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pairCuts_LikelihoodD.class.C
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1 // Class: ReadLikelihoodD
2 // Automatically generated by MethodBase::MakeClass
3 //
4 
5 /* configuration options =====================================================
6 
7 #GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
8 
9 Method : Likelihood::LikelihoodD
10 TMVA Release : 4.2.0 [262656]
11 ROOT Release : 5.34/38 [336422]
12 Creator : vassalli
13 Date : Thu Jan 17 19:10:59 2019
14 Host : Linux cvmfswrite02.sdcc.bnl.gov 3.10.0-693.11.6.el7.x86_64 #1 SMP Wed Jan 3 18:09:42 CST 2018 x86_64 x86_64 x86_64 GNU/Linux
15 Dir : /direct/phenix+u/vassalli/sphenix/single/Training
16 Training events: 17861
17 Analysis type : [Classification]
18 
19 
20 #OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-
21 
22 # Set by User:
23 V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
24 VarTransform: "Decorrelate" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
25 H: "False" [Print method-specific help message]
26 TransformOutput: "False" [Transform likelihood output by inverse sigmoid function]
27 # Default:
28 VerbosityLevel: "Default" [Verbosity level]
29 CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
30 IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
31 ##
32 
33 
34 #VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-
35 
36 NVar 10
37 track_deta track_deta track_deta track_deta 'F' [4.17232513428e-07,1.95571279526]
38 track_dlayer track_dlayer track_dlayer track_dlayer 'I' [0,26]
39 track_layer track_layer track_layer track_layer 'I' [0,13]
40 track_pT track_pT track_pT track_pT 'F' [0.21376606822,34.4252204895]
41 approach_dist approach_dist approach_dist approach_dist 'F' [2.03703564239e-07,36.641166687]
42 vtx_radius vtx_radius vtx_radius vtx_radius 'F' [1.04782679955e-06,209.171783447]
43 vtxTrack_dist vtxTrack_dist vtxTrack_dist vtxTrack_dist 'F' [0.0216154847294,25.1026344299]
44 photon_m photon_m photon_m photon_m 'F' [1.04462587833,46161.8671875]
45 photon_pT photon_pT photon_pT photon_pT 'F' [0.0145896151662,22595.2558594]
46 cluster_prob cluster_prob cluster_prob cluster_prob 'F' [0,0.999403715134]
47 NSpec 1
48 vtx_chi2 vtx_chi2 vtx_chi2 F 'F' [5.44684713083e-42,2.30723792151e-41]
49 
50 
51 ============================================================================ */
52 
53 #include <vector>
54 #include <cmath>
55 #include <string>
56 #include <iostream>
57 
58 #include <math.h>
59 #include <cstdlib>
60 #ifndef IClassifierReader__def
61 #define IClassifierReader__def
62 
63 class IClassifierReader {
64 
65  public:
66 
67  // constructor
68  IClassifierReader() : fStatusIsClean( true ) {}
69  virtual ~IClassifierReader() {}
70 
71  // return classifier response
72  virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
73 
74  // returns classifier status
75  bool IsStatusClean() const { return fStatusIsClean; }
76 
77  protected:
78 
79  bool fStatusIsClean;
80 };
81 
82 #endif
83 
84 class ReadLikelihoodD : public IClassifierReader {
85 
86  public:
87 
88  // constructor
89  ReadLikelihoodD( std::vector<std::string>& theInputVars )
90  : IClassifierReader(),
91  fClassName( "ReadLikelihoodD" ),
92  fNvars( 10 ),
93  fIsNormalised( false )
94  {
95  // the training input variables
96  const char* inputVars[] = { "track_deta", "track_dlayer", "track_layer", "track_pT", "approach_dist", "vtx_radius", "vtxTrack_dist", "photon_m", "photon_pT", "cluster_prob" };
97 
98  // sanity checks
99  if (theInputVars.size() <= 0) {
