/// Generates a random graph with exact degree sequence DegSeqV.
/// The generated graph has no self loops. The graph generation process
/// simulates the Configuration Model but if a duplicate edge occurs, we find a
/// random edge, break it and reconnect it with the duplicate.
PUNGraph GenDegSeq(const TIntV& DegSeqV, TRnd& Rnd) {
const int Nodes = DegSeqV.Len();
PUNGraph GraphPt = TUNGraph::New();
TUNGraph& Graph = *GraphPt;
Graph.Reserve(Nodes, -1);
TIntH DegH(DegSeqV.Len(), true);
IAssertR(DegSeqV.IsSorted(false), "DegSeqV must be sorted in descending order.");
int DegSum=0, edge=0;
for (int node = 0; node < Nodes; node++) {
IAssert(Graph.AddNode(node) == node);
DegH.AddDat(node, DegSeqV[node]);
DegSum += DegSeqV[node];
}
IAssert(DegSum % 2 == 0);
while (! DegH.Empty()) {
// pick random nodes and connect
const int NId1 = DegH.GetKey(DegH.GetRndKeyId(TInt::Rnd, 0.5));
const int NId2 = DegH.GetKey(DegH.GetRndKeyId(TInt::Rnd, 0.5));
IAssert(DegH.IsKey(NId1) && DegH.IsKey(NId2));
if (NId1 == NId2) {
if (DegH.GetDat(NId1) == 1) { continue; }
// find rnd edge, break it, and connect the endpoints to the nodes
const TIntPr Edge = TSnapDetail::GetRndEdgeNonAdjNode(GraphPt, NId1, -1);
if (Edge.Val1==-1) { continue; }
Graph.DelEdge(Edge.Val1, Edge.Val2);
Graph.AddEdge(Edge.Val1, NId1);
Graph.AddEdge(NId1, Edge.Val2);
if (DegH.GetDat(NId1) == 2) { DegH.DelKey(NId1); }
else { DegH.GetDat(NId1) -= 2; }
} else {
if (! Graph.IsEdge(NId1, NId2)) {
Graph.AddEdge(NId1, NId2); } // good edge
else {
// find rnd edge, break and cross-connect
const TIntPr Edge = TSnapDetail::GetRndEdgeNonAdjNode(GraphPt, NId1, NId2);
if (Edge.Val1==-1) {continue; }
Graph.DelEdge(Edge.Val1, Edge.Val2);
Graph.AddEdge(NId1, Edge.Val1);
Graph.AddEdge(NId2, Edge.Val2);
}
if (DegH.GetDat(NId1)==1) { DegH.DelKey(NId1); }
else { DegH.GetDat(NId1) -= 1; }
if (DegH.GetDat(NId2)==1) { DegH.DelKey(NId2); }
else { DegH.GetDat(NId2) -= 1; }
}
if (++edge % 1000 == 0) {
printf("\r %dk / %dk", edge/1000, DegSum/2000); }
}
return GraphPt;
}
void TTrawling::JoinItems(const TIntV& Item1, const TIntV& Item2, TIntV& JoinItem) {
int i = 0, j = 0;
JoinItem.Clr(false);
const int MaxL = Item1.Len()+1;
while (i < Item1.Len()) {
while (j < Item2.Len() && Item2[j] < Item1[i]) {
JoinItem.Add(Item2[j]); j++; }
JoinItem.Add(Item1[i]);
if (j < Item2.Len() && Item1[i] == Item2[j]) { j++; }
i++;
if (JoinItem.Len() > MaxL) { JoinItem.Clr(false); return; }
}
while (j < Item2.Len()) {
JoinItem.Add(Item2[j]); j++;
}
/*if (JoinItem.Len() > 3) {
Dump(Item1, "\n1:");
Dump(Item2, "2:");
Dump(JoinItem, "J:");
}*/
IAssert(JoinItem.IsSorted());
}
PBowDocBs TFtrGenBs::LoadCsv(TStr& FNm, const int& ClassId,
const TIntV& IgnoreIdV, const int& TrainLen) {
// feature generators
PFtrGenBs FtrGenBs = TFtrGenBs::New();
// CSV parsing stuff
PSIn SIn = TFIn::New(FNm);
char SsCh = ' '; TStrV FldValV;
// read the headers and initialise the feature generators
TSs::LoadTxtFldV(ssfCommaSep, SIn, SsCh, FldValV, false);
for (int FldValN = 0; FldValN < FldValV.Len(); FldValN++) {
const TStr& FldVal = FldValV[FldValN];
if (FldValN == ClassId) {
if (FldVal == "NOM") {
