void GetEigenVectorCentr(const PUNGraph& Graph, TIntFltH& EigenH, const double& Eps, const int& MaxIter) {
const int NNodes = Graph->GetNodes();
EigenH.Gen(NNodes);
for (TUNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++) {
EigenH.AddDat(NI.GetId(), 1.0/NNodes);
IAssert(NI.GetId() == EigenH.GetKey(EigenH.Len()-1));
}
TFltV TmpV(NNodes);
double diff = TFlt::Mx;
for (int iter = 0; iter < MaxIter; iter++) {
int j = 0;
for (TUNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++, j++) {
TmpV[j] = 0;
for (int e = 0; e < NI.GetOutDeg(); e++) {
TmpV[j] += EigenH.GetDat(NI.GetOutNId(e)); }
}
double sum = 0;
for (int i = 0; i < TmpV.Len(); i++) {
EigenH[i] = TmpV[i];
sum += EigenH[i];
}
for (int i = 0; i < EigenH.Len(); i++) {
EigenH[i] /= sum; }
if (fabs(diff-sum) < Eps) { break; }
//printf("\tdiff:%f\tsum:%f\n", fabs(diff-sum), sum);
diff = sum;
}
}
int GetWeightedPageRankMP1(const PNEANet Graph, TIntFltH& PRankH, const TStr& Attr, const double& C, const double& Eps, const int& MaxIter) {
if (!Graph->IsFltAttrE(Attr)) return -1;
TFltV Weights = Graph->GetFltAttrVecE(Attr);
int mxid = Graph->GetMxNId();
TFltV OutWeights(mxid);
Graph->GetWeightOutEdgesV(OutWeights, Weights);
/*for (TNEANet::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++) {
OutWeights[NI.GetId()] = Graph->GetWeightOutEdges(NI, Attr);
}*/
/*TIntFltH Weights;
for (TNEANet::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++) {
Weights.AddDat(NI.GetId(), Graph->GetWeightOutEdges(NI, Attr));
}*/
const int NNodes = Graph->GetNodes();
TVec<TNEANet::TNodeI> NV;
//const double OneOver = 1.0/double(NNodes);
PRankH.Gen(NNodes);
for (TNEANet::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++) {
NV.Add(NI);
PRankH.AddDat(NI.GetId(), 1.0/NNodes);
//IAssert(NI.GetId() == PRankH.GetKey(PRankH.Len()-1));
}
TFltV TmpV(NNodes);
for (int iter = 0; iter < MaxIter; iter++) {
#pragma omp parallel for schedule(dynamic,10000)
for (int j = 0; j < NNodes; j++) {
TNEANet::TNodeI NI = NV[j];
TmpV[j] = 0;
for (int e = 0; e < NI.GetInDeg(); e++) {
const int InNId = NI.GetInNId(e);
const TFlt OutWeight = OutWeights[InNId];
int EId = Graph->GetEId(InNId, NI.GetId());
const TFlt Weight = Weights[Graph->GetFltKeyIdE(EId)];
if (OutWeight > 0) {
TmpV[j] += PRankH.GetDat(InNId) * Weight / OutWeight; }
}
TmpV[j] = C*TmpV[j]; // Berkhin (the correct way of doing it)
//TmpV[j] = C*TmpV[j] + (1.0-C)*OneOver; // iGraph
}
double diff=0, sum=0, NewVal;
#pragma omp parallel for reduction(+:sum) schedule(dynamic,10000)
for (int i = 0; i < TmpV.Len(); i++) { sum += TmpV[i]; }
const double Leaked = (1.0-sum) / double(NNodes);
#pragma omp parallel for reduction(+:diff) schedule(dynamic,10000)
for (int i = 0; i < PRankH.Len(); i++) { // re-instert leaked PageRank
NewVal = TmpV[i] + Leaked; // Berkhin
//NewVal = TmpV[i] / sum; // iGraph
diff += fabs(NewVal-PRankH[i]);
PRankH[i] = NewVal;
}
if (diff < Eps) { break; }
}
return 0;
}
double TAGMFast::GetStepSizeByLineSearch(const int UID, const TIntFltH& DeltaV, const TIntFltH& GradV, const double& Alpha, const double& Beta, const int MaxIter) {