100  std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
101  fStatusIsClean = false;
102  }
103 
104  if (theInputVars.size() != fNvars) {
105  std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
106  << theInputVars.size() << " != " << fNvars << std::endl;
107  fStatusIsClean = false;
108  }
109 
110  // validate input variables
111  for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
112  if (theInputVars[ivar] != inputVars[ivar]) {
113  std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
114  << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
115  fStatusIsClean = false;
116  }
117  }
118 
119  // initialize min and max vectors (for normalisation)
120  fVmin[0] = -4.64050436019897;
121  fVmax[0] = 4.55152702331543;
122  fVmin[1] = -2.64709424972534;
123  fVmax[1] = 10.4751691818237;
124  fVmin[2] = -2.71628022193909;
125  fVmax[2] = 9.14456176757812;
126  fVmin[3] = -3.04766345024109;
127  fVmax[3] = 11.5378293991089;
128  fVmin[4] = -0.426839530467987;
129  fVmax[4] = 63.7131462097168;
130  fVmin[5] = -0.68628054857254;
131  fVmax[5] = 13.1620082855225;
132  fVmin[6] = -2.40324378013611;
133  fVmax[6] = 7.2221508026123;
134  fVmin[7] = -10.3819332122803;
135  fVmax[7] = 171.218048095703;
136  fVmin[8] = -2.87100577354431;
137  fVmax[8] = 58.8979034423828;
138  fVmin[9] = -1.93033838272095;
139  fVmax[9] = 4.228844165802;
140 
141  // initialize input variable types
142  fType[0] = 'F';
143  fType[1] = 'I';
144  fType[2] = 'I';
145  fType[3] = 'F';
146  fType[4] = 'F';
147  fType[5] = 'F';
148  fType[6] = 'F';
149  fType[7] = 'F';
150  fType[8] = 'F';
151  fType[9] = 'F';
152 
153  // initialize constants
154  Initialize();
155 
156  // initialize transformation
157  InitTransform();
158  }
159 
160  // destructor
161  virtual ~ReadLikelihoodD() {
162  Clear(); // method-specific
163  }
164 
165  // the classifier response
166  // "inputValues" is a vector of input values in the same order as the
167  // variables given to the constructor
168  double GetMvaValue( const std::vector<double>& inputValues ) const;
169 
170  private:
171 
172  // method-specific destructor
173  void Clear();
174 
175  // input variable transformation
176 
177  double fDecTF_1[3][10][10];
178  void InitTransform_1();
179  void Transform_1( std::vector<double> & iv, int sigOrBgd ) const;
180  void InitTransform();
181  void Transform( std::vector<double> & iv, int sigOrBgd ) const;
182 
183  // common member variables
184  const char* fClassName;
185 
186  const size_t fNvars;
187  size_t GetNvar() const { return fNvars; }
188  char GetType( int ivar ) const { return fType[ivar]; }
189 
190  // normalisation of input variables
191  const bool fIsNormalised;
192  bool IsNormalised() const { return fIsNormalised; }
193  double fVmin[10];
194  double fVmax[10];
195  double NormVariable( double x, double xmin, double xmax ) const {
196  // normalise to output range: [-1, 1]
197  return 2*(x - xmin)/(xmax - xmin) - 1.0;
198  }
199 
200  // type of input variable: 'F' or 'I'
201  char fType[10];
202 
203  // initialize internal variables
204  void Initialize();
205  double GetMvaValue__( const std::vector<double>& inputValues ) const;
206 
207  // private members (method specific)
208  double fEpsilon;
209  static float fRefS[][10000]; // signal reference vector [nvars][max_nbins]
210  static float fRefB[][10000]; // backgr reference vector [nvars][max_nbins]
211 
212 // if a variable has its PDF encoded as a spline0 --> treat it like an Integer valued one
213  bool fHasDiscretPDF[10];
