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Tools.cxx
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1// @(#)root/tmva $Id$
2// Author: Andreas Hoecker, Peter Speckmayer, Joerg Stelzer, Helge Voss, Jan Therhaag
3
4/**********************************************************************************
5 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
6 * Package: TMVA *
7 * Class : Tools *
8 * Web : http://tmva.sourceforge.net *
9 * *
10 * Description: *
11 * Implementation (see header for description) *
12 * *
13 * Authors (alphabetical): *
14 * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
15 * Peter Speckmayer <Peter.Speckmayer@cern.ch> - CERN, Switzerland *
16 * Jan Therhaag <Jan.Therhaag@cern.ch> - U of Bonn, Germany *
17 * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
18 * Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada *
19 * *
20 * Copyright (c) 2005-2011: *
21 * CERN, Switzerland *
22 * U. of Victoria, Canada *
23 * MPI-K Heidelberg, Germany *
24 * U. of Bonn, Germany *
25 * *
26 * Redistribution and use in source and binary forms, with or without *
27 * modification, are permitted according to the terms listed in LICENSE *
28 * (http://tmva.sourceforge.net/LICENSE) *
29 **********************************************************************************/
30
31/*! \class TMVA::Tools
32\ingroup TMVA
33Global auxiliary applications and data treatment routines.
34*/
35
36#include "TMVA/Tools.h"
37
38#include "TMVA/Config.h"
39#include "TMVA/Event.h"
40#include "TMVA/Version.h"
41#include "TMVA/PDF.h"
42#include "TMVA/MsgLogger.h"
43#include "TMVA/Types.h"
44
45#include "TObjString.h"
46#include "TMath.h"
47#include "TString.h"
48#include "TTree.h"
49#include "TLeaf.h"
50#include "TH1.h"
51#include "TH2.h"
52#include "TList.h"
53#include "TSpline.h"
54#include "TVector.h"
55#include "TMatrixD.h"
56#include "TMatrixDSymEigen.h"
57#include "TVectorD.h"
58#include "TTreeFormula.h"
59#include "TXMLEngine.h"
60#include "TROOT.h"
61
62#include <algorithm>
63#include <cstdlib>
64
65using namespace std;
66
67#if __cplusplus > 199711L
68std::atomic<TMVA::Tools*> TMVA::Tools::fgTools{0};
69#else
71#endif
72
75#if __cplusplus > 199711L
76 if(!fgTools) {
77 Tools* tmp = new Tools();
78 Tools* expected = 0;
79 if(! fgTools.compare_exchange_strong(expected,tmp)) {
80 //another thread beat us
81 delete tmp;
82 }
83 }
84 return *fgTools;
85#else
86 return fgTools?*(fgTools): *(fgTools = new Tools());
87#endif
88}
90 //NOTE: there is no thread safe way to do this so
91 // one must only call this method ones in an executable
92#if __cplusplus > 199711L
93 if (fgTools != 0) { delete fgTools.load(); fgTools=0; }
94#else
95 if (fgTools != 0) { delete fgTools; fgTools=0; }
96#endif
97}
98
99////////////////////////////////////////////////////////////////////////////////
100/// constructor
101
103 fRegexp("$&|!%^&()'<>?= "),
104 fLogger(new MsgLogger("Tools")),
105 fXMLEngine(new TXMLEngine())
106{
107}
108
109////////////////////////////////////////////////////////////////////////////////
110/// destructor
111
113{
114 delete fLogger;
115 delete fXMLEngine;
116}
117
118////////////////////////////////////////////////////////////////////////////////
119/// normalise to output range: [-1, 1]
120
122{
123 return 2*(x - xmin)/(xmax - xmin) - 1.0;
124}
125
126////////////////////////////////////////////////////////////////////////////////
127/// compute "separation" defined as
128/// \f[
129/// <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx
130/// \f]
131
133{
134 Double_t separation = 0;
135
136 // sanity checks
137 // signal and background histograms must have same number of bins and
138 // same limits
139 if ((S->GetNbinsX() != B->GetNbinsX()) || (S->GetNbinsX() <= 0)) {
140 Log() << kFATAL << "<GetSeparation> signal and background"
141 << " histograms have different number of bins: "
142 << S->GetNbinsX() << " : " << B->GetNbinsX() << Endl;
143 }
144
145 if (S->GetXaxis()->GetXmin() != B->GetXaxis()->GetXmin() ||
146 S->GetXaxis()->GetXmax() != B->GetXaxis()->GetXmax() ||
147 S->GetXaxis()->GetXmax() <= S->GetXaxis()->GetXmin()) {
148 Log() << kINFO << S->GetXaxis()->GetXmin() << " " << B->GetXaxis()->GetXmin()
149 << " " << S->GetXaxis()->GetXmax() << " " << B->GetXaxis()->GetXmax()
150 << " " << S->GetXaxis()->GetXmax() << " " << S->GetXaxis()->GetXmin() << Endl;
151 Log() << kFATAL << "<GetSeparation> signal and background"
152 << " histograms have different or invalid dimensions:" << Endl;
153 }
154
155 Int_t nstep = S->GetNbinsX();
156 Double_t intBin = (S->GetXaxis()->GetXmax() - S->GetXaxis()->GetXmin())/nstep;
157 Double_t nS = S->GetSumOfWeights()*intBin;
158 Double_t nB = B->GetSumOfWeights()*intBin;
159
160 if (nS > 0 && nB > 0) {
161 for (Int_t bin=0; bin<nstep; bin++) {
162 Double_t s = S->GetBinContent( bin+1 )/Double_t(nS);
163 Double_t b = B->GetBinContent( bin+1 )/Double_t(nB);
164 // separation
165 if (s + b > 0) separation += (s - b)*(s - b)/(s + b);
166 }
167 separation *= (0.5*intBin);
168 }
169 else {
170 Log() << kWARNING << "<GetSeparation> histograms with zero entries: "
171 << nS << " : " << nB << " cannot compute separation"
172 << Endl;
173 separation = 0;
174 }
175
176 return separation;
177}
178
179////////////////////////////////////////////////////////////////////////////////
180/// compute "separation" defined as
181/// \f[
182/// <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx
183/// \f]
184
185Double_t TMVA::Tools::GetSeparation( const PDF& pdfS, const PDF& pdfB ) const
186{
187 Double_t xmin = pdfS.GetXmin();
188 Double_t xmax = pdfS.GetXmax();
189 // sanity check
190 if (xmin != pdfB.GetXmin() || xmax != pdfB.GetXmax()) {
191 Log() << kFATAL << "<GetSeparation> Mismatch in PDF limits: "
192 << xmin << " " << pdfB.GetXmin() << xmax << " " << pdfB.GetXmax() << Endl;
193 }
194
195 Double_t separation = 0;
196 Int_t nstep = 100;
197 Double_t intBin = (xmax - xmin)/Double_t(nstep);
198 for (Int_t bin=0; bin<nstep; bin++) {
199 Double_t x = (bin + 0.5)*intBin + xmin;
200 Double_t s = pdfS.GetVal( x );
201 Double_t b = pdfB.GetVal( x );
202 // separation
203 if (s + b > 0) separation += (s - b)*(s - b)/(s + b);
204 }
205 separation *= (0.5*intBin);
206
207 return separation;
208}
209
210////////////////////////////////////////////////////////////////////////////////
211/// sanity check
212
213void TMVA::Tools::ComputeStat( const std::vector<TMVA::Event*>& events, std::vector<Float_t>* valVec,
214 Double_t& meanS, Double_t& meanB,
215 Double_t& rmsS, Double_t& rmsB,
217 Int_t signalClass, Bool_t norm )
218{
219 if (0 == valVec)
220 Log() << kFATAL << "<Tools::ComputeStat> value vector is zero pointer" << Endl;
221
222 if ( events.size() != valVec->size() )
223 Log() << kWARNING << "<Tools::ComputeStat> event and value vector have different lengths "
224 << events.size() << "!=" << valVec->size() << Endl;
225
226 Long64_t entries = valVec->size();
227
228 // first fill signal and background in arrays before analysis
229 Double_t* varVecS = new Double_t[entries];
230 Double_t* varVecB = new Double_t[entries];
231 Double_t* wgtVecS = new Double_t[entries];
232 Double_t* wgtVecB = new Double_t[entries];
233 xmin = +DBL_MAX;
234 xmax = -DBL_MAX;
235 Long64_t nEventsS = 0;
236 Long64_t nEventsB = 0;
237 Double_t xmin_ = 0, xmax_ = 0;
238
239 if (norm) {
240 xmin_ = *std::min( valVec->begin(), valVec->end() );
241 xmax_ = *std::max( valVec->begin(), valVec->end() );
242 }
243
244 for (Int_t ievt=0; ievt<entries; ievt++) {
245 Double_t theVar = (*valVec)[ievt];
246 if (norm) theVar = Tools::NormVariable( theVar, xmin_, xmax_ );
247
248 if (Int_t(events[ievt]->GetClass()) == signalClass ){
249 wgtVecS[nEventsS] = events[ievt]->GetWeight(); // this is signal
250 varVecS[nEventsS++] = theVar; // this is signal
251 }
252 else {
253 wgtVecB[nEventsB] = events[ievt]->GetWeight(); // this is signal
254 varVecB[nEventsB++] = theVar; // this is background
255 }
256
257 if (theVar > xmax) xmax = theVar;
258 if (theVar < xmin) xmin = theVar;
259 }
260 // ++nEventsS;
261 // ++nEventsB;
262
263 // basic statistics
264 // !!! TMath::Mean allows for weights, but NOT for negative weights
265 // and TMath::RMS doesn't allow for weights all together...
