void plotParitialDegDistribution(const PNGraph& graph, std::vector<int>& nodeList) {
std::map<int, int> inDegDistMap;
std::map<int, int> outDegDistMap;
for (int i = 0; i < nodeList.size(); ++i) {
int curNodeId = nodeList[i];
if (!graph->IsNode(curNodeId)) continue;
TNGraph::TNodeI ni = graph->GetNI(curNodeId);
int curNodeInDeg = ni.GetInDeg();
if (inDegDistMap.find(curNodeInDeg) == inDegDistMap.end()) {
inDegDistMap.insert(std::pair<int, int>(curNodeInDeg, 0));
}
inDegDistMap[curNodeInDeg]++;
int curNodeOutDeg = ni.GetOutDeg();
if (outDegDistMap.find(curNodeOutDeg) == outDegDistMap.end()) {
outDegDistMap.insert(std::pair<int, int>(curNodeOutDeg, 0));
}
outDegDistMap[curNodeOutDeg]++;
}
TFltPrV inDegDist;
for (std::map<int, int>::iterator itr = inDegDistMap.begin(); itr != inDegDistMap.end(); itr++) {
inDegDist.Add(TFltPr(itr->first, itr->second));
}
TFltPrV outDegDist;
for (std::map<int, int>::iterator itr = outDegDistMap.begin(); itr != outDegDistMap.end(); itr++) {
outDegDist.Add(TFltPr(itr->first, itr->second));
}
TGnuPlot plot1("inDegDistParitial", "");
plot1.AddPlot(inDegDist, gpwPoints, "");
plot1.SetScale(gpsLog10XY);
plot1.SavePng();
TGnuPlot plot2("outDegDistParitial", "");
plot2.AddPlot(outDegDist, gpwPoints, "");
plot2.SetScale(gpsLog10XY);
plot2.SavePng();
TGnuPlot plot3("DegDistParitial", "");
plot3.AddCmd("set key right top");
plot3.AddPlot(inDegDist, gpwPoints, "In Degree");
plot3.AddPlot(outDegDist, gpwPoints, "Out Degree");
plot3.SetScale(gpsLog10XY);
plot3.SavePng();
}
void getSampledDistance(const PNGraph& graph, std::vector<int> srcIds, std::vector<int> dstIds, int sampleSize, TFltPrV& ret) {
std::random_shuffle(srcIds.begin(), srcIds.end());
std::random_shuffle(dstIds.begin(), dstIds.end());
int distance[20];
for (int i = 0; i < 20; distance[i++] = 0);
int sampleCount = 0;
for (int i = 0; i < sampleSize; ) {
int srcNodeId = srcIds[rand() % srcIds.size()];
int dstNodeId = dstIds[rand() % dstIds.size()];
if (!graph->IsNode(srcNodeId)) continue;
if (!graph->IsNode(dstNodeId)) continue;
int shortDist = TSnap::GetShortPath(graph, srcNodeId, dstNodeId, true);
distance[shortDist]++;
sampleCount++;
printIntArray(distance, 20);
++i;
}
for (int i = 0; i < 20; ++i) {
ret.Add(TFltPr(i, distance[i]));
}
}
int TGnuPlot::AddExpFit(const int& PlotId, const TGpSeriesTy& SeriesTy, const double& FitXOffset, const TStr& Style) {
const TGpSeries& Plot = SeriesV[PlotId];
if(Plot.XYValV.Empty()) return -1;
const TFltKdV& XY = Plot.XYValV;
double A, B, R2, SigA, SigB, Chi2;
// power fit
TFltPrV XYPr;
int s;
for (s = 0; s < XY.Len(); s++) {
if (XY[s].Key-FitXOffset > 0) {
XYPr.Add(TFltPr(XY[s].Key-FitXOffset, XY[s].Dat)); } //!!! skip zero values
}
TSpecFunc::ExpFit(XYPr, A, B, SigA, SigB, Chi2, R2);
TStr Label, StyleStr=Style;
if (FitXOffset == 0) { Label = TStr::Fmt("%.4g exp(%.4g x) R^2:%.2g", A, B, R2); }
else { Label = TStr::Fmt("%.4g exp(%.4g x - %g) R^2:%.2g", A, B, FitXOffset, R2); }
if (StyleStr.Empty()) { StyleStr = "linewidth 3"; }
const int FitId = AddFunc(TStr::Fmt("%f*exp(%f*x-%f)", A, B, FitXOffset),
