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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
9Method : SVM::SVM
10TMVA Release : 4.2.1 [262657]
11ROOT Release : 6.16/01 [397313]
12Creator : sftnight
13Date : Sun Dec 19 22:13:35 2021
14Host : Linux root-ubuntu-2004-3 5.4.0-73-generic #82-Ubuntu SMP Wed Apr 14 17:39:42 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
15Dir : /home/sftnight/build/workspace/root-makedoc-v616/rootspi/rdoc/src/v6-16-00-patches/documentation/doxygen
16Training events: 2000
17Analysis type : [Classification]
18
19
20#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-
21
22# Set by User:
23VarTransform: "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)"]
24Gamma: "2.500000e-01" [RBF kernel parameter: Gamma (size of the Kernel)]
25Tol: "1.000000e-03" [Tolerance parameter]
26# Default:
27V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
28VerbosityLevel: "Default" [Verbosity level]
29H: "False" [Print method-specific help message]
30CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
31IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
32Kernel: "RBF" [Pick which kernel ( RBF or MultiGauss )]
33Order: "3" [Polynomial Kernel parameter: polynomial order]
34Theta: "1.000000e+00" [Polynomial Kernel parameter: polynomial theta]
35GammaList: "" [MultiGauss parameters]
36Tune: "All" [Tune Parameters]
37KernelList: "None" [Sum or product of kernels]
38Loss: "hinge" [Loss function]
39C: "1.000000e+00" [Cost parameter]
40MaxIter: "1000" [Maximum number of training loops]
41##
42
43
44#VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-
45
46NVar 4
47var1+var2 myvar1 myvar1 myvar1 'F' [-9.33803939819,7.69307804108]
48var1-var2 myvar2 myvar2 Expression 2 'F' [-3.25508260727,4.02912044525]
49var3 var3 var3 Variable 3 units 'F' [-5.2777428627,4.64297914505]
50var4 var4 var4 Variable 4 units 'F' [-5.6007027626,4.67435789108]
51NSpec 2
52var1*2 spec1 spec1 Spectator 1 units 'F' [-9.91655540466,8.7030172348]
53var1*3 spec2 spec2 Spectator 2 units 'F' [-14.874833107,13.0545253754]
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
67class 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
88class 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 {
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] = 0.99999988079071;
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 override;
154
155 private:
156
157 // method-specific destructor
158 void Clear();
159
160 // input variable transformation
161
162 double fOff_1[3][4];
163 double fScal_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 double fVmin[4];
178 double fVmax[4];
179 double NormVariable( double x, double xmin, double xmax ) const {
180 // normalise to output range: [-1, 1]
181 return 2*(x - xmin)/(xmax - xmin) - 1.0;
182 }
183
184 // type of input variable: 'F' or 'I'
185 char fType[4];
186
187 // initialize internal variables
188 void Initialize();
189 double GetMvaValue__( const std::vector<double>& inputValues ) const;
190
191 // private members (method specific)
192 // not implemented for class: "ReadSVM"
193 float fBparameter;
194 int fNOfSuppVec;
195 static float fAllSuppVectors[][1146];
196 static float fAlphaTypeCoef[1146];
197
198 // Kernel parameter(s)
199 float fGamma;
200};
201
202inline void ReadSVM::Initialize()
203{
204 fBparameter = -0.162949949502945;
205 fNOfSuppVec = 1146;