266 meanS = TMVA::Tools::Mean( nEventsS, varVecS, wgtVecS );
267 meanB = TMVA::Tools::Mean( nEventsB, varVecB, wgtVecB );
268 rmsS = TMVA::Tools::RMS ( nEventsS, varVecS, wgtVecS );
269 rmsB = TMVA::Tools::RMS ( nEventsB, varVecB, wgtVecB );
270
271 delete [] varVecS;
272 delete [] varVecB;
273 delete [] wgtVecS;
274 delete [] wgtVecB;
275}
276
277////////////////////////////////////////////////////////////////////////////////
278/// square-root of symmetric matrix
279/// of course the resulting sqrtMat is also symmetric, but it's easier to
280/// treat it as a general matrix
281
283{
284 Int_t n = symMat->GetNrows();
285
286 // compute eigenvectors
287 TMatrixDSymEigen* eigen = new TMatrixDSymEigen( *symMat );
288
289 // D = ST C S
290 TMatrixD* si = new TMatrixD( eigen->GetEigenVectors() );
291 TMatrixD* s = new TMatrixD( *si ); // copy
292 si->Transpose( *si ); // invert (= transpose)
293
294 // diagonal matrices
295 TMatrixD* d = new TMatrixD( n, n);
296 d->Mult( (*si), (*symMat) ); (*d) *= (*s);
297
298 // sanity check: matrix must be diagonal and positive definit
299 Int_t i, j;
300 Double_t epsilon = 1.0e-8;
301 for (i=0; i<n; i++) {
302 for (j=0; j<n; j++) {
303 if ((i != j && TMath::Abs((*d)(i,j))/TMath::Sqrt((*d)(i,i)*(*d)(j,j)) > epsilon) ||
304 (i == j && (*d)(i,i) < 0)) {
305 //d->Print();
306 Log() << kWARNING << "<GetSQRootMatrix> error in matrix diagonalization; printed S and B" << Endl;
307 }
308 }
309 }
310
311 // make exactly diagonal
312 for (i=0; i<n; i++) for (j=0; j<n; j++) if (j != i) (*d)(i,j) = 0;
313
314 // compute the square-root C' of covariance matrix: C = C'*C'
315 for (i=0; i<n; i++) (*d)(i,i) = TMath::Sqrt((*d)(i,i));
316
317 TMatrixD* sqrtMat = new TMatrixD( n, n );
318 sqrtMat->Mult( (*s), (*d) );
319 (*sqrtMat) *= (*si);
320
321 // invert square-root matrices
322 sqrtMat->Invert();
323
324 delete eigen;
325 delete s;
326 delete si;
327 delete d;
328
329 return sqrtMat;
330}
331
332////////////////////////////////////////////////////////////////////////////////
333/// turns covariance into correlation matrix
334
336{
337
338 if (covMat == 0) return 0;
339 // sanity check
340 Int_t nvar = covMat->GetNrows();
341 if (nvar != covMat->GetNcols())
342 Log() << kFATAL << "<GetCorrelationMatrix> input matrix not quadratic" << Endl;
343
344 Log() << kWARNING;
345 TMatrixD* corrMat = new TMatrixD( nvar, nvar );
346 for (Int_t ivar=0; ivar<nvar; ivar++) {
347 for (Int_t jvar=0; jvar<nvar; jvar++) {
348 if (ivar != jvar) {
349 Double_t d = (*covMat)(ivar, ivar)*(*covMat)(jvar, jvar);
350 if (d > 1E-20) {
351 (*corrMat)(ivar, jvar) = (*covMat)(ivar, jvar)/TMath::Sqrt(d);
352 } else {
353 Log() << "<GetCorrelationMatrix> zero variances for variables "
354 << "(" << ivar << ", " << jvar << ")" << Endl;
355 (*corrMat)(ivar, jvar) = 0;
356 }
357 if (TMath::Abs( (*corrMat)(ivar,jvar)) > 1){
358 Log() << kWARNING
359 << " Element corr("<<ivar<<","<<ivar<<")=" << (*corrMat)(ivar,jvar)
360 << " sigma2="<<d
361 << " cov("<<ivar<<","<<ivar<<")=" <<(*covMat)(ivar, ivar)
362 << " cov("<<jvar<<","<<jvar<<")=" <<(*covMat)(jvar, jvar)
363 << Endl;
364
365 }
366 }
367 else (*corrMat)(ivar, ivar) = 1.0;
368 }
369 }
370 Log() << Endl;
371 return corrMat;
372}
373
374////////////////////////////////////////////////////////////////////////////////
375/// projects variable from tree into normalised histogram
376
377TH1* TMVA::Tools::projNormTH1F( TTree* theTree, const TString& theVarName,
378 const TString& name, Int_t nbins,
379 Double_t xmin, Double_t xmax, const TString& cut )
380{
381 // needed because of ROOT bug (feature) that excludes events that have value == xmax
382 xmax += 0.00001;
383
384 TH1* hist = new TH1F( name, name, nbins, xmin, xmax );
385 hist->Sumw2(); // enable quadratic errors
386 theTree->Project( name, theVarName, cut );
387 NormHist( hist );
388 return hist;
389}
390
391////////////////////////////////////////////////////////////////////////////////
392/// normalises histogram
393
395{
396 if (!theHist) return 0;
397
398 if (theHist->GetSumw2N() == 0) theHist->Sumw2();
399 if (theHist->GetSumOfWeights() != 0) {
400 Double_t w = ( theHist->GetSumOfWeights()
401 *(theHist->GetXaxis()->GetXmax() - theHist->GetXaxis()->GetXmin())/theHist->GetNbinsX() );
402 if (w > 0) theHist->Scale( norm/w );
403 return w;
404 }
405
406 return 1.0;
407}
408
409////////////////////////////////////////////////////////////////////////////////
410/// Parse the string and cut into labels separated by ":"
411
412TList* TMVA::Tools::ParseFormatLine( TString formatString, const char* sep )
413{
414 TList* labelList = new TList();
415 labelList->SetOwner();
416 while (formatString.First(sep)==0) formatString.Remove(0,1); // remove initial separators
417
418 while (formatString.Length()>0) {
419 if (formatString.First(sep) == -1) { // no more separator
420 labelList->Add(new TObjString(formatString.Data()));
421 formatString="";
422 break;
423 }
424
425 Ssiz_t posSep = formatString.First(sep);
426 labelList->Add(new TObjString(TString(formatString(0,posSep)).Data()));
427 formatString.Remove(0,posSep+1);
428
429 while (formatString.First(sep)==0) formatString.Remove(0,1); // remove additional separators
430
431 }
432 return labelList;
433}
434
435////////////////////////////////////////////////////////////////////////////////
436/// parse option string for ANN methods
437/// default settings (should be defined in theOption string)
438///
439/// format and syntax of option string: "3000:N:N+2:N-3:6"
440///
441/// where:
442/// - 3000 - number of training cycles (epochs)
443/// - N - number of nodes in first hidden layer, where N is the number
444/// of discriminating variables used (note that the first ANN
445/// layer necessarily has N nodes, and hence is not given).
446/// - N+2 - number of nodes in 2nd hidden layer (2 nodes more than
447/// number of variables)
448/// - N-3 - number of nodes in 3rd hidden layer (3 nodes less than
449/// number of variables)
450/// - 6 - 6 nodes in last (4th) hidden layer (note that the last ANN
451/// layer in MVA has 2 nodes, each one for signal and background
452/// classes)
453
454vector<Int_t>* TMVA::Tools::ParseANNOptionString( TString theOptions, Int_t nvar,
455 vector<Int_t>* nodes )
456{
457 TList* list = TMVA::Tools::ParseFormatLine( theOptions, ":" );
458
459
460 // sanity check
461 if (list->GetSize() < 1) {
462 Log() << kFATAL << "<ParseANNOptionString> unrecognized option string: " << theOptions << Endl;
463 }
464
465 // add number of cycles
466 nodes->push_back( atoi( ((TObjString*)list->At(0))->GetString() ) );
467
468 Int_t a;
469 if (list->GetSize() > 1) {
470 for (Int_t i=1; i<list->GetSize(); i++) {
471 TString s = ((TObjString*)list->At(i))->GetString();
472 s.ToUpper();
473 if (s(0) == 'N') {
474 if (s.Length() > 1) nodes->push_back( nvar + atoi(&s[1]) );
475 else nodes->push_back( nvar );
476 }
477 else if ((a = atoi( s )) > 0) nodes->push_back( atoi(s ) );
478 else {
479 Log() << kFATAL << "<ParseANNOptionString> unrecognized option string: " << theOptions << Endl;
480 }
481 }
482 }
483
484 return nodes;
485}
486
487////////////////////////////////////////////////////////////////////////////////
488/// check quality of splining by comparing splines and histograms in each bin
489
490Bool_t TMVA::Tools::CheckSplines( const TH1* theHist, const TSpline* theSpline )
491{
492 const Double_t sanityCrit = 0.01; // relative deviation
493
494 Bool_t retval = kTRUE;
495 for (Int_t ibin=1; ibin<=theHist->GetNbinsX(); ibin++) {
496 Double_t x = theHist->GetBinCenter( ibin );
497 Double_t yh = theHist->GetBinContent( ibin ); // the histogram output
498 Double_t ys = theSpline->Eval( x ); // the spline output
499
500 if (ys + yh > 0) {
501 Double_t dev = 0.5*(ys - yh)/(ys + yh);
502 if (TMath::Abs(dev) > sanityCrit) {
503 Log() << kFATAL << "<CheckSplines> Spline failed sanity criterion; "
504 << " relative deviation from histogram: " << dev
505 << " in (bin, value): (" << ibin << ", " << x << ")" << Endl;
506 retval = kFALSE;
507 }
508 }
509 }
510
511 return retval;
512}
513
514////////////////////////////////////////////////////////////////////////////////
515/// computes difference between two vectors
516
517std::vector<Double_t> TMVA::Tools::MVADiff( std::vector<Double_t>& a, std::vector<Double_t>& b )
518{
519 if (a.size() != b.size()) {
520 throw;
521 }
522 vector<Double_t> result(a.size());
523 for (UInt_t i=0; i<a.size();i++) result[i]=a[i]-b[i];
524 return result;
525}
526
527////////////////////////////////////////////////////////////////////////////////
528/// scales double vector
529
530void TMVA::Tools::Scale( std::vector<Double_t>& v, Double_t f )
531{
532 for (UInt_t i=0; i<v.size();i++) v[i]*=f;
533}
534
535////////////////////////////////////////////////////////////////////////////////
536/// scales float vector
537
538void TMVA::Tools::Scale( std::vector<Float_t>& v, Float_t f )
539{
540 for (UInt_t i=0; i<v.size();i++) v[i]*=f;
541}
542
543////////////////////////////////////////////////////////////////////////////////
544/// sort 2D vector (AND in parallel a TString vector) in such a way
545/// that the "first vector is sorted" and the other vectors are reshuffled
546/// in the same way as necessary to have the first vector sorted.
547/// I.e. the correlation between the elements is kept.
548
549void TMVA::Tools::UsefulSortAscending( std::vector<vector<Double_t> >& v, std::vector<TString>* vs ){
550 UInt_t nArrays=v.size();
551 Double_t temp;
552 if (nArrays > 0) {
553 UInt_t sizeofarray=v[0].size();
554 for (UInt_t i=0; i<sizeofarray; i++) {
555 for (UInt_t j=sizeofarray-1; j>i; j--) {
556 if (v[0][j-1] > v[0][j]) {
557 for (UInt_t k=0; k< nArrays; k++) {
558 temp = v[k][j-1]; v[k][j-1] = v[k][j]; v[k][j] = temp;
559 }
560 if (nullptr != vs) {
561 TString temps = (*vs)[j-1]; (*vs)[j-1] = (*vs)[j]; (*vs)[j] = temps;
562 }
563 }
564 }
565 }
566 }
567}
568
569////////////////////////////////////////////////////////////////////////////////
570/// sort 2D vector (AND in parallel a TString vector) in such a way
571/// that the "first vector is sorted" and the other vectors are reshuffled
572/// in the same way as necessary to have the first vector sorted.
573/// I.e. the correlation between the elements is kept.