SeriesTy, Label, StyleStr);
return FitId;
/*SeriesV.Add();
TGpSeries& NewPlot = SeriesV.Last();
TFltKdV& EstXY = NewPlot.XYValV;
for (s = 0; s < XYPr.Len(); s++) {
EstXY.Add(TFltKd(XYPr[s].Val1+FitXOffset, A*exp(B*XYPr[s].Val1)));
}
NewPlot.SeriesTy = SeriesTy;
if (Style.Empty()) { NewPlot.WithStyle = "linewidth 3"; }
else { NewPlot.WithStyle = Style; }
return SeriesV.Len() - 1;*/
}
int TGnuPlot::AddLogFit(const int& PlotId, const TGpSeriesTy& SeriesTy, const TStr& Style) {
const TGpSeries& Plot = SeriesV[PlotId];
if(Plot.XYValV.Empty()) return -1;
const TFltKdV& XY = Plot.XYValV;
double A, B, R2, SigA, SigB, Chi2;
// power fit
TFltPrV XYPr;
int s;
for (s = 0; s < XY.Len(); s++) {
if (XY[s].Key > 0) {
XYPr.Add(TFltPr(XY[s].Key, XY[s].Dat)); } //!!! skip zero values
}
TSpecFunc::LogFit(XYPr, A, B, SigA, SigB, Chi2, R2);
TStr StyleStr=Style;
if (StyleStr.Empty()) { StyleStr = "linewidth 3"; }
const int FitId = AddFunc(TStr::Fmt("%f+%f*log(x)", A, B),
SeriesTy, TStr::Fmt("%.4g + %.4g log(x) R^2:%.2g", A, B, R2), StyleStr);
return FitId;
/*SeriesV.Add();
TGpSeries& NewPlot = SeriesV.Last();
TFltKdV& EstXY = NewPlot.XYValV;
for (s = 0; s < XYPr.Len(); s++) {
EstXY.Add(TFltKd(XYPr[s].Val1, A+B*log((double)XYPr[s].Val1)));
}
NewPlot.Label = TStr::Fmt("%.4g + %.4g log(x) R^2:%.2g", A, B, R2);
NewPlot.SeriesTy = SeriesTy;
if (Style.Empty()) { NewPlot.WithStyle = "linewidth 3"; }
else { NewPlot.WithStyle = Style; }
return SeriesV.Len() - 1;*/
}
// CHECK
void ExpBinning(const TFltPrV& deg, TFltPrV& degSparse, const int& BinRadix){
TFlt maxDeg(deg[deg.Len()-1].Val1.Val), minDeg(deg[0].Val1.Val);
bool maxPowerReached = false;
// idx - index of border, previdx - index of previous border
int power = 0, previdx = 0, idx, binSize;
TFltPr val;
double binBorder = 0.0;
while (binBorder <= minDeg)
binBorder = pow(static_cast<double>(BinRadix), power++);
TFltPr v(minDeg, deg[0].Val2.Val);
degSparse.Add(v);
bool isExact = false;
while (!maxPowerReached){
if (binBorder >= maxDeg){
// when last element of deg was previous bin border
if (previdx == deg.Len() - 1)
break;
// if we have another elements
binBorder = maxDeg;
maxPowerReached = true;
}
// find next element
idx = FindVal1Elem(deg, binBorder, isExact);
// if bin size == 0
if (previdx + 1 == idx && !isExact)
continue;
if (!isExact)
idx = idx - 1;
double sum = 0.0;
binSize = idx - previdx;
for (int i = previdx + 1; i <= idx; i++){
sum += deg[i].Val2.Val;
}
sum /= binSize;
// if prevBinBorder was the smallest degree, it can be more than binBorder / BinRadix
double SumBinBorder = previdx > 0 ? binBorder + static_cast<double>(binBorder) / BinRadix : binBorder + static_cast<double>(minDeg);
double avgDeg = SumBinBorder / 2.0;
val.Val1 = avgDeg; val.Val2 = sum;
degSparse.Add(val);
previdx = idx;
binBorder = pow(static_cast<double>(BinRadix), power++);
}
}
// Inverse participation ratio: normalize EigVec to have L2=1 and then I=sum_k EigVec[i]^4
// see Spectra of "real-world" graphs: Beyond the semicircle law by Farkas, Derenyi, Barabasi and Vicsek