214  int fNbin[10]; // number of bins (discrete variables may have less bins)
215  double fHistMin[10];
216  double fHistMax[10];
217  double TransformLikelihoodOutput( double, double ) const;
218 };
219 
220 inline void ReadLikelihoodD::Initialize()
221 {
222  fEpsilon = 2.2250738585072e-305;
223  fNbin[0] = 10000;
224  fHistMin[0] = -343.69677734375;
225  fHistMax[0] = 38.0898323059082;
226  fNbin[1] = 10000;
227  fHistMin[1] = -148;
228  fHistMax[1] = 20;
229  fNbin[2] = 10000;
230  fHistMin[2] = -40;
231  fHistMax[2] = 249;
232  fNbin[3] = 10000;
233  fHistMin[3] = -6.17905759811401;
234  fHistMax[3] = 989.641357421875;
235  fNbin[4] = 10000;
236  fHistMin[4] = -105.659767150879;
237  fHistMax[4] = 74.5078582763672;
238  fNbin[5] = 10000;
239  fHistMin[5] = -503.535064697266;
240  fHistMax[5] = 39.2586631774902;
241  fNbin[6] = 10000;
242  fHistMin[6] = -81.6677856445312;
243  fHistMax[6] = 86.4815139770508;
244  fNbin[7] = 10000;
245  fHistMin[7] = -175.707397460938;
246  fHistMax[7] = 7315.42822265625;
247  fNbin[8] = 10000;
248  fHistMin[8] = -176.395935058594;
249  fHistMax[8] = 427.576019287109;
250  fNbin[9] = 10000;
251  fHistMin[9] = -158.134368896484;
252  fHistMax[9] = 12.8934354782104;
253  fHasDiscretPDF[0] = false;
254  fHasDiscretPDF[1] = false;
255  fHasDiscretPDF[2] = false;
256  fHasDiscretPDF[3] = false;
257  fHasDiscretPDF[4] = false;
258  fHasDiscretPDF[5] = false;
259  fHasDiscretPDF[6] = false;
260  fHasDiscretPDF[7] = false;
261  fHasDiscretPDF[8] = false;
262  fHasDiscretPDF[9] = false;
263 }
264 
265 inline double ReadLikelihoodD::GetMvaValue__( const std::vector<double>& inputValues ) const
266 {
267  double ps(1), pb(1);
268  std::vector<double> inputValuesSig = inputValues;
269  std::vector<double> inputValuesBgd = inputValues;
270  Transform(inputValuesSig,0);
271  Transform(inputValuesBgd,1);
272  for (size_t ivar = 0; ivar < GetNvar(); ivar++) {
273 
274  // dummy at present... will be used for variable transforms
275  double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };
276 
277  for (int itype=0; itype < 2; itype++) {
278 
279  // interpolate linearly between adjacent bins
280  // this is not useful for discrete variables (or forced Spline0)
281  int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;
282 
283  // since the test data sample is in general different from the training sample
284  // it can happen that the min/max of the training sample are trespassed --> correct this
285  if (bin < 0) {
286  bin = 0;
287  x[itype] = fHistMin[ivar];
288  }
289  else if (bin >= fNbin[ivar]) {
290  bin = fNbin[ivar]-1;
291  x[itype] = fHistMax[ivar];
292  }
293 
294  // find corresponding histogram from cached indices
295  float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];
296 
297  // sanity check
298  if (ref < 0) {
299  std::cout << "Fatal error in ReadLikelihoodD: bin entry < 0 ==> abort" << std::endl;
300  std::exit(1);
301  }
302 
303  double p = ref;
304 
305  if (GetType(ivar) != 'I' && !fHasDiscretPDF[ivar]) {
306  float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
307  int nextbin = bin;
308  if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0)
309  nextbin++;
310  else
311  nextbin--;
312 
313  double refnext = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
314  float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
315 
316  double dx = bincenter - nextbincenter;
317  double dy = ref - refnext;
318  p += (x[itype] - bincenter) * dy/dx;
319  }
320 
321  if (p < fEpsilon) p = fEpsilon; // avoid zero response