574
575void TMVA::Tools::UsefulSortDescending( std::vector<std::vector<Double_t> >& v, std::vector<TString>* vs )
576{
577 UInt_t nArrays=v.size();
578 Double_t temp;
579 if (nArrays > 0) {
580 UInt_t sizeofarray=v[0].size();
581 for (UInt_t i=0; i<sizeofarray; i++) {
582 for (UInt_t j=sizeofarray-1; j>i; j--) {
583 if (v[0][j-1] < v[0][j]) {
584 for (UInt_t k=0; k< nArrays; k++) {
585 temp = v[k][j-1]; v[k][j-1] = v[k][j]; v[k][j] = temp;
586 }
587 if (nullptr != vs) {
588 TString temps = (*vs)[j-1]; (*vs)[j-1] = (*vs)[j]; (*vs)[j] = temps;
589 }
590 }
591 }
592 }
593 }
594}
595
596////////////////////////////////////////////////////////////////////////////////
597/// Mutual Information method for non-linear correlations estimates in 2D histogram
598/// Author: Moritz Backes, Geneva (2009)
599
601{
602 Double_t hi = h_.Integral();
603 if (hi == 0) return -1;
604
605 // copy histogram and rebin to speed up procedure
606 TH2F h( h_ );
607 h.RebinX(2);
608 h.RebinY(2);
609
610 Double_t mutualInfo = 0.;
611 Int_t maxBinX = h.GetNbinsX();
612 Int_t maxBinY = h.GetNbinsY();
613 for (Int_t x = 1; x <= maxBinX; x++) {
614 for (Int_t y = 1; y <= maxBinY; y++) {
615 Double_t p_xy = h.GetBinContent(x,y)/hi;
616 Double_t p_x = h.Integral(x,x,1,maxBinY)/hi;
617 Double_t p_y = h.Integral(1,maxBinX,y,y)/hi;
618 if (p_x > 0. && p_y > 0. && p_xy > 0.){
619 mutualInfo += p_xy*TMath::Log(p_xy / (p_x * p_y));
620 }
621 }
622 }
623
624 return mutualInfo;
625}
626
627////////////////////////////////////////////////////////////////////////////////
628/// Compute Correlation Ratio of 2D histogram to estimate functional dependency between two variables
629/// Author: Moritz Backes, Geneva (2009)
630
632{
633 Double_t hi = h_.Integral();
634 if (hi == 0.) return -1;
635
636 // copy histogram and rebin to speed up procedure
637 TH2F h( h_ );
638 h.RebinX(2);
639 h.RebinY(2);
640
641 Double_t corrRatio = 0.;
642 Double_t y_mean = h.ProjectionY()->GetMean();
643 for (Int_t ix=1; ix<=h.GetNbinsX(); ix++) {
644 corrRatio += (h.Integral(ix,ix,1,h.GetNbinsY())/hi)*pow((GetYMean_binX(h,ix)-y_mean),2);
645 }
646 corrRatio /= pow(h.ProjectionY()->GetRMS(),2);
647 return corrRatio;
648}
649
650////////////////////////////////////////////////////////////////////////////////
651/// Compute the mean in Y for a given bin X of a 2D histogram
652
654{
655 if (h.Integral(bin_x,bin_x,1,h.GetNbinsY()) == 0.) {return 0;}
656 Double_t y_bin_mean = 0.;
657 TH1* py = h.ProjectionY();
658 for (Int_t y = 1; y <= h.GetNbinsY(); y++){
659 y_bin_mean += h.GetBinContent(bin_x,y)*py->GetBinCenter(y);
660 }
661 y_bin_mean /= h.Integral(bin_x,bin_x,1,h.GetNbinsY());
662 return y_bin_mean;
663}
664
665////////////////////////////////////////////////////////////////////////////////
666/// Transpose quadratic histogram
667
669{
670 // sanity check
671 if (h.GetNbinsX() != h.GetNbinsY()) {
672 Log() << kFATAL << "<TransposeHist> cannot transpose non-quadratic histogram" << Endl;
673 }
674
675 TH2F *transposedHisto = new TH2F( h );
676 for (Int_t ix=1; ix <= h.GetNbinsX(); ix++){
677 for (Int_t iy=1; iy <= h.GetNbinsY(); iy++){
678 transposedHisto->SetBinContent(iy,ix,h.GetBinContent(ix,iy));
679 }
680 }
681
682 // copy stats (thanks to Swagato Banerjee for pointing out the missing stats information)
683 Double_t stats_old[7];
684 Double_t stats_new[7];
685
686 h.GetStats(stats_old);
687 stats_new[0] = stats_old[0];
688 stats_new[1] = stats_old[1];
689 stats_new[2] = stats_old[4];
690 stats_new[3] = stats_old[5];
691 stats_new[4] = stats_old[2];
692 stats_new[5] = stats_old[3];
693 stats_new[6] = stats_old[6];
694 transposedHisto->PutStats(stats_new);
695
696 return transposedHisto; // ownership returned
697}
698
699////////////////////////////////////////////////////////////////////////////////
700/// check for "silence" option in configuration option string
701
703{
704 Bool_t isSilent = kFALSE;
705
706 TString s( cs );
707 s.ToLower();
708 s.ReplaceAll(" ","");
709 if (s.Contains("silent") && !s.Contains("silent=f")) {
710 if (!s.Contains("!silent") || s.Contains("silent=t")) isSilent = kTRUE;
711 }
712
713 return isSilent;
714}
715
716////////////////////////////////////////////////////////////////////////////////
717/// check if verbosity "V" set in option
718
720{
721 Bool_t isVerbose = kFALSE;
722
723 TString s( cs );
724 s.ToLower();
725 s.ReplaceAll(" ","");
726 std::vector<TString> v = SplitString( s, ':' );
727 for (std::vector<TString>::iterator it = v.begin(); it != v.end(); ++it) {
728 if ((*it == "v" || *it == "verbose") && !it->Contains("!")) isVerbose = kTRUE;
729 }
730
731 return isVerbose;
732}
733
734////////////////////////////////////////////////////////////////////////////////
735/// sort vector
736
737void TMVA::Tools::UsefulSortDescending( std::vector<Double_t>& v )
738{
739 vector< vector<Double_t> > vtemp;
740 vtemp.push_back(v);
741 UsefulSortDescending(vtemp);
742 v = vtemp[0];
743}
744
745////////////////////////////////////////////////////////////////////////////////
746/// sort vector
747
748void TMVA::Tools::UsefulSortAscending( std::vector<Double_t>& v )
749{
750 vector<vector<Double_t> > vtemp;
751 vtemp.push_back(v);
752 UsefulSortAscending(vtemp);
753 v = vtemp[0];
754}
755
756////////////////////////////////////////////////////////////////////////////////
757/// find index of maximum entry in vector
758
759Int_t TMVA::Tools::GetIndexMaxElement( std::vector<Double_t>& v )
760{
761 if (v.empty()) return -1;
762
763 Int_t pos=0; Double_t mx=v[0];
764 for (UInt_t i=0; i<v.size(); i++){
765 if (v[i] > mx){
766 mx=v[i];
767 pos=i;
768 }
769 }
770 return pos;
771}
772
773////////////////////////////////////////////////////////////////////////////////
774/// find index of minimum entry in vector
775
776Int_t TMVA::Tools::GetIndexMinElement( std::vector<Double_t>& v )
777{
778 if (v.empty()) return -1;
779
780 Int_t pos=0; Double_t mn=v[0];
781 for (UInt_t i=0; i<v.size(); i++){
782 if (v[i] < mn){
783 mn=v[i];
784 pos=i;
785 }
786 }
787 return pos;
788}
789
790
791////////////////////////////////////////////////////////////////////////////////
792/// check if regular expression
793/// helper function to search for "$!%^&()'<>?= " in a string
794
796{
797 Bool_t regular = kFALSE;
798 for (Int_t i = 0; i < Tools::fRegexp.Length(); i++)
799 if (s.Contains( Tools::fRegexp[i] )) { regular = kTRUE; break; }
800
801 return regular;
802}
803
804////////////////////////////////////////////////////////////////////////////////
805/// replace regular expressions
806/// helper function to remove all occurrences "$!%^&()'<>?= " from a string
807/// and replace all ::,$,*,/,+,- with _M_,_S_,_T_,_D_,_P_,_M_ respectively
808
810{
811 TString snew = s;
812 for (Int_t i = 0; i < Tools::fRegexp.Length(); i++)
813 snew.ReplaceAll( Tools::fRegexp[i], r );
814
815 snew.ReplaceAll( "::", r );
816 snew.ReplaceAll( "$", "_S_" );
817 snew.ReplaceAll( "&", "_A_" );
818 snew.ReplaceAll( "%", "_MOD_" );
819 snew.ReplaceAll( "|", "_O_" );
820 snew.ReplaceAll( "*", "_T_" );
821 snew.ReplaceAll( "/", "_D_" );
822 snew.ReplaceAll( "+", "_P_" );
823 snew.ReplaceAll( "-", "_M_" );
824 snew.ReplaceAll( " ", "_" );
825 snew.ReplaceAll( "[", "_" );
826 snew.ReplaceAll( "]", "_" );
827 snew.ReplaceAll( "=", "_E_" );
828 snew.ReplaceAll( ">", "_GT_" );
829 snew.ReplaceAll( "<", "_LT_" );
830 snew.ReplaceAll( "(", "_" );
831 snew.ReplaceAll( ")", "_" );
832
833 return snew;
834}
835
836////////////////////////////////////////////////////////////////////////////////
837/// human readable color strings
838
840{
841 static const TString gClr_none = "" ;
842 static const TString gClr_white = "\033[1;37m"; // white
843 static const TString gClr_black = "\033[30m"; // black
844 static const TString gClr_blue = "\033[34m"; // blue
845 static const TString gClr_red = "\033[1;31m" ; // red
846 static const TString gClr_yellow = "\033[1;33m"; // yellow
847 static const TString gClr_darkred = "\033[31m"; // dark red
848 static const TString gClr_darkgreen = "\033[32m"; // dark green
849 static const TString gClr_darkyellow = "\033[33m"; // dark yellow
850
851 static const TString gClr_bold = "\033[1m" ; // bold
852 static const TString gClr_black_b = "\033[30m" ; // bold black
853 static const TString gClr_lblue_b = "\033[1;34m" ; // bold light blue
854 static const TString gClr_cyan_b = "\033[0;36m" ; // bold cyan
855 static const TString gClr_lgreen_b = "\033[1;32m"; // bold light green
856
857 static const TString gClr_blue_bg = "\033[44m"; // blue background
858 static const TString gClr_red_bg = "\033[1;41m"; // white on red background
859 static const TString gClr_whiteonblue = "\033[1;44m"; // white on blue background
860 static const TString gClr_whiteongreen = "\033[1;42m"; // white on green background
861 static const TString gClr_grey_bg = "\033[47m"; // grey background
862
863 static const TString gClr_reset = "\033[0m"; // reset
864
865 if (!gConfig().UseColor()) return gClr_none;
866
867 if (c == "white" ) return gClr_white;
868 if (c == "blue" ) return gClr_blue;
869 if (c == "black" ) return gClr_black;
870 if (c == "lightblue") return gClr_cyan_b;
871 if (c == "yellow") return gClr_yellow;
872 if (c == "red" ) return gClr_red;
873 if (c == "dred" ) return gClr_darkred;
874 if (c == "dgreen") return gClr_darkgreen;
875 if (c == "lgreenb") return gClr_lgreen_b;
