TCesna Class Reference

#include <agmattr.h>

Collaboration diagram for TCesna:

Public Member Functions
	TCesna ()
	TCesna (const PUNGraph &GraphPt, const THash< TInt, TIntV > &NIDAttrH, const int &InitComs, const int RndSeed=0)
void	Save (TSOut &SOut)
void	Load (TSIn &SIn, const int &RndSeed=0)
void	SetGraph (const PUNGraph &GraphPt, const THash< TInt, TIntV > &NIDAttrH)
void	SetRegCoef (const double _RegCoef)
double	GetRegCoef ()
void	SetWeightAttr (const double _WeightAttr)
double	GetWeightAttr ()
void	SetLassoCoef (const double _LassoCoef)
int	GetAttrs ()
double	GetComFromNID (const int &NID, const int &CID)
double	GetLassoCoef ()
void	InitW ()
void	SetAttrHoldOut (const int NID, const int KID)
void	SetAttrHoldOutForOneNode (const int NID)
void	GetW (TVec< TFltV > &_W)
void	SetW (TVec< TFltV > &_W)
void	RandomInit (const int InitComs)
void	NeighborComInit (const int InitComs)
void	NeighborComInit (TFltIntPrV &NIdPhiV, const int InitComs)
int	GetNumComs ()
void	SetCmtyVV (const TVec< TIntV > &CmtyVV)
double	Likelihood (const bool DoParallel=false)
double	LikelihoodForRow (const int UID)
double	LikelihoodForRow (const int UID, const TIntFltH &FU)
double	LikelihoodAttrKForRow (const int UID, const int K)
double	LikelihoodAttrKForRow (const int UID, const int K, const TIntFltH &FU)
double	LikelihoodAttrKForRow (const int UID, const int K, const TIntFltH &FU, const TFltV &WK)
double	LikelihoodForWK (const int K, const TFltV &WK)
double	LikelihoodForWK (const int K)
double	LikelihoodAttr ()
double	LikelihoodGraph ()
void	GenHoldOutAttr (const double HOFrac, TVec< TIntSet > &HOSetV)
void	SetHoldOut (const double HOFrac)
void	GradientForRow (const int UID, TIntFltH &GradU, const TIntSet &CIDSet)
void	GradientForWK (TFltV &GradV, const int K)
void	GetCmtyVV (TVec< TIntV > &CmtyVV)
void	GetCmtyVV (TVec< TIntV > &CmtyVV, TVec< TFltV > &Wck, const double Thres, const int MinSz=3)
	extract community affiliation from F_uc Wck[c][k] = W_c for k-th attribute More...
void	GetCmtyVV (TVec< TIntV > &CmtyVV, const double Thres, const int MinSz=3)
void	GetCmtyVV (TVec< TIntV > &CmtyVV, TVec< TFltV > &Wck)
void	GetCmtyVVUnSorted (TVec< TIntV > &CmtyVV)
void	GetCmtyVVUnSorted (TVec< TIntV > &CmtyVV, const double Thres, const int MinSz=3)
int	FindComs (TIntV &ComsV, const bool UseBIC=false, const double HOFrac=0.2, const int NumThreads=20, const TStr PlotLFNm=TStr(), const double StepAlpha=0.3, const double StepBeta=0.1)
int	FindComs (const int NumThreads, const int MaxComs, const int MinComs, const int DivComs, const TStr OutFNm, const bool UseBIC=false, const double HOFrac=0.1, const double StepAlpha=0.3, const double StepBeta=0.3)
	estimate number of communities using cross validation More...
void	DisplayAttrs (const int TopK, const TStrHash< TInt > &NodeNameH)
double	LikelihoodHoldOut ()
double	GetStepSizeByLineSearch (const int UID, const TIntFltH &DeltaV, const TIntFltH &GradV, const double &Alpha, const double &Beta, const int MaxIter=10)
double	GetStepSizeByLineSearchForWK (const int K, const TFltV &DeltaV, const TFltV &GradV, const double &Alpha, const double &Beta, const int MaxIter=10)
int	GetPositiveW ()
int	MLEGradAscent (const double &Thres, const int &MaxIter, const TStr PlotNm, const double StepAlpha=0.3, const double StepBeta=0.1)
int	MLEGradAscentParallel (const double &Thres, const int &MaxIter, const int ChunkNum, const int ChunkSize, const TStr PlotNm, const double StepAlpha=0.3, const double StepBeta=0.1)
int	MLEGradAscentParallel (const double &Thres, const int &MaxIter, const int ChunkNum, const TStr PlotNm=TStr(), const double StepAlpha=0.3, const double StepBeta=0.1)
double	GetCom (const int &NID, const int &CID)
double	GetAttr (const int &NID, const int &K)
void	AddCom (const int &NID, const int &CID, const double &Val)
void	DelCom (const int &NID, const int &CID)
double	DotProduct (const TIntFltH &UV, const TIntFltH &VV)
double	DotProduct (const int &UID, const int &VID)
double	Prediction (const TIntFltH &FU, const TIntFltH &FV)
double	PredictAttrK (const TIntFltH &FU, const TFltV &WK)
double	PredictAttrK (const TIntFltH &FU, const int K)
double	PredictAttrK (const int UID, const int K)
double	GetW (const int CID, const int K)
double	Prediction (const int &UID, const int &VID)
double	Sum (const TIntFltH &UV)
double	Norm2 (const TIntFltH &UV)
double	Sigmoid (const double X)

Public Attributes
TFlt	MinVal
TFlt	MaxVal
TFlt	MinValW
TFlt	MaxValW
TFlt	NegWgt
TFlt	LassoCoef
TFlt	WeightAttr
TFlt	PNoCom
TBool	DoParallel

Private Attributes
PUNGraph	G
TVec< TIntSet >	X
TVec< TIntFltH >	F
TVec< TFltV >	W
TInt	Attrs
TRnd	Rnd
TIntSet	NIDToIdx
TFlt	RegCoef
TFltV	SumFV
TInt	NumComs
TVec< TIntSet >	HOVIDSV
TVec< TIntSet >	HOKIDSV

Detailed Description

Definition at line 246 of file agmattr.h.

Constructor & Destructor Documentation

TCesna::TCesna ( )

inline

Definition at line 271 of file agmattr.h.

References G, and TUNGraph::New().

{ G = TUNGraph::New(10, -1); }

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TCesna::TCesna ( const PUNGraph &  GraphPt, const THash< TInt, TIntV > &  NIDAttrH, const int &  InitComs, const int  RndSeed = 0  )

inline

Definition at line 272 of file agmattr.h.

References NeighborComInit(), and SetGraph().

                                                                                                                 : Rnd(RndSeed), RegCoef(0), 
    MinVal(0.0), MaxVal(10.0), MinValW(-10.0), MaxValW(10.0), NegWgt(1.0), LassoCoef(1.0), WeightAttr(1.0) { SetGraph(GraphPt, NIDAttrH); NeighborComInit(InitComs); }

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Member Function Documentation

void TCesna::AddCom ( const int &  NID, const int &  CID, const double &  Val  )

inline

Definition at line 541 of file agmattr.h.

References F, and SumFV.

Referenced by MLEGradAscent(), NeighborComInit(), RandomInit(), and SetCmtyVV().

                                                                        {
    if (F[NID].IsKey(CID)) {
      SumFV[CID] -= F[NID].GetDat(CID);
    }
    F[NID].AddDat(CID) = Val;
    SumFV[CID] += Val;
  }

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void TCesna::DelCom ( const int &  NID, const int &  CID  )

inline

Definition at line 549 of file agmattr.h.

References F, and SumFV.

Referenced by MLEGradAscent().

                                                     {
    if (F[NID].IsKey(CID)) {
      SumFV[CID] -= F[NID].GetDat(CID);
      F[NID].DelKey(CID);
    }
  }

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void TCesna::DisplayAttrs ( const int  TopK, const TStrHash< TInt > &  NodeNameH  )

inline

Definition at line 470 of file agmattr.h.

References TStrHash< TDat, TStringPool, THashFunc >::GetKey(), TStrHash< TDat, TStringPool, THashFunc >::Len(), NIDToIdx, and X.

                                                                     {
    for (int u = 0; u < X.Len(); u++) {
      if (NodeNameH.Len() > 0) {
        printf("NID: %s\t Attrs: ", NodeNameH.GetKey(NIDToIdx[u]));
      } else {
        printf("NID: %d\t Attrs: ", NIDToIdx[u].Val);
      }
      for (int k = 0; k < X[u].Len(); k++) {
        printf("%d, ", X[u][k].Val);
      }
      printf("\n");
      if (u >= TopK) { break; }
    }
  }

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double TCesna::DotProduct ( const TIntFltH &  UV, const TIntFltH &  VV  )

inline

Definition at line 564 of file agmattr.h.