double StepSize = 1.0;
double InitLikelihood = LikelihoodForRow(UID);
TIntFltH NewVarV(DeltaV.Len());
for(int iter = 0; iter < MaxIter; iter++) {
for (int i = 0; i < DeltaV.Len(); i++){
int CID = DeltaV.GetKey(i);
double NewVal = GetCom(UID, CID) + StepSize * DeltaV.GetDat(CID);
if (NewVal < MinVal) { NewVal = MinVal; }
if (NewVal > MaxVal) { NewVal = MaxVal; }
NewVarV.AddDat(CID, NewVal);
}
if (LikelihoodForRow(UID, NewVarV) < InitLikelihood + Alpha * StepSize * DotProduct(GradV, DeltaV)) {
StepSize *= Beta;
} else {
break;
}
if (iter == MaxIter - 1) {
StepSize = 0.0;
break;
}
}
return StepSize;
}
void GetEigenVectorCentr(const PUNGraph& Graph, TIntFltH& NIdEigenH, const double& Eps, const int& MaxIter) {
const int NNodes = Graph->GetNodes();
NIdEigenH.Gen(NNodes);
// initialize vector values
for (TUNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++) {
NIdEigenH.AddDat(NI.GetId(), 1.0 / NNodes);
IAssert(NI.GetId() == NIdEigenH.GetKey(NIdEigenH.Len() - 1));
}
TFltV TmpV(NNodes);
for (int iter = 0; iter < MaxIter; iter++) {
int j = 0;
// add neighbor values
for (TUNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++, j++) {
TmpV[j] = 0;
for (int e = 0; e < NI.GetOutDeg(); e++) {
TmpV[j] += NIdEigenH.GetDat(NI.GetOutNId(e));
}
}
// normalize
double sum = 0;
for (int i = 0; i < TmpV.Len(); i++) {
sum += (TmpV[i] * TmpV[i]);
}
sum = sqrt(sum);
for (int i = 0; i < TmpV.Len(); i++) {
TmpV[i] /= sum;
}
// compute difference
double diff = 0.0;
j = 0;
for (TUNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++, j++) {
diff += fabs(NIdEigenH.GetDat(NI.GetId()) - TmpV[j]);
}
// set new values
j = 0;
for (TUNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++, j++) {
NIdEigenH.AddDat(NI.GetId(), TmpV[j]);
}
if (diff < Eps) {
break;
}
}
}
int main(int argc, char* argv[]) {
Env = TEnv(argc, argv, TNotify::StdNotify);
Env.PrepArgs(TStr::Fmt("Inverse PageRank. Build: %s, %s. Time: %s", __TIME__, __DATE__, TExeTm::GetCurTm()));
TExeTm ExeTm;
Try
const TStr Iput = Env.GetIfArgPrefixStr("-i:", "Input.txt", "Input File" );
const TStr Oput = Env.GetIfArgPrefixStr("-o:", "Output.txt", "Output File");
FILE* fpI = fopen(Iput.CStr(), "r");
FILE* fpO = fopen(Oput.CStr(), "w");
const double C = 0.85;
const int MaxIter = 50;
const double Eps = 1e-9;
PNGraph Graph = TSnap::LoadEdgeList< PNGraph > (Iput);
fprintf(fpO, "\nNodes: %d, Edges: %d\n\n", Graph->GetNodes(), Graph->GetEdges());
const int NNodes = Graph->GetNodes();
const double OneOver = (double) 1.0 / (double) NNodes;
TIntFltH PRankH;
PRankH.Gen(NNodes);
for (TNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++)
PRankH.AddDat(NI.GetId(), OneOver);
TFltV TmpV(NNodes);
for (int iter = 0; iter < MaxIter; iter++) {
int j = 0;
for (TNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++, j++) {
TmpV[j] = 0;
for (int e = 0; e < NI.GetOutDeg(); e++) {
const int OutNId = NI.GetOutNId(e);
const int InDeg = Graph->GetNI(OutNId).GetInDeg();
if (InDeg > 0)
TmpV[j] += PRankH.GetDat(OutNId) / InDeg;
}
TmpV[j] = C * TmpV[j];
}
for (int i = 0; i < PRankH.Len(); i++)
PRankH[i] = TmpV[i];