322 
323  if (itype == 0) ps *= p;
324  else pb *= p;
325  }
326  }
327 
328  // the likelihood ratio (transform it ?)
329  return TransformLikelihoodOutput( ps, pb );
330 }
331 
332 inline double ReadLikelihoodD::TransformLikelihoodOutput( double ps, double pb ) const
333 {
334  // returns transformed or non-transformed output
335  if (ps < fEpsilon) ps = fEpsilon;
336  if (pb < fEpsilon) pb = fEpsilon;
337  double r = ps/(ps + pb);
338  if (r >= 1.0) r = 1. - 1.e-15;
339 
340  if (false) {
341  // inverse Fermi function
342 
343  // sanity check
344  if (r <= 0.0) r = fEpsilon;
345  else if (r >= 1.0) r = 1. - 1.e-15;
346 
347  double tau = 15.0;
348  r = - log(1.0/r - 1.0)/tau;
349  }
350 
351  return r;
352 }
353 
354 // Clean up
355 inline void ReadLikelihoodD::Clear()
356 {
357  // nothing to clear
358 }
359 
360 // signal map
361 float ReadLikelihoodD::fRefS[][10000] =
362 {
363  { 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 1.41456941646356e-14, 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373 };
374 
375 // background map
376 float ReadLikelihoodD::fRefB[][10000] =
377 {
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0.077444144, 0.077470742, 0.077497259, 0.077523701, 0.077550061, 0.077576347, 0.077602558, 0.077628694, 0.077654742, 0.077680714, 0.07770662, 0.077732429, 0.077758163, 0.07778383, 0.077809416, 0.077834927, 0.077860355, 0.07788571, 0.077910982, 0.077936172, 0.077961281, 0.077986322, 0.078011282, 0.078036167, 0.07806097, 0.078085698, 0.078110345, 0.078134917, 0.078159407, 0.078183822, 0.078208163, 0.078232415, 0.0782566, 0.078280702, 0.07830473, 0.078328669, 0.078352533, 0.078376323, 0.078400046, 0.078423679, 0.078447238, 0.078470714, 0.078494109, 0.078517437, 0.078540683, 0.078563839, 0.078586936, 0.078609951, 0.078632876, 0.078655742, 0.078678511, 0.078701213, 0.078723826, 0.078746378, 0.078768834, 0.078791223 },
388 };
389 
390  inline double ReadLikelihoodD::GetMvaValue( const std::vector<double>& inputValues ) const
391  {
392  // classifier response value
393  double retval = 0;
394 
395  // classifier response, sanity check first
396  if (!IsStatusClean()) {
397  std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
398  << " because status is dirty" << std::endl;
399  retval = 0;
400  }
401  else {
402  if (IsNormalised()) {
403  // normalise variables
404  std::vector<double> iV;
405  iV.reserve(inputValues.size());
406  int ivar = 0;
407  for (std::vector<double>::const_iterator varIt = inputValues.begin();
408  varIt != inputValues.end(); varIt++, ivar++) {
409  iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
410  }
411  retval = GetMvaValue__( iV );
412  }
413  else {
414  retval = GetMvaValue__( inputValues );
415  }
416  }
417 
418  return retval;
419  }
420 
421 //_______________________________________________________________________
422 inline void ReadLikelihoodD::InitTransform_1()
423 {
424  // Decorrelation transformation, initialisation
425  fDecTF_1[0][0][0] = 18.9247419545;
426  fDecTF_1[0][0][1] = -0.0280792897409;
427  fDecTF_1[0][0][2] = 0.483774738868;
428  fDecTF_1[0][0][3] = 0.0239879772306;
429  fDecTF_1[0][0][4] = -0.0100067220544;
430  fDecTF_1[0][0][5] = 0.00743728580342;
431  fDecTF_1[0][0][6] = -0.0499215945277;
432  fDecTF_1[0][0][7] = -0.007787429103;
433  fDecTF_1[0][0][8] = 0.000239651463804;
434  fDecTF_1[0][0][9] = -0.182861600281;
435  fDecTF_1[0][1][0] = -0.0280792897409;
436  fDecTF_1[0][1][1] = 0.756090912957;
437  fDecTF_1[0][1][2] = 0.146475418396;
438  fDecTF_1[0][1][3] = 0.0173669816459;
