本文整理汇总了C++中Try::GetCmLn方法的典型用法代码示例。如果您正苦于以下问题：C++ Try::GetCmLn方法的具体用法？C++ Try::GetCmLn怎么用？C++ Try::GetCmLn使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Try的用法示例。
在下文中一共展示了Try::GetCmLn方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: main
int main(int argc, char* argv[]) {
Env = TEnv(argc, argv, TNotify::StdNotify);
Env.PrepArgs(
TStr::Fmt("Kronecker graphs. build: %s, %s. Time: %s", __TIME__,
__DATE__, TExeTm::GetCurTm()));
TExeTm ExeTm;
Try
Env = TEnv(argc, argv, TNotify::StdNotify);
const TStr InFNm = Env.GetIfArgPrefixStr("-i:", "../as20graph.txt",
"Input graph file (single directed edge per line)");
TStr OutFNm = Env.GetIfArgPrefixStr("-o:", "", "Output file prefix");
const TInt NZero = Env.GetIfArgPrefixInt("-n0:", 2,
"Innitiator matrix size");
const TStr InitMtx = Env.GetIfArgPrefixStr("-m:", "0.9 0.7; 0.5 0.2",
"Init Gradient Descent Matrix (R=random)").GetLc();
const TStr Perm =
Env.GetIfArgPrefixStr("-p:", "d",
"Initial node permutation: d:Degree, r:Random, o:Order").GetLc();
const TInt GradIter = Env.GetIfArgPrefixInt("-gi:", 50,
"Gradient descent iterations");
const TFlt LrnRate = Env.GetIfArgPrefixFlt("-l:", 1e-5,
"Learning rate");
const TFlt MnStep = Env.GetIfArgPrefixFlt("-mns:", 0.005,
"Minimum gradient step");
const TFlt MxStep = Env.GetIfArgPrefixFlt("-mxs:", 0.05,
"Maximum gradient step");
const TInt WarmUp = Env.GetIfArgPrefixInt("-w:", 10000,
"Samples to warm up");
const TInt NSamples = Env.GetIfArgPrefixInt("-s:", 100000,
"Samples per gradient estimation");
//const TInt GradType = Env.GetIfArgPrefixInt("-gt:", 1, "1:Grad1, 2:Grad2");
const bool ScaleInitMtx = Env.GetIfArgPrefixBool("-sim:", true,
"Scale the initiator to match the number of edges");
const TFlt PermSwapNodeProb =
Env.GetIfArgPrefixFlt("-nsp:", 1.0,
"Probability of using NodeSwap (vs. EdgeSwap) MCMC proposal distribution");
if (OutFNm.Empty()) {
OutFNm = TStr::Fmt("%s-fit%d", InFNm.GetFMid().CStr(), NZero());
}
// load graph
PNGraph G;
if (InFNm.GetFExt().GetLc() == ".ungraph") {
TFIn FIn(InFNm);
G = TSnap::ConvertGraph<PNGraph>(TUNGraph::Load(FIn), true);
} else if (InFNm.GetFExt().GetLc() == ".ngraph") {
TFIn FIn(InFNm);
G = TNGraph::Load(FIn);
} else {
G = TSnap::LoadEdgeList<PNGraph>(InFNm, 0, 1);
}
// fit
TKronMtx InitKronMtx =
InitMtx == "r" ?
TKronMtx::GetRndMtx(NZero, 0.1) :
TKronMtx::GetMtx(InitMtx);
InitKronMtx.Dump("INIT PARAM", true);
TKroneckerLL KronLL(G, InitKronMtx, PermSwapNodeProb);
if (ScaleInitMtx) {
InitKronMtx.SetForEdges(G->GetNodes(), G->GetEdges());
}
KronLL.InitLL(G, InitKronMtx);
InitKronMtx.Dump("SCALED PARAM", true);
KronLL.SetPerm(Perm.GetCh(0));
double LogLike = 0;
//if (GradType == 1) {
LogLike = KronLL.GradDescent(GradIter, LrnRate, MnStep, MxStep, WarmUp,
NSamples);
//} else if (GradType == 2) {
// LogLike = KronLL.GradDescent2(GradIter, LrnRate, MnStep, MxStep, WarmUp, NSamples); }
//else{ Fail; }
const TKronMtx& FitMtx = KronLL.GetProbMtx();
FILE *F = fopen(OutFNm.CStr(), "w");
fprintf(F, "Input\t%s\n", InFNm.CStr());
TStrV ParamV;
Env.GetCmLn().SplitOnAllCh(' ', ParamV);
fprintf(F, "Command line options\n");
for (int i = 0; i < ParamV.Len(); i++) {
fprintf(F, "\t%s\n",
ParamV[i].CStr() + (ParamV[i][0] == '-' ? 1 : 0));
}
fprintf(F, "Loglikelihood\t%10.2f\n", LogLike);
fprintf(F, "Absolute error (based on expected number of edges)\t%f\n",
KronLL.GetAbsErr());
fprintf(F, "RunTime\t%g\n", ExeTm.GetSecs());
fprintf(F, "Estimated initiator\t%s\n", FitMtx.GetMtxStr().CStr());
fclose(F);
Catch
printf("\nrun time: %s (%s)\n", ExeTm.GetTmStr(),
TSecTm::GetCurTm().GetTmStr().CStr());
return 0;
}