSeg
        virtual public double getGradCRF(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);
            //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);
                        }

                        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);
                        if (idSet != null)
                        {
                            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)}
        }