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Reference Guide
TMVAClassificationCategory_Likelihood.class.C
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1 // Class: ReadLikelihood
2 // Automatically generated by MethodBase::MakeClass
3 //
4 
5 /* configuration options =====================================================
6 
7 #GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
8 
9 Method : Likelihood::Likelihood
10 TMVA Release : 4.2.1 [262657]
11 ROOT Release : 6.12/07 [396295]
12 Creator : sftnight
13 Date : Sat Sep 29 23:25:45 2018
14 Host : Linux ec-ubuntu-14-04-x86-64-2 3.13.0-157-generic #207-Ubuntu SMP Mon Aug 20 16:44:59 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux
15 Dir : /mnt/build/workspace/root-makedoc-v612/rootspi/rdoc/src/v6-12-00-patches/documentation/doxygen
16 Training events: 10000
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 H: "False" [Print method-specific help message]
25 TransformOutput: "True" [Transform likelihood output by inverse sigmoid function]
26 # Default:
27 VerbosityLevel: "Default" [Verbosity level]
28 VarTransform: "None" [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)"]
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 4
37 var1 var1 var1 var1 'F' [-5.31190872192,4.21716070175]
38 var2 var2 var2 var2 'F' [-4.220682621,4.57174968719]
39 var3 var3 var3 var3 'F' [-4.58069658279,4.82019376755]
40 var4 var4 var4 var4 'F' [-5.10020828247,4.97665786743]
41 NSpec 5
42 eta eta eta eta 'F' [-2.49952101707,2.49999904633]
43 abs(eta)<=1.3 FisherCat_cat1 FisherCat_cat1 FisherCat:Category_Fisher_1 pass 'C' [0,1]
44 abs(eta)>1.3 FisherCat_cat2 FisherCat_cat2 FisherCat:Category_Fisher_2 pass 'C' [0,1]
45 abs(eta)<=1.3 LikelihoodCat_cat1 LikelihoodCat_cat1 LikelihoodCat:Category_Likelihood_1pass 'C' [0,1]
46 abs(eta)>1.3 LikelihoodCat_cat2 LikelihoodCat_cat2 LikelihoodCat:Category_Likelihood_2pass 'C' [0,1]
47 
48 
49 ============================================================================ */
50 
51 #include <array>
52 #include <vector>
53 #include <cmath>
54 #include <string>
55 #include <iostream>
56 
57 #include <math.h>
58 #include <cstdlib>
59 #ifndef IClassifierReader__def
60 #define IClassifierReader__def
61 
62 class IClassifierReader {
63 
64  public:
65 
66  // constructor
67  IClassifierReader() : fStatusIsClean( true ) {}
68  virtual ~IClassifierReader() {}
69 
70  // return classifier response
71  virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
72 
73  // returns classifier status
74  bool IsStatusClean() const { return fStatusIsClean; }
75 
76  protected:
77 
78  bool fStatusIsClean;
79 };
80 
81 #endif
82 
83 class ReadLikelihood : public IClassifierReader {
84 
85  public:
86 
87  // constructor
88  ReadLikelihood( std::vector<std::string>& theInputVars )
89  : IClassifierReader(),
90  fClassName( "ReadLikelihood" ),
91  fNvars( 4 ),
92  fIsNormalised( false )
93  {
94  // the training input variables
95  const char* inputVars[] = { "var1", "var2", "var3", "var4" };
96 
97  // sanity checks
98  if (theInputVars.size() <= 0) {
99  std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
100  fStatusIsClean = false;
101  }
102 
103  if (theInputVars.size() != fNvars) {
104  std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
105  << theInputVars.size() << " != " << fNvars << std::endl;
106  fStatusIsClean = false;
107  }
108 
109  // validate input variables
110  for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
111  if (theInputVars[ivar] != inputVars[ivar]) {
112  std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
113  << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
114  fStatusIsClean = false;
115  }
116  }
117 
118  // initialize min and max vectors (for normalisation)
119  fVmin[0] = 0;
120  fVmax[0] = 0;
121  fVmin[1] = 0;
122  fVmax[1] = 0;
123  fVmin[2] = 0;
124  fVmax[2] = 0;
125  fVmin[3] = 0;
126  fVmax[3] = 0;
127 
128  // initialize input variable types
129  fType[0] = 'F';
130  fType[1] = 'F';
131  fType[2] = 'F';
132  fType[3] = 'F';
133 
134  // initialize constants
135  Initialize();
136 
137  }
138 
139  // destructor
140  virtual ~ReadLikelihood() {
141  Clear(); // method-specific
142  }
143 
144  // the classifier response