References THash< TKey, TDat, THashFunc >::BegI(), THash< TKey, TDat, THashFunc >::EndI(), THash< TKey, TDat, THashFunc >::GetDat(), THash< TKey, TDat, THashFunc >::IsKey(), and THash< TKey, TDat, THashFunc >::Len().

Referenced by DotProduct(), GetStepSizeByLineSearch(), LikelihoodForRow(), and Prediction().

                                                                   {
    double DP = 0;
    if (UV.Len() > VV.Len()) {
      for (TIntFltH::TIter HI = UV.BegI(); HI < UV.EndI(); HI++) {
        if (VV.IsKey(HI.GetKey())) { 
          DP += VV.GetDat(HI.GetKey()) * HI.GetDat(); 
        }
      }
    } else {
      for (TIntFltH::TIter HI = VV.BegI(); HI < VV.EndI(); HI++) {
        if (UV.IsKey(HI.GetKey())) { 
          DP += UV.GetDat(HI.GetKey()) * HI.GetDat(); 
        }
      }
    }
    return DP;
  }

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double TCesna::DotProduct ( const int &  UID, const int &  VID  )

inline

Definition at line 581 of file agmattr.h.

References DotProduct(), and F.

                                                           {
    return DotProduct(F[UID], F[VID]);
  }

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int TCesna::FindComs	(	TIntV &	ComsV,
		const bool	UseBIC = false,
		const double	HOFrac = 0.2,
		const int	NumThreads = 20,
		const TStr	PlotLFNm = TStr(),
		const double	StepAlpha = 0.3,
		const double	StepBeta = 0.1
	)

Definition at line 361 of file agmattr.cpp.

References TVec< TVal, TSizeTy >::Add(), Attrs, TStr::Empty(), G, GenHoldOutAttr(), TCesnaUtil::GenHoldOutPairs(), TUNGraph::GetNodes(), HOKIDSV, HOVIDSV, TVec< TVal, TSizeTy >::Last(), TVec< TVal, TSizeTy >::Len(), Likelihood(), LikelihoodAttr(), LikelihoodHoldOut(), MLEGradAscent(), MLEGradAscentParallel(), TFlt::Mn, NeighborComInit(), TGnuPlot::PlotValV(), RandomInit(), Rnd, and WeightAttr.

Referenced by FindComs().

                                                                                                                                                                   {
  if (ComsV.Len() == 0) {
    int MaxComs = G->GetNodes() / 5;
    ComsV.Add(2);
    while(ComsV.Last() < MaxComs) { ComsV.Add(ComsV.Last() * 2); }
  }
  int MaxIterCV = 3;

  TVec<TVec<TIntSet> > HoldOutSets(MaxIterCV), HoldOutSetsAttr(MaxIterCV);
  TFltIntPrV NIdPhiV;
  TCesnaUtil::GetNIdPhiV<PUNGraph>(G, NIdPhiV);
  if (! UseBIC) { //if edges are many enough, use CV
    //printf("generating hold out set\n");
    TIntV NIdV1, NIdV2;
    G->GetNIdV(NIdV1);
    G->GetNIdV(NIdV2);
    for (int IterCV = 0; IterCV < MaxIterCV; IterCV++) {
      // generate holdout sets
      TCesnaUtil::GenHoldOutPairs(G, HoldOutSets[IterCV], HOFrac, Rnd);
      GenHoldOutAttr(HOFrac, HoldOutSetsAttr[IterCV]);
    }
    //printf("hold out set generated\n");
  }

  TFltV HOLV(ComsV.Len());
  TIntFltPrV ComsLV;
  for (int c = 0; c < ComsV.Len(); c++) {
    const int Coms = ComsV[c];
    //printf("Try number of Coms:%d\n", Coms);

    if (! UseBIC) { //if edges are many enough, use CV
      for (int IterCV = 0; IterCV < MaxIterCV; IterCV++) {
        HOVIDSV = HoldOutSets[IterCV];
        HOKIDSV = HoldOutSetsAttr[IterCV];
        NeighborComInit(NIdPhiV, Coms);
        //printf("Initialized\n");

        if (NumThreads == 1) {
          //printf("MLE without parallelization begins\n");
          MLEGradAscent(0.01, 100 * G->GetNodes(), "", StepAlpha, StepBeta);
          //printf("likelihood: train:%f, attr:%f, hold:%f\n", Likelihood(), LikelihoodAttr(), LikelihoodHoldOut());
        } else {
          //printf("MLE with parallelization begins\n");
          MLEGradAscentParallel(0.01, 100, NumThreads, "", StepAlpha, StepBeta);
        }
        double HOL = LikelihoodHoldOut();
        HOL = HOL < 0? HOL: TFlt::Mn;
        HOLV[c] += HOL;
      }
    }
    else {
      HOVIDSV.Gen(G->GetNodes());
      HOKIDSV.Gen(G->GetNodes());
      if (NumThreads == 1) {
        MLEGradAscent(0.005, 100 * G->GetNodes(), "", StepAlpha, StepBeta);
        printf("likelihood: train:%f, attr:%f, hold:%f\n", Likelihood(), LikelihoodAttr(), LikelihoodHoldOut());
      } else {
        MLEGradAscentParallel(0.005, 100, NumThreads, "", StepAlpha, StepBeta);
      }
      //int NumParams = G->GetNodes() * Coms + Coms * Attrs;
      double NumParams = (1.0 - WeightAttr) * Coms + WeightAttr * Coms * Attrs;
      double Observations = (1.0 - WeightAttr) * G->GetNodes() * (G->GetNodes() - 1) / 2 + WeightAttr * G->GetNodes() * Attrs;
      double BIC = 2 * Likelihood() - NumParams * log (Observations);
      HOLV[c] = BIC;
    }
  }
  int EstComs = 2;
  double MaxL = TFlt::Mn;
  if (UseBIC) {
    printf("Number of communities vs likelihood (criterion: BIC)\n");
  } else {
    printf("Number of communities vs likelihood (criterion: Cross validation)\n");
  }
  for (int c = 0; c < ComsV.Len(); c++) {
    ComsLV.Add(TIntFltPr(ComsV[c].Val, HOLV[c].Val));
    printf("%d(%f)\t", ComsV[c].Val, HOLV[c].Val);
    if (MaxL < HOLV[c]) {
      MaxL = HOLV[c];
      EstComs = ComsV[c];
    }
  }
  printf("\n");
  RandomInit(EstComs);
  HOVIDSV.Gen(G->GetNodes());
  HOKIDSV.Gen(G->GetNodes());
  if (! PlotLFNm.Empty()) {
    TGnuPlot::PlotValV(ComsLV, PlotLFNm, "hold-out likelihood", "communities", "likelihood");
  }
  return EstComs;
}

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int TCesna::FindComs	(	const int	NumThreads,
		const int	MaxComs,
		const int	MinComs,
		const int	DivComs,
		const TStr	OutFNm,
		const bool	UseBIC = false,
		const double	HOFrac = 0.1,
		const double	StepAlpha = 0.3,
		const double	StepBeta = 0.3
	)

estimate number of communities using cross validation

Definition at line 348 of file agmattr.cpp.

References TVec< TVal, TSizeTy >::Add(), FindComs(), TVec< TVal, TSizeTy >::Last(), TVec< TVal, TSizeTy >::Len(), TMath::Log(), and TInt::Val.

                                                                                                                                                                                                            {
    double ComsGap = exp(TMath::Log((double) MaxComs / (double) MinComs) / (double) DivComs);
    TIntV ComsV;
    ComsV.Add(MinComs);
    while (ComsV.Len() < DivComs) {
      int NewComs = int(ComsV.Last() * ComsGap);
      if (NewComs == ComsV.Last().Val) { NewComs++; }
      ComsV.Add(NewComs);
    }
    if (ComsV.Last() < MaxComs) { ComsV.Add(MaxComs); }
    return FindComs(ComsV, UseBIC, HOFrac, NumThreads, OutFNm, StepAlpha, StepBeta);
}

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void TCesna::GenHoldOutAttr ( const double  HOFrac, TVec< TIntSet > &  HOSetV  )

inline

Definition at line 397 of file agmattr.h.

