| 1 | /* ================================q======================================== *\
|
|---|
| 2 | !
|
|---|
| 3 | ! *
|
|---|
| 4 | ! * This file is part of MARS, the MAGIC Analysis and Reconstruction
|
|---|
| 5 | ! * Software. It is distributed to you in the hope that it can be a useful
|
|---|
| 6 | ! * and timesaving tool in analysing Data of imaging Cerenkov telescopes.
|
|---|
| 7 | ! * It is distributed WITHOUT ANY WARRANTY.
|
|---|
| 8 | ! *
|
|---|
| 9 | ! * Permission to use, copy, modify and distribute this software and its
|
|---|
| 10 | ! * documentation for any purpose is hereby granted without fee,
|
|---|
| 11 | ! * provided that the above copyright notice appear in all copies and
|
|---|
| 12 | ! * that both that copyright notice and this permission notice appear
|
|---|
| 13 | ! * in supporting documentation. It is provided "as is" without express
|
|---|
| 14 | ! * or implied warranty.
|
|---|
| 15 | ! *
|
|---|
| 16 | !
|
|---|
| 17 | !
|
|---|
| 18 | ! Author(s): Thomas Hengstebeck 2/2005 <mailto:hengsteb@physik.hu-berlin.de>
|
|---|
| 19 | ! Author(s): Thomas Bretz 8/2005 <mailto:tbretz@astro.uni-wuerzburg.de>
|
|---|
| 20 | !
|
|---|
| 21 | ! Copyright: MAGIC Software Development, 2000-2005
|
|---|
| 22 | !
|
|---|
| 23 | !
|
|---|
| 24 | \* ======================================================================== */
|
|---|
| 25 |
|
|---|
| 26 | /////////////////////////////////////////////////////////////////////////////
|
|---|
| 27 | //
|
|---|
| 28 | // MRanForestCalc
|
|---|
| 29 | //
|
|---|
| 30 | //
|
|---|
| 31 | ////////////////////////////////////////////////////////////////////////////
|
|---|
| 32 | #include "MRanForestCalc.h"
|
|---|
| 33 |
|
|---|
| 34 | #include <TF1.h>
|
|---|
| 35 | #include <TFile.h>
|
|---|
| 36 | #include <TGraph.h>
|
|---|
| 37 | #include <TVector.h>
|
|---|
| 38 |
|
|---|
| 39 | #include "MHMatrix.h"
|
|---|
| 40 |
|
|---|
| 41 | #include "MLog.h"
|
|---|
| 42 | #include "MLogManip.h"
|
|---|
| 43 |
|
|---|
| 44 | #include "MData.h"
|
|---|
| 45 | #include "MDataArray.h"
|
|---|
| 46 |
|
|---|
| 47 | #include "MRanForest.h"
|
|---|
| 48 | #include "MParameters.h"
|
|---|
| 49 |
|
|---|
| 50 | #include "MParList.h"
|
|---|
| 51 | #include "MTaskList.h"
|
|---|
| 52 | #include "MEvtLoop.h"
|
|---|
| 53 | #include "MRanForestGrow.h"
|
|---|
| 54 | #include "MFillH.h"
|
|---|
| 55 |
|
|---|
| 56 | ClassImp(MRanForestCalc);
|
|---|
| 57 |
|
|---|
| 58 | using namespace std;
|
|---|
| 59 |
|
|---|
| 60 | const TString MRanForestCalc::gsDefName = "MRanForestCalc";
|
|---|
| 61 | const TString MRanForestCalc::gsDefTitle = "RF for energy estimation";
|
|---|
| 62 |
|
|---|
| 63 | const TString MRanForestCalc::gsNameOutput = "RanForestOut";
|
|---|
| 64 |
|
|---|
| 65 | MRanForestCalc::MRanForestCalc(const char *name, const char *title)
|
|---|
| 66 | : fData(0), fRFOut(0), fTestMatrix(0),
|
|---|
| 67 | fNumTrees(-1), fNumTry(-1), fNdSize(-1), fNumObsoleteVariables(1),
|
|---|
| 68 | fLastDataColumnHasWeights(kFALSE),
|
|---|
| 69 | fNameOutput(gsNameOutput), fDebug(kFALSE), fEstimationMode(kMean)
|
|---|
| 70 | {
|
|---|
| 71 | fName = name ? name : gsDefName.Data();
|
|---|
| 72 | fTitle = title ? title : gsDefTitle.Data();
|
|---|
| 73 |
|
|---|
| 74 | // FIXME:
|
|---|
| 75 | fNumTrees = 100; //100
|
|---|
| 76 | fNumTry = 0; //3 0 means: in MRanForest estimated best value will be calculated
|
|---|
| 77 | fNdSize = 1; //1
|
|---|
| 78 | }
|
|---|
| 79 |
|
|---|
| 80 | MRanForestCalc::~MRanForestCalc()
|
|---|
| 81 | {
|
|---|
| 82 | fEForests.Delete();
|
|---|
| 83 | }
|
|---|
| 84 |
|
|---|
| 85 | // --------------------------------------------------------------------------
|
|---|
| 86 | //
|
|---|
| 87 | // ver=0: One yes/no-classification forest is trained for each bin.