145  // "inputValues" is a vector of input values in the same order as the
146  // variables given to the constructor
147  double GetMvaValue( const std::vector<double>& inputValues ) const;
148 
149  private:
150 
151  // method-specific destructor
152  void Clear();
153 
154  // common member variables
155  const char* fClassName;
156 
157  const size_t fNvars;
158  size_t GetNvar() const { return fNvars; }
159  char GetType( int ivar ) const { return fType[ivar]; }
160 
161  // normalisation of input variables
162  const bool fIsNormalised;
163  bool IsNormalised() const { return fIsNormalised; }
164  double fVmin[4];
165  double fVmax[4];
166  double NormVariable( double x, double xmin, double xmax ) const {
167  // normalise to output range: [-1, 1]
168  return 2*(x - xmin)/(xmax - xmin) - 1.0;
169  }
170 
171  // type of input variable: 'F' or 'I'
172  char fType[4];
173 
174  // initialize internal variables
175  void Initialize();
176  double GetMvaValue__( const std::vector<double>& inputValues ) const;
177 
178  // private members (method specific)
179  double fEpsilon;
180  static float fRefS[][10000]; // signal reference vector [nvars][max_nbins]
181  static float fRefB[][10000]; // backgr reference vector [nvars][max_nbins]
182 
183 // if a variable has its PDF encoded as a spline0 --> treat it like an Integer valued one
184  bool fHasDiscretPDF[4];
185  int fNbin[4]; // number of bins (discrete variables may have less bins)
186  double fHistMin[4];
187  double fHistMax[4];
188  double TransformLikelihoodOutput( double, double ) const;
189 };
190 
191 inline void ReadLikelihood::Initialize()
192 {
193  fEpsilon = 2.2250738585072e-305;
194  fNbin[0] = 10000;
195  fHistMin[0] = -5.31190872192383;
196  fHistMax[0] = 4.21716070175171;
197  fNbin[1] = 10000;
198  fHistMin[1] = -4.2206826210022;
199  fHistMax[1] = 4.57174968719482;
200  fNbin[2] = 10000;
201  fHistMin[2] = -4.58069658279419;
202  fHistMax[2] = 4.82019376754761;
203  fNbin[3] = 10000;
204  fHistMin[3] = -5.1002082824707;
205  fHistMax[3] = 4.97665786743164;
206  fHasDiscretPDF[0] = false;
207  fHasDiscretPDF[1] = false;
208  fHasDiscretPDF[2] = false;
209  fHasDiscretPDF[3] = false;
210 }
211 
212 inline double ReadLikelihood::GetMvaValue__( const std::vector<double>& inputValues ) const
213 {
214  double ps(1), pb(1);
215  std::vector<double> inputValuesSig = inputValues;
216  std::vector<double> inputValuesBgd = inputValues;
217  for (size_t ivar = 0; ivar < GetNvar(); ivar++) {
218 
219  // dummy at present... will be used for variable transforms
220  double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };
221 
222  for (int itype=0; itype < 2; itype++) {
223 
224  // interpolate linearly between adjacent bins
225  // this is not useful for discrete variables (or forced Spline0)
226  int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;
227 
228  // since the test data sample is in general different from the training sample
229  // it can happen that the min/max of the training sample are trespassed --> correct this
230  if (bin < 0) {
231  bin = 0;
232  x[itype] = fHistMin[ivar];
233  }
234  else if (bin >= fNbin[ivar]) {
235  bin = fNbin[ivar]-1;
236  x[itype] = fHistMax[ivar];
237  }
238 
239  // find corresponding histogram from cached indices
240  float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];
241 
242  // sanity check
243  if (ref < 0) {
244  std::cout << "Fatal error in ReadLikelihood: bin entry < 0 ==> abort" << std::endl;
245  std::exit(1);
246  }
247 
248  double p = ref;
249 
250  if (GetType(ivar) != 'I' && !fHasDiscretPDF[ivar]) {
251  float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
252  int nextbin = bin;
253  if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0)
254  nextbin++;
255  else
256  nextbin--;
257 
258  double refnext = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
259  float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
260 
261  double dx = bincenter - nextbincenter;
262  double dy = ref - refnext;
263  p += (x[itype] - bincenter) * dy/dx;
264  }
265 
266  if (p < fEpsilon) p = fEpsilon; // avoid zero response
267 
268  if (itype == 0) ps *= p;
269  else pb *= p;
270  }
271  }
272 
273  // the likelihood ratio (transform it ?)
