public double decodeViterbi(model m, dataSeq x, List <int> tags)
{
tags.Clear();
int nNode = x.Count;
int nTag = m.NTag;
dMatrix YY = new dMatrix(nTag, nTag);
double[] dAry = new double[nTag];
List <double> Y = new List <double>(dAry);
Viterbi viter = new Viterbi(nNode, nTag);
for (int i = 0; i < nNode; i++)
{
getLogYY(m, x, i, ref YY, ref Y, false, false);
viter.setScores(i, Y, YY);
}
List <int> states = new List <int>();
double numer = viter.runViterbi(ref states, false);
for (int i = 0; i < states.Count; i++)
{
int tag = states[i];
tags.Add(tag);
}
double Z = getZ(m, x, false);
return(Math.Exp(numer - Z));
}
public double getZ(model m, dataSeq x, bool mask)
{
belief bel = new belief(x.Count, m.NTag);
getBeliefs(bel, m, x, mask);
return(bel.Z);
}
//fast viterbi decode without probability
public void decodeViterbi_test(model m, dataSeq x, List <int> tags)
{
tags.Clear();
int nNode = x.Count;
int nState = m.NState;
dMatrix YY = new dMatrix(nState, nState);
double[] dAry = new double[nState];
List <double> Y = new List <double>(dAry);
Viterbi viter = new Viterbi(nNode, nState);
for (int i = 0; i < nNode; i++)
{
getLogYY(m, x, i, ref YY, ref Y, false, false);
viter.setScores(i, Y, YY);
}
List <int> states = new List <int>();
double numer = viter.runViterbi(ref states, false);
for (int i = 0; i < states.Count; i++)
{
int tag = m.hStateToTag(states[i]);
tags.Add(tag);
}
}
public static dataSet structSplit(dataSet X)
{
//make fractions
dataSet X2 = new dataSet(X.NTag, X.NFeature);
for (int t = 0; t < X.Count; t++)
{
dataSeq x = X[t];
if (Global.structReg && Global.miniSize != 0)
{
/*int step = getStep();
* //if (x.Count > 4 * step)
* if (x.Count > 4 * step && Global.segStep.ToString().Contains(".") == false)//divide x to 2 segments, then do fine segments
* {
* int rand = randomTool.getOneRandom_int(step, x.Count - step);
* dataSeq x1 = new dataSeq(x, 0, rand);
* dataSeq x2 = new dataSeq(x, rand, x.Count);
* getSegments(x1, X2);
* getSegments(x2, X2);
* }
* else*/
getSegments(x, X2);
}
else
{
X2.Add(x);
}
}
return(X2);
}
static void getSegments(dataSeq x, dataSet X2)
{
int rand = randomTool.getOneRandom_int(-100, 100);
if (rand <= 0)//forward
{
for (int node = 0; node < x.Count;)
{
int step = getStep();
dataSeq x2 = new dataSeq(x, node, step + Global.overlapLength);
X2.Add(x2);
node += step;
}
}
else//backward
{
for (int node = x.Count - 1; node >= 0;)
{
int step = getStep();
dataSeq x2 = new dataSeq(x, node, step + Global.overlapLength, false);
X2.Add(x2);
node -= step;
}
}
}
public void getYYandY(model m, dataSeq x, List <dMatrix> YYlist, List <List <double> > Ylist)
{
int nNodes = x.Count;
//int nTag = m.NTag;
int nTag = m.NTag;
double[] dAry = new double[nTag];
bool mask = false;
try
{
//Global.rwlock.AcquireReaderLock(Global.readWaitTime);
for (int i = 0; i < nNodes; i++)
{
dMatrix YYi = new dMatrix(nTag, nTag);
List <double> Yi = new List <double>(dAry);
//compute the Mi matrix
getLogYY(m, x, i, ref YYi, ref Yi, false, mask);
YYlist.Add(YYi);
Ylist.Add(Yi);
}
//Global.rwlock.ReleaseReaderLock();
}
catch (ApplicationException)
{
Console.WriteLine("read out time!");
}
}
public dataSeq(dataSeq x, int n, int length, bool forward = true)
{