void PlotInvParticipRat(const PUNGraph& Graph, const int& MaxEigVecs, const int& TimeLimit, const TStr& FNmPref, TStr DescStr) {
TFltPrV EigIprV;
GetInvParticipRat(Graph, MaxEigVecs, TimeLimit, EigIprV);
if (DescStr.Empty()) { DescStr = FNmPref; }
if (EigIprV.Empty()) { DescStr+=". FAIL"; EigIprV.Add(TFltPr(-1,-1)); return; }
TGnuPlot::PlotValV(EigIprV, "eigIPR."+FNmPref, TStr::Fmt("%s. G(%d, %d). Largest eig val = %f (%d values)",
DescStr.CStr(), Graph->GetNodes(), Graph->GetEdges(), EigIprV.Last().Val1(), EigIprV.Len()),
"Eigenvalue", "Inverse Participation Ratio of corresponding Eigenvector", gpsLog10Y, false, gpwPoints);
}
void TGStatVec::GetValV(const TGStatVal& XVal, const TGStatVal& YVal, TFltPrV& ValV) const {
ValV.Gen(Len(), 0);
double x;
for (int t = 0; t < Len(); t++) {
if (XVal == gsvTime) { x = t+1; }
else { x = At(t)->GetVal(XVal); }
ValV.Add(TFltPr(x, At(t)->GetVal(YVal)));
}
}
void TGnuPlot::MakeExpBins(const TFltPrV& XYValV, TFltPrV& ExpXYValV, const double& BinFactor, const double& MinYVal) {
TFltKdV KdV(XYValV.Len(), 0), OutV;
for (int i = 0; i < XYValV.Len(); i++) {
KdV.Add(TFltKd(XYValV[i].Val1, XYValV[i].Val2)); }
KdV.Sort();
TGnuPlot::MakeExpBins(KdV, OutV, BinFactor, MinYVal);
ExpXYValV.Gen(OutV.Len(), 0);
for (int i = 0; i < OutV.Len(); i++) {
ExpXYValV.Add(TFltPr(OutV[i].Key, OutV[i].Dat)); }
}
void TFfGGen::PlotFireSize(const TStr& FNmPref, const TStr& DescStr) {
TGnuPlot GnuPlot("fs."+FNmPref, TStr::Fmt("%s. Fire size. G(%d, %d)",
DescStr.CStr(), Graph->GetNodes(), Graph->GetEdges()));
GnuPlot.SetXYLabel("Vertex id (iterations)", "Fire size (node out-degree)");
TFltPrV IdToOutDegV;
for (TNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++) {
IdToOutDegV.Add(TFltPr(NI.GetId(), NI.GetOutDeg())); }
IdToOutDegV.Sort();
GnuPlot.AddPlot(IdToOutDegV, gpwImpulses, "Node out-degree");
GnuPlot.SavePng();
}
void GetCumDistr(const TFltPrV& nonCum, TFltPrV& res){
for (int i = nonCum.Len() - 1; i >=0; i--){
TFlt count;
if (i == nonCum.Len() - 1)
count = nonCum[i].Val2.Val;
else
count = nonCum[i].Val2.Val + res[res.Len()-1].Val2.Val;
TFltPr val(nonCum[i].Val1, count);
res.Add(val);
}
res.Sort();
}
void plotScatterLengthOfEachCascade(THash<TUInt,TSecTmV>& c1, THash<TUInt,TSecTmV>& c2)
{
printf("\n\nPlotting ...\n");
TFltPrV plotdata;
for(int q=0;q<c1.Len();q++)
{
TFltPr elem;
elem.Val1 = c1[q].Len();
elem.Val2 = c2[q].Len();
plotdata.Add(elem);
}
Tools::plotScatter(plotdata, "TwitterUrlsOverContents", "Urls on Twitter", "Contents on Twitter");
}
void plotScatterLengthOfEachCascade(THash<TStr,CascadeElementV>& quotes, THash<TUInt,TSecTmV>& twitter, char* name)
{
printf("\n\nPlotting ...\n");
TFltPrV plotdata;
for(int q=0;q<quotes.Len();q++)
{
TFltPr elem;
elem.Val1 = quotes[q].Len();
elem.Val2 = twitter[q].Len();
plotdata.Add(elem);
}
Tools::plotScatter(plotdata, name, "Blogs/News", TStr::Fmt("%s on Twitter",name).CStr());
}
void GetPoints(const TFlt& maxDegLog, const TFlt& minDegLog, const int& NInt, const TFltPrV& base, TFltPrV& points){
int beginIndex = 0;
// ignore nodes with zero degree (for Kronecker graphs)
/*if (base[0].Val1.Val != 0)