|
|---|
| 88 | // the yes/no classification is done using the grid
|
|---|
| 89 | // ver=1: One classification forest is trained. The last column contains a
|
|---|
| 90 | // value which is turned into a classifier by rf itself using the grid
|
|---|
| 91 | // ver=2: One classification forest is trained. The last column already contains
|
|---|
| 92 | // the classifier
|
|---|
| 93 | // ver=3: A regression forest is trained. The last column contains the
|
|---|
| 94 | // classifier
|
|---|
| 95 | //
|
|---|
| 96 | Int_t MRanForestCalc::Train(const MHMatrix &matrixtrain, const TArrayD &grid, Int_t ver)
|
|---|
| 97 | {
|
|---|
| 98 | gLog.Separator("MRanForestCalc - Train");
|
|---|
| 99 |
|
|---|
| 100 | if (!matrixtrain.GetColumns())
|
|---|
| 101 | {
|
|---|
| 102 | *fLog << err << "ERROR - MHMatrix does not contain rules... abort." << endl;
|
|---|
| 103 | return kFALSE;
|
|---|
| 104 | }
|
|---|
| 105 |
|
|---|
| 106 | const Int_t ncols = matrixtrain.GetM().GetNcols();
|
|---|
| 107 | const Int_t nrows = matrixtrain.GetM().GetNrows();
|
|---|
| 108 | if (ncols<=0 || nrows <=0)
|
|---|
| 109 | {
|
|---|
| 110 | *fLog << err << "ERROR - No. of columns or no. of rows of matrixtrain equal 0 ... abort." << endl;
|
|---|
| 111 | return kFALSE;
|
|---|
| 112 | }
|
|---|
| 113 |
|
|---|
| 114 | // rules (= combination of image par) to be used for energy estimation
|
|---|
| 115 | TFile fileRF(fFileName, "recreate");
|
|---|
| 116 | if (!fileRF.IsOpen())
|
|---|
| 117 | {
|
|---|
| 118 | *fLog << err << "ERROR - File to store RFs could not be opened... abort." << endl;
|
|---|
| 119 | return kFALSE;
|
|---|
| 120 | }
|
|---|
| 121 |
|
|---|
| 122 | // The number of columns which have to be removed for the training
|
|---|
| 123 | // The last data column may contain weight which also have to be removed
|
|---|
| 124 | const Int_t nobs = fNumObsoleteVariables + (fLastDataColumnHasWeights?1:0); // Number of obsolete columns
|
|---|
| 125 |
|
|---|
| 126 | const MDataArray &dcol = *matrixtrain.GetColumns();
|
|---|
| 127 |
|
|---|
| 128 | // Make a copy of the rules for accessing the train-data
|
|---|
| 129 | MDataArray usedrules;
|
|---|
| 130 | for (Int_t i=0; i<ncols; i++)
|
|---|
| 131 | if (i<ncols-nobs) // -3 is important!!!
