C++ TIntFltH::GetDat方法代码示例

// Test GetNodeClustCf (Vector)
TEST(triad, TestGetNodeClustCfVector) {
  // Test TUNGraph
  PUNGraph GraphTUN = TriadGetTestTUNGraph();
  TIntFltH NIdCCfH;

  TSnap::GetNodeClustCf(GraphTUN, NIdCCfH);
  for (int i = 0; i < GraphTUN->GetNodes(); i++) {
    double ClustCf = NIdCCfH.GetDat(i);
    VerifyNodeClustCf(i, ClustCf);
  }

  // TNGraph should be treated as TUNGraph for calculations
  PNGraph GraphTN = TriadGetTestTNGraph();
  NIdCCfH.Clr();

  TSnap::GetNodeClustCf(GraphTN, NIdCCfH);
  for (int i = 0; i < GraphTN->GetNodes(); i++) {
    double ClustCf = NIdCCfH.GetDat(i);
    VerifyNodeClustCf(i, ClustCf);
  }

  // TNEGraph should be treated as TUNGraph for calculations
  PNEGraph GraphTNE = TriadGetTestTNEGraph();
  NIdCCfH.Clr();

  TSnap::GetNodeClustCf(GraphTNE, NIdCCfH);
  for (int i = 0; i < GraphTNE->GetNodes(); i++) {
    double ClustCf = NIdCCfH.GetDat(i);
    VerifyNodeClustCf(i, ClustCf);
  }
}

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;
  }
}

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 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;
}

// Computes GINI coefficient of egonet as a subset of the parent graph (edges into and out of the egonet ARE considered)
double TSnap::GetGiniCoefficient(const TIntFltH DegH, const TIntV NIdV) {
  typename TIntV::TIter VI;
  typename TFltV::TIter DI;
  TFltV DegV;
  const int n = NIdV.Len();
  // DegV.Gen(n); // NOTE: don't use Gen() and Sort() on the same object (!)
  for (VI = NIdV.BegI(); VI < NIdV.EndI(); VI++) {
    DegV.Add(DegH.GetDat(VI->Val)); // might need to change this (in / out / undirected)
  }
  DegV.Sort();
  int i = 0;
  double numerator = 0.0, denominator = 0.0;
  for (DI = DegV.BegI(); DI < DegV.EndI(); DI++, i++) {
    numerator += (i + 1)*DegV[i];
    denominator += DegV[i];
  }
  return(double(2*numerator) / double(n*denominator) - double(n + 1) / double(n));
}

void getPageRankFromVect(const PNGraph& graph, std::vector<int> srcIds, std::vector<int> dstIds, int sampleSz, char* fileName) {
	std::random_shuffle(srcIds.begin(), srcIds.end());
	std::random_shuffle(dstIds.begin(), dstIds.end());
	std::ofstream outputFile;
	outputFile.open(fileName);

	for (int i = 0; i < sampleSz; ) {
		int srcNodeId = srcIds[rand() % srcIds.size()];
		int dstNodeId = dstIds[rand() % dstIds.size()];
		if (!graph->IsNode(srcNodeId)) continue;
		if (!graph->IsNode(dstNodeId)) continue;
		int shortPath = TSnap::GetShortPath(graph, srcNodeId, dstNodeId, true);
		if (shortPath > 4 || shortPath <= 2) continue;

		PNGraph subgraph = getFourHopGraph(graph, srcNodeId, dstNodeId);
		TIntFltH pageRankScores;
		TSnap::GetPageRank(subgraph, pageRankScores);

		// Calculate total PR score;
		/*
		double totalPR = 0.0;
		for (TIntFltH::TIter itr = pageRankScores.BegI(); itr != pageRankScores.EndI(); itr++) {
			totalPR += itr.GetDat();
		}*/

		int numOfNodesInSubGraph = subgraph->GetNodes();
		double normalizedSrcPR = pageRankScores.GetDat(srcNodeId) * numOfNodesInSubGraph;
		double normalizedDstPR = pageRankScores.GetDat(dstNodeId) * numOfNodesInSubGraph;

		char buffer[100];
		printf("%d, %d\n", i, numOfNodesInSubGraph);
		sprintf(buffer, "%d\t%f\t%d\t%f", srcNodeId, normalizedSrcPR, dstNodeId, normalizedDstPR);
		std::cout << buffer << std::endl;
		outputFile << buffer << std::endl;
		++i;
	}
	outputFile.close();
}

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 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;
//.........这里部分代码省略.........