points.Add(base[beginIndex]);
else {
points.Add(base[++beginIndex]);
}*/
points.Add(base[beginIndex]);
TFlt baseMaxDeg = base[base.Len()-1].Val1.Val,
baseMinDeg = base[beginIndex].Val1.Val;
for (int i = beginIndex + 1; i < NInt; i++){
// deg - degree to be found in base
TFlt degRound (pow (10, minDegLog.Val + i * (maxDegLog.Val - minDegLog.Val) / NInt));
TInt degInt(static_cast<int>(degRound.Val));
TFlt deg(degInt);
// if deg < baseMinDeg (for cases when baseMinDeg > minDeg)
if (deg.Val <= baseMinDeg)
continue;
// if deg > baseMaxDeg, add last point and finish
if (deg.Val >= baseMaxDeg){
points.Add(base[base.Len()-1]);
break;
}
// we have two cases: when we can find an exact value of deg, or when we have not this value
bool isExact = false;
int index = FindVal1Elem(base, deg, isExact);
if (isExact){
points.Add(base[index]);
}
else
{
TFltPr x;
x.Val1.Val = deg;
x.Val2.Val = ( base[index].Val2.Val + base [index + 1].Val2.Val ) / 2;
points.Add(x);
}
}
}
void plotpaths(char* fileName, TFltPrV& ret) {
int distance[10000];
for (int i = 0; i < 10000; distance[i++] = 0);
int lineCount = 1;
std::ifstream inputFile(fileName);
for (std::string line; std::getline(inputFile, line);) {
std::istringstream isss(line);
int a, c;
double b, d;
isss >> a;
ret.Add(TFltPr(lineCount++, a));
}
}
// MLE power-coefficient
int TGnuPlot::AddPwrFit2(const int& PlotId, const TGpSeriesTy& SeriesTy, const double& MinX, const TStr& Style) {
const TGpSeries& Plot = SeriesV[PlotId];
if(Plot.XYValV.Empty()) return -1;
const TFltKdV& XY = Plot.XYValV;
// power fit
TFltPrV XYPr;
double MinY = TFlt::Mx;
for (int s = 0; s < XY.Len(); s++) {
if (XY[s].Key > 0.0) {
XYPr.Add(TFltPr(XY[s].Key, XY[s].Dat));
MinY = TMath::Mn(MinY, XY[s].Dat());
}
}
if (XYPr.Empty()) return -1;
MinY = TMath::Mn(1.0, MinY);
// determine the sign of power coefficient
double CoefSign = 0.0;
{ double A, B, R2, SigA, SigB, Chi2;
TSpecFunc::PowerFit(XYPr, A, B, SigA, SigB, Chi2, R2);
CoefSign = B > 0.0 ? +1.0 : -1.0; }
const double PowerCf = CoefSign * TSpecFunc::GetPowerCoef(XYPr, MinX);
int Mid = (int) exp(log((double)XYPr.Len())/2.0);
if (Mid >= XYPr.Len()) { Mid = XYPr.Len()-1; }
const double MidX = XYPr[Mid].Val1();
const double MidY = XYPr[Mid].Val2();
const double B = MidY / pow(MidX, PowerCf);
TStr StyleStr=Style;
if (StyleStr.Empty()) { StyleStr = "linewidth 3"; }
const int FitId = AddFunc(TStr::Fmt("%f*x**%f", B, PowerCf),
SeriesTy, TStr::Fmt("MLE = x^{%.4g}", PowerCf), StyleStr);
return FitId;
/*SeriesV.Add();
TGpSeries& NewPlot = SeriesV.Last();
TFltKdV& XYFit = NewPlot.XYValV;
XYFit.Gen(XYPr.Len(), 0);
for (int s = 0; s < XYPr.Len(); s++) {
const double XVal = XYPr[s].Val1;
const double YVal = B * pow(XYPr[s].Val1(), PowerCf);
if (YVal < MinY || XVal < MinX) continue;
XYFit.Add(TFltKd(XVal, YVal));
}
NewPlot.Label = TStr::Fmt("PowerFit: %g", PowerCf);
NewPlot.SeriesTy = SeriesTy;
if (Style.Empty()) { NewPlot.WithStyle = "linewidth 3"; }
else { NewPlot.WithStyle = Style; }
return SeriesV.Len() - 1;*/
}
// linear fit on log-log scales{%