876 if (c == "dyellow") return gClr_darkyellow;
877
878 if (c == "bold") return gClr_bold;
879 if (c == "bblack") return gClr_black_b;
880
881 if (c == "blue_bgd") return gClr_blue_bg;
882 if (c == "red_bgd" ) return gClr_red_bg;
883
884 if (c == "white_on_blue" ) return gClr_whiteonblue;
885 if (c == "white_on_green") return gClr_whiteongreen;
886
887 if (c == "reset") return gClr_reset;
888
889 std::cout << "Unknown color " << c << std::endl;
890 exit(1);
891
892 return gClr_none;
893}
894
895////////////////////////////////////////////////////////////////////////////////
896/// formatted output of simple table
897
898void TMVA::Tools::FormattedOutput( const std::vector<Double_t>& values, const std::vector<TString>& V,
899 const TString titleVars, const TString titleValues, MsgLogger& logger,
900 TString format )
901{
902 // sanity check
903 UInt_t nvar = V.size();
904 if ((UInt_t)values.size() != nvar) {
905 logger << kFATAL << "<FormattedOutput> fatal error with dimensions: "
906 << values.size() << " OR " << " != " << nvar << Endl;
907 }
908
909 // find maximum length in V (and column title)
910 UInt_t maxL = 7;
911 std::vector<UInt_t> vLengths;
912 for (UInt_t ivar=0; ivar<nvar; ivar++) maxL = TMath::Max( (UInt_t)V[ivar].Length(), maxL );
913 maxL = TMath::Max( (UInt_t)titleVars.Length(), maxL );
914
915 // column length
916 UInt_t maxV = 7;
917 maxV = TMath::Max( (UInt_t)titleValues.Length() + 1, maxL );
918
919 // full column length
920 UInt_t clen = maxL + maxV + 3;
921
922 // bar line
923 for (UInt_t i=0; i<clen; i++) logger << "-";
924 logger << Endl;
925
926 // title bar
927 logger << setw(maxL) << titleVars << ":";
928 logger << setw(maxV+1) << titleValues << ":";
929 logger << Endl;
930 for (UInt_t i=0; i<clen; i++) logger << "-";
931 logger << Endl;
932
933 // the numbers
934 for (UInt_t irow=0; irow<nvar; irow++) {
935 logger << setw(maxL) << V[irow] << ":";
936 logger << setw(maxV+1) << Form( format.Data(), values[irow] );
937 logger << Endl;
938 }
939
940 // bar line
941 for (UInt_t i=0; i<clen; i++) logger << "-";
942 logger << Endl;
943}
944
945////////////////////////////////////////////////////////////////////////////////
946/// formatted output of matrix (with labels)
947
948void TMVA::Tools::FormattedOutput( const TMatrixD& M, const std::vector<TString>& V, MsgLogger& logger )
949{
950 // sanity check: matrix must be quadratic
951 UInt_t nvar = V.size();
952 if ((UInt_t)M.GetNcols() != nvar || (UInt_t)M.GetNrows() != nvar) {
953 logger << kFATAL << "<FormattedOutput> fatal error with dimensions: "
954 << M.GetNcols() << " OR " << M.GetNrows() << " != " << nvar << " ==> abort" << Endl;
955 }
956
957 // get length of each variable, and maximum length
958 UInt_t minL = 7;
959 UInt_t maxL = minL;
960 std::vector<UInt_t> vLengths;
961 for (UInt_t ivar=0; ivar<nvar; ivar++) {
962 vLengths.push_back(TMath::Max( (UInt_t)V[ivar].Length(), minL ));
963 maxL = TMath::Max( vLengths.back(), maxL );
964 }
965
966 // count column length
967 UInt_t clen = maxL+1;
968 for (UInt_t icol=0; icol<nvar; icol++) clen += vLengths[icol]+1;
969
970 // bar line
971 for (UInt_t i=0; i<clen; i++) logger << "-";
972 logger << Endl;
973
974 // title bar
975 logger << setw(maxL+1) << " ";
976 for (UInt_t icol=0; icol<nvar; icol++) logger << setw(vLengths[icol]+1) << V[icol];
977 logger << Endl;
978
979 // the numbers
980 for (UInt_t irow=0; irow<nvar; irow++) {
981 logger << setw(maxL) << V[irow] << ":";
982 for (UInt_t icol=0; icol<nvar; icol++) {
983 logger << setw(vLengths[icol]+1) << Form( "%+1.3f", M(irow,icol) );
984 }
985 logger << Endl;
986 }
987
988 // bar line
989 for (UInt_t i=0; i<clen; i++) logger << "-";
990 logger << Endl;
991}
992
993////////////////////////////////////////////////////////////////////////////////
994/// formatted output of matrix (with labels)
995
997 const std::vector<TString>& vert, const std::vector<TString>& horiz,
998 MsgLogger& logger )
999{
1000 // sanity check: matrix must be quadratic
1001 UInt_t nvvar = vert.size();
1002 UInt_t nhvar = horiz.size();
1003
1004 // get length of each variable, and maximum length
1005 UInt_t minL = 7;
1006 UInt_t maxL = minL;
1007 std::vector<UInt_t> vLengths;
1008 for (UInt_t ivar=0; ivar<nvvar; ivar++) {
1009 vLengths.push_back(TMath::Max( (UInt_t)vert[ivar].Length(), minL ));
1010 maxL = TMath::Max( vLengths.back(), maxL );
1011 }
1012
1013 // count column length
1014 UInt_t minLh = 7;
1015 UInt_t maxLh = minLh;
1016 std::vector<UInt_t> hLengths;
1017 for (UInt_t ivar=0; ivar<nhvar; ivar++) {
1018 hLengths.push_back(TMath::Max( (UInt_t)horiz[ivar].Length(), minL ));
1019 maxLh = TMath::Max( hLengths.back(), maxLh );
1020 }
1021
1022 UInt_t clen = maxLh+1;
1023 for (UInt_t icol=0; icol<nhvar; icol++) clen += hLengths[icol]+1;
1024
1025 // bar line
1026 for (UInt_t i=0; i<clen; i++) logger << "-";
1027 logger << Endl;
1028
1029 // title bar
1030 logger << setw(maxL+1) << " ";
1031 for (UInt_t icol=0; icol<nhvar; icol++) logger << setw(hLengths[icol]+1) << horiz[icol];
1032 logger << Endl;
1033
1034 // the numbers
1035 for (UInt_t irow=0; irow<nvvar; irow++) {
1036 logger << setw(maxL) << vert[irow] << ":";
1037 for (UInt_t icol=0; icol<nhvar; icol++) {
1038 logger << setw(hLengths[icol]+1) << Form( "%+1.3f", M(irow,icol) );
1039 }
1040 logger << Endl;
1041 }
1042
1043 // bar line
1044 for (UInt_t i=0; i<clen; i++) logger << "-";
1045 logger << Endl;
1046}
1047
1048////////////////////////////////////////////////////////////////////////////////
1049/// histogramming utility
1050
1052{
1053 return ( unit == "" ? title : ( title + " [" + unit + "]" ) );
1054}
1055
1056////////////////////////////////////////////////////////////////////////////////
1057/// histogramming utility
1058
1059TString TMVA::Tools::GetYTitleWithUnit( const TH1& h, const TString& unit, Bool_t normalised )
1060{
1061 TString retval = ( normalised ? "(1/N) " : "" );
1062 retval += Form( "dN_{ }/^{ }%.3g %s", h.GetXaxis()->GetBinWidth(1), unit.Data() );
1063 return retval;
1064}
1065
1066////////////////////////////////////////////////////////////////////////////////
1067/// writes a float value with the available precision to a stream
1068
1070{
1071 os << val << " :: ";
1072 void * c = &val;
1073 for (int i=0; i<4; i++) {
1074 Int_t ic = *((char*)c+i)-'\0';
1075 if (ic<0) ic+=256;
1076 os << ic << " ";
1077 }
1078 os << ":: ";
1079}
1080
1081////////////////////////////////////////////////////////////////////////////////
1082/// reads a float value with the available precision from a stream
1083
1085{
1086 Float_t a = 0;
1087 is >> a;
1088 TString dn;
1089 is >> dn;
1090 Int_t c[4];
1091 void * ap = &a;
1092 for (int i=0; i<4; i++) {
1093 is >> c[i];
1094 *((char*)ap+i) = '\0'+c[i];
1095 }
1096 is >> dn;
1097 val = a;
1098}
1099
1100// XML file reading/writing helper functions
1101
1102////////////////////////////////////////////////////////////////////////////////
1103/// add attribute from xml
1104
1105Bool_t TMVA::Tools::HasAttr( void* node, const char* attrname )
1106{
1107 return xmlengine().HasAttr(node, attrname);
1108}
1109
1110////////////////////////////////////////////////////////////////////////////////
1111/// add attribute from xml
1112
1113void TMVA::Tools::ReadAttr( void* node, const char* attrname, TString& value )
1114{
1115 if (!HasAttr(node, attrname)) {
1116 const char * nodename = xmlengine().GetNodeName(node);
1117 Log() << kFATAL << "Trying to read non-existing attribute '" << attrname << "' from xml node '" << nodename << "'" << Endl;
1118 }
1119 const char* val = xmlengine().GetAttr(node, attrname);
1120 value = TString(val);
1121}
1122
1123////////////////////////////////////////////////////////////////////////////////
1124/// add attribute to node
1125
1126void TMVA::Tools::AddAttr( void* node, const char* attrname, const char* value )
1127{
1128 if( node == 0 ) return;
1129 gTools().xmlengine().NewAttr(node, 0, attrname, value );
1130}
1131
1132////////////////////////////////////////////////////////////////////////////////
1133/// add child node
1134
1135void* TMVA::Tools::AddChild( void* parent, const char* childname, const char* content, bool isRootNode )
1136{
1137 if( !isRootNode && parent == 0 ) return 0;
1138 return gTools().xmlengine().NewChild(parent, 0, childname, content);
1139}
1140
1141////////////////////////////////////////////////////////////////////////////////
1142
1143Bool_t TMVA::Tools::AddComment( void* node, const char* comment ) {
1144 if( node == 0 ) return kFALSE;
1145 return gTools().xmlengine().AddComment(node, comment);
1146}
1147
1148////////////////////////////////////////////////////////////////////////////////
1149/// get parent node
1150
1151void* TMVA::Tools::GetParent( void* child)
1152{
1153 void* par = xmlengine().GetParent(child);
1154
1155 return par;
1156}
1157
1158////////////////////////////////////////////////////////////////////////////////
1159/// get child node
1160
1161void* TMVA::Tools::GetChild( void* parent, const char* childname )
1162{
1163 void* ch = xmlengine().GetChild(parent);
1164 if (childname != 0) {
1165 while (ch!=0 && strcmp(xmlengine().GetNodeName(ch),childname) != 0) ch = xmlengine().GetNext(ch);
1166 }
1167 return ch;
1168}
1169
1170////////////////////////////////////////////////////////////////////////////////
