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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 : 6.10/09 [395785]
12 Creator : sftnight
13 Date : Thu May 31 12:04:27 2018
14 Host : Linux SFT-ubuntu-1710-1 4.13.0-31-generic #34-Ubuntu SMP Fri Jan 19 16:34:46 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux
15 Dir : /mnt/build/workspace/root-makedoc-v610/rootspi/rdoc/src/v6-10-00-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 <vector>
59 #include <cmath>
60 #include <string>
61 #include <iostream>
62 
63 #ifndef IClassifierReader__def
64 #define IClassifierReader__def
65 
66 class IClassifierReader {
67 
68  public:
69 
70  // constructor
71  IClassifierReader() : fStatusIsClean( true ) {}
72  virtual ~IClassifierReader() {}
73 
74  // return classifier response
75  virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
76 
77  // returns classifier status
78  bool IsStatusClean() const { return fStatusIsClean; }
79 
80  protected:
81 
82  bool fStatusIsClean;
83 };
84 
85 #endif
86 
87 class ReadSVM : public IClassifierReader {
88 
89  public:
90 
91  // constructor
92  ReadSVM( std::vector<std::string>& theInputVars )
93  : IClassifierReader(),
94  fClassName( "ReadSVM" ),
95  fNvars( 4 ),
96  fIsNormalised( true )
97  {
98  // the training input variables
99  const char* inputVars[] = { "var1+var2", "var1-var2", "var3", "var4" };
100 
101  // sanity checks
102  if (theInputVars.size() <= 0) {
103  std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
104  fStatusIsClean = false;
105  }
106 
107  if (theInputVars.size() != fNvars) {
108  std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
109  << theInputVars.size() << " != " << fNvars << std::endl;
110  fStatusIsClean = false;
111  }
112 
113  // validate input variables
114  for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
115  if (theInputVars[ivar] != inputVars[ivar]) {
116  std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
117  << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
118  fStatusIsClean = false;
119  }
120  }
121 
122  // initialize min and max vectors (for normalisation)
123  fVmin[0] = -1;
124  fVmax[0] = 1;
125  fVmin[1] = -1;
126  fVmax[1] = 1;
127  fVmin[2] = -1;
128  fVmax[2] = 1;
129  fVmin[3] = -1;
130  fVmax[3] = 1;
131 
132  // initialize input variable types
133  fType[0] = 'F';
134  fType[1] = 'F';
135  fType[2] = 'F';
136  fType[3] = 'F';
137 
138  // initialize constants
139  Initialize();
140 
141  // initialize transformation
142  InitTransform();
143  }
144 
145  // destructor
146  virtual ~ReadSVM() {
147  Clear(); // method-specific
148  }
149 
150  // the classifier response
151  // "inputValues" is a vector of input values in the same order as the
152  // variables given to the constructor
153  double GetMvaValue( const std::vector<double>& inputValues ) const;
154 
155  private:
156 
157  // method-specific destructor
158  void Clear();
159 
160  // input variable transformation
161 
162  double fMin_1[3][4];
163  double fMax_1[3][4];
164  void InitTransform_1();
165  void Transform_1( std::vector<double> & iv, int sigOrBgd ) const;
166  void InitTransform();
167  void Transform( std::vector<double> & iv, int sigOrBgd ) const;
168 
169  // common member variables
170  const char* fClassName;
171 
172  const size_t fNvars;
173  size_t GetNvar() const { return fNvars; }
174  char GetType( int ivar ) const { return fType[ivar]; }
175 
176  // normalisation of input variables
177  const bool fIsNormalised;