References THashSet< TKey, THashFunc >::AddKey(), Attrs, F, G, TVec< TVal, TSizeTy >::Gen(), TUNGraph::GetNodes(), TRnd::GetUniDevInt(), THashSet< TKey, THashFunc >::IsKey(), and Rnd.

Referenced by FindComs(), and SetHoldOut().

                                                                  {
    HOSetV.Gen(F.Len());
    int HoldOutCnt = (int) ceil(HOFrac * G->GetNodes() * Attrs);
    TIntPrSet NIDKIDSet(HoldOutCnt);
    int Cnt = 0;
    for (int h = 0; h < 10 * HoldOutCnt; h++) {
      int UID = Rnd.GetUniDevInt(F.Len());
      int KID = Rnd.GetUniDevInt(Attrs);
      if (! NIDKIDSet.IsKey(TIntPr(UID, KID))) { 
        NIDKIDSet.AddKey(TIntPr(UID, KID)); 
        HOSetV[UID].AddKey(KID);
        Cnt++;
      }
      if (Cnt >= HoldOutCnt) { break; }
    }
    printf("%d hold out pairs generated for attributes\n", Cnt);
  }

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double TCesna::GetAttr ( const int &  NID, const int &  K  )

inline

Definition at line 534 of file agmattr.h.

References X.

Referenced by GradientForRow(), GradientForWK(), and LikelihoodAttrKForRow().

                                                      {
    if (X[NID].IsKey(K)) {
      return 1.0;
    } else {
      return 0.0;
    }
  }

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int TCesna::GetAttrs ( )

inline

Definition at line 321 of file agmattr.h.

References Attrs.

{ return Attrs; }

void TCesna::GetCmtyVV

(

TVec< TIntV > &

CmtyVV

)

Definition at line 289 of file agmattr.cpp.

References G, TUNGraph::GetEdges(), and TUNGraph::GetNodes().

Referenced by GetCmtyVV().

                                          {
  TVec<TFltV> TmpV;
  GetCmtyVV(CmtyVV, TmpV, sqrt(2.0 * (double) G->GetEdges() / G->GetNodes() / G->GetNodes()), 3);
}

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void TCesna::GetCmtyVV	(	TVec< TIntV > &	CmtyVV,
		TVec< TFltV > &	Wck,
		const double	Thres,
		const int	MinSz = 3
	)

extract community affiliation from F_uc Wck[c][k] = W_c for k-th attribute

Definition at line 296 of file agmattr.cpp.

References TVec< TVal, TSizeTy >::Add(), THash< TKey, TDat, THashFunc >::AddDat(), Attrs, F, TVec< TVal, TSizeTy >::Gen(), GetCom(), THash< TKey, TDat, THashFunc >::GetDat(), THash< TKey, TDat, THashFunc >::GetKey(), THashSet< TKey, THashFunc >::GetKeyV(), GetW(), IAssert, TVec< TVal, TSizeTy >::Len(), NIDToIdx, NumComs, THash< TKey, TDat, THashFunc >::SortByDat(), and SumFV.

                                                                                                 {
  CmtyVV.Gen(NumComs, 0);
  Wck.Gen(NumComs, 0);
  TIntFltH CIDSumFH(NumComs);
  for (int c = 0; c < SumFV.Len(); c++) {
    CIDSumFH.AddDat(c, SumFV[c]);
  }
  CIDSumFH.SortByDat(false);
  for (int c = 0; c < NumComs; c++) {
    int CID = CIDSumFH.GetKey(c);
    TIntFltH NIDFucH(F.Len() / 10);
    TIntV CmtyV;
    IAssert(SumFV[CID] == CIDSumFH.GetDat(CID));
    if (SumFV[CID] < Thres) { continue; }
    for (int u = 0; u < F.Len(); u++) {
      int NID = NIDToIdx[u];
      if (GetCom(u, CID) >= Thres) { NIDFucH.AddDat(NID, GetCom(u, CID)); }
    }
    NIDFucH.SortByDat(false);
    NIDFucH.GetKeyV(CmtyV);
    if (CmtyV.Len() < MinSz) { continue; }
    CmtyVV.Add(CmtyV); 
    TFltV WV(Attrs);
    for (int k = 0; k < Attrs; k++) {
      WV[k] = GetW(CID, k);
    }
    Wck.Add(WV);
  }
  if ( NumComs != CmtyVV.Len()) {
    printf("Community vector generated. %d communities are ommitted\n", NumComs.Val - CmtyVV.Len());
  }
}

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void TCesna::GetCmtyVV ( TVec< TIntV > &  CmtyVV, const double  Thres, const int  MinSz = 3  )

inline

Definition at line 438 of file agmattr.h.

References GetCmtyVV().

                                                                               {
    TVec<TFltV> TmpV;
    GetCmtyVV(CmtyVV, TmpV, Thres, MinSz); 
  }

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void TCesna::GetCmtyVV ( TVec< TIntV > &  CmtyVV, TVec< TFltV > &  Wck  )

inline

Definition at line 442 of file agmattr.h.

References G, GetCmtyVV(), TUNGraph::GetEdges(), and TUNGraph::GetNodes().

                                                        {
    GetCmtyVV(CmtyVV, Wck, sqrt(2.0 * (double) G->GetEdges() / G->GetNodes() / G->GetNodes()), 3);
  }

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void TCesna::GetCmtyVVUnSorted

(

TVec< TIntV > &

CmtyVV

)

Definition at line 329 of file agmattr.cpp.

References G, TUNGraph::GetEdges(), and TUNGraph::GetNodes().

                                                  {
  GetCmtyVVUnSorted(CmtyVV, sqrt(2.0 * (double) G->GetEdges() / G->GetNodes() / G->GetNodes()), 3);
}

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void TCesna::GetCmtyVVUnSorted	(	TVec< TIntV > &	CmtyVV,
		const double	Thres,
		const int	MinSz = 3
	)

Definition at line 333 of file agmattr.cpp.

References TVec< TVal, TSizeTy >::Add(), G, TVec< TVal, TSizeTy >::Gen(), GetCom(), TUNGraph::GetNodes(), TVec< TVal, TSizeTy >::Len(), NIDToIdx, and NumComs.

                                                                                       {
  CmtyVV.Gen(NumComs, 0);
  for (int c = 0; c < NumComs; c++) {
    TIntV CmtyV;
    for (int u = 0; u < G->GetNodes(); u++) {
      if (GetCom(u, c) > Thres) { CmtyV.Add(NIDToIdx[u]); }
    }
    if (CmtyV.Len() >= MinSz) { CmtyVV.Add(CmtyV); }
  }
  if ( NumComs != CmtyVV.Len()) {
    printf("Community vector generated. %d communities are ommitted\n", NumComs.Val - CmtyVV.Len());
  }
}

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double TCesna::GetCom ( const int &  NID, const int &  CID  )

inline

Definition at line 527 of file agmattr.h.

References F.

Referenced by GetCmtyVV(), GetCmtyVVUnSorted(), GetStepSizeByLineSearch(), GradientForRow(), GradientForWK(), LikelihoodForRow(), and MLEGradAscent().

                                                       {
    if (F[NID].IsKey(CID)) {
      return F[NID].GetDat(CID);
    } else {
      return 0.0;
    }
  }

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double TCesna::GetComFromNID ( const int &  NID, const int &  CID  )

inline

Definition at line 322 of file agmattr.h.

References F, THashSet< TKey, THashFunc >::GetKeyId(), and NIDToIdx.

                                                       {
    int NIdx = NIDToIdx.GetKeyId(NID);
    if (F[NIdx].IsKey(CID)) {
      return F[NIdx].GetDat(CID);
    } else {
      return 0.0;
    }
  }

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double TCesna::GetLassoCoef ( )

inline

Definition at line 330 of file agmattr.h.

References LassoCoef.

{ return LassoCoef; }

int TCesna::GetNumComs ( )

inline

Definition at line 351 of file agmattr.h.

References NumComs.

{ return NumComs; }

int TCesna::GetPositiveW ( )

inline

Definition at line 510 of file agmattr.h.

References Attrs, GetW(), and NumComs.

                     {
    int PosCnt = 0;
    for (int c = 0; c < NumComs; c++) {
      for (int k = 0; k < Attrs; k++) {
        if (GetW(c, k) > 0.0) { PosCnt++; }
      }
    }
    return PosCnt;
  }

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double TCesna::GetRegCoef ( )

inline

Definition at line 317 of file agmattr.h.

References RegCoef.