int start = -1, end = -1;
if (forward)//forward
{
start = n;
if (n + length < x.Count)
{
end = n + length;
}
else
{
end = x.Count;
}
}
else//backward
{
end = n + 1;
if (end - length >= 0)
{
start = end - length;
}
else
{
start = 0;
}
}
for (int i = start; i < end; i++)
{
featureTemps.Add(x.featureTemps[i]);
yGold.Add(x.yGold[i]);
}
}
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];
}
}
}
void updateWeights(dataSeq x, List <int> outStates, List <int> goldStates, float[] w, float[] accumW, int xsize, int k)
{
for (int n = 0; n < x.Count; n++)
{
int outState = outStates[n];
int goldState = goldStates[n];
//update the weights and accumulative weights
foreach (featureTemp im in _fGene.getFeatureTemp(x, n))
{
int f = _fGene.getNodeFeatID(im.id, outState);
float fv = (float)im.val;
w[f] -= fv;
float t = xsize - k;
accumW[f] -= t * fv;
f = _fGene.getNodeFeatID(im.id, goldState);
w[f] += fv;
accumW[f] += t * fv;
}
if (n > 0)
{
int f = _fGene.getEdgeFeatID(outStates[n - 1], outState);
float fv = 1;
w[f] -= fv;
float t = xsize - k;
accumW[f] -= t * fv;
f = _fGene.getEdgeFeatID(goldStates[n - 1], goldState);
w[f] += fv;
accumW[f] += t * fv;
}
}
}
//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 sgd_lazyReg()
{
List <double> w = _model.W;
int fsize = w.Count;
int xsize = _X.Count;
double[] ary = new double[fsize];
List <double> grad = new List <double>(ary);
List <int> ri = randomTool <int> .getShuffledIndexList(xsize);
double error = 0;
double r_k = 0;
for (int t = 0; t < xsize; t++)
{
int ii = ri[t];
dataSeq x = _X[ii];
baseHashSet <int> fset = new baseHashSet <int>();
double err = _grad.getGrad_SGD(grad, _model, x, fset);
error += err;
r_k = Global.rate0 * Math.Pow(Global.decayFactor, (double)Global.countWithIter / (double)xsize);
if (Global.countWithIter % (xsize / 4) == 0)
{
Global.swLog.WriteLine("iter{0} decay_rate={1}", Global.glbIter, r_k.ToString("e2"));
}
foreach (int i in fset)
{
w[i] -= r_k * grad[i];
//reset
grad[i] = 0;
}
Global.countWithIter++;
}
if (Global.reg != 0)
{
for (int i = 0; i < fsize; i++)
{
double grad_i = w[i] / (Global.reg * Global.reg);
w[i] -= r_k * grad_i;
}
double sum = listTool.squareSum(w);
error += sum / (2.0 * Global.reg * Global.reg);
}
Global.diff = convergeTest(error);
return(error);
}
//fast viterbi decode without probability
public void decodeViterbi_train(model m, dataSeq x, List <dMatrix> YYlist, List <List <double> > Ylist, List <int> tags)
{
int nNode = x.Count;
int nState = m.NState;
Viterbi viter = new Viterbi(nNode, nState);
for (int i = 0; i < nNode; i++)
{
viter.setScores(i, Ylist[i], YYlist[i]);
}
double numer = viter.runViterbi(ref tags, false);
}
//the scalar version
virtual 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);
List <double> w = m.W;
List <featureTemp> fList = _fGene.getFeatureTemp(x, i);
int nTag = m.NTag;
for (int j = 0; j < fList.Count; j++)
{
featureTemp ptr = fList[j];
int id = ptr.id;
double v = ptr.val;
for (int s = 0; s < nTag; s++)
{
int f = _fGene.getNodeFeatID(id, s);
Y[s] += w[f] * scalar * v;
}
}
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] * scalar;
}
}
}
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;
}
}
}
}