178  bool IsNormalised() const { return fIsNormalised; }
179  double fVmin[4];
180  double fVmax[4];
181  double NormVariable( double x, double xmin, double xmax ) const {
182  // normalise to output range: [-1, 1]
183  return 2*(x - xmin)/(xmax - xmin) - 1.0;
184  }
185 
186  // type of input variable: 'F' or 'I'
187  char fType[4];
188 
189  // initialize internal variables
190  void Initialize();
191  double GetMvaValue__( const std::vector<double>& inputValues ) const;
192 
193  // private members (method specific)
194  // not implemented for class: "ReadSVM"
195  float fBparameter;
196  int fNOfSuppVec;
197  static float fAllSuppVectors[][1171];
198  static float fAlphaTypeCoef[1171];
199 
200  // Kernel parameter(s)
201  float fGamma;
202 };
203 
204 inline void ReadSVM::Initialize()
205 {
206  fBparameter = 0.0021505260374397;
207  fNOfSuppVec = 1171;
208  fGamma = 0.25;
209 }
210 
211 inline double ReadSVM::GetMvaValue__(const std::vector<double>& inputValues ) const
212 {
213  double mvaval = 0;
214  double temp = 0;
215 
216  for (int ievt = 0; ievt < fNOfSuppVec; ievt++ ){
217  temp = 0;
218  for ( unsigned int ivar = 0; ivar < GetNvar(); ivar++ ) {
219  temp += (fAllSuppVectors[ivar][ievt] - inputValues[ivar])
220  * (fAllSuppVectors[ivar][ievt] - inputValues[ivar]);
221  }
222  mvaval += fAlphaTypeCoef[ievt] * exp( -fGamma * temp );
223  }
224  mvaval -= fBparameter;
225  return 1./(1. + exp(mvaval));
226 }
227 // Clean up
228 inline void ReadSVM::Clear()
229 {
230  // nothing to clear
231 }
232 
233 float ReadSVM::fAlphaTypeCoef[] =
234 { -1, -1, -1, -1, 1.79031240940094, 3.25342750549316, -1, -1, -1, -1, 3.60218167304993, -1, -1, -0.991728186607361, -1, -1, -1, 2.91623663902283, 9.65565122896805e-05, 0.0070298733189702, 0.522478520870209, -1, -1, 3.37394857406616, 0.350947707891464, 0.806878864765167, -1, -1, -1, -1, 0.720670342445374, -1, -1, -1, 0.537567913532257, -1, 0.00755005097016692, -1, 3.4170560836792, -1, -1, 2.91039681434631, 0.469379663467407, 0.127504050731659, -1, -1, 3.36805701255798, 3.31421160697937, 2.91140556335449, -1, -1, -1, 0.019097039476037, -1, -1, -1, 1.98014295101166, 0.557818114757538, -1, 3.29127597808838, 0.00615879707038403, 1.30762827396393, 2.60360956192017, -1, 0.00167241040617228, -1, -1, 0.100060328841209, 0.0048934337683022, 2.52274060249329, -1, 0.00119973625987768, -1, -1, -1, -1, 0.00176339456811547, -1, -1, 0.000761684728786349, -1, 1.73234176635742, 3.40583443641663, 5.33956990693696e-05, -1, -1, 2.43203091621399, -1, 0.0093670217320323, -1, -1, -1, 2.33860468864441, 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235 
236 float ReadSVM::fAllSuppVectors[][1171] =
237 {
238  { -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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245 };
246 
247  inline double ReadSVM::GetMvaValue( const std::vector<double>& inputValues ) const
248  {
249  // classifier response value
250  double retval = 0;
251 
252  // classifier response, sanity check first
253  if (!IsStatusClean()) {
254  std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
255  << " because status is dirty" << std::endl;
256  retval = 0;
257  }
258  else {
259  if (IsNormalised()) {
260  // normalise variables
261  std::vector<double> iV;
262  iV.reserve(inputValues.size());
263  int ivar = 0;
264  for (std::vector<double>::const_iterator varIt = inputValues.begin();
265  varIt != inputValues.end(); varIt++, ivar++) {
266  iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
267  }