FtrGenBs->PutClsFtrGen(TFtrGenNominal::New());
} else if (FldVal == "MULTI-NOM") {
FtrGenBs->PutClsFtrGen(TFtrGenMultiNom::New());
} else {
TExcept::Throw("Wrong class type '" + FldVal + "', should be NOM or MULTI-NOM!");
}
} else if (!IgnoreIdV.IsIn(FldValN)) {
if (FldVal == TFtrGenNumeric::GetType()) {
FtrGenBs->AddFtrGen(TFtrGenNumeric::New());
} else if (FldVal == TFtrGenNominal::GetType()) {
FtrGenBs->AddFtrGen(TFtrGenNominal::New());
} else if (FldVal == TFtrGenToken::GetType()) {
FtrGenBs->AddFtrGen(TFtrGenToken::New(
TSwSet::New(swstNone), TStemmer::New(stmtNone)));
} else if (FldVal == TFtrGenSparseNumeric::GetType()) {
FtrGenBs->AddFtrGen(TFtrGenSparseNumeric::New());
} else if (FldVal == TFtrGenMultiNom::GetType()) {
FtrGenBs->AddFtrGen(TFtrGenMultiNom::New());
} else {
TExcept::Throw("Wrong type '" + FldVal + "'!");
}
}
}
const int Flds = FldValV.Len();
// read the lines and feed them to the feature generators
int Recs = 0;
while (!SIn->Eof()) {
if (Recs == TrainLen) { break; }
Recs++; printf("%7d\r", Recs);
TSs::LoadTxtFldV(ssfCommaSep, SIn, SsCh, FldValV, false);
// make sure line still has the same number of fields as the header
EAssertR(FldValV.Len() == Flds,
TStr::Fmt("Wrong number of fields in line %d! Found %d and expected %d!",
Recs + 1, FldValV.Len(), Flds));
// go over lines
try {
TStrV FtrValV;
for (int FldValN = 0; FldValN < FldValV.Len(); FldValN++) {
const TStr& FldVal = FldValV[FldValN];
if (FldValN == ClassId) {
FtrGenBs->UpdateCls(FldVal);
} else if (!IgnoreIdV.IsIn(FldValN)) {
FtrValV.Add(FldVal);
}
}
FtrGenBs->Update(FtrValV);
} catch (PExcept Ex) {
TExcept::Throw(TStr::Fmt("Error in line %d: '%s'!",
Recs+1, Ex->GetMsgStr().CStr()));
}
}
// read the file again and feed it to the training set
PBowDocBs BowDocBs = FtrGenBs->MakeBowDocBs();
// we read and ignore the headers since we parsed them already
SIn = TFIn::New(FNm); SsCh = ' ';
TSs::LoadTxtFldV(ssfCommaSep, SIn, SsCh, FldValV, false);
// read the lines and feed them to the training set
Recs = 0;
while (!SIn->Eof()){
Recs++; printf("%7d\r", Recs);
TSs::LoadTxtFldV(ssfCommaSep, SIn, SsCh, FldValV, false);
// make sure line still has the same number of fields as the header
EAssertR(FldValV.Len() == Flds,
TStr::Fmt("Wrong number of fields in line %s! Found %d and expected %d!",
Recs + 1, FldValV.Len(), Flds));
// go over lines and construct the sparse vector
TStrV FtrValV; TStr ClsFtrVal;
try {
for (int FldValN = 0; FldValN < FldValV.Len(); FldValN++) {
const TStr& FldVal = FldValV[FldValN];
if (FldValN == ClassId) {
ClsFtrVal = FldVal;
} else if (!IgnoreIdV.IsIn(FldValN)) {
FtrValV.Add(FldVal);
}
}
} catch (PExcept Ex) {
TExcept::Throw(TStr::Fmt("Error in line %d: '%s'!",
Recs+1, Ex->GetMsgStr().CStr()));
}
// add the feature vector to trainsets
FtrGenBs->AddBowDoc(BowDocBs, TStr::Fmt("Line-%d", Recs), FtrValV, ClsFtrVal);
}
// prepare training and testing doc ids
TIntV AllDIdV; BowDocBs->GetAllDIdV(AllDIdV); IAssert(AllDIdV.IsSorted());
TIntV TrainDIdV = AllDIdV; TrainDIdV.Trunc(TrainLen);
BowDocBs->PutTrainDIdV(TrainDIdV);
TIntV TestDIdV = AllDIdV; TestDIdV.Minus(TrainDIdV);
BowDocBs->PutTestDIdV(TestDIdV);
return BowDocBs;
}