override 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 nState = m.NState;
foreach (featureTemp ft in fList)
{
for (int s = 0; s < nState; s++)
{
int f = _fGene.getNodeFeatID(ft.id, s);
Y[s] += w[f] * ft.val;
}
}
if (i > 0)
{
foreach (featureTemp im in fList)
{
for (int s = 0; s < nState; s++)
{
for (int sPre = 0; sPre < nState; sPre++)
{
int f = _fGene.getEdgeFeatID(im.id, sPre, s);
YY[sPre, s] += w[f] * 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;
}
}
}
}
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));
}
public void getYYandY(model m, dataSeq x, List <dMatrix> YYlist, List <List <double> > Ylist, List <dMatrix> maskYYlist, List <List <double> > maskYlist)
{
int nNodes = x.Count;
//int nTag = m.NTag;
int nState = m.NState;
double[] dAry = new double[nState];
bool mask = false;
try
{
//Global.rwlock.AcquireReaderLock(Global.readWaitTime);
for (int i = 0; i < nNodes; i++)
{
dMatrix YYi = new dMatrix(nState, nState);
List <double> Yi = new List <double>(dAry);
//compute the Mi matrix
getLogYY(m, x, i, ref YYi, ref Yi, false, mask);
YYlist.Add(YYi);
Ylist.Add(Yi);
maskYYlist.Add(new dMatrix(YYi));
maskYlist.Add(new List <double>(Yi));
}
//Global.rwlock.ReleaseReaderLock();
}
catch (ApplicationException)
{
Console.WriteLine("read out time!");
}
//get the masked YY and Y
double maskValue = double.MinValue;
dMatrix statesPerNodes = m.getStatesPerNode(x);
for (int i = 0; i < nNodes; i++)
{
List <double> Y = maskYlist[i];
List <int> tagList = x.getTags();
for (int s = 0; s < Y.Count; s++)
{
if (statesPerNodes[i, s] == 0)
{
Y[s] = maskValue;
}
}
}
}
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;
}
}
}
}
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 dataSeq(dataSeq x, int n, int length)
{
int end = 0;
if (n + length < x.Count)
{
end = n + length;
}
else
{
end = x.Count;
}
for (int i = n; i < end; i++)
{
featureTemps.Add(x.featureTemps[i]);
yGold.Add(x.yGold[i]);
}
}
public dMatrix getStatesPerNode(dataSeq x)
{
int n = x.Count;
dMatrix spn = x.GoldStatesPerNode;
if (spn == null || spn.R == 0)
{
List <int> tList = x.getTags();
spn = new dMatrix(tList.Count, _nTag);
for (int i = 0; i < tList.Count; i++)
{
int tag = tList[i];
spn[i, tag] = 1;
}
x.GoldStatesPerNode = spn;
}
return(spn);
}
public datasetList(string fileFeature, string fileTag)
{
StreamReader srfileFeature = new StreamReader(fileFeature);
StreamReader srfileTag = new StreamReader(fileTag);
string txt = srfileFeature.ReadToEnd();
txt = txt.Replace("\r", "");
string[] fAry = txt.Split(Global.triLineEndAry, StringSplitOptions.RemoveEmptyEntries);
txt = srfileTag.ReadToEnd();
txt = txt.Replace("\r", "");
string[] tAry = txt.Split(Global.triLineEndAry, StringSplitOptions.RemoveEmptyEntries);
if (fAry.Length != tAry.Length)
{
throw new Exception("error");
}
_nFeature = int.Parse(fAry[0]);
_nTag = int.Parse(tAry[0]);
for (int i = 1; i < fAry.Length; i++)
{
string fBlock = fAry[i];
string tBlock = tAry[i];
dataSet ds = new dataSet();
string[] fbAry = fBlock.Split(Global.biLineEndAry, StringSplitOptions.RemoveEmptyEntries);
string[] lbAry = tBlock.Split(Global.biLineEndAry, StringSplitOptions.RemoveEmptyEntries);
for (int k = 0; k < fbAry.Length; k++)
{
string fm = fbAry[k];
