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Reference Guide
TMVAClassification_SVM.class.C
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1 // Class: ReadSVM
2 // Automatically generated by MethodBase::MakeClass
3 //
4 
5 /* configuration options =====================================================
6 
7 #GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
8 
9 Method : SVM::SVM
10 TMVA Release : 4.2.1 [262657]
11 ROOT Release : 6.14/05 [396805]
12 Creator : sftnight
13 Date : Fri Nov 2 10:41:09 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-v614/rootspi/rdoc/src/v6-14-00-patches/documentation/doxygen
16 Training events: 2000
17 Analysis type : [Classification]
18 
19 
20 #OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-
21 
22 # Set by User:
23 VarTransform: "Norm" [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)"]
24 Gamma: "2.500000e-01" [RBF kernel parameter: Gamma (size of the Kernel)]
25 Tol: "1.000000e-03" [Tolerance parameter]
26 # Default:
27 V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
28 VerbosityLevel: "Default" [Verbosity level]
29 H: "False" [Print method-specific help message]
30 CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
31 IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
32 Kernel: "RBF" [Pick which kernel ( RBF or MultiGauss )]
33 Order: "3" [Polynomial Kernel parameter: polynomial order]
34 Theta: "1.000000e+00" [Polynomial Kernel parameter: polynomial theta]
35 GammaList: "" [MultiGauss parameters]
36 Tune: "All" [Tune Parameters]
37 KernelList: "None" [Sum or product of kernels]
38 Loss: "hinge" [Loss function]
39 C: "1.000000e+00" [Cost parameter]
40 MaxIter: "1000" [Maximum number of training loops]
41 ##
42 
43 
44 #VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-
45 
46 NVar 4
47 var1+var2 myvar1 myvar1 myvar1 'F' [-8.14423561096,7.26972866058]
48 var1-var2 myvar2 myvar2 Expression 2 'F' [-3.96643972397,4.0258936882]
49 var3 var3 var3 Variable 3 units 'F' [-5.03730010986,4.27845287323]
50 var4 var4 var4 Variable 4 units 'F' [-5.95050764084,4.64035463333]
51 NSpec 2
52 var1*2 spec1 spec1 Spectator 1 units 'F' [-9.91655540466,8.67800140381]
53 var1*3 spec2 spec2 Spectator 2 units 'F' [-14.874833107,13.0170021057]
54 
55 
56 ============================================================================ */
57 
58 #include <array>
59 #include <vector>
60 #include <cmath>
61 #include <string>
62 #include <iostream>
63 
64 #ifndef IClassifierReader__def
65 #define IClassifierReader__def
66 
67 class IClassifierReader {
68 
69  public:
70 
71  // constructor
72  IClassifierReader() : fStatusIsClean( true ) {}
73  virtual ~IClassifierReader() {}
74 
75  // return classifier response
76  virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
77 
78  // returns classifier status
79  bool IsStatusClean() const { return fStatusIsClean; }
80 
81  protected:
82 
83  bool fStatusIsClean;
84 };
85 
86 #endif
87 
88 class ReadSVM : public IClassifierReader {
89 
90  public:
91 
92  // constructor
93  ReadSVM( std::vector<std::string>& theInputVars )
94  : IClassifierReader(),
95  fClassName( "ReadSVM" ),
96  fNvars( 4 ),
97  fIsNormalised( true )
98  {
99  // the training input variables
100  const char* inputVars[] = { "var1+var2", "var1-var2", "var3", "var4" };
101 
102  // sanity checks
103  if (theInputVars.size() <= 0) {
104  std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
105  fStatusIsClean = false;
106  }
107 
108  if (theInputVars.size() != fNvars) {
109  std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
110  << theInputVars.size() << " != " << fNvars << std::endl;
111  fStatusIsClean = false;
112  }
113 
114  // validate input variables
115  for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
116  if (theInputVars[ivar] != inputVars[ivar]) {
117  std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
118  << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
119  fStatusIsClean = false;
120  }
121  }
122 
123  // initialize min and max vectors (for normalisation)
124  fVmin[0] = -1;
125  fVmax[0] = 1;
126  fVmin[1] = -1;
127  fVmax[1] = 1;
128  fVmin[2] = -1;
129  fVmax[2] = 1;
130  fVmin[3] = -1;
131  fVmax[3] = 1;
132 
133  // initialize input variable types
134  fType[0] = 'F';
135  fType[1] = 'F';
136  fType[2] = 'F';
137  fType[3] = 'F';
138 
139  // initialize constants
140  Initialize();
141 
142  // initialize transformation
143  InitTransform();
144  }
145 