206 fGamma = 0.25;
207}
208
209inline double ReadSVM::GetMvaValue__(const std::vector<double>& inputValues ) const
210{
211 double mvaval = 0;
212 double temp = 0;
213
214 for (int ievt = 0; ievt < fNOfSuppVec; ievt++ ){
215 temp = 0;
216 for ( unsigned int ivar = 0; ivar < GetNvar(); ivar++ ) {
217 temp += (fAllSuppVectors[ivar][ievt] - inputValues[ivar])
218 * (fAllSuppVectors[ivar][ievt] - inputValues[ivar]);
219 }
220 mvaval += fAlphaTypeCoef[ievt] * exp( -fGamma * temp );
221 }
222 mvaval -= fBparameter;
223 return 1./(1. + exp(mvaval));
224}
225// Clean up
226inline void ReadSVM::Clear()
227{
228 // nothing to clear
229}
230
231float ReadSVM::fAlphaTypeCoef[] =
232{ -1, 0.58891236782074, -1, -1, -1, -1, 3.32657241821289, 2.18013024330139, 0.0248334426432848, -1, 0.011335433460772, 0.940118849277496, -1, -1, -1, -1, 2.13269019126892, -1, -1, 0.011907871812582, -1, -1, -1, -1, 0.705736100673676, -1, 0.371660768985748, -1, -1, 0.892130255699158, 2.15806913375854, -1, 0.179787963628769, -1, 0.038511399179697, -1, 0.000279079307802022, 0.0536592714488506, -1, 3.33324718475342, 0.106782831251621, 0.0318171270191669, -1, 0.013180473819375, -1, 0.244615092873573, 0.123856388032436, 0.00573757011443377, -1, 1.78156065940857, -1, 2.63460755348206, -1, 0.170236229896545, -1, 0.000873383076395839, 3.33350300788879, 0.0196853503584862, 3.33293008804321, -1, -1, -1, 0.102613791823387, -1, -1, -1, 0.0251902304589748, 0.0394790358841419, -1, -1, -1, 0.000260632776189595, 0.0088065518066287, 0.100062042474747, -1, 0.411309629678726, 1.59163653850555, -1, 0.141127705574036, -1, 1.03443956375122, -1, 0.000887303496710956, -1, -1, -1, -1, -1, -1, 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233
234float ReadSVM::fAllSuppVectors[][1146] =
235{
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243};
244
245 inline double ReadSVM::GetMvaValue( const std::vector<double>& inputValues ) const
246 {
247 // classifier response value
248 double retval = 0;
249
250 // classifier response, sanity check first
251 if (!IsStatusClean()) {
252 std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
253 << " because status is dirty" << std::endl;
254 retval = 0;
255 }
256 else {
257 // normalise variables
258 std::vector<double> iV;
259 iV.reserve(inputValues.size());
260 int ivar = 0;
261 for (std::vector<double>::const_iterator varIt = inputValues.begin();
262 varIt != inputValues.end(); varIt++, ivar++) {
263 iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
264 }
265 Transform( iV, -1 );
266 retval = GetMvaValue__( iV );
267 }
268
269 return retval;
270 }
271
272//_______________________________________________________________________
273inline void ReadSVM::InitTransform_1()
274{
275 double fMin_1[3][4];
276 double fMax_1[3][4];
277 // Normalization transformation, initialisation
278 fMin_1[0][0] = -4.33593845367;
279 fMax_1[0][0] = 6.3994679451;
280 fScal_1[0][0] = 2.0/(fMax_1[0][0]-fMin_1[0][0]);
281 fOff_1[0][0] = fMin_1[0][0]*fScal_1[0][0]+1.;
282 fMin_1[1][0] = -9.33803939819;
283 fMax_1[1][0] = 7.69307804108;
284 fScal_1[1][0] = 2.0/(fMax_1[1][0]-fMin_1[1][0]);
285 fOff_1[1][0] = fMin_1[1][0]*fScal_1[1][0]+1.;
286 fMin_1[2][0] = -9.33803939819;
287 fMax_1[2][0] = 7.69307804108;
288 fScal_1[2][0] = 2.0/(fMax_1[2][0]-fMin_1[2][0]);
289 fOff_1[2][0] = fMin_1[2][0]*fScal_1[2][0]+1.;
290 fMin_1[0][1] = -3.20988440514;
