Author: Lorenzo Moneta
This notebook tutorial was automatically generated with ROOTBOOK-izer (Beta) from the macro found in the ROOT repository on Thursday, January 19, 2017 at 04:33 PM.
Function (a 4d gaussian)
In [1]:
%%cpp -d
#include "TMath.h"
#include "TF2.h"
#include "TStopwatch.h"
#include "Math/DistSampler.h"
#include "Math/DistSamplerOptions.h"
#include "Math/MinimizerOptions.h"
#include "Math/Factory.h"
#include "TKDTreeBinning.h"
#include "TTree.h"
#include "TFile.h"
#include "TMatrixDSym.h"
#include "TVectorD.h"
#include "TCanvas.h"
#include <cmath>
Gauss nd function make a class in order to avoid constructing the matrices for every call This however requires that the code must be compiled with ACLIC
In [2]:
bool debug = false;
Define the gausnd strcture
In [3]:
struct GausND {
TVectorD X;
TVectorD Mu;
TMatrixDSym CovMat;
GausND( int dim ) :
X(TVectorD(dim)),
Mu(TVectorD(dim)),
CovMat(TMatrixDSym(dim) )
{}
double operator() (double *x, double *p) {
// 4 parameters
int dim = X.GetNrows();
int k = 0;
for (int i = 0; i<dim; ++i) { X[i] = x[i] - p[k]; k++; }
for (int i = 0; i<dim; ++i) {
CovMat(i,i) = p[k]*p[k];
k++;
}
for (int i = 0; i<dim; ++i) {
for (int j = i+1; j<dim; ++j) {
// p now are the correlations N(N-1)/2
CovMat(i,j) = p[k]*sqrt(CovMat(i,i)*CovMat(j,j));
CovMat(j,i) = CovMat(i,j);
k++;
}
}
if (debug) {
X.Print();
CovMat.Print();
}
double det = CovMat.Determinant();
if (det <= 0) {
Fatal("GausND","Determinant is <= 0 det = %f",det);
CovMat.Print();
return 0;
}
double norm = std::pow( 2. * TMath::Pi(), dim/2) * sqrt(det);
// compute the gaussians
CovMat.Invert();
double fval = std::exp( - 0.5 * CovMat.Similarity(X) )/ norm;
if (debug) {
std::cout << "det " << det << std::endl;
std::cout << "norm " << norm << std::endl;
std::cout << "fval " << fval << std::endl;
}
return fval;
}
};
Use the math namespace
In [4]:
%%cpp -d
// This is a workaround to make sure the namespace is used inside functions
using namespace ROOT::Math;
In [5]:
const int N = 10000;
/*const int NBin = 1000;*/
const int DIM = 4;
double xmin[] = {-10,-10,-10, -10};
double xmax[] = { 10, 10, 10, 10};
double par0[] = { 1., -1., 2, 0, // the gaussian mu
1, 2, 1, 3, // the sigma
0.5,0.,0.,0.,0.,0.8 }; // the correlation
const int NPAR = DIM + DIM*(DIM+1)/2; // 14 in the 4d case
Generate the sample
In [6]:
GausND gaus4d(4);
TF1 * f = new TF1("functionND",gaus4d,0,1,14);
f->SetParameters(par0);
double x0[] = {0,0,0,0};
For debugging
In [7]:
if (debug) f->EvalPar(x0,0);
debug = false;
TString name;
for (int i = 0; i < NPAR; ++i ) {
if (i < DIM) f->SetParName(i, name.Format("mu_%d",i+1) );
else if (i < 2*DIM) f->SetParName(i, name.Format("sig_%d",i-DIM+1) );
else if (i < 2*DIM) f->SetParName(i, name.Format("sig_%d",i-2*DIM+1) );
}
/*ROOT::Math::DistSamplerOptions::SetDefaultSampler("Foam");*/
DistSampler * sampler = Factory::CreateDistSampler();
if (sampler == 0) {
Info("multidimSampling","Default sampler %s is not available try with Foam ",
ROOT::Math::DistSamplerOptions::DefaultSampler().c_str() );
ROOT::Math::DistSamplerOptions::SetDefaultSampler("Foam");
}
sampler = Factory::CreateDistSampler();
if (sampler == 0) {
Error("multidimSampling","Foam sampler is not available - exit ");
return;
}
sampler->SetFunction(*f,DIM);
sampler->SetRange(xmin,xmax);
bool ret = sampler->Init();
std::vector<double> data1(DIM*N);
double v[DIM];
TStopwatch w;
if (!ret) {
Error("Sampler::Init","Error initializing unuran sampler");
return;
}
Generate the data
In [8]:
w.Start();
for (int i = 0; i < N; ++i) {
sampler->Sample(v);
for (int j = 0; j < DIM; ++j)
data1[N*j + i] = v[j];
}
w.Stop();
// w.Print();
Fill tree with data
In [9]:
TFile * file = new TFile("multiDimSampling.root","RECREATE");
double x[DIM];
TTree * t1 = new TTree("t1","Tree from Unuran");
t1->Branch("x",x,"x[4]/D");
for (int i = 0; i < N; ++i) {
for (int j = 0; j < DIM; ++j) {
x[j] = data1[i+N*j];
}
t1->Fill();
}
Plot the data
In [10]:
t1->Draw("x[0]:x[1]:x[2]:x[3]","","candle");
TCanvas * c2 = new TCanvas();
c2->Divide(3,2);
int ic=1;
c2->cd(ic++);
t1->Draw("x[0]:x[1]");
c2->cd(ic++);
t1->Draw("x[0]:x[2]");
c2->cd(ic++);
t1->Draw("x[0]:x[3]");
c2->cd(ic++);
t1->Draw("x[1]:x[2]");
c2->cd(ic++);
t1->Draw("x[1]:x[3]");
c2->cd(ic++);
t1->Draw("x[2]:x[3]");
t1->Write();
file->Close();
Draw all canvases
In [11]:
gROOT->GetListOfCanvases()->Draw()