274  return TransformLikelihoodOutput( ps, pb );
275 }
276 
277 inline double ReadLikelihood::TransformLikelihoodOutput( double ps, double pb ) const
278 {
279  // returns transformed or non-transformed output
280  if (ps < fEpsilon) ps = fEpsilon;
281  if (pb < fEpsilon) pb = fEpsilon;
282  double r = ps/(ps + pb);
283  if (r >= 1.0) r = 1. - 1.e-15;
284 
285  if (true) {
286  // inverse Fermi function
287 
288  // sanity check
289  if (r <= 0.0) r = fEpsilon;
290  else if (r >= 1.0) r = 1. - 1.e-15;
291 
292  double tau = 15.0;
293  r = - log(1.0/r - 1.0)/tau;
294  }
295 
296  return r;
297 }
298 
299 // Clean up
300 inline void ReadLikelihood::Clear()
301 {
302  // nothing to clear
303 }
304 
305 // signal map
306 float ReadLikelihood::fRefS[][10000] =
307 {
308  { 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 2.12706447846379e-15, 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0.00175789662171155, 0.00174106808844954, 0.00172429822850972, 0.00170758296735585, 0.00169093371368945, 0.00167433568276465, 0.00165774102788419, 0.00164120504632592, 0.00162478617858142, 0.00160836696159095, 0.00159200292546302, 0.00157570082228631, 0.00155939871910959, 0.00154320988804102, 0.00152702478226274, 0.00151089811697602, 0.00149483373388648, 0.00147881673183292, 0.00146286922972649, 0.00144697655923665, 0.00143096700776368, 0.00141530449036509, 0.00139941181987524, 0.00138369458727539, 0.00136814906727523, 0.00135243544355035, 0.00133683148305863, 0.00132135185413063, 0.00130586116574705, 0.0012904986506328, 0.00127512856852263, 0.00125982449389994, 0.00124457140918821, 0.00122932193335146, 0.00121418957132846, 0.00119905336759984, 0.00118404172826558, 0.00116902263835073, 0.00115401076618582, 0.0011391049483791, 0.00112426863051951, 0.00110942858736962, 0.00109470565803349, 0.00108003744389862, 0.00106531439814717, 0.00105070823337883, 0.00103615713305771, 0.00102166796568781, 0.00100723735522479, 0.000992806744761765, 0.000978434574790299, 0.000964117294643074, 0.000949924229644239, 0.000935723888687789, 0.000921527273021638, 0.000907447538338602, 0.000893367803655565, 0.000879342900589108, 0.000865380105096847, 0.000851537974085659, 0.000837630068417639, 0.000823842769023031, 0.000810051802545786, 0.000796385167632252, 0.000782711198553443, 0.000769099337048829, 0.000755542307160795, 0.000742051051929593, 0.000728610961232334, 0.000715174595825374, 0.000701855111401528, 0.000688594183884561, 0.000675274699460715, 0.000662127102259547, 0.00064898666460067, 0.000635839067399502, 0.000622812076471746, 0.000609785027336329, 0.000596816476900131, 0.00058396125677973, 0.000571109645534307, 0.000558258150704205, 0.000545523420441896, 0.00053284369641915, 0.00052016758127138, 0.000507608405314386, 0.000498888897709548, 0.000490204198285937, 0.000481548719108105, 0.000473039457574487, 0.00046447166823782, 0.000456137873698026, 0.000447745551355183, 0.000439499446656555, 0.000431341090006754, 0.000423211953602731, 0.000415229063946754, 0.000407187646487728, 0.000399380136514083, 0.000391601904993877, 0.000383794424124062, 0.00037619168870151, 0.000368647364666685, 0.000361044600140303, 0.000353675713995472, 0.000346336106304079, 0.000338967249263078, 0.000331861607264727, 0.000324697408359498, 0.000317679456202313, 0.000310690724290907, 0.00030376052018255, 0.000296947255264968, 0.000290192430838943, 0.000283466943074018, 0.000276975304586813, 0.000270337506663054, 0.000263933616224676, 