C++ TRnd::GetUniDev方法代码示例

/// Generates a small-world graph using the Watts-Strogatz model.
/// We assume a circle where each node creates links to NodeOutDeg other nodes. 
/// This way at the end each node is connected to 2*NodeOutDeg other nodes.
/// See: Collective dynamics of 'small-world' networks. Watts and Strogatz.
/// URL: http://research.yahoo.com/files/w_s_NATURE_0.pdf
PUNGraph GenSmallWorld(const int& Nodes, const int& NodeOutDeg, const double& RewireProb, TRnd& Rnd) {
  THashSet<TIntPr> EdgeSet(Nodes*NodeOutDeg);
  
  IAssertR(Nodes > NodeOutDeg, TStr::Fmt("Insufficient nodes for out degree, %d!", NodeOutDeg));
  for (int node = 0; node < Nodes; node++) {
    const int src = node;
    for (int edge = 1; edge <= NodeOutDeg; edge++) {
      int dst = (node+edge) % Nodes;      // edge to next neighbor
      if (Rnd.GetUniDev() < RewireProb) { // random edge
        dst = Rnd.GetUniDevInt(Nodes);
        while (dst == src || EdgeSet.IsKey(TIntPr(src, dst))) {
          dst = Rnd.GetUniDevInt(Nodes); }
      }
      EdgeSet.AddKey(TIntPr(src, dst));
    }
  }
  PUNGraph GraphPt = TUNGraph::New();
  TUNGraph& Graph = *GraphPt;
  Graph.Reserve(Nodes, EdgeSet.Len());
  int node;
  for (node = 0; node < Nodes; node++) {
    IAssert(Graph.AddNode(node) == node);
  }
  for (int edge = 0; edge < EdgeSet.Len(); edge++) {
    Graph.AddEdge(EdgeSet[edge].Val1, EdgeSet[edge].Val2);
  }
  Graph.Defrag();
  return GraphPt;
}

int main() {
  TLSHash LSH(7, 7, DIM, TLSHash::EUCLIDEAN);
  LSH.Init();

  TRnd Gen;
  Gen.Randomize();

  TVec<TFltV> DataV;
  for (int i=0; i<1000000; i++) {
    TFltV Datum;
    for (int j=0; j<3; j++) {
      Datum.Add(Gen.GetUniDev()*2100);
    }
    DataV.Add(Datum);
  }
  LSH.AddV(DataV);
  
  TVec<TPair<TFltV, TFltV> > NeighborsV = LSH.GetAllCandidatePairs();
  printf("Number of Candidates: %d\n", NeighborsV.Len());

  NeighborsV = LSH.GetAllNearPairs();
  printf("Number of Close Pairs: %d\n", NeighborsV.Len());
  for (int i=0; i<NeighborsV.Len(); i++) {
    outputPoint(NeighborsV[i].GetVal1());
    printf(" ");
    outputPoint(NeighborsV[i].GetVal2());
    printf("\n");
  }
  return 0;
}

//Initialize positive embeddings
void InitPosEmb(TIntV& Vocab, int& Dimensions, TRnd& Rnd, TVVec<TFlt, int64>& SynPos) {
  SynPos = TVVec<TFlt, int64>(Vocab.Len(),Dimensions);
  for (int64 i = 0; i < SynPos.GetXDim(); i++) {
    for (int j = 0; j < SynPos.GetYDim(); j++) {
      SynPos(i,j) =(Rnd.GetUniDev()-0.5)/Dimensions;
    }
  }
}

/// Generates a random scale-free network using the Copying Model.
/// The generating process operates as follows: Node u is added to a graph, it
/// selects a random node v, and with prob Beta it links to v, with 1-Beta 
/// links u links to neighbor of v. The power-law degree exponent is -1/(1-Beta).
/// See: Stochastic models for the web graph.
/// Kumar, Raghavan, Rajagopalan, Sivakumar, Tomkins, Upfal.
/// URL: http://snap.stanford.edu/class/cs224w-readings/kumar00stochastic.pdf
PNGraph GenCopyModel(const int& Nodes, const double& Beta, TRnd& Rnd) {
  PNGraph GraphPt = TNGraph::New();
  TNGraph& Graph = *GraphPt;
  Graph.Reserve(Nodes, Nodes);
  const int startNId = Graph.AddNode();
  Graph.AddEdge(startNId, startNId);
  for (int n = 1; n < Nodes; n++) {
    const int rnd = Graph.GetRndNId();
    const int NId = Graph.AddNode();
    if (Rnd.GetUniDev() < Beta) {
      Graph.AddEdge(NId, rnd); }
    else {
      const TNGraph::TNodeI NI = Graph.GetNI(rnd);
      const int rnd2 = Rnd.GetUniDevInt(NI.GetOutDeg());
      Graph.AddEdge(NId, NI.GetOutNId(rnd2));
    }
  }
  return GraphPt;
}

void TTransCorpus::Shuffle(const PTransCorpus& InFirstTransCorpus, 
        const PTransCorpus& InSecondTransCorpus, TRnd& Rnd, const double& SwapProb,
        PTransCorpus& OutFirstTransCorpus, PTransCorpus& OutSecondTransCorpus) {