/*
double diff = 0, sum = 0, NewVal;
for (int i = 0; i < TmpV.Len(); i++)
sum += TmpV[i];
const double Leaked = (double) (1.0 - sum) / (double) NNodes;
for (int i = 0; i < PRankH.Len(); i++) {
NewVal = TmpV[i] + Leaked;
diff += fabs(NewVal - PRankH[i]);
PRankH[i] = NewVal;
}
if (diff < Eps)
break;
*/
}
fprintf(fpO, "Node ID\t\tInverse PageRank\n");
for (TNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++){
int Id = NI.GetId();
double ipr = PRankH.GetDat(Id);
fprintf(fpO, "%d\t\t\t%.5lf\n", Id, ipr);
}
Catch
printf("\nRun Time: %s (%s)\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
return 0;
}
int main(int argc, char* argv[]) {
Env = TEnv(argc, argv, TNotify::StdNotify);
Env.PrepArgs(TStr::Fmt("Trust Rank. Build: %s, %s. Time: %s", __TIME__, __DATE__, TExeTm::GetCurTm()));
TExeTm ExeTm;
Try
const TStr Gnod = Env.GetIfArgPrefixStr("-g:", "Gnode.txt", "Good Nodes");
const TStr Bnod = Env.GetIfArgPrefixStr("-b:", "Bnode.txt", "Bad Nodes" );
const TStr Iput = Env.GetIfArgPrefixStr("-i:", "Input.txt", "Input File");
const TStr Oput = Env.GetIfArgPrefixStr("-o:", "Output.txt", "Output File");
const double C = 0.85;
const int MaxIter = 50;
const double Eps = 1e-9;
FILE* fpO = fopen(Oput.CStr(), "w");
PNGraph Graph = TSnap::LoadEdgeList< PNGraph > (Iput);
fprintf(fpO, "\nNodes: %d, Edges: %d\n\n", Graph->GetNodes(), Graph->GetEdges());
const int NNodes = Graph->GetNodes();
TIntFltH TRankH;
TRankH.Gen(NNodes);
int maxNId = 0, NId = 0, ret = 0;
for (TNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++)
maxNId = max(maxNId, NI.GetId());
TFltV initialTrustScore(maxNId + 1);
for (int i = 0; i < initialTrustScore.Len(); i++)
initialTrustScore[i] = 0.5;
FILE* fpI = fopen(Gnod.CStr(), "r");
while (true) {
ret = fscanf(fpI, "%d", &NId);
if (ret == EOF) break;
if (Graph->IsNode(NId))
initialTrustScore[NId] = 1.0;
}
fclose(fpI);
fpI = fopen(Bnod.CStr(), "r");
while (true) {
ret = fscanf(fpI, "%d", &NId);
if (ret == EOF) break;
if (Graph->IsNode(NId))
initialTrustScore[NId] = 0.0;
}
fclose(fpI);
double Tot = 0.0;
for(int i = 0; i < initialTrustScore.Len(); i++)
Tot += initialTrustScore[i];
for(int i = 0; i < initialTrustScore.Len(); i++)
initialTrustScore[i] /= Tot;
for (TNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++)
TRankH.AddDat( NI.GetId(), initialTrustScore[NI.GetId()] );
TFltV TmpV(NNodes);
for (int iter = 0; iter < MaxIter; iter++) {
int j = 0;
for (TNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++, j++) {
TmpV[j] = 0;
for (int e = 0; e < NI.GetOutDeg(); e++) {
const int OutNId = NI.GetOutNId(e);
const int InDeg = Graph->GetNI(InNId).GetInDeg();
if (InDeg > 0)
TmpV[j] += (double) TRankH.GetDat(OutNId) / (double) InDeg;
}
TmpV[j] = C * TmpV[j] + (1.0 - C) * initialTrustScore[NI.GetId()];
}
for (int i = 0; i < TRankH.Len(); i++)
TRankH[i] = TmpV[i];
}
fprintf(fpO, "Node ID\t\tTrustRank\n");
for (TNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++){
int Id = NI.GetId();
double tr = TRankH.GetDat(Id);
fprintf(fpO, "%d\t\t\t%.5lf\n", Id, tr);
}
fclose(fpO);
Catch