0.000257558975135908, 0.000251242832746357, 0.000245043600443751, 0.000238902895944193, 0.000232849924941547, 0.00022682617418468, 0.0002208608930232, 0.000215012623812072, 0.000209310528589413, 0.00020354994921945, 0.000197935602045618, 0.000192350475117564, 0.000186999270226806, 0.000181501876795664, 0.000176238419953734, 0.000171004183357581, 0.000165828445460647, 0.000160681942361407, 0.000155681642354466, 0.000150769075844437, 0.000145885758684017, 0.000141148630063981, 0.000136353031848557, 0.000131703622173518, 0.000127171209896915, 0.000122697252663784, 0.000118252552056219, 0.000113954054540955, 0.000109597072878387, 0.000105474020529073, 0.000101292491308413, 9.72571579040959e-05, 9.33095652726479e-05, 8.94789336598478e-05, 8.5706778918393e-05, 8.18761545815505e-05, 7.82794450060464e-05, 7.46242585591972e-05, 7.11152824806049e-05, 6.76355484756641e-05, 6.43897437839769e-05, 6.09977287240326e-05, 5.78396502532996e-05, 5.47108102182392e-05, 5.16404543304816e-05, 4.85993405163754e-05, 4.57044334325474e-05, 4.28972562076524e-05, 4.01193137804512e-05, 3.74875780835282e-05, 3.47973618772812e-05, 3.23410786222667e-05, 2.98263112199493e-05, 2.74577505479101e-05, 2.51184337685117e-05, 2.29253219004022e-05, 2.07614502869546e-05, 1.86560646397993e-05, 1.66676454682602e-05, 1.4737706806045e-05, 1.28370102174813e-05, 1.10825212686905e-05, 9.41575672186445e-06, 7.77823424868984e-06, 6.19919410382863e-06, 4.73711997983628e-06, 3.33352886627836e-06, 1.95917937162449e-06, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 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2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15, 2.00795745827797e-15 },
312 };
313 
314 // background map
315 float ReadLikelihood::fRefB[][10000] =
316 {
317  { 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 2.1078004e-15, 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321 };
322 
323  inline double ReadLikelihood::GetMvaValue( const std::vector<double>& inputValues ) const
324  {
325  // classifier response value
326  double retval = 0;
327 
328  // classifier response, sanity check first
329  if (!IsStatusClean()) {
330  std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
331  << " because status is dirty" << std::endl;
332  retval = 0;
333  }
334  else {
335  if (IsNormalised()) {
336  // normalise variables
337  std::vector<double> iV;
338  iV.reserve(inputValues.size());
339  int ivar = 0;
340  for (std::vector<double>::const_iterator varIt = inputValues.begin();
341  varIt != inputValues.end(); varIt++, ivar++) {
342  iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
343  }
344  retval = GetMvaValue__( iV );
345  }
346  else {
347  retval = GetMvaValue__( inputValues );
348  }
349  }
350 
351  return retval;
352  }
xmin
float xmin
Definition: THbookFile.cxx:93
TGeoUnit::ps
static constexpr double ps
Definition: TGeoSystemOfUnits.h:165
RooStats::HistFactory::Constraint::GetType
Type GetType(const std::string &Name)
Definition: Systematics.cxx:34
x
Double_t x[n]
Definition: legend1.C:17
tau
you should not use this method at all Int_t Int_t Double_t Double_t Double_t Int_t Double_t Double_t Double_t tau
Definition: TRolke.cxx:630
r
ROOT::R::TRInterface & r
Definition: Object.C:4
TMVA::TMVAGlob::Initialize
void Initialize(Bool_t useTMVAStyle=kTRUE)
Definition: tmvaglob.cxx:176
xmax
float xmax
Definition: THbookFile.cxx:93
fType
PyObject * fType
Definition: MethodProxy.cxx:120
log
double log(double)