    // prepare new corpuses
    OutFirstTransCorpus = TTransCorpus::New();
    OutSecondTransCorpus = TTransCorpus::New();
    // swap sentences
    TIntV FirstSentIdV, SecondSentIdV;
    InFirstTransCorpus->GetSentIdV(FirstSentIdV);
    InSecondTransCorpus->GetSentIdV(SecondSentIdV);
    for (int SentIdN = 0; SentIdN < FirstSentIdV.Len(); SentIdN++) {
        // get sentence id
        const int SentId = FirstSentIdV[SentIdN];
        // check if id same in both cases
        IAssert(SecondSentIdV[SentIdN] == SentId);
        // read sentences
        TStr OrgStr1 = InFirstTransCorpus->GetOrgStr(SentId);
        TStr OrgStr2 = InSecondTransCorpus->GetOrgStr(SentId);
        IAssert(OrgStr1 == OrgStr2 );
        TStr RefTransStr1 = InFirstTransCorpus->GetRefTransStrV(SentId)[0];
        TStr RefTransStr2 = InSecondTransCorpus->GetRefTransStrV(SentId)[0];
        IAssert(RefTransStr1 == RefTransStr2);
        TStr FirstTransStr = InFirstTransCorpus->GetTransStr(SentId);
        TStr SecondTransStr = InSecondTransCorpus->GetTransStr(SentId);
        // swap sentences
        if (Rnd.GetUniDev() < SwapProb) { 
            // we swap
            OutFirstTransCorpus->AddSentence(SentId, OrgStr1, SecondTransStr, RefTransStr1);
            OutSecondTransCorpus->AddSentence(SentId, OrgStr1, FirstTransStr, RefTransStr1);
        } else {
            // no swap
            OutFirstTransCorpus->AddSentence(SentId, OrgStr1, FirstTransStr, RefTransStr1);
            OutSecondTransCorpus->AddSentence(SentId, OrgStr1, SecondTransStr, RefTransStr1);
        }
    }
}

/// R-MAT Generator. The modes is based on the recursive descent into a 2x2
/// matrix [A,B; C, 1-(A+B+C)].
/// See: R-MAT Generator: A Recursive Model for Graph Mining. 
/// D. Chakrabarti, Y. Zhan and C. Faloutsos, in SIAM Data Mining 2004. 
/// URL: http://www.cs.cmu.edu/~deepay/mywww/papers/siam04.pdf
PNGraph GenRMat(const int& Nodes, const int& Edges, const double& A, const double& B, const double& C, TRnd& Rnd) {
  PNGraph GraphPt = TNGraph::New();
  TNGraph& Graph = *GraphPt;
  Graph.Reserve(Nodes, Edges);
  IAssert(A+B+C < 1.0);
  int rngX, rngY, offX, offY;
  int Depth=0, Collisions=0, Cnt=0, PctDone=0;
  const int EdgeGap = Edges / 100 + 1;
  // sum of parameters (probabilities)
  TVec<double> sumA(128, 0), sumAB(128, 0), sumAC(128, 0), sumABC(128, 0);  // up to 2^128 vertices ~ 3.4e38
  for (int i = 0; i < 128; i++) {
    const double a = A * (Rnd.GetUniDev() + 0.5);
    const double b = B * (Rnd.GetUniDev() + 0.5);
    const double c = C * (Rnd.GetUniDev() + 0.5);
    const double d = (1.0 - (A+B+C)) * (Rnd.GetUniDev() + 0.5);
    const double abcd = a+b+c+d;
    sumA.Add(a / abcd);
    sumAB.Add((a+b) / abcd);
    sumAC.Add((a+c) / abcd);
    sumABC.Add((a+b+c) / abcd);
  }
  // nodes
  for (int node = 0; node < Nodes; node++) {
    IAssert(Graph.AddNode(-1) == node);
  }
  // edges
  for (int edge = 0; edge < Edges; ) {
    rngX = Nodes;  rngY = Nodes;  offX = 0;  offY = 0;
    Depth = 0;
    // recurse the matrix
    while (rngX > 1 || rngY > 1) {
      const double RndProb = Rnd.GetUniDev();
      if (rngX>1 && rngY>1) {
        if (RndProb < sumA[Depth]) { rngX/=2; rngY/=2; }
        else if (RndProb < sumAB[Depth]) { offX+=rngX/2;  rngX-=rngX/2;  rngY/=2; }
        else if (RndProb < sumABC[Depth]) { offY+=rngY/2;  rngX/=2;  rngY-=rngY/2; }
        else { offX+=rngX/2;  offY+=rngY/2;  rngX-=rngX/2;  rngY-=rngY/2; }
      } else
      if (rngX>1) { // row vector
        if (RndProb < sumAC[Depth]) { rngX/=2; rngY/=2; }
        else { offX+=rngX/2;  rngX-=rngX/2;  rngY/=2; }
      } else
      if (rngY>1) { // column vector
        if (RndProb < sumAB[Depth]) { rngX/=2; rngY/=2; }
        else { offY+=rngY/2;  rngX/=2;  rngY-=rngY/2; }
      } else { Fail; }
      Depth++;
    }
    // add edge
    const int NId1 = offX;
    const int NId2 = offY;
    if (NId1 != NId2 && ! Graph.IsEdge(NId1, NId2)) {
      Graph.AddEdge(NId1, NId2);
      if (++Cnt > EdgeGap) {
        Cnt=0;  printf("\r  %d%% edges", ++PctDone); }
      edge++;
    } else {
      Collisions++; }
  }
  printf("\r  RMat: nodes:%d, edges:%d, Iterations:%d, Collisions:%d (%.1f%%).\n", Nodes, Edges,
    Edges+Collisions, Collisions, 100*Collisions/double(Edges+Collisions));
  Graph.Defrag();
  return GraphPt;
}

int64 RndUnigramInt(TIntV& KTable, TFltV& UTable, TRnd& Rnd) {
  TInt X = KTable[static_cast<int64>(Rnd.GetUniDev()*KTable.Len())];
  double Y = Rnd.GetUniDev();
  return Y < UTable[X] ? X : KTable[X];
}