printf("\nRun Time: %s (%s)\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
return 0;
}
int TAGMFast::MLEGradAscentParallel(const double& Thres, const int& MaxIter, const int ChunkNum, const int ChunkSize, const TStr PlotNm, const double StepAlpha, const double StepBeta) {
//parallel
time_t InitTime = time(NULL);
uint64 StartTm = TSecTm::GetCurTm().GetAbsSecs();
TExeTm ExeTm, CheckTm;
double PrevL = Likelihood(true);
TIntFltPrV IterLV;
int PrevIter = 0;
int iter = 0;
TIntV NIdxV(F.Len(), 0);
for (int i = 0; i < F.Len(); i++) { NIdxV.Add(i); }
TIntV NIDOPTV(F.Len()); //check if a node needs optimization or not 1: does not require optimization
NIDOPTV.PutAll(0);
TVec<TIntFltH> NewF(ChunkNum * ChunkSize);
TIntV NewNIDV(ChunkNum * ChunkSize);
for (iter = 0; iter < MaxIter; iter++) {
NIdxV.Clr(false);
for (int i = 0; i < F.Len(); i++) {
if (NIDOPTV[i] == 0) { NIdxV.Add(i); }
}
IAssert (NIdxV.Len() <= F.Len());
NIdxV.Shuffle(Rnd);
// compute gradient for chunk of nodes
#pragma omp parallel for schedule(static, 1)
for (int TIdx = 0; TIdx < ChunkNum; TIdx++) {
TIntFltH GradV;
for (int ui = TIdx * ChunkSize; ui < (TIdx + 1) * ChunkSize; ui++) {
NewNIDV[ui] = -1;
if (ui > NIdxV.Len()) { continue; }
int u = NIdxV[ui]; //
//find set of candidate c (we only need to consider c to which a neighbor of u belongs to)
TUNGraph::TNodeI UI = G->GetNI(u);
TIntSet CIDSet(5 * UI.GetDeg());
TIntFltH CurFU = F[u];
for (int e = 0; e < UI.GetDeg(); e++) {
if (HOVIDSV[u].IsKey(UI.GetNbrNId(e))) { continue; }
TIntFltH& NbhCIDH = F[UI.GetNbrNId(e)];
for (TIntFltH::TIter CI = NbhCIDH.BegI(); CI < NbhCIDH.EndI(); CI++) {
CIDSet.AddKey(CI.GetKey());
}
}
if (CIDSet.Empty()) {
CurFU.Clr();
}
else {
for (TIntFltH::TIter CI = CurFU.BegI(); CI < CurFU.EndI(); CI++) { //remove the community membership which U does not share with its neighbors
if (! CIDSet.IsKey(CI.GetKey())) {
CurFU.DelIfKey(CI.GetKey());
}
}
GradientForRow(u, GradV, CIDSet);
if (Norm2(GradV) < 1e-4) { NIDOPTV[u] = 1; continue; }
double LearnRate = GetStepSizeByLineSearch(u, GradV, GradV, StepAlpha, StepBeta, 5);
if (LearnRate <= 1e-5) { NewNIDV[ui] = -2; continue; }
for (int ci = 0; ci < GradV.Len(); ci++) {
int CID = GradV.GetKey(ci);
double Change = LearnRate * GradV.GetDat(CID);
double NewFuc = CurFU.IsKey(CID)? CurFU.GetDat(CID) + Change : Change;
if (NewFuc <= 0.0) {
CurFU.DelIfKey(CID);
} else {
CurFU.AddDat(CID) = NewFuc;
}
}
CurFU.Defrag();
}
//store changes
NewF[ui] = CurFU;
NewNIDV[ui] = u;
}
}
int NumNoChangeGrad = 0;
int NumNoChangeStepSize = 0;
for (int ui = 0; ui < NewNIDV.Len(); ui++) {
int NewNID = NewNIDV[ui];
if (NewNID == -1) { NumNoChangeGrad++; continue; }
if (NewNID == -2) { NumNoChangeStepSize++; continue; }
for (TIntFltH::TIter CI = F[NewNID].BegI(); CI < F[NewNID].EndI(); CI++) {
SumFV[CI.GetKey()] -= CI.GetDat();
}
}
#pragma omp parallel for
for (int ui = 0; ui < NewNIDV.Len(); ui++) {
int NewNID = NewNIDV[ui];
if (NewNID < 0) { continue; }
F[NewNID] = NewF[ui];
}
for (int ui = 0; ui < NewNIDV.Len(); ui++) {
int NewNID = NewNIDV[ui];
if (NewNID < 0) { continue; }