146  // destructor
147  virtual ~ReadSVM() {
148  Clear(); // method-specific
149  }
150 
151  // the classifier response
152  // "inputValues" is a vector of input values in the same order as the
153  // variables given to the constructor
154  double GetMvaValue( const std::vector<double>& inputValues ) const override;
155 
156  private:
157 
158  // method-specific destructor
159  void Clear();
160 
161  // input variable transformation
162 
163  double fMin_1[3][4];
164  double fMax_1[3][4];
165  void InitTransform_1();
166  void Transform_1( std::vector<double> & iv, int sigOrBgd ) const;
167  void InitTransform();
168  void Transform( std::vector<double> & iv, int sigOrBgd ) const;
169 
170  // common member variables
171  const char* fClassName;
172 
173  const size_t fNvars;
174  size_t GetNvar() const { return fNvars; }
175  char GetType( int ivar ) const { return fType[ivar]; }
176 
177  // normalisation of input variables
178  const bool fIsNormalised;
179  bool IsNormalised() const { return fIsNormalised; }
180  double fVmin[4];
181  double fVmax[4];
182  double NormVariable( double x, double xmin, double xmax ) const {
183  // normalise to output range: [-1, 1]
184  return 2*(x - xmin)/(xmax - xmin) - 1.0;
185  }
186 
187  // type of input variable: 'F' or 'I'
188  char fType[4];
189 
190  // initialize internal variables
191  void Initialize();
192  double GetMvaValue__( const std::vector<double>& inputValues ) const;
193 
194  // private members (method specific)
195  // not implemented for class: "ReadSVM"
196  float fBparameter;
197  int fNOfSuppVec;
198  static float fAllSuppVectors[][1171];
199  static float fAlphaTypeCoef[1171];
200 
201  // Kernel parameter(s)
202  float fGamma;
203 };
204 
205 inline void ReadSVM::Initialize()
206 {
207  fBparameter = 0.00662573566660285;
208  fNOfSuppVec = 1171;
209  fGamma = 0.25;
210 }
211 
212 inline double ReadSVM::GetMvaValue__(const std::vector<double>& inputValues ) const
213 {
214  double mvaval = 0;
215  double temp = 0;
216 
217  for (int ievt = 0; ievt < fNOfSuppVec; ievt++ ){
218  temp = 0;
219  for ( unsigned int ivar = 0; ivar < GetNvar(); ivar++ ) {
220  temp += (fAllSuppVectors[ivar][ievt] - inputValues[ivar])
221  * (fAllSuppVectors[ivar][ievt] - inputValues[ivar]);
222  }
223  mvaval += fAlphaTypeCoef[ievt] * exp( -fGamma * temp );
224  }
225  mvaval -= fBparameter;
226  return 1./(1. + exp(mvaval));
227 }
228 // Clean up
229 inline void ReadSVM::Clear()
230 {
231  // nothing to clear
232 }
233 
234 float ReadSVM::fAlphaTypeCoef[] =
235 { -1, -1, -1, -1, 1.78999531269073, 3.25285124778748, -1, -1, -1, -1, 3.60154366493225, -1, -1, -0.901240766048431, -1, -1, -1, 2.91571998596191, 9.65394065133296e-05, 0.00702862860634923, 0.522385954856873, -1, -1, 3.37335109710693, 0.350885540246964, 0.806735992431641, -1, -0.930398941040039, -1, -1, 0.720542728900909, -1, -1, -1, 0.537472665309906, -1, 0.00754871405661106, -1, 3.41645097732544, -1, -1, 2.9098813533783, 0.469296544790268, 0.127481460571289, -1, -1, 3.36746048927307, 3.31362462043762, 2.9108898639679, -1, -1, -1, 0.019093656912446, -1, -1, -1, 1.9797922372818, 0.5577192902565, -1, 3.29069304466248, 0.00615770602598786, 1.30739676952362, 2.60314846038818, -1, 0.00167211424559355, -1, -1, 0.10004261136055, 0.00489256717264652, 2.52229380607605, -1, 0.00119952380191535, -1, -1, -1, -1, 0.00176308222580701, -1, -1, 0.000761549803428352, -1, 1.73203492164612, 3.4052312374115, 5.338624396245e-05, -1, -1, 2.43160033226013, -1, 0.00936536304652691, -1, -1, -1, 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236 
237 float ReadSVM::fAllSuppVectors[][1171] =
238 {
239  { -0.266774773597717, -0.325933456420898, -0.217129349708557, -0.0176641941070557, 0.0365585088729858, 0.0444620847702026, 0.460650205612183, 0.0176531076431274, -0.208876371383667, 0.0585358142852783, 0.0158555507659912, 0.254392623901367, 0.0277608633041382, 0.159626364707947, 0.0838032960891724, 0.336062550544739, -0.239717364311218, -0.283523321151733, 0.992382526397705, 0.669888854026794, 0.284940958023071, -0.127230226993561, 0.216871500015259, 0.0388877391815186, 0.424784183502197, 0.396058320999146, -0.23619145154953, 0.300818562507629, -0.1159947514534, -0.0367396473884583, 0.448601365089417, 0.104373693466187, -0.123367309570312, 0.0393267869949341, 0.448378324508667, -0.380622923374176, 0.722771883010864, 0.302169680595398, 0.0477370023727417, 0.117727398872375, 0.363363862037659, 0.00253748893737793, 0.276192903518677, 0.470348477363586, 0.270477771759033, 0.256522417068481, -0.0989335775375366, 0.171757221221924, -0.314709186553955, 0.195376753807068, 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246 };