439  fDecTF_1[0][1][4] = 0.00173748689519;
440  fDecTF_1[0][1][5] = 0.0240062809462;
441  fDecTF_1[0][1][6] = -0.157574982831;
442  fDecTF_1[0][1][7] = -0.00330066996778;
443  fDecTF_1[0][1][8] = 0.000138457397819;
444  fDecTF_1[0][1][9] = -0.024397030166;
445  fDecTF_1[0][2][0] = 0.483774738868;
446  fDecTF_1[0][2][1] = 0.146475418396;
447  fDecTF_1[0][2][2] = 4.13545455721;
448  fDecTF_1[0][2][3] = -0.00531019608838;
449  fDecTF_1[0][2][4] = -0.0111571235826;
450  fDecTF_1[0][2][5] = -0.0562948397049;
451  fDecTF_1[0][2][6] = -0.0902367818498;
452  fDecTF_1[0][2][7] = 0.00488169562262;
453  fDecTF_1[0][2][8] = -0.000143934539319;
454  fDecTF_1[0][2][9] = -0.0530266764679;
455  fDecTF_1[0][3][0] = 0.0239879772306;
456  fDecTF_1[0][3][1] = 0.0173669816459;
457  fDecTF_1[0][3][2] = -0.00531019608838;
458  fDecTF_1[0][3][3] = 0.289721672896;
459  fDecTF_1[0][3][4] = -0.00179735919007;
460  fDecTF_1[0][3][5] = 0.0165077487501;
461  fDecTF_1[0][3][6] = -0.0614947360463;
462  fDecTF_1[0][3][7] = 0.0217903569969;
463  fDecTF_1[0][3][8] = -0.00075499559555;
464  fDecTF_1[0][3][9] = 0.0787266699998;
465  fDecTF_1[0][4][0] = -0.0100067220544;
466  fDecTF_1[0][4][1] = 0.00173748689519;
467  fDecTF_1[0][4][2] = -0.0111571235826;
468  fDecTF_1[0][4][3] = -0.00179735919007;
469  fDecTF_1[0][4][4] = 1.54963021203;
470  fDecTF_1[0][4][5] = -0.0038884150045;
471  fDecTF_1[0][4][6] = -0.0312208450526;
472  fDecTF_1[0][4][7] = -0.00231358436524;
473  fDecTF_1[0][4][8] = 6.64443181951e-05;
474  fDecTF_1[0][4][9] = -0.00366401295157;
475  fDecTF_1[0][5][0] = 0.00743728580342;
476  fDecTF_1[0][5][1] = 0.0240062809462;
477  fDecTF_1[0][5][2] = -0.0562948397049;
478  fDecTF_1[0][5][3] = 0.0165077487501;
479  fDecTF_1[0][5][4] = -0.0038884150045;
480  fDecTF_1[0][5][5] = 0.196084782709;
481  fDecTF_1[0][5][6] = -0.0763824468393;
482  fDecTF_1[0][5][7] = -0.0111948124532;
483  fDecTF_1[0][5][8] = 0.000301212374743;
484  fDecTF_1[0][5][9] = -0.00122339217863;
485  fDecTF_1[0][6][0] = -0.0499215945277;
486  fDecTF_1[0][6][1] = -0.157574982831;
487  fDecTF_1[0][6][2] = -0.0902367818498;
488  fDecTF_1[0][6][3] = -0.0614947360463;
489  fDecTF_1[0][6][4] = -0.0312208450526;
490  fDecTF_1[0][6][5] = -0.0763824468393;
491  fDecTF_1[0][6][6] = 0.551074561097;
492  fDecTF_1[0][6][7] = -0.00177623442059;
493  fDecTF_1[0][6][8] = -8.95394485675e-07;
494  fDecTF_1[0][6][9] = 0.0217050764548;
495  fDecTF_1[0][7][0] = -0.007787429103;
496  fDecTF_1[0][7][1] = -0.00330066996778;
497  fDecTF_1[0][7][2] = 0.00488169562262;
498  fDecTF_1[0][7][3] = 0.0217903569969;
499  fDecTF_1[0][7][4] = -0.00231358436524;
500  fDecTF_1[0][7][5] = -0.0111948124532;
501  fDecTF_1[0][7][6] = -0.00177623442059;
502  fDecTF_1[0][7][7] = 0.160835955065;
503  fDecTF_1[0][7][8] = -0.0048126962052;
504  fDecTF_1[0][7][9] = -0.00349070341448;
505  fDecTF_1[0][8][0] = 0.000239651463804;
506  fDecTF_1[0][8][1] = 0.000138457397819;
507  fDecTF_1[0][8][2] = -0.000143934539319;
508  fDecTF_1[0][8][3] = -0.00075499559555;
509  fDecTF_1[0][8][4] = 6.64443181951e-05;
510  fDecTF_1[0][8][5] = 0.000301212374743;
511  fDecTF_1[0][8][6] = -8.95394485675e-07;
512  fDecTF_1[0][8][7] = -0.0048126962052;
513  fDecTF_1[0][8][8] = 0.0020258343497;
514  fDecTF_1[0][8][9] = 0.000100247374144;
515  fDecTF_1[0][9][0] = -0.182861600281;
516  fDecTF_1[0][9][1] = -0.024397030166;
517  fDecTF_1[0][9][2] = -0.0530266764679;
518  fDecTF_1[0][9][3] = 0.0787266699998;
519  fDecTF_1[0][9][4] = -0.00366401295157;
520  fDecTF_1[0][9][5] = -0.00122339217863;
521  fDecTF_1[0][9][6] = 0.0217050764548;
