void node2vec(PWNet& InNet, double& ParamP, double& ParamQ, int& Dimensions,
int& WalkLen, int& NumWalks, int& WinSize, int& Iter, bool& Verbose,
bool& OutputWalks, TVVec<TInt, int64>& WalksVV, TIntFltVH& EmbeddingsHV) {
//Preprocess transition probabilities
PreprocessTransitionProbs(InNet, ParamP, ParamQ, Verbose);
TIntV NIdsV;
for (TWNet::TNodeI NI = InNet->BegNI(); NI < InNet->EndNI(); NI++) {
NIdsV.Add(NI.GetId());
}
//Generate random walks
int64 AllWalks = (int64)NumWalks * NIdsV.Len();
WalksVV = TVVec<TInt, int64>(AllWalks,WalkLen);
TRnd Rnd(time(NULL));
int64 WalksDone = 0;
for (int64 i = 0; i < NumWalks; i++) {
NIdsV.Shuffle(Rnd);
#pragma omp parallel for schedule(dynamic)
for (int64 j = 0; j < NIdsV.Len(); j++) {
if ( Verbose && WalksDone%10000 == 0 ) {
printf("\rWalking Progress: %.2lf%%",(double)WalksDone*100/(double)AllWalks);fflush(stdout);
}
TIntV WalkV;
SimulateWalk(InNet, NIdsV[j], WalkLen, Rnd, WalkV);
for (int64 k = 0; k < WalkV.Len(); k++) {
WalksVV.PutXY(i*NIdsV.Len()+j, k, WalkV[k]);
}
WalksDone++;
}
}
if (Verbose) {
printf("\n");
fflush(stdout);
}
//Learning embeddings
if (!OutputWalks) {
LearnEmbeddings(WalksVV, Dimensions, WinSize, Iter, Verbose, EmbeddingsHV);
}
}