{ return RegCoef; }

double TCesna::GetStepSizeByLineSearch	(	const int	UID,
		const TIntFltH &	DeltaV,
		const TIntFltH &	GradV,
		const double &	Alpha,
		const double &	Beta,
		const int	MaxIter = 10
	)

Definition at line 480 of file agmattr.cpp.

References DotProduct(), GetCom(), THash< TKey, TDat, THashFunc >::GetDat(), THash< TKey, TDat, THashFunc >::GetKey(), THash< TKey, TDat, THashFunc >::Len(), LikelihoodForRow(), MaxVal, and MinVal.

Referenced by MLEGradAscent(), and MLEGradAscentParallel().

                                                                                                                                                               {
  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;
}

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double TCesna::GetStepSizeByLineSearchForWK ( const int  K, const TFltV &  DeltaV, const TFltV &  GradV, const double &  Alpha, const double &  Beta, const int  MaxIter = 10  )

inline

Definition at line 486 of file agmattr.h.

References TLinAlg::DotProduct(), IAssert, TVec< TVal, TSizeTy >::Len(), LikelihoodForWK(), MaxValW, MinValW, NumComs, and W.

Referenced by MLEGradAscent(), and MLEGradAscentParallel().

                                                                                                                                                             {
    double StepSize = 1.0;
    double InitLikelihood = LikelihoodForWK(K);
    TFltV NewVarV(DeltaV.Len());
    IAssert(DeltaV.Len() == NumComs + 1);
    for(int iter = 0; iter < MaxIter; iter++) {
      for (int c = 0; c < DeltaV.Len(); c++){
        double NewVal = W[K][c] + StepSize * DeltaV[c];
        if (NewVal < MinValW) { NewVal = MinValW; }
        if (NewVal > MaxValW) { NewVal = MaxValW; }
        NewVarV[c] = NewVal;
      }
      if (LikelihoodForWK(K, NewVarV) < InitLikelihood + Alpha * StepSize * TLinAlg::DotProduct(GradV, DeltaV)) {
        StepSize *= Beta;
      } else {
        break;
      }
      if (iter == MaxIter - 1) { 
        StepSize = 0.0;
        break;
      }
    }
    return StepSize;
  }

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void TCesna::GetW ( TVec< TFltV > &  _W )

inline

Definition at line 346 of file agmattr.h.

References W.

Referenced by GetCmtyVV(), GetPositiveW(), GradientForRow(), and GradientForWK().

{ _W = W; }

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double TCesna::GetW ( const int  CID, const int  K  )

inline

Definition at line 603 of file agmattr.h.

References W.

                                                 {
    return W[K][CID];
  }

double TCesna::GetWeightAttr ( )

inline

Definition at line 319 of file agmattr.h.

References WeightAttr.

{ return WeightAttr; }

void TCesna::GradientForRow	(	const int	UID,
		TIntFltH &	GradU,
		const TIntSet &	CIDSet
	)

Definition at line 213 of file agmattr.cpp.

References THash< TKey, TDat, THashFunc >::AddDat(), Attrs, F, G, THash< TKey, TDat, THashFunc >::Gen(), TVec< TVal, TSizeTy >::Gen(), GetAttr(), GetCom(), TUNGraph::TNodeI::GetDeg(), THashSet< TKey, THashFunc >::GetKey(), TUNGraph::TNodeI::GetNbrNId(), TUNGraph::GetNI(), GetW(), HOKIDSV, HOVIDSV, IAssert, THash< TKey, TDat, THashFunc >::Len(), TVec< TVal, TSizeTy >::Len(), THashSet< TKey, THashFunc >::Len(), NegWgt, PredictAttrK(), Prediction(), RegCoef, SumFV, W, and WeightAttr.

Referenced by MLEGradAscent(), and MLEGradAscentParallel().

                                                                                 {
  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());
  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++) {
    GradV[c] *= (1.0 - WeightAttr);
  }
  //add attribute part
  TFltV AttrPredV(Attrs);
  for (int k = 0; k < Attrs; k++) {
    if (HOKIDSV[UID].IsKey(k)) { continue; }
    AttrPredV[k] = PredictAttrK(F[UID], W[k]);
  }
  for (int c = 0; c < GradV.Len(); c++) {
    for (int k = 0; k < Attrs; k++) {
      if (HOKIDSV[UID].IsKey(k)) { continue; }
      GradV[c] += WeightAttr * (GetAttr(UID, k) - AttrPredV[k]) * GetW(CIDV[c], k);
    }
  }


  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);
  }
}

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void TCesna::GradientForWK ( TFltV &  GradV, const int  K  )

inline

Definition at line 421 of file agmattr.h.

References F, TVec< TVal, TSizeTy >::Gen(), GetAttr(), GetCom(), GetW(), HOKIDSV, LassoCoef, TVec< TVal, TSizeTy >::Len(), NumComs, PredictAttrK(), and TMath::Sign().

Referenced by MLEGradAscent(), and MLEGradAscentParallel().

                                                {
    GradV.Gen(NumComs + 1);
    for (int u = 0; u < F.Len(); u++) {
      if (HOKIDSV[u].IsKey(K)) { continue; }
      double Pred = PredictAttrK(u, K);
      for (TIntFltH::TIter CI = F[u].BegI(); CI < F[u].EndI(); CI++) { 
        GradV[CI.GetKey()] += (GetAttr(u, K) - Pred) * GetCom(u, CI.GetKey());
      }
      GradV[NumComs] += (GetAttr(u, K) - Pred);
    }
    
    for (int c = 0; c < GradV.Len() - 1; c++) {
      GradV[c] -= LassoCoef * TMath::Sign(GetW(c, K));
    }
  }

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void TCesna::InitW ( )

inline

Definition at line 331 of file agmattr.h.

References Attrs, NumComs, and W.

Referenced by NeighborComInit(), RandomInit(), SetCmtyVV(), and SetGraph().

               { // initialize W
    W.Gen(Attrs);
    for (int k = 0; k < Attrs; k++) {
      W[k].Gen(NumComs + 1);
    }
  }

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double TCesna::Likelihood

(

const bool

DoParallel = false

)

Definition at line 137 of file agmattr.cpp.

References F, and LikelihoodForRow().

Referenced by FindComs(), LikelihoodGraph(), MLEGradAscent(), and MLEGradAscentParallel().

                                                { 
  TExeTm ExeTm;
  double L = 0.0;
  if (_DoParallel) {
  #pragma omp parallel for 
    for (int u = 0; u < F.Len(); u++) {
      double LU = LikelihoodForRow(u);
      #pragma omp atomic
        L += LU;
    }
  }
  else {
    for (int u = 0; u < F.Len(); u++) {
      double LU = LikelihoodForRow(u);
        L += LU;
    }
  }
  return L;
}

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double TCesna::LikelihoodAttr ( )

inline

Definition at line 371 of file agmattr.h.

References Attrs, F, HOKIDSV, LikelihoodAttrKForRow(), and W.

Referenced by FindComs(), and LikelihoodGraph().

                          {
    double L = 0.0;
    for (int k = 0; k < Attrs; k++) {
      for (int u = 0; u < F.Len(); u++) {
        if (HOKIDSV[u].IsKey(k)) { continue; }
        L += LikelihoodAttrKForRow(u, k, F[u], W[k]);
      }
    }
    return L;
  }

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double TCesna::LikelihoodAttrKForRow ( const int  UID, const int  K  )

inline

Definition at line 356 of file agmattr.h.

References F, and LikelihoodAttrKForRow().

Referenced by LikelihoodAttr(), LikelihoodAttrKForRow(), LikelihoodForRow(), LikelihoodForWK(), and LikelihoodHoldOut().

{ return LikelihoodAttrKForRow(UID, K, F[UID]); }

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double TCesna::LikelihoodAttrKForRow ( const int  UID, const int  K, const TIntFltH &  FU  )

inline

Definition at line 357 of file agmattr.h.

References LikelihoodAttrKForRow(), and W.

Referenced by LikelihoodAttrKForRow().

{ return LikelihoodAttrKForRow(UID, K, FU, W[K]); }

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double TCesna::LikelihoodAttrKForRow	(	const int	UID,
		const int	K,
		const TIntFltH &	FU,
		const TFltV &	WK
	)

Definition at line 202 of file agmattr.cpp.

References GetAttr(), and PredictAttrK().

                                                                                                    {
  double Prob = PredictAttrK(FU, WK);
  double L = 0.0;
  if (GetAttr(UID, K)) { 
    L = Prob == 0.0? -100.0: log(Prob);
  } else {
    L = Prob == 1.0? -100.0: log(1.0 - Prob);
  }
  return L;
}

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double TCesna::LikelihoodForRow

(

const int

UID

)

Definition at line 157 of file agmattr.cpp.