int TGnuPlot::AddPwrFit1(const int& PlotId, const TGpSeriesTy& SeriesTy, const TStr& Style) {
if (PlotId < 0 || PlotId >= SeriesV.Len()) return -1;
const TGpSeries& Plot = SeriesV[PlotId];
if(Plot.XYValV.Empty()) return -1;
const TFltKdV& XY = Plot.XYValV;
double A, B, R2, SigA, SigB, Chi2, MinY = TFlt::Mx, MinX = TFlt::Mx;
// power fit
TFltPrV XYPr;
int s;
for (s = 0; s < XY.Len(); s++) {
if (XY[s].Key > 0) {
XYPr.Add(TFltPr(XY[s].Key, XY[s].Dat)); //!!! skip zero values
MinX = TMath::Mn(MinX, XY[s].Key());
MinY = TMath::Mn(MinY, XY[s].Dat());
}
}
MinY = TMath::Mn(1.0, MinY);
TSpecFunc::PowerFit(XYPr, A, B, SigA, SigB, Chi2, R2);
TStr StyleStr=Style;
if (StyleStr.Empty()) { StyleStr = "linewidth 3"; }
const int FitId = AddFunc(TStr::Fmt("%f*x**%f", A, B),
SeriesTy, TStr::Fmt("%.1g * x^{%.4g} R^2:%.2g", A, B, R2), StyleStr);
return FitId;
/*SeriesV.Add();
TGpSeries& NewPlot = SeriesV.Last();
const int FitId = SeriesV.Len() - 1;
NewPlot.DataFNm = ;
TFltKdV& EstXY = NewPlot.XYValV;
for (s = 0; s < XYPr.Len(); s++) {
const double YVal = A*pow(XYPr[s].Val1(), B);
if (YVal < MinY) continue;
EstXY.Add(TFltKd(XYPr[s].Val1, YVal));
}
NewPlot.Label = ;
NewPlot.SeriesTy = SeriesTy;
if (Style.Empty()) { NewPlot.WithStyle = "linewidth 3"; }
else { NewPlot.WithStyle = Style; }
//if (MinX < 5.0) MinX = 5.0;
//AddPwrFit2(PlotId, SeriesTy, MinX);*/
}
void GetInvParticipRat(const PUNGraph& Graph, int MaxEigVecs, int TimeLimit, TFltPrV& EigValIprV) {
TUNGraphMtx GraphMtx(Graph);
TFltVV EigVecVV;
TFltV EigValV;
TExeTm ExeTm;
if (MaxEigVecs<=1) { MaxEigVecs=1000; }
int EigVecs = TMath::Mn(Graph->GetNodes(), MaxEigVecs);
printf("start %d vecs...", EigVecs);
try {
TSparseSVD::Lanczos2(GraphMtx, EigVecs, TimeLimit, ssotFull, EigValV, EigVecVV, false);
} catch(...) {
printf("\n ***EXCEPTION: TRIED %d GOT %d values** \n", EigVecs, EigValV.Len()); }
printf(" ***TRIED %d GOT %d values in %s\n", EigVecs, EigValV.Len(), ExeTm.GetStr());
TFltV EigVec;
EigValIprV.Clr();
if (EigValV.Empty()) { return; }
for (int v = 0; v < EigVecVV.GetCols(); v++) {
EigVecVV.GetCol(v, EigVec);
EigValIprV.Add(TFltPr(EigValV[v], GetInvParticipRat(EigVec)));
}
EigValIprV.Sort();
}
// get degrees from current and add it to degrees
void AddDegreeStat(const TFltPrV& current, TFltPrV& degrees, TIntPrV& samples){
for (int j = 0; j < current.Len(); j++){
const TFltPr& elem = current[j];
const double& deg = elem.Val1.Val, &nodesCount = elem.Val2.Val;
bool wasFound = false;
// silly search
for (int k = 0; k < degrees.Len(); k++){
if (degrees[k].Val1.Val == deg){
degrees[k].Val2.Val += nodesCount;
samples[k].Val2.Val++;
wasFound = true; break;
}
}
if (!wasFound){
TFlt d(deg), n(nodesCount);
TFltPr val(d,n);
degrees.Add(val);
TInt di(static_cast<int>(deg));
TIntPr valI(di, 1);
samples.Add(valI);
}
}
}
void plotPR(char* fileName, TFltPrV& ret) {
int distance[10000];
for (int i = 0; i < 10000; distance[i++] = 0);
std::ifstream inputFile(fileName);
for (std::string line; std::getline(inputFile, line);) {
std::istringstream isss(line);
int a, c;
double b, d;
isss >> a >> b >> c >> d;
int val = (int)(d * 1000);