291 fMax_1[0][1] = 4.02912044525;
292 fScal_1[0][1] = 2.0/(fMax_1[0][1]-fMin_1[0][1]);
293 fOff_1[0][1] = fMin_1[0][1]*fScal_1[0][1]+1.;
294 fMin_1[1][1] = -3.25508260727;
295 fMax_1[1][1] = 3.36500358582;
296 fScal_1[1][1] = 2.0/(fMax_1[1][1]-fMin_1[1][1]);
297 fOff_1[1][1] = fMin_1[1][1]*fScal_1[1][1]+1.;
298 fMin_1[2][1] = -3.25508260727;
299 fMax_1[2][1] = 4.02912044525;
300 fScal_1[2][1] = 2.0/(fMax_1[2][1]-fMin_1[2][1]);
301 fOff_1[2][1] = fMin_1[2][1]*fScal_1[2][1]+1.;
302 fMin_1[0][2] = -2.60635733604;
303 fMax_1[0][2] = 3.86989831924;
304 fScal_1[0][2] = 2.0/(fMax_1[0][2]-fMin_1[0][2]);
305 fOff_1[0][2] = fMin_1[0][2]*fScal_1[0][2]+1.;
306 fMin_1[1][2] = -5.2777428627;
307 fMax_1[1][2] = 4.64297914505;
308 fScal_1[1][2] = 2.0/(fMax_1[1][2]-fMin_1[1][2]);
309 fOff_1[1][2] = fMin_1[1][2]*fScal_1[1][2]+1.;
310 fMin_1[2][2] = -5.2777428627;
311 fMax_1[2][2] = 4.64297914505;
312 fScal_1[2][2] = 2.0/(fMax_1[2][2]-fMin_1[2][2]);
313 fOff_1[2][2] = fMin_1[2][2]*fScal_1[2][2]+1.;
314 fMin_1[0][3] = -2.1695792675;
315 fMax_1[0][3] = 4.5351858139;
316 fScal_1[0][3] = 2.0/(fMax_1[0][3]-fMin_1[0][3]);
317 fOff_1[0][3] = fMin_1[0][3]*fScal_1[0][3]+1.;
318 fMin_1[1][3] = -5.6007027626;
319 fMax_1[1][3] = 4.67435789108;
320 fScal_1[1][3] = 2.0/(fMax_1[1][3]-fMin_1[1][3]);
321 fOff_1[1][3] = fMin_1[1][3]*fScal_1[1][3]+1.;
322 fMin_1[2][3] = -5.6007027626;
323 fMax_1[2][3] = 4.67435789108;
324 fScal_1[2][3] = 2.0/(fMax_1[2][3]-fMin_1[2][3]);
325 fOff_1[2][3] = fMin_1[2][3]*fScal_1[2][3]+1.;
326}
327
328//_______________________________________________________________________
329inline void ReadSVM::Transform_1( std::vector<double>& iv, int cls) const
330{
331 // Normalization transformation
332 if (cls < 0 || cls > 2) {
333 if (2 > 1 ) cls = 2;
334 else cls = 2;
335 }
336 const int nVar = 4;
337
338 // get indices of used variables
339
340 // define the indices of the variables which are transformed by this transformation
341 static std::vector<int> indicesGet;
342 static std::vector<int> indicesPut;
343
344 if ( indicesGet.empty() ) {
345 indicesGet.reserve(fNvars);
346 indicesGet.push_back( 0);
347 indicesGet.push_back( 1);
348 indicesGet.push_back( 2);
349 indicesGet.push_back( 3);
350 }
351 if ( indicesPut.empty() ) {
352 indicesPut.reserve(fNvars);
353 indicesPut.push_back( 0);
354 indicesPut.push_back( 1);
355 indicesPut.push_back( 2);
356 indicesPut.push_back( 3);
357 }
358
359 static std::vector<double> dv;
360 dv.resize(nVar);
361 for (int ivar=0; ivar<nVar; ivar++) dv[ivar] = iv[indicesGet.at(ivar)];
362 for (int ivar=0;ivar<4;ivar++) {
363 double offset = fOff_1[cls][ivar];
364 double scale = fScal_1[cls][ivar];
365 iv[indicesPut.at(ivar)] = scale*dv[ivar]-offset;
366 }
367}
368
369//_______________________________________________________________________
370inline void ReadSVM::InitTransform()
371{
372 InitTransform_1();
373}
374
375//_______________________________________________________________________
376inline void ReadSVM::Transform( std::vector<double>& iv, int sigOrBgd ) const
377{
378 Transform_1( iv, sigOrBgd );
379}
PyObject * fType
float xmin
Definition: THbookFile.cxx:93
float xmax
Definition: THbookFile.cxx:93
double exp(double)
Double_t x[n]
Definition: legend1.C:17
Type GetType(const std::string &Name)
Definition: Systematics.cxx:34
void Initialize(Bool_t useTMVAStyle=kTRUE)
Definition: tmvaglob.cxx:176