References F.

Referenced by GetStepSizeByLineSearch(), and Likelihood().

                                             {
  return LikelihoodForRow(UID, F[UID]);
}

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double TCesna::LikelihoodForRow	(	const int	UID,
		const TIntFltH &	FU
	)

Definition at line 161 of file agmattr.cpp.

References Attrs, THash< TKey, TDat, THashFunc >::BegI(), DotProduct(), THash< TKey, TDat, THashFunc >::EndI(), F, G, TVec< TVal, TSizeTy >::Gen(), GetCom(), TUNGraph::TNodeI::GetDeg(), TUNGraph::TNodeI::GetNbrNId(), TUNGraph::GetNI(), HOKIDSV, HOVIDSV, TVec< TVal, TSizeTy >::Len(), LikelihoodAttrKForRow(), NegWgt, Norm2(), Prediction(), RegCoef, Sum(), SumFV, and WeightAttr.

                                                                 {
  double L = 0.0;
  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);
  for (int e = 0; e < NI.GetDeg(); e++) {
    int v = NI.GetNbrNId(e);
    if (v == UID) { continue; }
    if (HOVIDSV[UID].IsKey(v)) { continue; }
    L += log (1.0 - Prediction(FU, F[v])) + NegWgt * DotProduct(FU, F[v]);
  }
  for (TIntFltH::TIter HI = FU.BegI(); HI < FU.EndI(); HI++) {
    double HOSum = HOVIDSV[UID].Len() > 0?  HOSumFV[HI.GetKey()].Val: 0.0;//subtract Hold out pairs only if hold out pairs exist
    L -= NegWgt * (SumFV[HI.GetKey()] - HOSum - GetCom(UID, HI.GetKey())) * HI.GetDat();
  }
  //add regularization
  if (RegCoef > 0.0) { //L1
    L -= RegCoef * Sum(FU);
  }
  if (RegCoef < 0.0) { //L2
    L += RegCoef * Norm2(FU);
  }
  L *= (1.0 - WeightAttr);
  // add attribute part
  for (int k = 0; k < Attrs; k++) {
    if (HOKIDSV[UID].IsKey(k)) { continue; }
    L += WeightAttr * LikelihoodAttrKForRow(UID, k, FU);
  }
  return L;
}

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double TCesna::LikelihoodForWK ( const int  K, const TFltV &  WK  )

inline

Definition at line 359 of file agmattr.h.

References F, HOKIDSV, LassoCoef, TVec< TVal, TSizeTy >::Len(), and LikelihoodAttrKForRow().

Referenced by GetStepSizeByLineSearchForWK().

                                                       {
    double L = 0.0;
    for (int u = 0; u < F.Len(); u++) {
      if (HOKIDSV[u].IsKey(K)) { continue; }
      L += LikelihoodAttrKForRow(u, K, F[u], WK);
    }
    for (int c = 0; c < WK.Len() - 1; c++) {
      L -= LassoCoef * fabs(WK[c]);
    } 
    return L;
  }

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double TCesna::LikelihoodForWK ( const int  K )

inline

Definition at line 370 of file agmattr.h.

References LikelihoodForWK(), and W.

Referenced by LikelihoodForWK().

{ return LikelihoodForWK(K, W[K]); }

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double TCesna::LikelihoodGraph ( )

inline

Definition at line 381 of file agmattr.h.

References F, Likelihood(), LikelihoodAttr(), Norm2(), RegCoef, Sum(), and WeightAttr.

                           {
    double L = Likelihood();
    //add regularization
    if (RegCoef > 0.0) { //L1
      for (int u = 0; u < F.Len(); u++) {
        L += RegCoef * Sum(F[u]);
      }
    }
    if (RegCoef < 0.0) { //L2
      for (int u = 0; u < F.Len(); u++) {
        L -= RegCoef * Norm2(F[u]);
      }
    }

    return L - WeightAttr * LikelihoodAttr();
  }

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double TCesna::LikelihoodHoldOut

(

)

Definition at line 452 of file agmattr.cpp.

References Attrs, G, HOKIDSV, HOVIDSV, IAssert, TUNGraph::IsEdge(), LikelihoodAttrKForRow(), NegWgt, Prediction(), and WeightAttr.

Referenced by FindComs().

                                 { 
  double L = 0.0;
  for (int u = 0; u < HOVIDSV.Len(); u++) {
    for (int e = 0; e < HOVIDSV[u].Len(); e++) {
      int VID = HOVIDSV[u][e];
      if (VID == u) { continue; } 
      double Pred = Prediction(u, VID);
      if (G->IsEdge(u, VID)) {
        L += log(1.0 - Pred);
      }
      else {
        L += NegWgt * log(Pred);
      }
      //printf("NODE %d, %d: Dot Prod: %f, Prediction: %f L:%f\n", u, VID, DotProduct(F[u], F[VID]), Pred, L);
    } 
  }
  L *= (1.0 - WeightAttr);
  //printf("likelihood for hold out network:%f\n", L);
  for (int u = 0; u < HOKIDSV.Len(); u++) {
    for (int e = 0; e < HOKIDSV[u].Len(); e++) {
      IAssert(HOKIDSV[u][e] < Attrs);
      L += WeightAttr * LikelihoodAttrKForRow(u, HOKIDSV[u][e]);
      //printf("asbefsafd ATTRIBUTE %d, NODE %d:%f\n", u, HOKIDSV[u][e].Val, LikelihoodAttrKForRow(u, HOKIDSV[u][e]));
    }
  }
  return L;
}

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void TCesna::Load ( TSIn &  SIn, const int &  RndSeed = 0  )

inline

Definition at line 294 of file agmattr.h.

References Attrs, F, G, HOKIDSV, HOVIDSV, LassoCoef, TUNGraph::Load(), TVec< TVal, TSizeTy >::Load(), THashSet< TKey, THashFunc >::Load(), TInt::Load(), TFlt::Load(), MaxVal, MaxValW, MinVal, MinValW, NegWgt, NIDToIdx, NumComs, PNoCom, RegCoef, SumFV, W, and X.

                                               {
    G->Load(SIn);
    X.Load(SIn);
    F.Load(SIn);
    W.Load(SIn);
    Attrs.Load(SIn);
    NIDToIdx.Load(SIn);
    RegCoef.Load(SIn);
    LassoCoef.Load(SIn);
    SumFV.Load(SIn);
    NumComs.Load(SIn);
    HOVIDSV.Load(SIn);
    HOKIDSV.Load(SIn);
    MinVal.Load(SIn);
    MaxVal.Load(SIn);
    MinValW.Load(SIn);
    MaxValW.Load(SIn);
    NegWgt.Load(SIn);
    PNoCom.Load(SIn);
  }

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int TCesna::MLEGradAscent	(	const double &	Thres,
		const int &	MaxIter,
		const TStr	PlotNm,
		const double	StepAlpha = 0.3,
		const double	StepBeta = 0.1
	)

Definition at line 505 of file agmattr.cpp.

References TVec< TVal, TSizeTy >::Add(), AddCom(), THashSet< TKey, THashFunc >::AddKey(), Attrs, DelCom(), TStr::Empty(), F, G, GetCom(), THash< TKey, TDat, THashFunc >::GetDat(), THash< TKey, TDat, THashFunc >::GetKey(), TUNGraph::GetNodes(), GetStepSizeByLineSearch(), GetStepSizeByLineSearchForWK(), TExeTm::GetTmStr(), GradientForRow(), GradientForWK(), THash< TKey, TDat, THashFunc >::Len(), TVec< TVal, TSizeTy >::Len(), Likelihood(), MaxValW, MinValW, TFlt::Mn, TLinAlg::Norm2(), Norm2(), NumComs, TGnuPlot::PlotValV(), Rnd, and W.

Referenced by FindComs().

                                                                                                                                   {
  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); }
  TIntFltH GradV;
  //consider all C
  TIntSet CIDSet(NumComs);
  for (int c = 0; c < NumComs; c++) { CIDSet.AddKey(c); }
  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(20 * 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)));
      }
    }
    // fit W (logistic regression)
    for (int k = 0; k < Attrs; k++) {
      TFltV GradWV(NumComs);
      GradientForWK(GradWV, k);
      if (TLinAlg::Norm2(GradWV) < 1e-4) { continue; }
      double LearnRate = GetStepSizeByLineSearchForWK(k, GradWV, GradWV, StepAlpha, StepBeta);
      if (LearnRate == 0.0) { continue; }
      for (int c = 0; c < GradWV.Len(); c++){
        W[k][c] += LearnRate * GradWV[c];
        if (W[k][c] < MinValW) { W[k][c] = MinValW; }
        if (W[k][c] > MaxValW) { W[k][c] = MaxValW; }
      }
    }
    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;
}

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int TCesna::MLEGradAscentParallel	(	const double &	Thres,
		const int &	MaxIter,
		const int	ChunkNum,
		const int	ChunkSize,
		const TStr	PlotNm,
		const double	StepAlpha = 0.3,
		const double	StepBeta = 0.1
	)

Definition at line 594 of file agmattr.cpp.