247 
248  inline double ReadSVM::GetMvaValue( const std::vector<double>& inputValues ) const
249  {
250  // classifier response value
251  double retval = 0;
252 
253  // classifier response, sanity check first
254  if (!IsStatusClean()) {
255  std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
256  << " because status is dirty" << std::endl;
257  retval = 0;
258  }
259  else {
260  if (IsNormalised()) {
261  // normalise variables
262  std::vector<double> iV;
263  iV.reserve(inputValues.size());
264  int ivar = 0;
265  for (std::vector<double>::const_iterator varIt = inputValues.begin();
266  varIt != inputValues.end(); varIt++, ivar++) {
267  iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
268  }
269  Transform( iV, -1 );
270  retval = GetMvaValue__( iV );
271  }
272  else {
273  std::vector<double> iV;
274  int ivar = 0;
275  for (std::vector<double>::const_iterator varIt = inputValues.begin();
276  varIt != inputValues.end(); varIt++, ivar++) {
277  iV.push_back(*varIt);
278  }
279  Transform( iV, -1 );
280  retval = GetMvaValue__( iV );
281  }
282  }
283 
284  return retval;
285  }
286 
287 //_______________________________________________________________________
288 inline void ReadSVM::InitTransform_1()
289 {
290  // Normalization transformation, initialisation
291  fMin_1[0][0] = -4.94358778;
292  fMax_1[0][0] = 6.3994679451;
293  fMin_1[1][0] = -8.14423561096;
294  fMax_1[1][0] = 7.26972866058;
295  fMin_1[2][0] = -8.14423561096;
296  fMax_1[2][0] = 7.26972866058;
297  fMin_1[0][1] = -3.96643972397;
298  fMax_1[0][1] = 3.11266636848;
299  fMin_1[1][1] = -3.25508260727;
300  fMax_1[1][1] = 4.0258936882;
301  fMin_1[2][1] = -3.96643972397;
302  fMax_1[2][1] = 4.0258936882;
303  fMin_1[0][2] = -2.78645992279;
304  fMax_1[0][2] = 3.50111722946;
305  fMin_1[1][2] = -5.03730010986;
306  fMax_1[1][2] = 4.27845287323;
307  fMin_1[2][2] = -5.03730010986;
308  fMax_1[2][2] = 4.27845287323;
309  fMin_1[0][3] = -2.42712664604;
310  fMax_1[0][3] = 4.5351858139;
311  fMin_1[1][3] = -5.95050764084;
312  fMax_1[1][3] = 4.64035463333;
313  fMin_1[2][3] = -5.95050764084;
314  fMax_1[2][3] = 4.64035463333;
315 }
316 
317 //_______________________________________________________________________
318 inline void ReadSVM::Transform_1( std::vector<double>& iv, int cls) const
319 {
320  // Normalization transformation
321  if (cls < 0 || cls > 2) {
322  if (2 > 1 ) cls = 2;
323  else cls = 2;
324  }
325  const int nVar = 4;
326 
327  // get indices of used variables
328 
329  // define the indices of the variables which are transformed by this transformation
330  static std::vector<int> indicesGet;
331  static std::vector<int> indicesPut;
332 
333  if ( indicesGet.empty() ) {
334  indicesGet.reserve(fNvars);
335  indicesGet.push_back( 0);
336  indicesGet.push_back( 1);
337  indicesGet.push_back( 2);
338  indicesGet.push_back( 3);
339  }
340  if ( indicesPut.empty() ) {
341  indicesPut.reserve(fNvars);
342  indicesPut.push_back( 0);
343  indicesPut.push_back( 1);
344  indicesPut.push_back( 2);
345  indicesPut.push_back( 3);
346  }
347 
348  static std::vector<double> dv;
349  dv.resize(nVar);
350  for (int ivar=0; ivar<nVar; ivar++) dv[ivar] = iv[indicesGet.at(ivar)];
351  for (int ivar=0;ivar<4;ivar++) {
352  double offset = fMin_1[cls][ivar];
353  double scale = 1.0/(fMax_1[cls][ivar]-fMin_1[cls][ivar]);
354  iv[indicesPut.at(ivar)] = (dv[ivar]-offset)*scale * 2 - 1;
355  }
356 }
357 
358 //_______________________________________________________________________
359 inline void ReadSVM::InitTransform()
360 {
361  InitTransform_1();
362 }
363 
364 //_______________________________________________________________________
365 inline void ReadSVM::Transform( std::vector<double>& iv, int sigOrBgd ) const
366 {
367  Transform_1( iv, sigOrBgd );
368 }
xmin
float xmin
Definition: THbookFile.cxx:93
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Type GetType(const std::string &Name)
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Double_t x[n]
Definition: legend1.C:17
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Definition: tmvaglob.cxx:176
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Definition: THbookFile.cxx:93
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