val -= (val % 100);
if (val >= 10000) continue;
//double idd = std::stold(line);
printf("%d\n", val);
distance[val]++;
}
for (int i = 0; i < 10000; ++i) {
if (distance[i] == 0) continue;
ret.Add(TFltPr(i, distance[i]));
}
}
int main(int argc, char* argv[]) {
Env = TEnv(argc, argv, TNotify::StdNotify);
Env.PrepArgs(TStr::Fmt("\nNETINF. build: %s, %s. Time: %s", __TIME__, __DATE__, TExeTm::GetCurTm()));
TExeTm ExeTm;
Try
const TStr InFNm = Env.GetIfArgPrefixStr("-i:", "example-cascades.txt", "Input cascades (one file)");
const TStr GroundTruthFNm = Env.GetIfArgPrefixStr("-n:", "example-network.txt", "Input ground-truth network (one file)");
const TStr OutFNm = Env.GetIfArgPrefixStr("-o:", "network", "Output file name(s) prefix");
const TStr Iters = Env.GetIfArgPrefixStr("-e:", "5", "Number of iterations");
const double alpha = Env.GetIfArgPrefixFlt("-a:", 1.0, "Alpha for transmission model");
const int Model = Env.GetIfArgPrefixInt("-m:", 0, "0:exponential, 1:power law, 2:rayleigh");
const int Top =Env.GetIfArgPrefixInt("-t:", 10, "select top k as friends");
const int TakeAdditional = Env.GetIfArgPrefixInt("-s:", 1, "How much additional files to create?\n\
1:info about each edge, 2:objective function value (+upper bound), 3:Precision-recall plot, 4:all-additional-files (default:1)\n");
bool ComputeBound = false, ComputeInfo = false; bool CompareGroundTruth = false;
switch (TakeAdditional) {
case 1 : ComputeInfo = true; break;
case 2 : ComputeBound = true; break;
case 3 : CompareGroundTruth = true; break;
case 4 :
ComputeInfo = true;
// ComputeBound = true;
CompareGroundTruth = true; break;
default: FailR("Bad -s: parameter.");
}
TNetInfBs NIB(ComputeBound, CompareGroundTruth, Top);
printf("\nLoading input cascades: %s\n", InFNm.CStr());
// load cascade from file
TFIn FIn(InFNm);
NIB.LoadCascadesTxt(FIn, Model, alpha);
// load ground truth network
if (CompareGroundTruth) {
TFIn FInG(GroundTruthFNm);
NIB.LoadGroundTruthTxt(FInG);
}
NIB.Init();
printf("cascades:%d nodes:%d potential edges:%d\nRunning NETINF...\n", NIB.GetCascs(), NIB.GetNodes(), NIB.CascPerEdge.Len());
NIB.GreedyOpt(Iters.GetInt());
// plot showing precision/recall using groundtruth
if (CompareGroundTruth)
TGnuPlot::PlotValV(NIB.PrecisionRecall, TStr::Fmt("%s-precision-recall", OutFNm.CStr()), "Precision Recall", "Recall",
"Precision", gpsAuto, false, gpwLinesPoints, false);
// plot objective function
if (ComputeBound) {
TFltPrV Gains;
for (int i=0; i<NIB.EdgeInfoH.Len(); i++)
Gains.Add(TFltPr((double)(i+1), NIB.EdgeInfoH[i].MarginalGain));
TGnuPlot::PlotValV(Gains, TStr::Fmt("%s-objective", OutFNm.CStr()), "Objective Function", "Iters", "Objective Function");
}
// save network in plain text
NIB.SavePlaneTextNet(TStr::Fmt("%s.txt", OutFNm.CStr()));
// save edge info
if (ComputeInfo)
NIB.SaveEdgeInfo(TStr::Fmt("%s-edge.info", OutFNm.CStr()));
// save obj+bound info
if (ComputeBound)
NIB.SaveObjInfo(TStr::Fmt("%s-obj", OutFNm.CStr()));
Catch
printf("\nrun time: %s (%s)\n", ExeTm.GetTmStr(), TSecTm::GetCurTm().GetTmStr().CStr());
return 0;
}