for (TIntFltH::TIter CI = F[NewNID].BegI(); CI < F[NewNID].EndI(); CI++) {
SumFV[CI.GetKey()] += CI.GetDat();
}
}
// update the nodes who are optimal
for (int ui = 0; ui < NewNIDV.Len(); ui++) {
int NewNID = NewNIDV[ui];
if (NewNID < 0) { continue; }
TUNGraph::TNodeI UI = G->GetNI(NewNID);
NIDOPTV[NewNID] = 0;
for (int e = 0; e < UI.GetDeg(); e++) {
NIDOPTV[UI.GetNbrNId(e)] = 0;
}
}
int OPTCnt = 0;
for (int i = 0; i < NIDOPTV.Len(); i++) { if (NIDOPTV[i] == 1) { OPTCnt++; } }
if (! PlotNm.Empty()) {
printf("\r%d iterations [%s] %d secs", iter * ChunkSize * ChunkNum, ExeTm.GetTmStr(), int(TSecTm::GetCurTm().GetAbsSecs() - StartTm));
if (PrevL > TFlt::Mn) { printf(" (%f) %d g %d s %d OPT", PrevL, NumNoChangeGrad, NumNoChangeStepSize, OPTCnt); }
fflush(stdout);
}
if ((iter - PrevIter) * ChunkSize * ChunkNum >= G->GetNodes()) {
PrevIter = iter;
double CurL = Likelihood(true);
IterLV.Add(TIntFltPr(iter * ChunkSize * ChunkNum, CurL));
printf("\r%d iterations, Likelihood: %f, Diff: %f [%d secs]", iter, CurL, CurL - PrevL, int(time(NULL) - InitTime));
fflush(stdout);
if (CurL - PrevL <= Thres * fabs(PrevL)) {
break;
}
else {
PrevL = CurL;
}
}
}
if (! PlotNm.Empty()) {
printf("\nMLE completed with %d iterations(%s secs)\n", iter, int(TSecTm::GetCurTm().GetAbsSecs() - StartTm));
TGnuPlot::PlotValV(IterLV, PlotNm + ".likelihood_Q");[]
} else {
int TAGMFast::MLEGradAscent(const double& Thres, const int& MaxIter, const TStr PlotNm, const double StepAlpha, const double StepBeta) {
time_t InitTime = time(NULL);
TExeTm ExeTm, CheckTm;
int iter = 0, PrevIter = 0;
TIntFltPrV IterLV;
TUNGraph::TNodeI UI;
double PrevL = TFlt::Mn, CurL = 0.0;
TIntV NIdxV(F.Len(), 0);
for (int i = 0; i < F.Len(); i++) { NIdxV.Add(i); }
IAssert(NIdxV.Len() == F.Len());
TIntFltH GradV;
while(iter < MaxIter) {
NIdxV.Shuffle(Rnd);
for (int ui = 0; ui < F.Len(); ui++, iter++) {
int u = NIdxV[ui]; //
//find set of candidate c (we only need to consider c to which a neighbor of u belongs to)
UI = G->GetNI(u);
TIntSet CIDSet(5 * UI.GetDeg());
for (int e = 0; e < UI.GetDeg(); e++) {
if (HOVIDSV[u].IsKey(UI.GetNbrNId(e))) { continue; }
TIntFltH& NbhCIDH = F[UI.GetNbrNId(e)];
for (TIntFltH::TIter CI = NbhCIDH.BegI(); CI < NbhCIDH.EndI(); CI++) {
CIDSet.AddKey(CI.GetKey());
}
}
for (TIntFltH::TIter CI = F[u].BegI(); CI < F[u].EndI(); CI++) { //remove the community membership which U does not share with its neighbors
if (! CIDSet.IsKey(CI.GetKey())) {
DelCom(u, CI.GetKey());
}
}
if (CIDSet.Empty()) { continue; }
GradientForRow(u, GradV, CIDSet);
if (Norm2(GradV) < 1e-4) { continue; }
double LearnRate = GetStepSizeByLineSearch(u, GradV, GradV, StepAlpha, StepBeta);
if (LearnRate == 0.0) { continue; }
for (int ci = 0; ci < GradV.Len(); ci++) {
int CID = GradV.GetKey(ci);
double Change = LearnRate * GradV.GetDat(CID);
double NewFuc = GetCom(u, CID) + Change;
if (NewFuc <= 0.0) {
DelCom(u, CID);
} else {
AddCom(u, CID, NewFuc);
}
}
if (! PlotNm.Empty() && (iter + 1) % G->GetNodes() == 0) {
IterLV.Add(TIntFltPr(iter, Likelihood(false)));
}
}
printf("\r%d iterations (%f) [%lu sec]", iter, CurL, time(NULL) - InitTime);