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 GetWeightedPageRankMP2(const PNEANet Graph, TIntFltH& PRankH, const TStr& Attr, const double& C, const double& Eps, const int& MaxIter) {
  if (!Graph->IsFltAttrE(Attr)) return -1;
  const int NNodes = Graph->GetNodes();
  TVec<TNEANet::TNodeI> NV;

  //const double OneOver = 1.0/double(NNodes);
  PRankH.Gen(NNodes);
  int MxId;

  for (TNEANet::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++) {
    NV.Add(NI);
    PRankH.AddDat(NI.GetId(), 1.0/NNodes);
    int Id = NI.GetId();
    if (Id > MxId) {
      MxId = Id;
    }
  }

  TFltV PRankV(MxId+1);
  TFltV OutWeights(MxId+1);

  TFltV Weights = Graph->GetFltAttrVecE(Attr);

  #pragma omp parallel for schedule(dynamic,10000)
  for (int j = 0; j < NNodes; j++) {
    TNEANet::TNodeI NI = NV[j];
    int Id = NI.GetId();
    OutWeights[Id] = Graph->GetWeightOutEdges(NI, Attr);
    PRankV[Id] = 1/NNodes;
  }

  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];
      TFlt Tmp = 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) {
          Tmp += PRankH.GetDat(InNId) * Weight / OutWeight; 
        }
      }
      TmpV[j] =  C*Tmp; // Berkhin (the correct way of doing it)
      //TmpV[j] =  C*TmpV[j] + (1.0-C)*OneOver; // iGraph
    }

    double sum = 0;
    #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);

    double diff = 0;
    #pragma omp parallel for reduction(+:diff) schedule(dynamic,10000)
    for (int i = 0; i < NNodes; i++) {
      TNEANet::TNodeI NI = NV[i];
      double NewVal = TmpV[i] + Leaked; // Berkhin
      //NewVal = TmpV[i] / sum;  // iGraph
      int Id = NI.GetId();
      diff += fabs(NewVal-PRankV[Id]);
      PRankV[Id] = NewVal;
    }
    if (diff < Eps) { break; }
  }

  #pragma omp parallel for schedule(dynamic,10000)
  for (int i = 0; i < NNodes; i++) {
    TNEANet::TNodeI NI = NV[i];
    PRankH[i] = PRankV[NI.GetId()];
  }

  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;
//.........这里部分代码省略.........

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;
}

int main(int argc, char* argv[]) {
  
  setbuf(stdout, NULL); // disables the buffer so that print statements are not buffered and display immediately (?)
   
  Env = TEnv(argc, argv, TNotify::StdNotify);
  Env.PrepArgs(TStr::Fmt("Node centrality. build: %s, %s. Time: %s", __TIME__, __DATE__, TExeTm::GetCurTm()));
  
  TExeTm ExeTm;
  
  Try
  
  const TStr InFNm = Env.GetIfArgPrefixStr("-i:", "", "input network");
  const TStr OutFNm = Env.GetIfArgPrefixStr("-o:", "", "output prefix (filename extensions added)");
  const TStr BseFNm = OutFNm.RightOfLast('/');
  const int k = Env.GetIfArgPrefixInt("-k:", 1, "depth of weighted degree distributions (1 / 2 / ...)");
  const bool c = Env.GetIfArgPrefixBool("-c:", false, "collate centralities into matrix (T / F)");
  
  // Load graph and create directed and undirected graphs (pointer to the same memory)
  printf("\nLoading %s...", InFNm.CStr());
  PFltWNGraph WGraph = TSnap::LoadFltWEdgeList<TWNGraph>(InFNm);
  printf(" DONE\n");
  printf("  nodes: %d\n", WGraph->GetNodes());
  printf("  edges: %d\n", WGraph->GetEdges());
  printf("  time elapsed: %s (%s)\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
  