string tm = lbAry[k];
dataSeq seq = new dataSeq();
seq.read(fm, tm);
ds.Add(seq);
}
Add(ds);
}
srfileFeature.Close();
srfileTag.Close();
}
//fast viterbi decode without probability
public void decodeViterbi_test(model m, dataSeq x, List <int> tags)
{
tags.Clear();
int nNode = x.Count;
int nTag = m.NTag;
dMatrix YY = new dMatrix(nTag, nTag);
double[] dAry = new double[nTag];
List <double> Y = new List <double>(dAry);
Viterbi viter = new Viterbi(nNode, nTag);
for (int i = 0; i < nNode; i++)
{
getLogYY(m, x, i, ref YY, ref Y, false, false);
viter.setScores(i, Y, YY);
}
viter.runViterbi(ref tags);
}
public dMatrix getStatesPerNode(dataSeq x)
{
int n = x.Count;
dMatrix spn = x.GoldStatesPerNode;
if (spn == null || spn.R == 0)
{
List <int> tList = x.getTags();
spn = new dMatrix(tList.Count, _nHiddenState);
for (int i = 0; i < tList.Count; i++)
{
int tag = tList[i];
List <int> states = tagToHiddenStates(tag);
foreach (int s in states)
{
spn[i, s] = 1;
}
}
x.GoldStatesPerNode = spn;
}
return(spn);
}
public dataSeqTest(dataSeq x, List <int> yOutput)
{
_x = x;
_yOutput = yOutput;
}
virtual public void getFeatures(dataSeq x, int node, ref List <List <int> > nodeFeature, ref int[,] edgeFeature)
{
throw new Exception("error");
}
public List <featureTemp> getFeatureTemp(dataSeq x, int node)
{
return(x.getFeatureTemp(node));
}
//ADF training
public double adf()
{
float[] w = _model.W;
int fsize = w.Length;
int xsize = _X.Count;
List <double> grad = new List <double>(new double[fsize]);
double error = 0;
List <int> featureCountList = new List <int>(new int[fsize]);
List <int> ri = randomTool <int> .getShuffledIndexList(xsize);//random shuffle of training samples
Global.interval = xsize / Global.nUpdate;
int nSample = 0;//#sample in an update interval
for (int t = 0; t < xsize; t += Global.miniBatch)
{
List <dataSeq> XX = new List <dataSeq>();
bool end = false;
for (int k = t; k < t + Global.miniBatch; k++)
{
int i = ri[k];
dataSeq x = _X[i];
XX.Add(x);
if (k == xsize - 1)
{
end = true;
break;
}
}
int mbSize = XX.Count;
nSample += mbSize;
baseHashSet <int> fSet = new baseHashSet <int>();
double err = _grad.getGrad_SGD_miniBatch(grad, _model, XX, fSet);
error += err;
foreach (int i in fSet)
{
featureCountList[i]++;
}
bool check = false;
for (int k = t; k < t + Global.miniBatch; k++)
{
if (t != 0 && k % Global.interval == 0)
{
check = true;
}
}
//update decay rates
if (check || end)
{
for (int i = 0; i < fsize; i++)
{
int v = featureCountList[i];
double u = (double)v / (double)nSample;
double eta = Global.upper - (Global.upper - Global.lower) * u;
Global.decayList[i] *= eta;
}
//reset
for (int i = 0; i < featureCountList.Count; i++)
{
featureCountList[i] = 0;
}
}
//update weights
foreach (int i in fSet)
{
w[i] -= (float)(Global.decayList[i] * grad[i]);
//reset
grad[i] = 0;
}
//reg
if (check || end)
{
if (Global.reg != 0)
{
for (int i = 0; i < fsize; i++)
{
double grad_i = w[i] / (Global.reg * Global.reg) * ((double)nSample / (double)xsize);
w[i] -= (float)(Global.decayList[i] * grad_i);
}
}
//reset
nSample = 0;
}
Global.countWithIter += mbSize;
}
if (Global.reg != 0)
{
double sum = arrayTool.squareSum(w);
error += sum / (2.0 * Global.reg * Global.reg);
}