|
|---|
| 132 | usedrules.AddEntry(dcol[i].GetRule());
|
|---|
| 133 | else
|
|---|
| 134 | *fLog << inf << "Skipping " << dcol[i].GetRule() << " for training" << endl;
|
|---|
| 135 |
|
|---|
| 136 | // In the case of regression store the rule to be regessed in the
|
|---|
| 137 | // last entry of your rules
|
|---|
| 138 | MDataArray rules(usedrules);
|
|---|
| 139 | rules.AddEntry(ver<3?"Classification":dcol[ncols-1].GetRule().Data());
|
|---|
| 140 |
|
|---|
| 141 | // prepare train-matrix finally used
|
|---|
| 142 | TMatrix mat(matrixtrain.GetM());
|
|---|
| 143 |
|
|---|
| 144 | // Resize it such that the obsolete columns are removed
|
|---|
| 145 | mat.ResizeTo(nrows, ncols-nobs+1);
|
|---|
| 146 |
|
|---|
| 147 | if (fDebug)
|
|---|
| 148 | gLog.SetNullOutput(kTRUE);
|
|---|
| 149 |
|
|---|
| 150 | // In the case one independant RF is trained for each bin (e.g.
|
|---|
| 151 | // energy-bin) train all of them
|
|---|
| 152 | const Int_t nbins = ver>0 ? 1 : grid.GetSize()-1;
|
|---|
| 153 | for (Int_t ie=0; ie<nbins; ie++)
|
|---|
| 154 | {
|
|---|
| 155 | // In the case weights should be used initialize the
|
|---|
| 156 | // corresponding array
|
|---|
| 157 | TArrayF weights(nrows);
|
|---|
| 158 | if (fLastDataColumnHasWeights)
|
|---|
| 159 | for (Int_t j=0; j<nrows; j++)
|
|---|
| 160 | {
|
|---|
| 161 | weights[j] = matrixtrain.GetM()(j, ncols-nobs);
|
|---|
| 162 | if (j%100==0)
|
|---|
| 163 | cout << weights[j] << " ";
|
|---|
| 164 | }
|
|---|
| 165 |
|
|---|
| 166 | // Setup the matrix such that the last comlumn contains
|
|---|
| 167 | // the classifier or the regeression target value
|
|---|
| 168 | switch (ver)
|
|---|
| 169 | {
|
|---|
| 170 | case 0: // Replace last column by a classification which is 1 in
|
|---|
| 171 | // the case the event belongs to this bin, 0 otherwise
|
|---|
| 172 | {
|
|---|
| 173 | Int_t irows=0;
|
|---|
| 174 | for (Int_t j=0; j<nrows; j++)
|
|---|
| 175 | {
|
|---|
| 176 | const Double_t value = matrixtrain.GetM()(j,ncols-1);
|
|---|
| 177 | const Bool_t inside = value>grid[ie] && value<=grid[ie+1];
|
|---|
| 178 |
|
|---|
| 179 | mat(j, ncols-nobs) = inside ? 1 : 0;
|
|---|
| 180 |
|
|---|
| 181 | if (inside)
|
|---|
| 182 | irows++;
|
|---|
| 183 | }
|
|---|
| 184 | if (irows==0)
|
|---|
| 185 | *fLog << warn << "WARNING - Skipping";
|
|---|
| 186 | else
|
|---|
| 187 | *fLog << inf << "Training RF for";
|
|---|
| 188 |
|
|---|
| 189 | *fLog << " bin " << ie << " (" << grid[ie] << ", " << grid[ie+1] << ") " << irows << "/" << nrows << endl;
|
|---|
| 190 |
|
|---|
| 191 | if (irows==0)
|
|---|
| 192 | continue;
|
|---|
| 193 | }
|
|---|
| 194 | break;
|
|---|
| 195 |
|
|---|
| 196 | case 1: // Use last column as classifier or for regression
|
|---|
| 197 | case 2:
|
|---|
| 198 | case 3:
|
|---|
| 199 | for (Int_t j=0; j<nrows; j++)
|
|---|
| 200 | mat(j, ncols-nobs) = matrixtrain.GetM()(j,ncols-1);