References TVec< TVal, TSizeTy >::Add(), Attrs, THash< TKey, TDat, THashFunc >::BegI(), TStr::CStr(), TStr::Empty(), THashKeyDatI< TKey, TDat >::EndI, THash< TKey, TDat, THashFunc >::EndI(), F, G, TSecTm::GetAbsSecs(), TSecTm::GetCurTm(), THash< TKey, TDat, THashFunc >::GetDat(), TVec< TVal, TSizeTy >::GetDat(), TUNGraph::TNodeI::GetDeg(), THash< TKey, TDat, THashFunc >::GetKey(), TUNGraph::TNodeI::GetNbrNId(), TUNGraph::GetNI(), TUNGraph::GetNodes(), GetStepSizeByLineSearch(), GetStepSizeByLineSearchForWK(), TUInt64::GetStr(), TExeTm::GetTmStr(), GradientForRow(), GradientForWK(), HOVIDSV, IAssert, THash< TKey, TDat, THashFunc >::Len(), TVec< TVal, TSizeTy >::Len(), Likelihood(), MaxValW, MinValW, TFlt::Mn, TLinAlg::Norm2(), Norm2(), NumComs, TGnuPlot::PlotValV(), TVec< TVal, TSizeTy >::PutAll(), Rnd, SumFV, and W.

Referenced by FindComs(), and MLEGradAscentParallel().

                                                                                                                                                                                    {
  //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);
          if (LearnRate == 0.0) { 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++; } }
    // fit W (logistic regression)
    if (! PlotNm.Empty()) {
      printf("\r%d iterations [%s] %s secs", iter * ChunkSize * ChunkNum, ExeTm.GetTmStr(), TUInt64::GetStr(TSecTm::GetCurTm().GetAbsSecs()-StartTm).CStr());
      if (PrevL > TFlt::Mn) { printf(" (%f) %d g %d s %d OPT", PrevL, NumNoChangeGrad, NumNoChangeStepSize, OPTCnt); }
      fflush(stdout);
    }
    if (iter == 0 || (iter - PrevIter) * ChunkSize * ChunkNum >= G->GetNodes()) {
  #pragma omp parallel for
      for (int k = 0; k < Attrs; k++) {
        TFltV GradWV(NumComs);
        GradientForWK(GradWV, k);
        if (TLinAlg::Norm2(GradWV) < 1e-4) { continue; }
        double LearnRate = GetStepSizeByLineSearchForWK(k, GradWV, GradWV, StepAlpha, StepBeta);
        if (LearnRate == 0.0) { continue; }
        for (int c = 0; c < GradWV.Len(); c++){
          W[k][c] += LearnRate * GradWV[c];
          if (W[k][c] < MinValW) { W[k][c] = MinValW; }
          if (W[k][c] > MaxValW) { W[k][c] = MaxValW; }
        }
      }
      PrevIter = iter;
      double CurL = Likelihood(true);
      IterLV.Add(TIntFltPr(iter * ChunkSize * ChunkNum, CurL));
      printf("\r%d iterations, Likelihood: %f, Diff: %f [%lu secs]", iter, CurL,  CurL - PrevL, 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, TUInt64::GetStr(TSecTm::GetCurTm().GetAbsSecs()-StartTm).CStr());
    TGnuPlot::PlotValV(IterLV, PlotNm + ".likelihood_Q");
  } else {
    printf("\rMLE completed with %d iterations(%lu secs)", iter, time(NULL) - InitTime);
    fflush(stdout);
  }
  return iter;
}

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int TCesna::MLEGradAscentParallel ( const double &  Thres, const int &  MaxIter, const int  ChunkNum, const TStr  PlotNm = TStr(), const double  StepAlpha = 0.3, const double  StepBeta = 0.1  )

inline

Definition at line 521 of file agmattr.h.

References G, TUNGraph::GetNodes(), and MLEGradAscentParallel().

                                                                                                                                                                                {
    int ChunkSize = G->GetNodes() / 10 / ChunkNum;
    if (ChunkSize == 0) { ChunkSize = 1; }
    return MLEGradAscentParallel(Thres, MaxIter, ChunkNum, ChunkSize, PlotNm, StepAlpha, StepBeta);
  }

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void TCesna::NeighborComInit

(

const int

InitComs

)

Definition at line 32 of file agmattr.cpp.

References F, and G.

Referenced by FindComs(), and TCesna().

                                               {
  //initialize with best neighborhood communities (Gleich et.al. KDD'12)
  TFltIntPrV NIdPhiV(F.Len(), 0);
  TCesnaUtil::GetNIdPhiV<PUNGraph>(G, NIdPhiV);
  NeighborComInit(NIdPhiV, InitComs);
}

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void TCesna::NeighborComInit	(	TFltIntPrV &	NIdPhiV,
		const int	InitComs
	)

Definition at line 39 of file agmattr.cpp.

References AddCom(), F, G, TVec< TVal, TSizeTy >::Gen(), TUNGraph::TNodeI::GetDeg(), TUNGraph::TNodeI::GetNbrNId(), TUNGraph::GetNI(), TUNGraph::GetNodes(), TRnd::GetUniDev(), TRnd::GetUniDevInt(), InitW(), TVec< TVal, TSizeTy >::Len(), NumComs, Rnd, TVec< TVal, TSizeTy >::Sort(), and SumFV.

                                                                    {
  //initialize with best neighborhood communities (Gleich et.al. KDD'12)
  NIdPhiV.Sort(true);
  F.Gen(G->GetNodes());
  SumFV.Gen(InitComs);
  NumComs = InitComs;
  TIntSet InvalidNIDS(F.Len());
  TIntV ChosenNIDV(InitComs, 0); //FOR DEBUG
  //choose nodes with local minimum in conductance
  int CurCID = 0;
  for (int ui = 0; ui < NIdPhiV.Len(); ui++) {
    int UID = NIdPhiV[ui].Val2;
    fflush(stdout);
    if (InvalidNIDS.IsKey(UID)) { continue; }
    ChosenNIDV.Add(UID); //FOR DEBUG
    //add the node and its neighbors to the current community
    AddCom(UID, CurCID, 1.0);
    TUNGraph::TNodeI NI = G->GetNI(UID);
    fflush(stdout);
    for (int e = 0; e < NI.GetDeg(); e++) {
      AddCom(NI.GetNbrNId(e), CurCID, 1.0);
    }
    //exclude its neighbors from the next considerations
    for (int e = 0; e < NI.GetDeg(); e++) {
      InvalidNIDS.AddKey(NI.GetNbrNId(e));
    }
    CurCID++;
    fflush(stdout);
    if (CurCID >= NumComs) { break;  }
  }
  if (NumComs > CurCID) {
    printf("%d communities needed to fill randomly\n", NumComs - CurCID);
  }
  //assign a member to zero-member community (if any)
  for (int c = 0; c < SumFV.Len(); c++) {
    if (SumFV[c] == 0.0) {
      int ComSz = 10;
      for (int u = 0; u < ComSz; u++) {
        int UID = Rnd.GetUniDevInt(G->GetNodes());
        AddCom(UID, c, Rnd.GetUniDev());
      }
    }
  }
  InitW();
}

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double TCesna::Norm2 ( const TIntFltH &  UV )

inline

Definition at line 616 of file agmattr.h.

References THash< TKey, TDat, THashFunc >::BegI(), and THash< TKey, TDat, THashFunc >::EndI().

Referenced by LikelihoodForRow(), LikelihoodGraph(), MLEGradAscent(), and MLEGradAscentParallel().

                                          {
    double N = 0.0;
    for (TIntFltH::TIter HI = UV.BegI(); HI < UV.EndI(); HI++) {
      N += HI.GetDat() * HI.GetDat();
    }
    return N;
  }

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double TCesna::PredictAttrK ( const TIntFltH &  FU, const TFltV &  WK  )

inline

Definition at line 589 of file agmattr.h.