1171/// XML helpers
1172
1173void* TMVA::Tools::GetNextChild( void* prevchild, const char* childname )
1174{
1175 void* ch = xmlengine().GetNext(prevchild);
1176 if (childname != 0) {
1177 while (ch!=0 && strcmp(xmlengine().GetNodeName(ch),childname)!=0) ch = xmlengine().GetNext(ch);
1178 }
1179 return ch;
1180}
1181
1182////////////////////////////////////////////////////////////////////////////////
1183/// XML helpers
1184
1185const char* TMVA::Tools::GetContent( void* node )
1186{
1187 return xmlengine().GetNodeContent(node);
1188}
1189
1190////////////////////////////////////////////////////////////////////////////////
1191/// XML helpers
1192
1193const char* TMVA::Tools::GetName( void* node )
1194{
1195 return xmlengine().GetNodeName(node);
1196}
1197
1198////////////////////////////////////////////////////////////////////////////////
1199/// XML helpers
1200
1201Bool_t TMVA::Tools::AddRawLine( void* node, const char * raw )
1202{
1203 return xmlengine().AddRawLine( node, raw );
1204}
1205
1206////////////////////////////////////////////////////////////////////////////////
1207/// splits the option string at 'separator' and fills the list
1208/// 'splitV' with the primitive strings
1209
1210std::vector<TString> TMVA::Tools::SplitString(const TString& theOpt, const char separator ) const
1211{
1212 std::vector<TString> splitV;
1213 TString splitOpt(theOpt);
1214 splitOpt.ReplaceAll("\n"," ");
1215 splitOpt = splitOpt.Strip(TString::kBoth,separator);
1216 while (splitOpt.Length()>0) {
1217 if ( !splitOpt.Contains(separator) ) {
1218 splitV.push_back(splitOpt);
1219 break;
1220 }
1221 else {
1222 TString toSave = splitOpt(0,splitOpt.First(separator));
1223 splitV.push_back(toSave);
1224 splitOpt = splitOpt(splitOpt.First(separator),splitOpt.Length());
1225 }
1226 splitOpt = splitOpt.Strip(TString::kLeading,separator);
1227 }
1228 return splitV;
1229}
1230
1231////////////////////////////////////////////////////////////////////////////////
1232/// string tools
1233
1235{
1236 std::stringstream s;
1237 s << i;
1238 return TString(s.str().c_str());
1239}
1240
1241////////////////////////////////////////////////////////////////////////////////
1242/// string tools
1243
1245{
1246 std::stringstream s;
1247 s << Form( "%5.8e", d );
1248 return TString(s.str().c_str());
1249}
1250
1251////////////////////////////////////////////////////////////////////////////////
1252/// XML helpers
1253
1254void TMVA::Tools::WriteTMatrixDToXML( void* node, const char* name, TMatrixD* mat )
1255{
1256 void* matnode = xmlengine().NewChild(node, 0, name);
1257 xmlengine().NewAttr(matnode,0,"Rows", StringFromInt(mat->GetNrows()) );
1258 xmlengine().NewAttr(matnode,0,"Columns", StringFromInt(mat->GetNcols()) );
1259 std::stringstream s;
1260 for (Int_t row = 0; row<mat->GetNrows(); row++) {
1261 for (Int_t col = 0; col<mat->GetNcols(); col++) {
1262 s << Form( "%5.15e ", (*mat)[row][col] );
1263 }
1264 }
1265 xmlengine().AddRawLine( matnode, s.str().c_str() );
1266}
1267
1268////////////////////////////////////////////////////////////////////////////////
1269
1270void TMVA::Tools::WriteTVectorDToXML( void* node, const char* name, TVectorD* vec )
1271{
1272 TMatrixD mat(1,vec->GetNoElements(),&((*vec)[0]));
1273 WriteTMatrixDToXML( node, name, &mat );
1274}
1275
1276////////////////////////////////////////////////////////////////////////////////
1277
1278void TMVA::Tools::ReadTVectorDFromXML( void* node, const char* name, TVectorD* vec )
1279{
1280 TMatrixD mat(1,vec->GetNoElements(),&((*vec)[0]));
1281 ReadTMatrixDFromXML( node, name, &mat );
1282 for (int i=0;i<vec->GetNoElements();++i) (*vec)[i] = mat[0][i];
1283}
1284
1285////////////////////////////////////////////////////////////////////////////////
1286
1287void TMVA::Tools::ReadTMatrixDFromXML( void* node, const char* name, TMatrixD* mat )
1288{
1289 if (strcmp(xmlengine().GetNodeName(node),name)!=0){
1290 Log() << kWARNING << "Possible Error: Name of matrix in weight file"
1291 << " does not match name of matrix passed as argument!" << Endl;
1292 }
1293 Int_t nrows, ncols;
1294 ReadAttr( node, "Rows", nrows );
1295 ReadAttr( node, "Columns", ncols );
1296 if (mat->GetNrows() != nrows || mat->GetNcols() != ncols){
1297 Log() << kWARNING << "Possible Error: Dimension of matrix in weight file"
1298 << " does not match dimension of matrix passed as argument!" << Endl;
1299 mat->ResizeTo(nrows,ncols);
1300 }
1301 const char* content = xmlengine().GetNodeContent(node);
1302 std::stringstream s(content);
1303 for (Int_t row = 0; row<nrows; row++) {
1304 for (Int_t col = 0; col<ncols; col++) {
1305 s >> (*mat)[row][col];
1306 }
1307 }
1308}
1309
1310////////////////////////////////////////////////////////////////////////////////
1311/// direct output, eg, when starting ROOT session -> no use of Logger here
1312
1314{
1315 std::cout << std::endl;
1316 std::cout << Color("bold") << "TMVA -- Toolkit for Multivariate Data Analysis" << Color("reset") << std::endl;
1317 std::cout << " " << "Version " << TMVA_RELEASE << ", " << TMVA_RELEASE_DATE << std::endl;
1318 std::cout << " " << "Copyright (C) 2005-2010 CERN, MPI-K Heidelberg, Us of Bonn and Victoria" << std::endl;
1319 std::cout << " " << "Home page: http://tmva.sf.net" << std::endl;
1320 std::cout << " " << "Citation info: http://tmva.sf.net/citeTMVA.html" << std::endl;
1321 std::cout << " " << "License: http://tmva.sf.net/LICENSE" << std::endl << std::endl;
1322}
1323
1324////////////////////////////////////////////////////////////////////////////////
1325/// prints the TMVA release number and date
1326
1328{
1329 logger << "___________TMVA Version " << TMVA_RELEASE << ", " << TMVA_RELEASE_DATE
1330 << "" << Endl;
1331}
1332
1333////////////////////////////////////////////////////////////////////////////////
1334/// prints the ROOT release number and date
1335
1337{
1338 static const char * const months[] = { "Jan","Feb","Mar","Apr","May",
1339 "Jun","Jul","Aug","Sep","Oct",
1340 "Nov","Dec" };
1341 Int_t idatqq = gROOT->GetVersionDate();
1342 Int_t iday = idatqq%100;
1343 Int_t imonth = (idatqq/100)%100;
1344 Int_t iyear = (idatqq/10000);
1345 TString versionDate = Form("%s %d, %4d",months[imonth-1],iday,iyear);
1346
1347 logger << kHEADER ;
1348 logger << "You are running ROOT Version: " << gROOT->GetVersion() << ", " << versionDate << Endl;
1349}
1350
1351////////////////////////////////////////////////////////////////////////////////
1352/// various kinds of welcome messages
1353/// ASCII text generated by this site: http://www.network-science.de/ascii/
1354
1356{
1357 switch (msgType) {
1358
1359 case kStandardWelcomeMsg:
1360 logger << Color("white") << "TMVA -- Toolkit for Multivariate Analysis" << Color("reset") << Endl;
1361 logger << "Copyright (C) 2005-2006 CERN, LAPP & MPI-K Heidelberg and Victoria U." << Endl;
1362 logger << "Home page http://tmva.sourceforge.net" << Endl;
1363 logger << "All rights reserved, please read http://tmva.sf.net/license.txt" << Endl << Endl;
1364 break;
1365
1366 case kIsometricWelcomeMsg:
1367 logger << " ___ ___ ___ ___ " << Endl;
1368 logger << " /\\ \\ /\\__\\ /\\__\\ /\\ \\ " << Endl;
1369 logger << " \\:\\ \\ /::| | /:/ / /::\\ \\ " << Endl;
1370 logger << " \\:\\ \\ /:|:| | /:/ / /:/\\:\\ \\ " << Endl;
1371 logger << " /::\\ \\ /:/|:|__|__ /:/__/ ___ /::\\~\\:\\ \\ " << Endl;
1372 logger << " /:/\\:\\__\\ /:/ |::::\\__\\ |:| | /\\__\\ /:/\\:\\ \\:\\__\\ " << Endl;
1373 logger << " /:/ \\/__/ \\/__/~~/:/ / |:| |/:/ / \\/__\\:\\/:/ / " << Endl;
1374 logger << "/:/ / /:/ / |:|__/:/ / \\::/ / " << Endl;
1375 logger << "\\/__/ /:/ / \\::::/__/ /:/ / " << Endl;
1376 logger << " /:/ / ~~~~ /:/ / " << Endl;
1377 logger << " \\/__/ \\/__/ " << Endl << Endl;
1378 break;
1379
1380 case kBlockWelcomeMsg:
1381 logger << Endl;
1382 logger << "_|_|_|_|_| _| _| _| _| _|_| " << Endl;
1383 logger << " _| _|_| _|_| _| _| _| _| " << Endl;
1384 logger << " _| _| _| _| _| _| _|_|_|_| " << Endl;
1385 logger << " _| _| _| _| _| _| _| " << Endl;
1386 logger << " _| _| _| _| _| _| " << Endl << Endl;
1387 break;
1388
1389 case kLeanWelcomeMsg:
1390 logger << Endl;
1391 logger << "_/_/_/_/_/ _/ _/ _/ _/ _/_/ " << Endl;
1392 logger << " _/ _/_/ _/_/ _/ _/ _/ _/ " << Endl;
1393 logger << " _/ _/ _/ _/ _/ _/ _/_/_/_/ " << Endl;
1394 logger << " _/ _/ _/ _/ _/ _/ _/ " << Endl;
1395 logger << "_/ _/ _/ _/ _/ _/ " << Endl << Endl;
1396 break;
1397
1398 case kLogoWelcomeMsg:
1399 logger << Endl;
1400 logger << "_/_/_/_/_/ _| _| _| _| _|_| " << Endl;
1401 logger << " _/ _|_| _|_| _| _| _| _| " << Endl;
1402 logger << " _/ _| _| _| _| _| _|_|_|_| " << Endl;
1403 logger << " _/ _| _| _| _| _| _| " << Endl;
1404 logger << "_/ _| _| _| _| _| " << Endl << Endl;
1405 break;
1406
1407 case kSmall1WelcomeMsg:
1408 logger << " _____ __ ____ ___ " << Endl;
1409 logger << "|_ _| \\/ \\ \\ / /_\\ " << Endl;
1410 logger << " | | | |\\/| |\\ V / _ \\ " << Endl;
1411 logger << " |_| |_| |_| \\_/_/ \\_\\" << Endl << Endl;
1412 break;
1413
1414 case kSmall2WelcomeMsg:
1415 logger << " _____ __ ____ ___ " << Endl;
1416 logger << "|_ _| \\/ \\ \\ / / \\ " << Endl;
1417 logger << " | | | |\\/| |\\ \\ / / _ \\ " << Endl;
1418 logger << " | | | | | | \\ V / ___ \\ " << Endl;
1419 logger << " |_| |_| |_| \\_/_/ \\_\\ " << Endl << Endl;
1420 break;
1421
1422 case kOriginalWelcomeMsgColor:
1423 logger << kINFO << "" << Color("red")