522  fDecTF_1[0][9][7] = -0.00349070341448;
523  fDecTF_1[0][9][8] = 0.000100247374144;
524  fDecTF_1[0][9][9] = 3.31009046513;
525  fDecTF_1[1][0][0] = 2.33930209654;
526  fDecTF_1[1][0][1] = 0.000860583367685;
527  fDecTF_1[1][0][2] = 0.0192405183216;
528  fDecTF_1[1][0][3] = 0.0519032301307;
529  fDecTF_1[1][0][4] = -0.0133721177107;
530  fDecTF_1[1][0][5] = 0.0268194157571;
531  fDecTF_1[1][0][6] = -0.220158505899;
532  fDecTF_1[1][0][7] = -0.000638236155502;
533  fDecTF_1[1][0][8] = 0.00128338867698;
534  fDecTF_1[1][0][9] = -0.00588074988197;
535  fDecTF_1[1][1][0] = 0.000860583367685;
536  fDecTF_1[1][1][1] = 0.357445245062;
537  fDecTF_1[1][1][2] = 0.0281597837732;
538  fDecTF_1[1][1][3] = 0.00964247838537;
539  fDecTF_1[1][1][4] = -0.0271262631444;
540  fDecTF_1[1][1][5] = 0.0051446437914;
541  fDecTF_1[1][1][6] = -0.0531513480337;
542  fDecTF_1[1][1][7] = -0.000299589606821;
543  fDecTF_1[1][1][8] = 0.000621528226397;
544  fDecTF_1[1][1][9] = 0.00340370225621;
545  fDecTF_1[1][2][0] = 0.0192405183216;
546  fDecTF_1[1][2][1] = 0.0281597837732;
547  fDecTF_1[1][2][2] = 0.557502783253;
548  fDecTF_1[1][2][3] = -0.13721560892;
549  fDecTF_1[1][2][4] = -0.0377368345547;
550  fDecTF_1[1][2][5] = -0.014240927028;
551  fDecTF_1[1][2][6] = 0.0786067074464;
552  fDecTF_1[1][2][7] = 0.000745273657034;
553  fDecTF_1[1][2][8] = -0.00178106704059;
554  fDecTF_1[1][2][9] = 0.0314322234037;
555  fDecTF_1[1][3][0] = 0.0519032301307;
556  fDecTF_1[1][3][1] = 0.00964247838537;
557  fDecTF_1[1][3][2] = -0.13721560892;
558  fDecTF_1[1][3][3] = 0.614293992772;
559  fDecTF_1[1][3][4] = -0.00124594789504;
560  fDecTF_1[1][3][5] = -0.0153919892404;
561  fDecTF_1[1][3][6] = 0.0723834732431;
562  fDecTF_1[1][3][7] = -0.00140740264929;
563  fDecTF_1[1][3][8] = 0.00258499908209;
564  fDecTF_1[1][3][9] = 0.0952834002272;
565  fDecTF_1[1][4][0] = -0.0133721177107;
566  fDecTF_1[1][4][1] = -0.0271262631444;
567  fDecTF_1[1][4][2] = -0.0377368345547;
568  fDecTF_1[1][4][3] = -0.00124594789504;
569  fDecTF_1[1][4][4] = 2.03416216621;
570  fDecTF_1[1][4][5] = -0.000430981365373;
571  fDecTF_1[1][4][6] = 0.00261540881852;
572  fDecTF_1[1][4][7] = 6.43867691835e-05;
573  fDecTF_1[1][4][8] = -0.000120378893913;
574  fDecTF_1[1][4][9] = -0.0130287294054;
575  fDecTF_1[1][5][0] = 0.0268194157571;
576  fDecTF_1[1][5][1] = 0.0051446437914;
577  fDecTF_1[1][5][2] = -0.014240927028;
578  fDecTF_1[1][5][3] = -0.0153919892404;
579  fDecTF_1[1][5][4] = -0.000430981365373;
580  fDecTF_1[1][5][5] = 0.0872082001506;
581  fDecTF_1[1][5][6] = -0.0779716965123;
582  fDecTF_1[1][5][7] = 0.000374970143209;
583  fDecTF_1[1][5][8] = -0.00078762905851;
584  fDecTF_1[1][5][9] = 0.00189623007041;
585  fDecTF_1[1][6][0] = -0.220158505899;
586  fDecTF_1[1][6][1] = -0.0531513480337;
587  fDecTF_1[1][6][2] = 0.0786067074464;
588  fDecTF_1[1][6][3] = 0.0723834732431;
589  fDecTF_1[1][6][4] = 0.00261540881852;
590  fDecTF_1[1][6][5] = -0.0779716965123;
591  fDecTF_1[1][6][6] = 0.817500386634;
592  fDecTF_1[1][6][7] = -0.0018077973255;
593  fDecTF_1[1][6][8] = 0.00368586605177;
594  fDecTF_1[1][6][9] = -0.0154799619654;
595  fDecTF_1[1][7][0] = -0.000638236155502;
596  fDecTF_1[1][7][1] = -0.000299589606821;
597  fDecTF_1[1][7][2] = 0.000745273657034;
598  fDecTF_1[1][7][3] = -0.00140740264929;
599  fDecTF_1[1][7][4] = 6.43867691835e-05;
600  fDecTF_1[1][7][5] = 0.000374970143209;
601  fDecTF_1[1][7][6] = -0.0018077973255;
602  fDecTF_1[1][7][7] = 0.00619839533299;
603  fDecTF_1[1][7][8] = -0.00795575256909;
