Example #1
0
// generates Kronecker model using configuration model of small model network
// and compare it to big network
void KroneckerBySample(vector<TStr> CommandLineArgs){
    Try
        Env = TEnv(CommandLineArgs[KRONTEST], TNotify::NullNotify);
    // generation of big model and its Kronecker product is required
    const TStr Gen = Env.GetIfArgPrefixStr("-gen:", "model+kron", "Generation of sample or/and its Kronecker product (model, kron, model+kron)");
    // plot type
    const TStr Plt = Env.GetIfArgPrefixStr("-plt:", "all", "Type of plots (cum, noncum, all)");
    // time estimates file name
    const TStr StatFile = Env.GetIfArgPrefixStr("-ot:", "stat.tab", "Name of output file with statistics");

    TFile = OpenFile(StatFile.CStr());
    CheckParams(Gen, Plt);
    
    PyInit("PySettings.txt");
      

    if (Gen != "none")
    {
        vector <TStr> Parameters;
        GetParameters(CommandLineArgs, Parameters);
        // name is empty
        Parameters.push_back("");
        GetGraphs(Parameters, Gen, Plt);
    }
    

    Py_Finalize();

    Catch
}
Example #2
0
void ReadPNGraphFromFile(const TStr args, PNGraph& G){
    Try
        Env = TEnv(args, TNotify::StdNotify);
    const TStr InFNm = Env.GetIfArgPrefixStr("-i:", "../as20graph.txt", "Input graph file (single directed edge per line)");
    // load graph
    G = TSnap::LoadEdgeList<PNGraph>(InFNm, 0, 1);
    Catch
}
Example #3
0
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;
}