  // Declare variables
  TIntFltVH FirstWDegVH;
  TIntFltVH kWInDegVH, kWOutDegVH, kWDegVH;
  TIntFltVH WDegCentrVH, WEigCentrVH;
  TFltV WEigDiffV;
  TIntFltH WPgRH;
  double WPgRDiff;
  TFltWNGraph::TNodeI NI;
  TFltV::TIter VI;
  
  // CENTRALITY (computations)
  
  // Weighted first degree distributions
  
  printf("\nComputing weighted degree distributions...");
  TSnap::GetWDegVH(WGraph, FirstWDegVH);
  printf(" DONE (time elapsed: %s (%s))\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
  
  // 1:k degree distributions
  
  printf("Computing egonet degrees for k = 1 to %d (in / out / undirected)\n", k);
  TSnap::TFixedMemorykWDeg<TFlt, TWNGraph> FixedMemorykWDeg(WGraph, k);
  printf("  ...");
  FixedMemorykWDeg.GetkWInDegSeqH(kWInDegVH);
  printf(" DONE (time elapsed: %s (%s))\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
  printf("  ...");
  FixedMemorykWDeg.GetkWOutDegSeqH(kWOutDegVH);
  printf(" DONE (time elapsed: %s (%s))\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
  printf("  ...");
  FixedMemorykWDeg.GetkWDegSeqH(kWDegVH);
  printf(" DONE (time elapsed: %s (%s))\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
  
  // Centrality measures
  
  printf("Computing weighted degree centrality...");
  TSnap::GetWDegreeCentrVH(WGraph, WDegCentrVH, 0.5);
  printf(" DONE (time elapsed: %s (%s))\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
  
  printf("Computing weighted eigenvector centrality...");
  WEigDiffV = TSnap::GetWEigenVectorCentrVH<TFlt>(WGraph, WEigCentrVH, 1e-4, 1000);
  printf(" DONE (time elapsed: %s (%s))\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
  printf("  convergence differences (in / out / undirected)\n");
  printf("    %f\n", double(WEigDiffV[0]));
  printf("    %f\n", double(WEigDiffV[1]));
  printf("    %f\n", double(WEigDiffV[2]));
  
  printf("Computing weighted PageRank centrality...");
  WPgRDiff = TSnap::GetWPageRank<TFlt>(WGraph, WPgRH, 0.85, 1e-4, 1000);
  printf(" DONE (time elapsed: %s (%s))\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
  printf("  convergence difference: %f\n", double(WPgRDiff));
  
  // OUTPUTTING (mostly verbose printing statements, don't get scared)
  
  if (c) {
    
    printf("\nSaving %s.wcentr...", BseFNm.CStr());
    const TStr AggFNm = TStr::Fmt("%s.wcentr", OutFNm.CStr());
    FILE *F = fopen(AggFNm.CStr(), "wt");
    fprintf(F,"# Node centrality distributions on the directed / undirected graph (as applicable)\n");
    fprintf(F,"# Nodes: %d\tEdges: %d\n", WGraph->GetNodes(), WGraph->GetEdges());
    fprintf(F,"# NodeId\tWInDegCentr\tWOutDegCentr\tWDegCentr\tWInEigCentr\tWOutEigCentr\tWEigCentr\tWPgRCentr\n");
    for (NI = WGraph->BegNI(); NI < WGraph->EndNI(); NI++) {
      const int NId = NI.GetId(); fprintf(F, "%d", NId);
      const TFltV WDegCentrV = WDegCentrVH.GetDat(NId);
      for (VI = WDegCentrV.BegI(); VI < WDegCentrV.EndI(); VI++) { fprintf(F, "\t%f", VI->Val); }
      const TFltV WEigCentrV = WEigCentrVH.GetDat(NId);
      for (VI = WEigCentrV.BegI(); VI < WEigCentrV.EndI(); VI++) { fprintf(F, "\t%f", VI->Val); }
      const double WPgRCentr = WPgRH.GetDat(NId); fprintf(F, "\t%f", WPgRCentr);
      fprintf(F, "\n");
    }
    printf(" DONE\n");
    
  } else {
    
    printf("\nSaving %s.wdeg.centr...", BseFNm.CStr());
//.........这里部分代码省略.........