604  fDecTF_1[1][7][9] = -0.000226694354106;
605  fDecTF_1[1][8][0] = 0.00128338867698;
606  fDecTF_1[1][8][1] = 0.000621528226397;
607  fDecTF_1[1][8][2] = -0.00178106704059;
608  fDecTF_1[1][8][3] = 0.00258499908209;
609  fDecTF_1[1][8][4] = -0.000120378893913;
610  fDecTF_1[1][8][5] = -0.00078762905851;
611  fDecTF_1[1][8][6] = 0.00368586605177;
612  fDecTF_1[1][8][7] = -0.00795575256909;
613  fDecTF_1[1][8][8] = 0.0191531361068;
614  fDecTF_1[1][8][9] = 0.000508214243734;
615  fDecTF_1[1][9][0] = -0.00588074988197;
616  fDecTF_1[1][9][1] = 0.00340370225621;
617  fDecTF_1[1][9][2] = 0.0314322234037;
618  fDecTF_1[1][9][3] = 0.0952834002272;
619  fDecTF_1[1][9][4] = -0.0130287294054;
620  fDecTF_1[1][9][5] = 0.00189623007041;
621  fDecTF_1[1][9][6] = -0.0154799619654;
622  fDecTF_1[1][9][7] = -0.000226694354106;
623  fDecTF_1[1][9][8] = 0.000508214243734;
624  fDecTF_1[1][9][9] = 2.64778096445;
625  fDecTF_1[2][0][0] = 2.89480512943;
626  fDecTF_1[2][0][1] = -0.0228013553407;
627  fDecTF_1[2][0][2] = 0.0664177556478;
628  fDecTF_1[2][0][3] = 0.068177747169;
629  fDecTF_1[2][0][4] = 0.0212028321184;
630  fDecTF_1[2][0][5] = -0.00838088232452;
631  fDecTF_1[2][0][6] = -0.145387638443;
632  fDecTF_1[2][0][7] = -0.000100450131257;
633  fDecTF_1[2][0][8] = 5.22744757105e-05;
634  fDecTF_1[2][0][9] = -0.105730189073;
635  fDecTF_1[2][1][0] = -0.0228013553407;
636  fDecTF_1[2][1][1] = 0.449884339424;
637  fDecTF_1[2][1][2] = 0.034880927654;
638  fDecTF_1[2][1][3] = 0.0194524170714;
639  fDecTF_1[2][1][4] = -0.0144537360175;
640  fDecTF_1[2][1][5] = -0.00302510501934;
641  fDecTF_1[2][1][6] = -0.082084258524;
642  fDecTF_1[2][1][7] = -4.22605109857e-05;
643  fDecTF_1[2][1][8] = 4.71313261788e-05;
644  fDecTF_1[2][1][9] = -0.00781367316754;
645  fDecTF_1[2][2][0] = 0.0664177556478;
646  fDecTF_1[2][2][1] = 0.034880927654;
647  fDecTF_1[2][2][2] = 0.720026051396;
648  fDecTF_1[2][2][3] = -0.0549665112657;
649  fDecTF_1[2][2][4] = -0.0307109427126;
650  fDecTF_1[2][2][5] = -0.0189033947758;
651  fDecTF_1[2][2][6] = 0.0585611996645;
652  fDecTF_1[2][2][7] = -0.000158896415639;
653  fDecTF_1[2][2][8] = -2.15468010135e-06;
654  fDecTF_1[2][2][9] = 0.0392907189702;
655  fDecTF_1[2][3][0] = 0.068177747169;
656  fDecTF_1[2][3][1] = 0.0194524170714;
657  fDecTF_1[2][3][2] = -0.0549665112657;
658  fDecTF_1[2][3][3] = 0.33834989421;
659  fDecTF_1[2][3][4] = -0.00590409750552;
660  fDecTF_1[2][3][5] = 0.0221139851684;
661  fDecTF_1[2][3][6] = -0.0255032923112;
662  fDecTF_1[2][3][7] = -1.92463569084e-05;
663  fDecTF_1[2][3][8] = -0.000142601255434;
664  fDecTF_1[2][3][9] = 0.0899089639958;
665  fDecTF_1[2][4][0] = 0.0212028321184;
666  fDecTF_1[2][4][1] = -0.0144537360175;
667  fDecTF_1[2][4][2] = -0.0307109427126;
668  fDecTF_1[2][4][3] = -0.00590409750552;
669  fDecTF_1[2][4][4] = 1.73893527884;
670  fDecTF_1[2][4][5] = 0.00355491788726;
671  fDecTF_1[2][4][6] = -0.0197280799458;
672  fDecTF_1[2][4][7] = 1.50234540163e-05;
673  fDecTF_1[2][4][8] = -1.21305104635e-05;
674  fDecTF_1[2][4][9] = 4.35709180726e-05;
675  fDecTF_1[2][5][0] = -0.00838088232452;
676  fDecTF_1[2][5][1] = -0.00302510501934;
677  fDecTF_1[2][5][2] = -0.0189033947758;
678  fDecTF_1[2][5][3] = 0.0221139851684;
679  fDecTF_1[2][5][4] = 0.00355491788726;
680  fDecTF_1[2][5][5] = 0.0715050436792;
681  fDecTF_1[2][5][6] = -0.0748556176847;
682  fDecTF_1[2][5][7] = -9.55527190761e-05;
683  fDecTF_1[2][5][8] = 8.75025182385e-05;
684  fDecTF_1[2][5][9] = -0.00819603757198;
685  fDecTF_1[2][6][0] = -0.145387638443;
686  fDecTF_1[2][6][1] = -0.082084258524;