Global.diff = convergeTest(error);
return(error);
}
public double sgd_exactReg()
{
double scalar = 1, scalarOld = 1;
float[] w = _model.W;
int fsize = w.Length;
int xsize = _X.Count;
double newReg = Global.reg * Math.Sqrt(xsize);
double oldReg = Global.reg;
Global.reg = newReg;
double[] tmpAry = new double[fsize];
List <double> grad = new List <double>(tmpAry);
List <int> ri = randomTool <int> .getShuffledIndexList(xsize);
double error = 0;
double r_k = 0;
for (int t = 0; t < xsize; t++)
{
int ii = ri[t];
dataSeq x = _X[ii];
baseHashSet <int> fset = new baseHashSet <int>();
double err = _grad.getGrad_SGD(grad, scalar, _model, x, fset);
error += err;
//decaying rate: r_k = r_0 * beta^(k/N), with 0<r_0<=1, 0<beta<1
r_k = Global.rate0 * Math.Pow(Global.decayFactor, (double)Global.countWithIter / (double)xsize);
if (Global.countWithIter % (xsize / 4) == 0)
{
Global.swLog.WriteLine("iter{0} decay_rate={1}", Global.glbIter, r_k.ToString("e2"));
}
//reg
if (t % Global.scalarResetStep == 0)
{
//reset
for (int i = 0; i < fsize; i++)
{
w[i] *= (float)scalar;
}
scalar = scalarOld = 1;
}
else
{
scalarOld = scalar;
scalar *= 1 - r_k / (Global.reg * Global.reg);
}
foreach (int i in fset)
{
double realWeight = w[i] * scalarOld;
double grad_i = grad[i] + realWeight / (Global.reg * Global.reg);
realWeight = realWeight - r_k * grad_i;
w[i] = (float)(realWeight / scalar);
//reset
grad[i] = 0;
}
Global.countWithIter++;
}
//recover the real weights
for (int i = 0; i < fsize; i++)
{
w[i] *= (float)scalar;
}
if (Global.reg != 0.0)
{
double sum = arrayTool.squareSum(w);
error += sum / (2.0 * Global.reg * Global.reg);
}
Global.diff = convergeTest(error);
Global.reg = oldReg;
return(error);
}
//SGD with lazy reg
public double sgd_lazyReg()
{
float[] w = _model.W;
int fsize = w.Length;
int xsize = _X.Count;
double[] ary = new double[fsize];
List <double> grad = new List <double>(ary);
List <int> ri = randomTool <int> .getShuffledIndexList(xsize);
double error = 0;
double r_k = 0;
for (int t = 0; t < xsize; t += Global.miniBatch)
{
List <dataSeq> XX = new List <dataSeq>();
for (int k = t; k < t + Global.miniBatch; k++)
{
int i = ri[k];
dataSeq x = _X[i];
XX.Add(x);
if (k == xsize - 1)
{
break;
}
}
int mbSize = XX.Count;
baseHashSet <int> fset = new baseHashSet <int>();
double err = _grad.getGrad_SGD_miniBatch(grad, _model, XX, fset);
error += err;
//decaying rate: r_k = r_0 * beta^(k/N), with 0<r_0<=1, 0<beta<1
r_k = Global.rate0 * Math.Pow(Global.decayFactor, (double)Global.countWithIter / (double)xsize);
if (Global.countWithIter % (xsize / 4) == 0)
{
Global.swLog.WriteLine("iter{0} decay_rate={1}", Global.glbIter, r_k.ToString("e2"));
}
foreach (int i in fset)
{
//because dgrad[i] is the grad of -log(obj), minus the gradient to find the minumum point
w[i] -= (float)(r_k * grad[i]);
//reset
grad[i] = 0;
}
Global.countWithIter += mbSize;
}
if (Global.reg != 0)
{
for (int i = 0; i < fsize; i++)
{
double grad_i = w[i] / (Global.reg * Global.reg);
w[i] -= (float)(r_k * grad_i);
}
double sum = arrayTool.squareSum(w);
error += sum / (2.0 * Global.reg * Global.reg);
}
Global.diff = convergeTest(error);
return(error);
}
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;
}