1424 << "_______________________________________" << Color("reset") << Endl;
1425 logger << kINFO << "" << Color("blue")
1426 << Color("red_bgd") << Color("bwhite") << " // " << Color("reset")
1427 << Color("white") << Color("blue_bgd")
1428 << "|\\ /|| \\ // /\\\\\\\\\\\\\\\\\\\\\\\\ \\ \\ \\ " << Color("reset") << Endl;
1429 logger << kINFO << ""<< Color("blue")
1430 << Color("red_bgd") << Color("white") << "// " << Color("reset")
1431 << Color("white") << Color("blue_bgd")
1432 << "| \\/ || \\// /--\\\\\\\\\\\\\\\\\\\\\\\\ \\ \\ \\" << Color("reset") << Endl;
1433 break;
1434
1435 case kOriginalWelcomeMsgBW:
1436 logger << kINFO << ""
1437 << "_______________________________________" << Endl;
1438 logger << kINFO << " // "
1439 << "|\\ /|| \\ // /\\\\\\\\\\\\\\\\\\\\\\\\ \\ \\ \\ " << Endl;
1440 logger << kINFO << "// "
1441 << "| \\/ || \\// /--\\\\\\\\\\\\\\\\\\\\\\\\ \\ \\ \\" << Endl;
1442 break;
1443
1444 default:
1445 logger << kFATAL << "unknown message type: " << msgType << Endl;
1446 }
1447}
1448
1449////////////////////////////////////////////////////////////////////////////////
1450/// kinds of TMVA citation
1451
1453{
1454 switch (citType) {
1455
1456 case kPlainText:
1457 logger << "A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne, H. Voss" << Endl;
1458 logger << "\"TMVA - Toolkit for Multivariate Data Analysis\" PoS ACAT:040,2007. e-Print: physics/0703039" << Endl;
1459 break;
1460
1461 case kBibTeX:
1462 logger << "@Article{TMVA2007," << Endl;
1463 logger << " author = \"Hoecker, Andreas and Speckmayer, Peter and Stelzer, Joerg " << Endl;
1464 logger << " and Therhaag, Jan and von Toerne, Eckhard and Voss, Helge\"," << Endl;
1465 logger << " title = \"{TMVA: Toolkit for multivariate data analysis}\"," << Endl;
1466 logger << " journal = \"PoS\"," << Endl;
1467 logger << " volume = \"ACAT\"," << Endl;
1468 logger << " year = \"2007\"," << Endl;
1469 logger << " pages = \"040\"," << Endl;
1470 logger << " eprint = \"physics/0703039\"," << Endl;
1471 logger << " archivePrefix = \"arXiv\"," << Endl;
1472 logger << " SLACcitation = \"%%CITATION = PHYSICS/0703039;%%\"" << Endl;
1473 logger << "}" << Endl;
1474 break;
1475
1476 case kLaTeX:
1477 logger << "%\\cite{TMVA2007}" << Endl;
1478 logger << "\\bibitem{TMVA2007}" << Endl;
1479 logger << " A.~Hoecker, P.~Speckmayer, J.~Stelzer, J.~Therhaag, E.~von Toerne, H.~Voss" << Endl;
1480 logger << " %``TMVA: Toolkit for multivariate data analysis,''" << Endl;
1481 logger << " PoS A {\\bf CAT} (2007) 040" << Endl;
1482 logger << " [arXiv:physics/0703039]." << Endl;
1483 logger << " %%CITATION = POSCI,ACAT,040;%%" << Endl;
1484 break;
1485
1486 case kHtmlLink:
1487 // logger << kINFO << " " << Endl;
1488 logger << kHEADER << gTools().Color("bold")
1489 << "Thank you for using TMVA!" << gTools().Color("reset") << Endl;
1490 logger << kINFO << gTools().Color("bold")
1491 << "For citation information, please visit: http://tmva.sf.net/citeTMVA.html"
1492 << gTools().Color("reset") << Endl;
1493 }
1494}
1495
1496////////////////////////////////////////////////////////////////////////////////
1497
1499{
1500 return !(h.GetXaxis()->GetXbins()->fN);
1501}
1502
1503////////////////////////////////////////////////////////////////////////////////
1504
1505std::vector<TMatrixDSym*>*
1506TMVA::Tools::CalcCovarianceMatrices( const std::vector<const Event*>& events, Int_t maxCls, VariableTransformBase* transformBase )
1507{
1508 std::vector<Event*> eventVector;
1509 for (std::vector<const Event*>::const_iterator it = events.begin(), itEnd = events.end(); it != itEnd; ++it)
1510 {
1511 eventVector.push_back (new Event(*(*it)));
1512 }
1513 std::vector<TMatrixDSym*>* returnValue = CalcCovarianceMatrices (eventVector, maxCls, transformBase);
1514 for (std::vector<Event*>::const_iterator it = eventVector.begin(), itEnd = eventVector.end(); it != itEnd; ++it)
1515 {
1516 delete (*it);
1517 }
1518 return returnValue;
1519}
1520
1521////////////////////////////////////////////////////////////////////////////////
1522/// compute covariance matrices
1523
1524std::vector<TMatrixDSym*>*
1525TMVA::Tools::CalcCovarianceMatrices( const std::vector<Event*>& events, Int_t maxCls, VariableTransformBase* transformBase )
1526{
1527 if (events.empty()) {
1528 Log() << kWARNING << " Asked to calculate a covariance matrix for an empty event vectors.. sorry cannot do that -> return NULL"<<Endl;
1529 return 0;
1530 }
1531
1532 UInt_t nvars=0, ntgts=0, nspcts=0;
1533 if (transformBase)
1534 transformBase->CountVariableTypes( nvars, ntgts, nspcts );
1535 else {
1536 nvars =events.at(0)->GetNVariables ();
1537 ntgts =events.at(0)->GetNTargets ();
1538 nspcts=events.at(0)->GetNSpectators();
1539 }
1540
1541
1542 // init matrices
1543 Int_t matNum = maxCls;
1544 if (maxCls > 1 ) matNum++; // if more than one classes, then produce one matrix for all events as well (beside the matrices for each class)
1545
1546 std::vector<TVectorD*>* vec = new std::vector<TVectorD*>(matNum);
1547 std::vector<TMatrixD*>* mat2 = new std::vector<TMatrixD*>(matNum);
1548 std::vector<Double_t> count(matNum);
1549 count.assign(matNum,0);
1550
1551 Int_t cls = 0;
1552 TVectorD* v;
1553 TMatrixD* m;
1554 UInt_t ivar=0, jvar=0;
1555 for (cls = 0; cls < matNum ; cls++) {
1556 vec->at(cls) = new TVectorD(nvars);
1557 mat2->at(cls) = new TMatrixD(nvars,nvars);
1558 v = vec->at(cls);
1559 m = mat2->at(cls);
1560
1561 for (ivar=0; ivar<nvars; ivar++) {
1562 (*v)(ivar) = 0;
1563 for (jvar=0; jvar<nvars; jvar++) {
1564 (*m)(ivar, jvar) = 0;
1565 }
1566 }
1567 }
1568
1569 // perform event loop
1570 for (UInt_t i=0; i<events.size(); i++) {
1571
1572 // fill the event
1573 const Event * ev = events[i];
1574 cls = ev->GetClass();
1575 Double_t weight = ev->GetWeight();
1576
1577 std::vector<Float_t> input;
1578 std::vector<Char_t> mask; // entries with kTRUE must not be transformed
1579 // Bool_t hasMaskedEntries = kFALSE;
1580 if (transformBase) {
1581 /* hasMaskedEntries = */ transformBase->GetInput (ev, input, mask);
1582 } else {
1583 for (ivar=0; ivar<nvars; ++ivar) {
1584 input.push_back (ev->GetValue(ivar));
1585 }
1586 }
1587
1588 if (maxCls > 1) {
1589 v = vec->at(matNum-1);
1590 m = mat2->at(matNum-1);
1591
1592 count.at(matNum-1)+=weight; // count used events
1593 for (ivar=0; ivar<nvars; ivar++) {
1594
1595 Double_t xi = input.at (ivar);
1596 (*v)(ivar) += xi*weight;
1597 (*m)(ivar, ivar) += (xi*xi*weight);
1598
1599 for (jvar=ivar+1; jvar<nvars; jvar++) {
1600 Double_t xj = input.at (jvar);
1601 (*m)(ivar, jvar) += (xi*xj*weight);
1602 (*m)(jvar, ivar) = (*m)(ivar, jvar); // symmetric matrix
1603 }
1604 }
1605 }
1606
1607 count.at(cls)+=weight; // count used events
1608 v = vec->at(cls);
1609 m = mat2->at(cls);
1610 for (ivar=0; ivar<nvars; ivar++) {
1611 Double_t xi = input.at (ivar);
1612 (*v)(ivar) += xi*weight;
1613 (*m)(ivar, ivar) += (xi*xi*weight);
1614
1615 for (jvar=ivar+1; jvar<nvars; jvar++) {
1616 Double_t xj = input.at (jvar);
1617 (*m)(ivar, jvar) += (xi*xj*weight);
1618 (*m)(jvar, ivar) = (*m)(ivar, jvar); // symmetric matrix
1619 }
1620 }
1621 }
1622
1623 // variance-covariance
1624 std::vector<TMatrixDSym*>* mat = new std::vector<TMatrixDSym*>(matNum);
1625 for (cls = 0; cls < matNum; cls++) {
1626 v = vec->at(cls);
1627 m = mat2->at(cls);
1628
1629 mat->at(cls) = new TMatrixDSym(nvars);
1630
1631 Double_t n = count.at(cls);
1632 for (ivar=0; ivar<nvars; ivar++) {
1633 for (jvar=0; jvar<nvars; jvar++) {
1634 (*(mat->at(cls)))(ivar, jvar) = (*m)(ivar, jvar)/n - (*v)(ivar)*(*v)(jvar)/(n*n);
1635 }
1636 }
1637 delete v;
1638 delete m;
1639 }
1640
1641 delete mat2;
1642 delete vec;
1643
1644 return mat;
1645}
1646
1647////////////////////////////////////////////////////////////////////////////////
1648/// Return the weighted mean of an array defined by the first and
1649/// last iterators. The w iterator should point to the first element
1650/// of a vector of weights of the same size as the main array.
1651
1652template <typename Iterator, typename WeightIterator>
1653Double_t TMVA::Tools::Mean ( Iterator first, Iterator last, WeightIterator w)
1654{
1655 Double_t sum = 0;
1656 Double_t sumw = 0;
1657 int i = 0;
1658 if (w==NULL)
1659 {
1660 while ( first != last )
1661 {
1662 // if ( *w < 0) {
1663 // ::Error("TMVA::Tools::Mean","w[%d] = %.4e < 0 ?!",i,*w);
1664 // return 0;
1665 // } // SURE, why wouldn't you allow for negative event weights here ?? :)
1666 sum += (*first);
1667 sumw += 1.0 ;
1668 ++first;
1669 ++i;
1670 }
1671 if (sumw <= 0) {
1672 ::Error("TMVA::Tools::Mean","sum of weights <= 0 ?! that's a bit too much of negative event weights :) ");
1673 return 0;
1674 }
1675 }
1676 else
1677 {
1678 while ( first != last )
1679 {
1680 // if ( *w < 0) {
1681 // ::Error("TMVA::Tools::Mean","w[%d] = %.4e < 0 ?!",i,*w);
1682 // return 0;
1683 // } // SURE, why wouldn't you allow for negative event weights here ?? :)
1684 sum += (*w) * (*first);
1685 sumw += (*w) ;
1686 ++w;
1687 ++first;
1688 ++i;
1689 }
1690 if (sumw <= 0) {
1691 ::Error("TMVA::Tools::Mean","sum of weights <= 0 ?! that's a bit too much of negative event weights :) ");
1692 return 0;
1693 }
1694 }
1695 return sum/sumw;
1696}
1697
1698////////////////////////////////////////////////////////////////////////////////
1699/// Return the weighted mean of an array a with length n.