687  fDecTF_1[2][6][2] = 0.0585611996645;
688  fDecTF_1[2][6][3] = -0.0255032923112;
689  fDecTF_1[2][6][4] = -0.0197280799458;
690  fDecTF_1[2][6][5] = -0.0748556176847;
691  fDecTF_1[2][6][6] = 0.585895128047;
692  fDecTF_1[2][6][7] = -3.24208759224e-05;
693  fDecTF_1[2][6][8] = -2.14963232801e-05;
694  fDecTF_1[2][6][9] = -0.0126828319522;
695  fDecTF_1[2][7][0] = -0.000100450131257;
696  fDecTF_1[2][7][1] = -4.22605109857e-05;
697  fDecTF_1[2][7][2] = -0.000158896415639;
698  fDecTF_1[2][7][3] = -1.92463569084e-05;
699  fDecTF_1[2][7][4] = 1.50234540163e-05;
700  fDecTF_1[2][7][5] = -9.55527190761e-05;
701  fDecTF_1[2][7][6] = -3.24208759224e-05;
702  fDecTF_1[2][7][7] = 0.00399076512031;
703  fDecTF_1[2][7][8] = -0.000575458044623;
704  fDecTF_1[2][7][9] = 8.73639085678e-06;
705  fDecTF_1[2][8][0] = 5.22744757105e-05;
706  fDecTF_1[2][8][1] = 4.71313261788e-05;
707  fDecTF_1[2][8][2] = -2.15468010135e-06;
708  fDecTF_1[2][8][3] = -0.000142601255434;
709  fDecTF_1[2][8][4] = -1.21305104635e-05;
710  fDecTF_1[2][8][5] = 8.75025182385e-05;
711  fDecTF_1[2][8][6] = -2.14963232801e-05;
712  fDecTF_1[2][8][7] = -0.000575458044623;
713  fDecTF_1[2][8][8] = 0.00262340318274;
714  fDecTF_1[2][8][9] = 8.65260783569e-06;
715  fDecTF_1[2][9][0] = -0.105730189073;
716  fDecTF_1[2][9][1] = -0.00781367316754;
717  fDecTF_1[2][9][2] = 0.0392907189702;
718  fDecTF_1[2][9][3] = 0.0899089639958;
719  fDecTF_1[2][9][4] = 4.35709180726e-05;
720  fDecTF_1[2][9][5] = -0.00819603757198;
721  fDecTF_1[2][9][6] = -0.0126828319522;
722  fDecTF_1[2][9][7] = 8.73639085678e-06;
723  fDecTF_1[2][9][8] = 8.65260783569e-06;
724  fDecTF_1[2][9][9] = 2.89359038103;
725 }
726 
727 //_______________________________________________________________________
728 inline void ReadLikelihoodD::Transform_1( std::vector<double>& iv, int cls) const
729 {
730  // Decorrelation transformation
731  if (cls < 0 || cls > 2) {
732  if (2 > 1 ) cls = 2;
733  else cls = 2;
734  }
735 
736  // define the indices of the variables which are transformed by this transformation
737  static std::vector<int> indicesGet;
738  static std::vector<int> indicesPut;
739 
740  if ( indicesGet.empty() ) {
741  indicesGet.reserve(fNvars);
742  indicesGet.push_back( 0);
743  indicesGet.push_back( 1);
744  indicesGet.push_back( 2);
745  indicesGet.push_back( 3);
746  indicesGet.push_back( 4);
747  indicesGet.push_back( 5);
748  indicesGet.push_back( 6);
749  indicesGet.push_back( 7);
750  indicesGet.push_back( 8);
751  indicesGet.push_back( 9);
752  }
753  if ( indicesPut.empty() ) {
754  indicesPut.reserve(fNvars);
755  indicesPut.push_back( 0);
756  indicesPut.push_back( 1);
757  indicesPut.push_back( 2);
758  indicesPut.push_back( 3);
759  indicesPut.push_back( 4);
760  indicesPut.push_back( 5);
761  indicesPut.push_back( 6);
762  indicesPut.push_back( 7);
763  indicesPut.push_back( 8);
764  indicesPut.push_back( 9);
765  }
766 
767  std::vector<double> tv;
768  for (int i=0; i<10;i++) {
769  double v = 0;
770  for (int j=0; j<10; j++)
771  v += iv[indicesGet.at(j)] * fDecTF_1[cls][i][j];
772  tv.push_back(v);
773  }
774  for (int i=0; i<10;i++) iv[indicesPut.at(i)] = tv[i];
775 }
776 
777 //_______________________________________________________________________
778 inline void ReadLikelihoodD::InitTransform()
779 {
780  InitTransform_1();
781 }
782 
783 //_______________________________________________________________________
784 inline void ReadLikelihoodD::Transform( std::vector<double>& iv, int sigOrBgd ) const
785 {
786  Transform_1( iv, sigOrBgd );
787 }