58 #ifndef IClassifierReader__def 59 #define IClassifierReader__def 61 class IClassifierReader {
66 IClassifierReader() : fStatusIsClean(
true ) {}
67 virtual ~IClassifierReader() {}
70 virtual double GetMvaValue(
const std::vector<double>& inputValues )
const = 0;
73 bool IsStatusClean()
const {
return fStatusIsClean; }
82 class ReadLikelihood :
public IClassifierReader {
87 ReadLikelihood( std::vector<std::string>& theInputVars )
88 : IClassifierReader(),
89 fClassName(
"ReadLikelihood" ),
91 fIsNormalised(
false )
94 const char* inputVars[] = {
"var1",
"var2",
"var3",
"var4" };
97 if (theInputVars.size() <= 0) {
98 std::cout <<
"Problem in class \"" << fClassName <<
"\": empty input vector" << std::endl;
99 fStatusIsClean =
false;
102 if (theInputVars.size() != fNvars) {
103 std::cout <<
"Problem in class \"" << fClassName <<
"\": mismatch in number of input values: " 104 << theInputVars.size() <<
" != " << fNvars << std::endl;
105 fStatusIsClean =
false;
109 for (
size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
110 if (theInputVars[ivar] != inputVars[ivar]) {
111 std::cout <<
"Problem in class \"" << fClassName <<
"\": mismatch in input variable names" << std::endl
112 <<
" for variable [" << ivar <<
"]: " << theInputVars[ivar].c_str() <<
" != " << inputVars[ivar] << std::endl;
113 fStatusIsClean =
false;
139 virtual ~ReadLikelihood() {
146 double GetMvaValue(
const std::vector<double>& inputValues )
const;
154 const char* fClassName;
157 size_t GetNvar()
const {
return fNvars; }
161 const bool fIsNormalised;
162 bool IsNormalised()
const {
return fIsNormalised; }
165 double NormVariable(
double x,
double xmin,
double xmax )
const {
167 return 2*(x -
xmin)/(xmax - xmin) - 1.0;
175 double GetMvaValue__(
const std::vector<double>& inputValues )
const;
179 static float fRefS[][10000];
180 static float fRefB[][10000];
183 bool fHasDiscretPDF[4];
187 double TransformLikelihoodOutput(
double,
double )
const;
192 fEpsilon = 2.2250738585072e-305;
194 fHistMin[0] = -5.31190872192383;
195 fHistMax[0] = 4.21716070175171;
197 fHistMin[1] = -4.2206826210022;
198 fHistMax[1] = 4.57174968719482;
200 fHistMin[2] = -4.58069658279419;
201 fHistMax[2] = 4.82019376754761;
203 fHistMin[3] = -5.1002082824707;
204 fHistMax[3] = 4.97665786743164;
205 fHasDiscretPDF[0] =
false;
206 fHasDiscretPDF[1] =
false;
207 fHasDiscretPDF[2] =
false;
208 fHasDiscretPDF[3] =
false;
211 inline double ReadLikelihood::GetMvaValue__(
const std::vector<double>& inputValues )
const 214 std::vector<double> inputValuesSig = inputValues;
215 std::vector<double> inputValuesBgd = inputValues;
216 for (
size_t ivar = 0; ivar < GetNvar(); ivar++) {
219 double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };
221 for (
int itype=0; itype < 2; itype++) {
225 int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;
231 x[itype] = fHistMin[ivar];
233 else if (bin >= fNbin[ivar]) {
235 x[itype] = fHistMax[ivar];
239 float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];
243 std::cout <<
"Fatal error in ReadLikelihood: bin entry < 0 ==> abort" << std::endl;
249 if (
GetType(ivar) !=
'I' && !fHasDiscretPDF[ivar]) {
250 float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
252 if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0)
257 double refnext = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
258 float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
260 double dx = bincenter - nextbincenter;
261 double dy = ref - refnext;
262 p += (x[itype] - bincenter) * dy/dx;
265 if (p < fEpsilon) p = fEpsilon;
267 if (itype == 0) ps *= p;
273 return TransformLikelihoodOutput( ps, pb );
276 inline double ReadLikelihood::TransformLikelihoodOutput(
double ps,
double pb )
const 279 if (ps < fEpsilon) ps = fEpsilon;
280 if (pb < fEpsilon) pb = fEpsilon;
281 double r = ps/(ps + pb);
282 if (r >= 1.0) r = 1. - 1.e-15;
288 if (r <= 0.0) r = fEpsilon;
289 else if (r >= 1.0) r = 1. - 1.e-15;
292 r = -
log(1.0/r - 1.0)/
tau;
299 inline void ReadLikelihood::Clear()
305 float ReadLikelihood::fRefS[][10000] =
307 { 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 2.13177758154055e-15, 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314 float ReadLikelihood::fRefB[][10000] =
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322 inline double ReadLikelihood::GetMvaValue(
const std::vector<double>& inputValues )
const 328 if (!IsStatusClean()) {
329 std::cout <<
"Problem in class \"" << fClassName <<
"\": cannot return classifier response" 330 <<
" because status is dirty" << std::endl;
334 if (IsNormalised()) {
336 std::vector<double> iV;
337 iV.reserve(inputValues.size());
339 for (std::vector<double>::const_iterator varIt = inputValues.begin();
340 varIt != inputValues.end(); varIt++, ivar++) {
341 iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
343 retval = GetMvaValue__( iV );
346 retval = GetMvaValue__( inputValues );
Type GetType(const std::string &Name)
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
void Initialize(Bool_t useTMVAStyle=kTRUE)