fflush(stdout);
if (iter - PrevIter >= 2 * G->GetNodes() && iter > 10000) {
PrevIter = iter;
CurL = Likelihood();
if (PrevL > TFlt::Mn && ! PlotNm.Empty()) {
printf("\r%d iterations, Likelihood: %f, Diff: %f", iter, CurL, CurL - PrevL);
}
fflush(stdout);
if (CurL - PrevL <= Thres * fabs(PrevL)) { break; }
else { PrevL = CurL; }
}
}
printf("\n");
printf("MLE for Lambda completed with %d iterations(%s)\n", iter, ExeTm.GetTmStr());
if (! PlotNm.Empty()) {
TGnuPlot::PlotValV(IterLV, PlotNm + ".likelihood_Q");
}
return iter;
}
void TAGMFast::GradientForRow(const int UID, TIntFltH& GradU, const TIntSet& CIDSet) {
GradU.Gen(CIDSet.Len());
TFltV HOSumFV; //adjust for Fv of v hold out
if (HOVIDSV[UID].Len() > 0) {
HOSumFV.Gen(SumFV.Len());
for (int e = 0; e < HOVIDSV[UID].Len(); e++) {
for (int c = 0; c < SumFV.Len(); c++) {
HOSumFV[c] += GetCom(HOVIDSV[UID][e], c);
}
}
}
TUNGraph::TNodeI NI = G->GetNI(UID);
int Deg = NI.GetDeg();
TFltV PredV(Deg), GradV(CIDSet.Len());
TIntV CIDV(CIDSet.Len());
if (DoParallel && Deg + CIDSet.Len() > 10) {
#pragma omp parallel for schedule(static, 1)
for (int e = 0; e < Deg; e++) {
if (NI.GetNbrNId(e) == UID) { continue; }
if (HOVIDSV[UID].IsKey(NI.GetNbrNId(e))) { continue; }
PredV[e] = Prediction(UID, NI.GetNbrNId(e));
}
#pragma omp parallel for schedule(static, 1)
for (int c = 0; c < CIDSet.Len(); c++) {
int CID = CIDSet.GetKey(c);
double Val = 0.0;
for (int e = 0; e < Deg; e++) {
int VID = NI.GetNbrNId(e);
if (VID == UID) { continue; }
if (HOVIDSV[UID].IsKey(VID)) { continue; }
Val += PredV[e] * GetCom(VID, CID) / (1.0 - PredV[e]) + NegWgt * GetCom(VID, CID);
}
double HOSum = HOVIDSV[UID].Len() > 0? HOSumFV[CID].Val: 0.0;//subtract Hold out pairs only if hold out pairs exist
Val -= NegWgt * (SumFV[CID] - HOSum - GetCom(UID, CID));
CIDV[c] = CID;
GradV[c] = Val;
}
}
else {
for (int e = 0; e < Deg; e++) {
if (NI.GetNbrNId(e) == UID) { continue; }
if (HOVIDSV[UID].IsKey(NI.GetNbrNId(e))) { continue; }
PredV[e] = Prediction(UID, NI.GetNbrNId(e));
}
for (int c = 0; c < CIDSet.Len(); c++) {
int CID = CIDSet.GetKey(c);
double Val = 0.0;
for (int e = 0; e < Deg; e++) {
int VID = NI.GetNbrNId(e);
if (VID == UID) { continue; }
if (HOVIDSV[UID].IsKey(VID)) { continue; }
Val += PredV[e] * GetCom(VID, CID) / (1.0 - PredV[e]) + NegWgt * GetCom(VID, CID);
}
double HOSum = HOVIDSV[UID].Len() > 0? HOSumFV[CID].Val: 0.0;//subtract Hold out pairs only if hold out pairs exist
Val -= NegWgt * (SumFV[CID] - HOSum - GetCom(UID, CID));
CIDV[c] = CID;
GradV[c] = Val;
}
}
//add regularization
if (RegCoef > 0.0) { //L1
for (int c = 0; c < GradV.Len(); c++) {
GradV[c] -= RegCoef;
}
}
if (RegCoef < 0.0) { //L2
for (int c = 0; c < GradV.Len(); c++) {
GradV[c] += 2 * RegCoef * GetCom(UID, CIDV[c]);
}
}
for (int c = 0; c < GradV.Len(); c++) {
if (GetCom(UID, CIDV[c]) == 0.0 && GradV[c] < 0.0) { continue; }
if (fabs(GradV[c]) < 0.0001) { continue; }
GradU.AddDat(CIDV[c], GradV[c]);
}
for (int c = 0; c < GradU.Len(); c++) {
if (GradU[c] >= 10) { GradU[c] = 10; }
if (GradU[c] <= -10) { GradU[c] = -10; }
IAssert(GradU[c] >= -10);
}
}
