// this = m1 * m2; (right multiply) public void multiply(dMatrix m1, dMatrix m2) { if (m2._r != m1._c) { throw new Exception("error"); } _biAry = new double[m1._r, m2._c]; int m1r, i, m2c; for (m1r = 0; m1r < m1._r; m1r++) { for (m2c = 0; m2c < m2._c; m2c++) { double value = 0; for (i = 0; i < m1._c; i++) { value += m1[m1r, i] * m2[i, m2c]; } _biAry[m1r, m2c] = value; } } }
public void getBeliefs(belief bel, model m, dataSeq x, bool mask) { int nNodes = x.Count; int nStates = m.NTag; dMatrix YY = new dMatrix(nStates, nStates); double[] dAry = new double[nStates]; List <double> Y = new List <double>(dAry); List <double> alpha_Y = new List <double>(dAry); List <double> newAlpha_Y = new List <double>(dAry); List <double> tmp_Y = new List <double>(dAry); for (int i = nNodes - 1; i > 0; i--) { getLogYY(m, x, i, ref YY, ref Y, false, mask); listTool.listSet(ref tmp_Y, bel.belState[i]); listTool.listAdd(ref tmp_Y, Y); logMultiply(YY, tmp_Y, bel.belState[i - 1]); } //compute Alpha values for (int i = 0; i < nNodes; i++) { getLogYY(m, x, i, ref YY, ref Y, false, mask); if (i > 0) { listTool.listSet(ref tmp_Y, alpha_Y); YY.transpose(); logMultiply(YY, tmp_Y, newAlpha_Y); listTool.listAdd(ref newAlpha_Y, Y); } else { listTool.listSet(ref newAlpha_Y, Y); } if (i > 0) { listTool.listSet(ref tmp_Y, Y); listTool.listAdd(ref tmp_Y, bel.belState[i]); YY.transpose(); bel.belEdge[i].set(YY); for (int yPre = 0; yPre < nStates; yPre++) { for (int y = 0; y < nStates; y++) { bel.belEdge[i][yPre, y] += tmp_Y[y] + alpha_Y[yPre]; } } } List <double> tmp = bel.belState[i]; listTool.listAdd(ref tmp, newAlpha_Y); listTool.listSet(ref alpha_Y, newAlpha_Y); } double Z = logSum(alpha_Y); for (int i = 0; i < nNodes; i++) { List <double> tmp = bel.belState[i]; listTool.listAdd(ref tmp, -Z); listTool.listExp(ref tmp); } for (int i = 1; i < nNodes; i++) { bel.belEdge[i].add(-Z); bel.belEdge[i].eltExp(); } bel.Z = Z; }
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; //node feature foreach (featureTemp im in fList) { nodeFeature[] features = Global.idNodeFeatures[im.id]; foreach (nodeFeature feat in features) { int f = feat._id; int s = feat._s; Y[s] += w[f] * im.val; } } if (i > 0) { //non-rich edge if (Global.useTraditionalEdge) { 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]; } } } //rich edge foreach (featureTemp im in fList) { edgeFeature[] features = Global.idEdgeFeatures[im.id]; foreach (edgeFeature feat in features) { YY[feat._sPre, feat._s] += w[feat._id] * im.val; } } //rich2 if (Global.richFeat2) { List <featureTemp> fList2 = _fGene.getFeatureTemp(x, i - 1); foreach (featureTemp im in fList2) { edgeFeature[] features = Global.idEdgeFeatures2[im.id]; foreach (edgeFeature feat in features) { YY[feat._sPre, feat._s] += w[feat._id] * im.val; } } } } double maskValue = double.MinValue; if (takeExp) { listTool.listExp(ref Y); YY.eltExp(); maskValue = 0; } if (mask) { dMatrix statesPerNodes = m.getStatesPerNode(x); for (int s = 0; s < Y.Count; s++) { if (statesPerNodes[i, s] == 0) { Y[s] = maskValue; } } } }
//get beliefs (mariginal probabilities) public void getBeliefs(belief bel, model m, dataSeq x, List <dMatrix> YYlist, List <List <double> > Ylist) { int nNodes = x.Count; int nTag = m.NTag; //dMatrix YY = new dMatrix(nTag, nTag); double[] dAry = new double[nTag]; //List<double> Y = new List<double>(dAry); List <double> alpha_Y = new List <double>(dAry); List <double> newAlpha_Y = new List <double>(dAry);//marginal probability from left to current node (including values of the current node) List <double> tmp_Y = new List <double>(dAry); //compute beta values in a backward scan for (int i = nNodes - 1; i > 0; i--) { dMatrix YY = YYlist[i]; List <double> Y = Ylist[i]; //compute the Mi matrix //getLogYY(m, x, i, ref YY, ref Y, false, mask); listTool.listSet(ref tmp_Y, bel.belState[i]);//this is meaningful from the 2nd round listTool.listAdd(ref tmp_Y, Y); logMultiply(YY, tmp_Y, bel.belState[i - 1]); } //compute alpha values for (int i = 0; i < nNodes; i++) { dMatrix YY = null; if (i > 0) { YY = new dMatrix(YYlist[i]);//should use the copy to avoid change } List <double> Y = Ylist[i]; //compute the Mi matrix //getLogYY(m, x, i, ref YY, ref Y, false, mask); if (i > 0) { listTool.listSet(ref tmp_Y, alpha_Y);//this is meaningful from the 2nd round YY.transpose(); logMultiply(YY, tmp_Y, newAlpha_Y); listTool.listAdd(ref newAlpha_Y, Y); } else { listTool.listSet(ref newAlpha_Y, Y); } //setting marginal probability on edges if (i > 0) { //beta + Y listTool.listSet(ref tmp_Y, Y); listTool.listAdd(ref tmp_Y, bel.belState[i]); //YY YY.transpose(); bel.belEdge[i].set(YY); //belief = alpha + YY + beta + Y for (int yPre = 0; yPre < nTag; yPre++) { for (int y = 0; y < nTag; y++) { bel.belEdge[i][yPre, y] += tmp_Y[y] + alpha_Y[yPre]; } } } //setting marginal probability on nodes List <double> tmp = bel.belState[i]; //beta listTool.listAdd(ref tmp, newAlpha_Y); //belief = alpha + beta listTool.listSet(ref alpha_Y, newAlpha_Y); } double Z = logSum(alpha_Y); for (int i = 0; i < nNodes; i++) { List <double> tmp = bel.belState[i]; listTool.listAdd(ref tmp, -Z); listTool.listExp(ref tmp); } for (int i = 1; i < nNodes; i++) { bel.belEdge[i].add(-Z); bel.belEdge[i].eltExp(); } bel.Z = Z;//the overall potential function value }
//the scalar version override public void getLogYY(double scalar, 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] * scalar * ft.val; } } if (i > 0) { //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); YY[sPre, s] += w[f] * scalar; } } } //rich 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); YY[sPre, s] += w[f] * scalar * im.val; } } } } } 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; } } } }