References THash< TKey, TDat, THashFunc >::BegI(), THash< TKey, TDat, THashFunc >::EndI(), TVec< TVal, TSizeTy >::Last(), and Sigmoid().

Referenced by GradientForRow(), GradientForWK(), LikelihoodAttrKForRow(), and PredictAttrK().

                                                                  {
    double DP = 0.0;
    for (TIntFltH::TIter FI = FU.BegI(); FI < FU.EndI(); FI++) {
      DP += FI.GetDat() * WK[FI.GetKey()];
    }
    DP += WK.Last();
    return Sigmoid(DP);
  }

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double TCesna::PredictAttrK ( const TIntFltH &  FU, const int  K  )

inline

Definition at line 597 of file agmattr.h.

References PredictAttrK(), and W.

                                                              {
    return PredictAttrK(FU, W[K]);
  }

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double TCesna::PredictAttrK ( const int  UID, const int  K  )

inline

Definition at line 600 of file agmattr.h.

References F, PredictAttrK(), and W.

                                                         {
    return PredictAttrK(F[UID], W[K]);
  }

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double TCesna::Prediction ( const TIntFltH &  FU, const TIntFltH &  FV  )

inline

Definition at line 584 of file agmattr.h.

References DotProduct(), TStr::Fmt(), IAssertR, and PNoCom.

Referenced by GradientForRow(), LikelihoodForRow(), LikelihoodHoldOut(), and Prediction().

                                                                   {
    double DP = log (1.0 / (1.0 - PNoCom)) + DotProduct(FU, FV);
    IAssertR(DP > 0.0, TStr::Fmt("DP: %f", DP));
    return exp(- DP);
  }

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double TCesna::Prediction ( const int &  UID, const int &  VID  )

inline

Definition at line 606 of file agmattr.h.

References F, and Prediction().

                                                           {
    return Prediction(F[UID], F[VID]);
  }

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void TCesna::RandomInit

(

const int

InitComs

)

Definition at line 9 of file agmattr.cpp.

References AddCom(), F, G, TVec< TVal, TSizeTy >::Gen(), TUNGraph::TNodeI::GetDeg(), TUNGraph::GetNI(), TUNGraph::GetNodes(), TRnd::GetUniDev(), TRnd::GetUniDevInt(), InitW(), TVec< TVal, TSizeTy >::Len(), NumComs, Rnd, and SumFV.

Referenced by FindComs().

                                          {
  F.Gen(G->GetNodes());
  SumFV.Gen(InitComs);
  NumComs = InitComs;
  for (int u = 0; u < F.Len(); u++) {
    //assign to just one community
    int Mem = G->GetNI(u).GetDeg();
    if (Mem > 10) { Mem = 10; }
    for (int c = 0; c < Mem; c++) {
      int CID = Rnd.GetUniDevInt(InitComs);
      AddCom(u, CID, Rnd.GetUniDev());
    }
  }
  //assign a member to zero-member community (if any)
  for (int c = 0; c < SumFV.Len(); c++) {
    if (SumFV[c] == 0.0) {
      int UID = Rnd.GetUniDevInt(G->GetNodes());
      AddCom(UID, c, Rnd.GetUniDev());
    }
  }
  InitW();
}

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void TCesna::Save ( TSOut &  SOut )

inline

Definition at line 274 of file agmattr.h.

References Attrs, F, G, HOKIDSV, HOVIDSV, LassoCoef, MaxVal, MaxValW, MinVal, MinValW, NegWgt, NIDToIdx, NumComs, PNoCom, RegCoef, TUNGraph::Save(), TVec< TVal, TSizeTy >::Save(), THashSet< TKey, THashFunc >::Save(), TInt::Save(), TFlt::Save(), SumFV, W, and X.

                         {
    G->Save(SOut);
    X.Save(SOut);
    F.Save(SOut);
    W.Save(SOut);
    Attrs.Save(SOut);
    NIDToIdx.Save(SOut);
    RegCoef.Save(SOut);
    LassoCoef.Save(SOut);
    SumFV.Save(SOut);
    NumComs.Save(SOut);
    HOVIDSV.Save(SOut);
    HOKIDSV.Save(SOut);
    MinVal.Save(SOut);
    MaxVal.Save(SOut);
    MinValW.Save(SOut);
    MaxValW.Save(SOut);
    NegWgt.Save(SOut);
    PNoCom.Save(SOut);
  }

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void TCesna::SetAttrHoldOut ( const int  NID, const int  KID  )

inline

Definition at line 337 of file agmattr.h.

References THashSet< TKey, THashFunc >::GetKeyId(), HOKIDSV, and NIDToIdx.

Referenced by SetAttrHoldOutForOneNode().

                                                    {
    int NIdx = NIDToIdx.GetKeyId(NID);
    HOKIDSV[NIdx].AddKey(KID);
  }

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void TCesna::SetAttrHoldOutForOneNode ( const int  NID )

inline

Definition at line 341 of file agmattr.h.

References Attrs, and SetAttrHoldOut().

                                               {
    for (int k = 0; k < Attrs; k++) {
      SetAttrHoldOut(NID, k);
    }
  }

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void TCesna::SetCmtyVV

(

const TVec< TIntV > &

CmtyVV

)

Definition at line 85 of file agmattr.cpp.

References AddCom(), F, G, TVec< TVal, TSizeTy >::Gen(), THashSet< TKey, THashFunc >::GetKeyId(), TUNGraph::GetNodes(), InitW(), THashSet< TKey, THashFunc >::IsKey(), TVec< TVal, TSizeTy >::Len(), NIDToIdx, NumComs, and SumFV.

                                                {
  F.Gen(G->GetNodes());
  SumFV.Gen(CmtyVV.Len());
  NumComs = CmtyVV.Len();
  InitW();
  for (int c = 0; c < CmtyVV.Len(); c++) {
    for (int u = 0; u < CmtyVV[c].Len(); u++) {
      int UID = CmtyVV[c][u];
      if (! NIDToIdx.IsKey(UID)) { continue; }
      AddCom(NIDToIdx.GetKeyId(UID), c, 1.0);
    }
  }
}

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void TCesna::SetGraph	(	const PUNGraph &	GraphPt,
		const THash< TInt, TIntV > &	NIDAttrH
	)

Definition at line 99 of file agmattr.cpp.

References THashSet< TKey, THashFunc >::AddKeyV(), Attrs, TSnap::DelSelfEdges(), DoParallel, G, THashSet< TKey, THashFunc >::Gen(), THash< TKey, TDat, THashFunc >::GetKey(), THashSet< TKey, THashFunc >::GetKeyId(), TUNGraph::GetNIdV(), TUNGraph::GetNodes(), TSnap::GetSubGraph(), HOKIDSV, HOVIDSV, IAssert, InitW(), THashSet< TKey, THashFunc >::IsKey(), TUNGraph::IsNode(), THash< TKey, TDat, THashFunc >::Len(), TVec< TVal, TSizeTy >::Len(), TFlt::Mx, NegWgt, NIDToIdx, PNoCom, and X.

Referenced by TCesna().

                                                                                 {
  HOVIDSV.Gen(GraphPt->GetNodes());  
  HOKIDSV.Gen(GraphPt->GetNodes());  
  X.Gen(GraphPt->GetNodes());
  TIntV NIDV;
  GraphPt->GetNIdV(NIDV);
  NIDToIdx.Gen(NIDV.Len());
  NIDToIdx.AddKeyV(NIDV);
  // check that nodes IDs are {0,1,..,Nodes-1}
  printf("rearrage nodes\n");
  G = TSnap::GetSubGraph(GraphPt, NIDV, true);
  for (int nid = 0; nid < G->GetNodes(); nid++) {
    IAssert(G->IsNode(nid)); 
  }
  TSnap::DelSelfEdges(G);
  
  PNoCom = 1.0 / (double) G->GetNodes();
  DoParallel = false;
  if (1.0 / PNoCom > sqrt(TFlt::Mx)) { PNoCom = 0.99 / sqrt(TFlt::Mx); } // to prevent overflow
  NegWgt = 1.0;

  // add node attributes, find number of attributes
  int NumAttr = 0;
  for (int u = 0; u < NIDAttrH.Len(); u++) {
    int UID = NIDAttrH.GetKey(u);
    if (! NIDToIdx.IsKey(UID)) { continue; }
    X[NIDToIdx.GetKeyId(UID)].Gen(NIDAttrH[u].Len());
    for (int k = 0; k < NIDAttrH[u].Len(); k++) {
      int KID = NIDAttrH[u][k];
      IAssert (KID >= 0);
      X[NIDToIdx.GetKeyId(UID)].AddKey(KID);
      if (NumAttr < KID + 1) { NumAttr = KID + 1; } 
    }
  }
  Attrs = NumAttr;
  InitW();
}

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void TCesna::SetHoldOut ( const double  HOFrac )

inline

Definition at line 414 of file agmattr.h.

