void TNetInfBs::GenCascade(TCascade& C, const int& TModel, const double &window, TIntPrIntH& EdgesUsed, const double& delta,
const double& std_waiting_time, const double& std_beta) {
TIntFltH InfectedNIdH; TIntH InfectedBy;
double GlobalTime; int StartNId;
double alpha, beta;
if (GroundTruth->GetNodes() == 0)
return;
while (C.Len() < 2) {
C.Clr();
InfectedNIdH.Clr();
InfectedBy.Clr();
GlobalTime = 0;
StartNId = GroundTruth->GetRndNId();
InfectedNIdH.AddDat(StartNId) = GlobalTime;
while (true) {
// sort by time & get the oldest node that did not run infection
InfectedNIdH.SortByDat(true);
const int& NId = InfectedNIdH.BegI().GetKey();
GlobalTime = InfectedNIdH.BegI().GetDat();
// all the nodes has run infection
if (GlobalTime >= window)
break;
// add current oldest node to the network and set its time
C.Add(NId, GlobalTime);
// run infection from the current oldest node
const TNGraph::TNodeI NI = GroundTruth->GetNI(NId);
for (int e = 0; e < NI.GetOutDeg(); e++) {
const int DstNId = NI.GetOutNId(e);
beta = Betas.GetDat(TIntPr(NId, DstNId));
// flip biased coin (set by beta)
if (TInt::Rnd.GetUniDev() > beta+std_beta*TFlt::Rnd.GetNrmDev())
continue;
alpha = Alphas.GetDat(TIntPr(NId, DstNId));
// not infecting the parent
if (InfectedBy.IsKey(NId) && InfectedBy.GetDat(NId).Val == DstNId)
continue;
double sigmaT;
switch (TModel) {
case 0:
// exponential with alpha parameter
sigmaT = TInt::Rnd.GetExpDev(alpha);
break;
case 1:
// power-law with alpha parameter
sigmaT = TInt::Rnd.GetPowerDev(alpha);
while (sigmaT < delta) { sigmaT = TInt::Rnd.GetPowerDev(alpha); }
break;
case 2:
// rayleigh with alpha parameter
sigmaT = TInt::Rnd.GetRayleigh(1/sqrt(alpha));
break;
default:
sigmaT = 1;
break;
}
// avoid negative time diffs in case of noise
if (std_waiting_time > 0)
sigmaT = TFlt::GetMx(0.0, sigmaT + std_waiting_time*TFlt::Rnd.GetNrmDev());
double t1 = GlobalTime + sigmaT;
if (InfectedNIdH.IsKey(DstNId)) {
double t2 = InfectedNIdH.GetDat(DstNId);
if (t2 > t1 && t2 != window) {
InfectedNIdH.GetDat(DstNId) = t1;
InfectedBy.GetDat(DstNId) = NId;
}
} else {
InfectedNIdH.AddDat(DstNId) = t1;
InfectedBy.AddDat(DstNId) = NId;
}
}
// we cannot delete key (otherwise, we cannot sort), so we assign a big time (window cut-off)
InfectedNIdH.GetDat(NId) = window;
}
}
C.Sort();
for (TIntH::TIter EI = InfectedBy.BegI(); EI < InfectedBy.EndI(); EI++) {
TIntPr Edge(EI.GetDat().Val, EI.GetKey().Val);
if (!EdgesUsed.IsKey(Edge)) EdgesUsed.AddDat(Edge) = 0;
EdgesUsed.GetDat(Edge) += 1;
}
}
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 {
