// 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;
}
}
}
}