268  Transform( iV, -1 );
269  retval = GetMvaValue__( iV );
270  }
271  else {
272  std::vector<double> iV;
273  int ivar = 0;
274  for (std::vector<double>::const_iterator varIt = inputValues.begin();
275  varIt != inputValues.end(); varIt++, ivar++) {
276  iV.push_back(*varIt);
277  }
278  Transform( iV, -1 );
279  retval = GetMvaValue__( iV );
280  }
281  }
282 
283  return retval;
284  }
285 
286 //_______________________________________________________________________
287 inline void ReadSVM::InitTransform_1()
288 {
289  // Normalization transformation, initialisation
290  fMin_1[0][0] = -4.94358778;
291  fMax_1[0][0] = 6.3994679451;
292  fMin_1[1][0] = -8.14423561096;
293  fMax_1[1][0] = 7.26972866058;
294  fMin_1[2][0] = -8.14423561096;
295  fMax_1[2][0] = 7.26972866058;
296  fMin_1[0][1] = -3.96643972397;
297  fMax_1[0][1] = 3.11266636848;
298  fMin_1[1][1] = -3.25508260727;
299  fMax_1[1][1] = 4.0258936882;
300  fMin_1[2][1] = -3.96643972397;
301  fMax_1[2][1] = 4.0258936882;
302  fMin_1[0][2] = -2.78645992279;
303  fMax_1[0][2] = 3.50111722946;
304  fMin_1[1][2] = -5.03730010986;
305  fMax_1[1][2] = 4.27845287323;
306  fMin_1[2][2] = -5.03730010986;
307  fMax_1[2][2] = 4.27845287323;
308  fMin_1[0][3] = -2.42712664604;
309  fMax_1[0][3] = 4.5351858139;
310  fMin_1[1][3] = -5.95050764084;
311  fMax_1[1][3] = 4.64035463333;
312  fMin_1[2][3] = -5.95050764084;
313  fMax_1[2][3] = 4.64035463333;
314 }
315 
316 //_______________________________________________________________________
317 inline void ReadSVM::Transform_1( std::vector<double>& iv, int cls) const
318 {
319  // Normalization transformation
320  if (cls < 0 || cls > 2) {
321  if (2 > 1 ) cls = 2;
322  else cls = 2;
323  }
324  const int nVar = 4;
325 
326  // get indices of used variables
327 
328  // define the indices of the variables which are transformed by this transformation
329  static std::vector<int> indicesGet;
330  static std::vector<int> indicesPut;
331 
332  if ( indicesGet.empty() ) {
333  indicesGet.reserve(fNvars);
334  indicesGet.push_back( 0);
335  indicesGet.push_back( 1);
336  indicesGet.push_back( 2);
337  indicesGet.push_back( 3);
338  }
339  if ( indicesPut.empty() ) {
340  indicesPut.reserve(fNvars);
341  indicesPut.push_back( 0);
342  indicesPut.push_back( 1);
343  indicesPut.push_back( 2);
344  indicesPut.push_back( 3);
345  }
346 
347  static std::vector<double> dv;
348  dv.resize(nVar);
349  for (int ivar=0; ivar<nVar; ivar++) dv[ivar] = iv[indicesGet.at(ivar)];
350  for (int ivar=0;ivar<4;ivar++) {
351  double offset = fMin_1[cls][ivar];
352  double scale = 1.0/(fMax_1[cls][ivar]-fMin_1[cls][ivar]);
353  iv[indicesPut.at(ivar)] = (dv[ivar]-offset)*scale * 2 - 1;
354  }
355 }
356 
357 //_______________________________________________________________________
358 inline void ReadSVM::InitTransform()
359 {
360  InitTransform_1();
361 }
362 
363 //_______________________________________________________________________
364 inline void ReadSVM::Transform( std::vector<double>& iv, int sigOrBgd ) const
365 {
366  Transform_1( iv, sigOrBgd );
367 }
xmin
float xmin
Definition: THbookFile.cxx:93
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Type GetType(const std::string &Name)
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Definition: legend1.C:17
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Definition: tmvaglob.cxx:176
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Definition: THbookFile.cxx:93
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