References G, GenHoldOutAttr(), TCesnaUtil::GenHoldOutPairs(), HOKIDSV, HOVIDSV, and Rnd.

                                       { 
    TVec<TIntSet> HoldOut; 
    TCesnaUtil::GenHoldOutPairs(G, HoldOut, HOFrac, Rnd); 
    GenHoldOutAttr(HOFrac, HOKIDSV);
    HOVIDSV = HoldOut; 
  }

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void TCesna::SetLassoCoef ( const double  _LassoCoef )

inline

Definition at line 320 of file agmattr.h.

References LassoCoef.

{ LassoCoef = _LassoCoef; }

void TCesna::SetRegCoef ( const double  _RegCoef )

inline

Definition at line 316 of file agmattr.h.

References RegCoef.

{ RegCoef = _RegCoef; }

void TCesna::SetW ( TVec< TFltV > &  _W )

inline

Definition at line 347 of file agmattr.h.

References W.

{ W = _W; }

void TCesna::SetWeightAttr ( const double  _WeightAttr )

inline

Definition at line 318 of file agmattr.h.

References IAssert, and WeightAttr.

{ IAssert (_WeightAttr <= 1.0 && _WeightAttr >= 0.0); WeightAttr = _WeightAttr; }

double TCesna::Sigmoid ( const double  X )

inline

Definition at line 632 of file agmattr.h.

Referenced by PredictAttrK().

                                        {
    return 1.0 / ( 1.0 + exp(-X));
  }

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double TCesna::Sum ( const TIntFltH &  UV )

inline

Definition at line 609 of file agmattr.h.

References THash< TKey, TDat, THashFunc >::BegI(), and THash< TKey, TDat, THashFunc >::EndI().

Referenced by LikelihoodForRow(), and LikelihoodGraph().

                                        {
    double N = 0.0;
    for (TIntFltH::TIter HI = UV.BegI(); HI < UV.EndI(); HI++) {
      N += HI.GetDat();
    }
    return N;
  }

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Member Data Documentation

TInt TCesna::Attrs

private

Definition at line 252 of file agmattr.h.

Referenced by FindComs(), GenHoldOutAttr(), GetAttrs(), GetCmtyVV(), GetPositiveW(), GradientForRow(), InitW(), LikelihoodAttr(), LikelihoodForRow(), LikelihoodHoldOut(), Load(), MLEGradAscent(), MLEGradAscentParallel(), Save(), SetAttrHoldOutForOneNode(), and SetGraph().

TBool TCesna::DoParallel

Definition at line 269 of file agmattr.h.

Referenced by SetGraph().

TVec<TIntFltH> TCesna::F

private

Definition at line 250 of file agmattr.h.

Referenced by AddCom(), DelCom(), DotProduct(), GenHoldOutAttr(), GetCmtyVV(), GetCom(), GetComFromNID(), GradientForRow(), GradientForWK(), Likelihood(), LikelihoodAttr(), LikelihoodAttrKForRow(), LikelihoodForRow(), LikelihoodForWK(), LikelihoodGraph(), Load(), MLEGradAscent(), MLEGradAscentParallel(), NeighborComInit(), PredictAttrK(), Prediction(), RandomInit(), Save(), and SetCmtyVV().

PUNGraph TCesna::G

private

Definition at line 248 of file agmattr.h.

Referenced by FindComs(), GenHoldOutAttr(), GetCmtyVV(), GetCmtyVVUnSorted(), GradientForRow(), LikelihoodForRow(), LikelihoodHoldOut(), Load(), MLEGradAscent(), MLEGradAscentParallel(), NeighborComInit(), RandomInit(), Save(), SetCmtyVV(), SetGraph(), SetHoldOut(), and TCesna().

TVec<TIntSet> TCesna::HOKIDSV

private

Definition at line 259 of file agmattr.h.

Referenced by FindComs(), GradientForRow(), GradientForWK(), LikelihoodAttr(), LikelihoodForRow(), LikelihoodForWK(), LikelihoodHoldOut(), Load(), Save(), SetAttrHoldOut(), SetGraph(), and SetHoldOut().

TVec<TIntSet> TCesna::HOVIDSV

private

Definition at line 258 of file agmattr.h.

Referenced by FindComs(), GradientForRow(), LikelihoodForRow(), LikelihoodHoldOut(), Load(), MLEGradAscentParallel(), Save(), SetGraph(), and SetHoldOut().

TFlt TCesna::LassoCoef

Definition at line 266 of file agmattr.h.

Referenced by GetLassoCoef(), GradientForWK(), LikelihoodForWK(), Load(), Save(), and SetLassoCoef().

TFlt TCesna::MaxVal

Definition at line 262 of file agmattr.h.

Referenced by GetStepSizeByLineSearch(), Load(), and Save().

TFlt TCesna::MaxValW

Definition at line 264 of file agmattr.h.

Referenced by GetStepSizeByLineSearchForWK(), Load(), MLEGradAscent(), MLEGradAscentParallel(), and Save().

TFlt TCesna::MinVal

Definition at line 261 of file agmattr.h.

Referenced by GetStepSizeByLineSearch(), Load(), and Save().

TFlt TCesna::MinValW

Definition at line 263 of file agmattr.h.

Referenced by GetStepSizeByLineSearchForWK(), Load(), MLEGradAscent(), MLEGradAscentParallel(), and Save().

TFlt TCesna::NegWgt

Definition at line 265 of file agmattr.h.

Referenced by GradientForRow(), LikelihoodForRow(), LikelihoodHoldOut(), Load(), Save(), and SetGraph().

TIntSet TCesna::NIDToIdx

private

Definition at line 254 of file agmattr.h.

Referenced by DisplayAttrs(), GetCmtyVV(), GetCmtyVVUnSorted(), GetComFromNID(), Load(), Save(), SetAttrHoldOut(), SetCmtyVV(), and SetGraph().

TInt TCesna::NumComs

private

Definition at line 257 of file agmattr.h.

Referenced by GetCmtyVV(), GetCmtyVVUnSorted(), GetNumComs(), GetPositiveW(), GetStepSizeByLineSearchForWK(), GradientForWK(), InitW(), Load(), MLEGradAscent(), MLEGradAscentParallel(), NeighborComInit(), RandomInit(), Save(), and SetCmtyVV().

TFlt TCesna::PNoCom

Definition at line 268 of file agmattr.h.

Referenced by Load(), Prediction(), Save(), and SetGraph().

TFlt TCesna::RegCoef

private

Definition at line 255 of file agmattr.h.

Referenced by GetRegCoef(), GradientForRow(), LikelihoodForRow(), LikelihoodGraph(), Load(), Save(), and SetRegCoef().

TRnd TCesna::Rnd

private

Definition at line 253 of file agmattr.h.

Referenced by FindComs(), GenHoldOutAttr(), MLEGradAscent(), MLEGradAscentParallel(), NeighborComInit(), RandomInit(), and SetHoldOut().

TFltV TCesna::SumFV

private

Definition at line 256 of file agmattr.h.

Referenced by AddCom(), DelCom(), GetCmtyVV(), GradientForRow(), LikelihoodForRow(), Load(), MLEGradAscentParallel(), NeighborComInit(), RandomInit(), Save(), and SetCmtyVV().

TVec<TFltV> TCesna::W

private

Definition at line 251 of file agmattr.h.

Referenced by GetStepSizeByLineSearchForWK(), GetW(), GradientForRow(), InitW(), LikelihoodAttr(), LikelihoodAttrKForRow(), LikelihoodForWK(), Load(), MLEGradAscent(), MLEGradAscentParallel(), PredictAttrK(), Save(), and SetW().

TFlt TCesna::WeightAttr

Definition at line 267 of file agmattr.h.

Referenced by FindComs(), GetWeightAttr(), GradientForRow(), LikelihoodForRow(), LikelihoodGraph(), LikelihoodHoldOut(), and SetWeightAttr().

TVec<TIntSet> TCesna::X

private

Definition at line 249 of file agmattr.h.

Referenced by DisplayAttrs(), GetAttr(), Load(), Save(), and SetGraph().

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The documentation for this class was generated from the following files:

• snap-adv/agmattr.h
• snap-adv/agmattr.cpp