1700
1701template <typename T>
1703{
1704 if (w) {
1705 return TMVA::Tools::Mean(a, a+n, w);
1706 } else {
1707 return TMath::Mean(a, a+n);
1708 }
1709}
1710
1711////////////////////////////////////////////////////////////////////////////////
1712/// Return the Standard Deviation of an array defined by the iterators.
1713/// Note that this function returns the sigma(standard deviation) and
1714/// not the root mean square of the array.
1715
1716template <typename Iterator, typename WeightIterator>
1717Double_t TMVA::Tools::RMS(Iterator first, Iterator last, WeightIterator w)
1718{
1719
1720 Double_t sum = 0;
1721 Double_t sum2 = 0;
1722 Double_t sumw = 0;
1723
1724 Double_t adouble;
1725 if (w==NULL)
1726 {
1727 while ( first != last ) {
1728 adouble=Double_t(*first);
1729 sum += adouble;
1730 sum2 += adouble*adouble;
1731 sumw += 1.0;
1732 ++first;
1733 }
1734 }
1735 else
1736 {
1737 while ( first != last ) {
1738 adouble=Double_t(*first);
1739 sum += adouble * (*w);
1740 sum2 += adouble*adouble * (*w);
1741 sumw += (*w);
1742 ++first;
1743 ++w;
1744 }
1745 }
1746 Double_t norm = 1./sumw;
1747 Double_t mean = sum*norm;
1748 Double_t rms = TMath::Sqrt(TMath::Abs(sum2*norm -mean*mean));
1749 return rms;
1750}
1751
1752////////////////////////////////////////////////////////////////////////////////
1753/// Return the Standard Deviation of an array a with length n.
1754/// Note that this function returns the sigma(standard deviation) and
1755/// not the root mean square of the array.
1756
1757template <typename T>
1759{
1760
1761 if (w) {
1762 return TMVA::Tools::RMS(a, a+n, w);
1763 } else {
1764 return TMath::RMS(a, a+n);
1765 }
1766}
1767
1768////////////////////////////////////////////////////////////////////////////////
1769/// get the cumulative distribution of a histogram
1770
1772{
1773 TH1* cumulativeDist= (TH1*) h->Clone(Form("%sCumul",h->GetTitle()));
1774 //cumulativeDist->Smooth(5); // with this, I get less beautiful ROC curves, hence out!
1775
1776 Float_t partialSum = 0;
1777 Float_t inverseSum = 0.;
1778
1779 Float_t val;
1780 for (Int_t ibinEnd=1, ibin=cumulativeDist->GetNbinsX(); ibin >=ibinEnd ; ibin--){
1781 val = cumulativeDist->GetBinContent(ibin);
1782 if (val>0) inverseSum += val;
1783 }
1784 inverseSum = 1/inverseSum; // as I learned multiplications are much faster than division, and later I need one per bin. Well, not that it would really matter here I guess :)
1785
1786 for (Int_t ibinEnd=1, ibin=cumulativeDist->GetNbinsX(); ibin >=ibinEnd ; ibin--){
1787 val = cumulativeDist->GetBinContent(ibin);
1788 if (val>0) partialSum += val;
1789 cumulativeDist->SetBinContent(ibin,partialSum*inverseSum);
1790 }
1791 return cumulativeDist;
1792}
1793
1794void TMVA::Tools::ReadAttr(void *node, const char *attrname, float &value)
1795{
1796 // read attribute from xml
1797 const char *val = xmlengine().GetAttr(node, attrname);
1798 if (val == nullptr) {
1799 const char *nodename = xmlengine().GetNodeName(node);
1800 Log() << kFATAL << "Trying to read non-existing attribute '" << attrname << "' from xml node '" << nodename << "'"
1801 << Endl;
1802 } else
1803 value = atof(val);
1804}
1805
1806void TMVA::Tools::ReadAttr(void *node, const char *attrname, int &value)
1807{
1808 // read attribute from xml
1809 const char *val = xmlengine().GetAttr(node, attrname);
1810 if (val == nullptr) {
1811 const char *nodename = xmlengine().GetNodeName(node);
1812 Log() << kFATAL << "Trying to read non-existing attribute '" << attrname << "' from xml node '" << nodename << "'"
1813 << Endl;
1814 } else
1815 value = atoi(val);
1816}
1817
1818void TMVA::Tools::ReadAttr(void *node, const char *attrname, short &value)
1819{
1820 // read attribute from xml
1821 const char *val = xmlengine().GetAttr(node, attrname);
1822 if (val == nullptr) {
1823 const char *nodename = xmlengine().GetNodeName(node);
1824 Log() << kFATAL << "Trying to read non-existing attribute '" << attrname << "' from xml node '" << nodename << "'"
1825 << Endl;
1826 } else
1827 value = atoi(val);
1828}
ROOT::R::TRInterface & r
Definition: Object.C:4
#define d(i)
Definition: RSha256.hxx:102
#define b(i)
Definition: RSha256.hxx:100
#define f(i)
Definition: RSha256.hxx:104
#define c(i)
Definition: RSha256.hxx:101
#define h(i)
Definition: RSha256.hxx:106
int Int_t
Definition: RtypesCore.h:43
int Ssiz_t
Definition: RtypesCore.h:65
unsigned int UInt_t
Definition: RtypesCore.h:44
const Bool_t kFALSE
Definition: RtypesCore.h:90
long Long_t
Definition: RtypesCore.h:52
bool Bool_t
Definition: RtypesCore.h:61
double Double_t
Definition: RtypesCore.h:57
long long Long64_t
Definition: RtypesCore.h:71
float Float_t
Definition: RtypesCore.h:55
const Bool_t kTRUE
Definition: RtypesCore.h:89
void Error(const char *location, const char *msgfmt,...)
char name[80]
Definition: TGX11.cxx:109
float xmin
Definition: THbookFile.cxx:93
float xmax
Definition: THbookFile.cxx:93
float type_of_call hi(const int &, const int &)
double pow(double, double)
TMatrixTSym< Double_t > TMatrixDSym
TMatrixT< Double_t > TMatrixD
Definition: TMatrixDfwd.h:22
#define gROOT
Definition: TROOT.h:406
char * Form(const char *fmt,...)
TVectorT< Double_t > TVectorD
Definition: TVectorDfwd.h:22
#define TMVA_RELEASE
Definition: Version.h:44
#define TMVA_RELEASE_DATE
Definition: Version.h:45
Double_t GetXmax() const
Definition: TAxis.h:134
Double_t GetXmin() const
Definition: TAxis.h:133
virtual void SetOwner(Bool_t enable=kTRUE)
Set whether this collection is the owner (enable==true) of its content.
virtual Int_t GetSize() const
Return the capacity of the collection, i.e.
Definition: TCollection.h:182
1-D histogram with a float per channel (see TH1 documentation)}
Definition: TH1.h:571
The TH1 histogram class.
Definition: TH1.h:56
virtual Double_t GetBinCenter(Int_t bin) const
Return bin center for 1D histogram.
Definition: TH1.cxx:8597
TAxis * GetXaxis()
Get the behaviour adopted by the object about the statoverflows. See EStatOverflows for more informat...
Definition: TH1.h:316
virtual Int_t GetNbinsX() const
Definition: TH1.h:292
virtual void SetBinContent(Int_t bin, Double_t content)
Set bin content see convention for numbering bins in TH1::GetBin In case the bin number is greater th...
Definition: TH1.cxx:8678
virtual Double_t GetBinContent(Int_t bin) const
Return content of bin number bin.
Definition: TH1.cxx:4907
virtual void Scale(Double_t c1=1, Option_t *option="")
Multiply this histogram by a constant c1.
Definition: TH1.cxx:6246
virtual Int_t GetSumw2N() const
Definition: TH1.h:310
virtual Double_t GetSumOfWeights() const
Return the sum of weights excluding under/overflows.
Definition: TH1.cxx:7426
virtual void Sumw2(Bool_t flag=kTRUE)
Create structure to store sum of squares of weights.
Definition: TH1.cxx:8476
2-D histogram with a float per channel (see TH1 documentation)}
Definition: TH2.h:251
Service class for 2-Dim histogram classes.
Definition: TH2.h:30
virtual void PutStats(Double_t *stats)
Replace current statistics with the values in array stats.
Definition: TH2.cxx:2349
virtual Double_t Integral(Option_t *option="") const
Return integral of bin contents.
Definition: TH2.cxx:1188
virtual void SetBinContent(Int_t bin, Double_t content)
Set bin content.
Definition: TH2.cxx:2480
A doubly linked list.
Definition: TList.h:44
virtual void Add(TObject *obj)
Definition: TList.h:87
virtual TObject * At(Int_t idx) const
Returns the object at position idx. Returns 0 if idx is out of range.
Definition: TList.cxx:356
Float_t GetValue(UInt_t ivar) const
return value of i'th variable
Definition: Event.cxx:236
Double_t GetWeight() const
return the event weight - depending on whether the flag IgnoreNegWeightsInTraining is or not.
Definition: Event.cxx:381
UInt_t GetClass() const
Definition: Event.h:86
ostringstream derivative to redirect and format output
Definition: MsgLogger.h:59
PDF wrapper for histograms; uses user-defined spline interpolation.
Definition: PDF.h:63
Double_t GetXmin() const
Definition: PDF.h:104
Double_t GetXmax() const
Definition: PDF.h:105
Double_t GetVal(Double_t x) const
returns value PDF(x)
Definition: PDF.cxx:700
Global auxiliary applications and data treatment routines.
Definition: Tools.h:78
void ComputeStat(const std::vector< TMVA::Event * > &, std::vector< Float_t > *, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Int_t signalClass, Bool_t norm=kFALSE)
sanity check
Definition: Tools.cxx:213
void * GetParent(void *child)
get parent node
Definition: Tools.cxx:1151
void FormattedOutput(const std::vector< Double_t > &, const std::vector< TString > &, const TString titleVars, const TString titleValues, MsgLogger &logger, TString format="%+1.3f")
formatted output of simple table
Definition: Tools.cxx:898
void UsefulSortDescending(std::vector< std::vector< Double_t > > &, std::vector< TString > *vs=0)
sort 2D vector (AND in parallel a TString vector) in such a way that the "first vector is sorted" and...
Definition: Tools.cxx:575
Bool_t HistoHasEquidistantBins(const TH1 &h)
Definition: Tools.cxx:1498
TList * ParseFormatLine(TString theString, const char *sep=":")
Parse the string and cut into labels separated by ":".
Definition: Tools.cxx:412
Double_t NormVariable(Double_t x, Double_t xmin, Double_t xmax)
normalise to output range: [-1, 1]
Definition: Tools.cxx:121
void WriteFloatArbitraryPrecision(Float_t val, std::ostream &os)
writes a float value with the available precision to a stream
Definition: Tools.cxx:1069
void * GetNextChild(void *prevchild, const char *childname=0)
XML helpers.
Definition: Tools.cxx:1173
TString GetYTitleWithUnit(const TH1 &h, const TString &unit, Bool_t normalised)
histogramming utility
Definition: Tools.cxx:1059
Double_t GetSeparation(TH1 *S, TH1 *B) const
compute "separation" defined as
Definition: Tools.cxx:132
static Tools & Instance()
Definition: Tools.cxx:74
TMatrixD * GetSQRootMatrix(TMatrixDSym *symMat)
square-root of symmetric matrix of course the resulting sqrtMat is also symmetric,...
Definition: Tools.cxx:282
std::vector< Double_t > MVADiff(std::vector< Double_t > &, std::vector< Double_t > &)
computes difference between two vectors
Definition: Tools.cxx:517
Int_t GetIndexMinElement(std::vector< Double_t > &)
find index of minimum entry in vector
Definition: Tools.cxx:776
TH1 * projNormTH1F(TTree *theTree, const TString &theVarName, const TString &name, Int_t nbins, Double_t xmin, Double_t xmax, const TString &cut)
projects variable from tree into normalised histogram
Definition: Tools.cxx:377
void ROOTVersionMessage(MsgLogger &logger)
prints the ROOT release number and date
Definition: Tools.cxx:1336
TString ReplaceRegularExpressions(const TString &s, const TString &replace="+")
replace regular expressions helper function to remove all occurrences "$!%^&()'<>?...
Definition: Tools.cxx:809
void * AddChild(void *parent, const char *childname, const char *content=0, bool isRootNode=false)
add child node
Definition: Tools.cxx:1135
void ReadTVectorDFromXML(void *node, const char *name, TVectorD *vec)
Definition: Tools.cxx:1278
TH1 * GetCumulativeDist(TH1 *h)
get the cumulative distribution of a histogram
Definition: Tools.cxx:1771
void ReadFloatArbitraryPrecision(Float_t &val, std::istream &is)
reads a float value with the available precision from a stream
Definition: Tools.cxx:1084
Bool_t AddRawLine(void *node, const char *raw)
XML helpers.
Definition: Tools.cxx:1201
~Tools()
destructor
Definition: Tools.cxx:112
Bool_t ContainsRegularExpression(const TString &s)
check if regular expression helper function to search for "$!%^&()'<>?= " in a string
Definition: Tools.cxx:795
static void DestroyInstance()
Definition: Tools.cxx:89
Double_t Mean(Long64_t n, const T *a, const Double_t *w=0)
Return the weighted mean of an array a with length n.
Definition: Tools.cxx:1702
Double_t GetMutualInformation(const TH2F &)
Mutual Information method for non-linear correlations estimates in 2D histogram Author: Moritz Backes...
Definition: Tools.cxx:600
std::vector< TString > SplitString(const TString &theOpt, const char separator) const
splits the option string at 'separator' and fills the list 'splitV' with the primitive strings
Definition: Tools.cxx:1210
const TString & Color(const TString &)
human readable color strings
Definition: Tools.cxx:839
TString StringFromDouble(Double_t d)
string tools
Definition: Tools.cxx:1244
Bool_t CheckForVerboseOption(const TString &) const
check if verbosity "V" set in option
Definition: Tools.cxx:719
Double_t RMS(Long64_t n, const T *a, const Double_t *w=0)
Return the Standard Deviation of an array a with length n.
Definition: Tools.cxx:1758
const char * GetContent(void *node)
XML helpers.
Definition: Tools.cxx:1185
void * GetChild(void *parent, const char *childname=0)
get child node
Definition: Tools.cxx:1161
void WriteTVectorDToXML(void *node, const char *name, TVectorD *vec)
Definition: Tools.cxx:1270
TXMLEngine & xmlengine()
Definition: Tools.h:268
void Scale(std::vector< Double_t > &, Double_t)
scales double vector
Definition: Tools.cxx:530
Bool_t CheckSplines(const TH1 *, const TSpline *)
check quality of splining by comparing splines and histograms in each bin
Definition: Tools.cxx:490
const char * GetName(void *node)
XML helpers.
Definition: Tools.cxx:1193
const TMatrixD * GetCorrelationMatrix(const TMatrixD *covMat)
turns covariance into correlation matrix
Definition: Tools.cxx:335
void WriteTMatrixDToXML(void *node, const char *name, TMatrixD *mat)
XML helpers.
Definition: Tools.cxx:1254
Double_t GetCorrelationRatio(const TH2F &)
Compute Correlation Ratio of 2D histogram to estimate functional dependency between two variables Aut...
Definition: Tools.cxx:631
void ReadAttr(void *node, const char *, T &value)
read attribute from xml
Definition: Tools.h:335
Bool_t AddComment(void *node, const char *comment)
Definition: Tools.cxx:1143
void ReadTMatrixDFromXML(void *node, const char *name, TMatrixD *mat)
Definition: Tools.cxx:1287
TString GetXTitleWithUnit(const TString &title, const TString &unit)
histogramming utility
Definition: Tools.cxx:1051
void AddAttr(void *node, const char *, const T &value, Int_t precision=16)
add attribute to xml
Definition: Tools.h:353
TString StringFromInt(Long_t i)
string tools
Definition: Tools.cxx:1234
Tools()
constructor
Definition: Tools.cxx:102
Double_t NormHist(TH1 *theHist, Double_t norm=1.0)
normalises histogram
Definition: Tools.cxx:394
std::vector< TMatrixDSym * > * CalcCovarianceMatrices(const std::vector< Event * > &events, Int_t maxCls, VariableTransformBase *transformBase=0)
compute covariance matrices
Definition: Tools.cxx:1525
Double_t GetYMean_binX(const TH2 &, Int_t bin_x)
Compute the mean in Y for a given bin X of a 2D histogram.
Definition: Tools.cxx:653
void TMVACitation(MsgLogger &logger, ECitation citType=kPlainText)
kinds of TMVA citation
Definition: Tools.cxx:1452
void TMVAVersionMessage(MsgLogger &logger)
prints the TMVA release number and date
Definition: Tools.cxx:1327
void TMVAWelcomeMessage()
direct output, eg, when starting ROOT session -> no use of Logger here
Definition: Tools.cxx:1313
std::vector< Int_t > * ParseANNOptionString(TString theOptions, Int_t nvar, std::vector< Int_t > *nodes)
parse option string for ANN methods default settings (should be defined in theOption string)
Definition: Tools.cxx:454
Int_t GetIndexMaxElement(std::vector< Double_t > &)
find index of maximum entry in vector
Definition: Tools.cxx:759
TH2F * TransposeHist(const TH2F &)
Transpose quadratic histogram.
Definition: Tools.cxx:668
static Tools * fgTools
Definition: Tools.h:234
EWelcomeMessage
Definition: Tools.h:200
Bool_t HasAttr(void *node, const char *attrname)
add attribute from xml
Definition: Tools.cxx:1105
void UsefulSortAscending(std::vector< std::vector< Double_t > > &, std::vector< TString > *vs=0)
sort 2D vector (AND in parallel a TString vector) in such a way that the "first vector is sorted" and...
Definition: Tools.cxx:549
const TString fRegexp
Definition: Tools.h:228
Bool_t CheckForSilentOption(const TString &) const
check for "silence" option in configuration option string
Definition: Tools.cxx:702
Linear interpolation class.
virtual Bool_t GetInput(const Event *event, std::vector< Float_t > &input, std::vector< Char_t > &mask, Bool_t backTransform=kFALSE) const
select the values from the event
virtual void CountVariableTypes(UInt_t &nvars, UInt_t &ntgts, UInt_t &nspcts) const
count variables, targets and spectators
TMatrixDSymEigen.
const TMatrixD & GetEigenVectors() const
Int_t GetNrows() const
Definition: TMatrixTBase.h:124
Int_t GetNcols() const
Definition: TMatrixTBase.h:127
virtual TMatrixTBase< Element > & ResizeTo(Int_t nrows, Int_t ncols, Int_t=-1)
Set size of the matrix to nrows x ncols New dynamic elements are created, the overlapping part of the...
Definition: TMatrixT.cxx:1213
TMatrixT< Element > & Invert(Double_t *det=0)
Invert the matrix and calculate its determinant.
Definition: TMatrixT.cxx:1399
TMatrixT< Element > & Transpose(const TMatrixT< Element > &source)
Transpose matrix source.
Definition: TMatrixT.cxx:1472
void Mult(const TMatrixT< Element > &a, const TMatrixT< Element > &b)
General matrix multiplication. Create a matrix C such that C = A * B.
Definition: TMatrixT.cxx:651
Collectable string class.
Definition: TObjString.h:28
Base class for spline implementation containing the Draw/Paint methods.
Definition: TSpline.h:22
virtual Double_t Eval(Double_t x) const =0
Basic string class.
Definition: TString.h:131
Ssiz_t Length() const
Definition: TString.h:405
TSubString Strip(EStripType s=kTrailing, char c=' ') const
Return a substring of self stripped at beginning and/or end.
Definition: TString.cxx:1106
Ssiz_t First(char c) const
Find first occurrence of a character c.
Definition: TString.cxx:499
const char * Data() const
Definition: TString.h:364
TString & ReplaceAll(const TString &s1, const TString &s2)
Definition: TString.h:687
@ kLeading
Definition: TString.h:262
@ kBoth
Definition: TString.h:262
TString & Remove(Ssiz_t pos)
Definition: TString.h:668
Bool_t Contains(const char *pat, ECaseCompare cmp=kExact) const
Definition: TString.h:619
A TTree represents a columnar dataset.
Definition: TTree.h:78
virtual Long64_t Project(const char *hname, const char *varexp, const char *selection="", Option_t *option="", Long64_t nentries=kMaxEntries, Long64_t firstentry=0)
Make a projection of a tree using selections.
Definition: TTree.cxx:7401
Int_t GetNoElements() const
Definition: TVectorT.h:76
XMLNodePointer_t NewChild(XMLNodePointer_t parent, XMLNsPointer_t ns, const char *name, const char *content=nullptr)
create new child element for parent node
Definition: TXMLEngine.cxx:709
XMLAttrPointer_t NewAttr(XMLNodePointer_t xmlnode, XMLNsPointer_t, const char *name, const char *value)
creates new attribute for xmlnode, namespaces are not supported for attributes
Definition: TXMLEngine.cxx:580
Bool_t AddComment(XMLNodePointer_t parent, const char *comment)
Adds comment line to the node.
Definition: TXMLEngine.cxx:872
RooCmdArg Color(Color_t color)
Double_t y[n]
Definition: legend1.C:17
Double_t x[n]
Definition: legend1.C:17
const Int_t n
Definition: legend1.C:16
static double B[]
double T(double x)
Definition: ChebyshevPol.h:34
static const std::string separator("@@@")
static const std::string comment("comment")
TClass * GetClass(T *)
Definition: TClass.h:658
RooArgSet S(const RooAbsArg &v1)
static constexpr double s
Config & gConfig()
Tools & gTools()
MsgLogger & Endl(MsgLogger &ml)
Definition: MsgLogger.h:158
Short_t Max(Short_t a, Short_t b)
Definition: TMathBase.h:212
constexpr Double_t E()
Base of natural log:
Definition: TMath.h:97
Double_t Log(Double_t x)
Definition: TMath.h:750
Double_t Mean(Long64_t n, const T *a, const Double_t *w=0)
Return the weighted mean of an array a with length n.
Definition: TMath.h:1063
Double_t Sqrt(Double_t x)
Definition: TMath.h:681
Double_t RMS(Long64_t n, const T *a, const Double_t *w=0)
Return the Standard Deviation of an array a with length n.
Definition: TMath.h:1157
Short_t Abs(Short_t d)
Definition: TMathBase.h:120
Definition: first.py:1
auto * m
Definition: textangle.C:8
auto * a
Definition: textangle.C:12
static long int sum(long int i)
Definition: Factory.cxx:2275
REAL epsilon
Definition: triangle.c:617