|
|---|
| 201 | break;
|
|---|
| 202 | }
|
|---|
| 203 |
|
|---|
| 204 | MHMatrix matrix(mat, &rules, "MatrixTrain");
|
|---|
| 205 |
|
|---|
| 206 | MParList plist;
|
|---|
| 207 | MTaskList tlist;
|
|---|
| 208 | plist.AddToList(&tlist);
|
|---|
| 209 | plist.AddToList(&matrix);
|
|---|
| 210 |
|
|---|
| 211 | MRanForest rf;
|
|---|
| 212 | rf.SetNumTrees(fNumTrees);
|
|---|
| 213 | rf.SetNumTry(fNumTry);
|
|---|
| 214 | rf.SetNdSize(fNdSize);
|
|---|
| 215 | rf.SetClassify(ver<3 ? kTRUE : kFALSE);
|
|---|
| 216 | if (ver==1)
|
|---|
| 217 | rf.SetGrid(grid);
|
|---|
| 218 | if (fLastDataColumnHasWeights)
|
|---|
| 219 | rf.SetWeights(weights);
|
|---|
| 220 |
|
|---|
| 221 | plist.AddToList(&rf);
|
|---|
| 222 |
|
|---|
| 223 | MRanForestGrow rfgrow;
|
|---|
| 224 | tlist.AddToList(&rfgrow);
|
|---|
| 225 |
|
|---|
| 226 | MFillH fillh("MHRanForestGini");
|
|---|
| 227 | tlist.AddToList(&fillh);
|
|---|
| 228 |
|
|---|
| 229 | MEvtLoop evtloop;
|
|---|
| 230 | evtloop.SetParList(&plist);
|
|---|
| 231 | evtloop.SetDisplay(fDisplay);
|
|---|
| 232 | evtloop.SetLogStream(fLog);
|
|---|
| 233 |
|
|---|
| 234 | if (!evtloop.Eventloop())
|
|---|
| 235 | return kFALSE;
|
|---|
| 236 |
|
|---|
| 237 | if (fDebug)
|
|---|
| 238 | gLog.SetNullOutput(kFALSE);
|
|---|
| 239 |
|
|---|
| 240 | if (ver==0)
|
|---|
| 241 | {
|
|---|
| 242 | // Calculate bin center
|
|---|
| 243 | const Double_t E = (TMath::Log10(grid[ie])+TMath::Log10(grid[ie+1]))/2;
|
|---|
| 244 |
|
|---|
| 245 | // save whole forest
|
|---|
| 246 | rf.SetUserVal(E);
|
|---|
| 247 | rf.SetName(Form("%.10f", E));
|
|---|
| 248 | }
|
|---|
| 249 |
|
|---|
| 250 | rf.Write();
|
|---|
| 251 | }
|
|---|
| 252 |
|
|---|
| 253 | // save rules
|
|---|
| 254 | usedrules.Write("rules");
|
|---|
| 255 |
|
|---|
| 256 | return kTRUE;
|
|---|
| 257 | }
|
|---|
| 258 |
|
|---|
| 259 | Int_t MRanForestCalc::ReadForests(MParList &plist)
|
|---|
| 260 | {
|
|---|
| 261 | TFile fileRF(fFileName, "read");
|
|---|
| 262 | if (!fileRF.IsOpen())
|
|---|
| 263 | {
|
|---|
| 264 | *fLog << err << dbginf << "File containing RFs could not be opened... aborting." << endl;
|
|---|
| 265 | return kFALSE;
|
|---|
| 266 | }
|
|---|
| 267 |
|
|---|
| 268 | fEForests.Delete();
|
|---|
| 269 |
|
|---|
| 270 | TIter Next(fileRF.GetListOfKeys());
|
|---|
| 271 | TObject *o=0;
|
|---|
| 272 | while ((o=Next()))
|
|---|
| 273 | {
|
|---|
| 274 | MRanForest *forest=0;
|
|---|
| 275 | fileRF.GetObject(o->GetName(), forest);
|
|---|
| 276 | if (!forest)
|
|---|
| 277 | continue;
|
|---|
| 278 |
|
|---|
| 279 | forest->SetUserVal(atof(o->GetName()));
|
|---|
| 280 |
|
|---|
| 281 | fEForests.Add(forest);
|
|---|
| 282 | }
|
|---|
| 283 |
|
|---|
| 284 | // Maybe fEForests[0].fRules could be used instead?
|
|---|
| 285 | if (fData->Read("rules")<=0)
|
|---|
| 286 | {
|
|---|
| 287 | *fLog << err << "ERROR - Reading 'rules' from file " << fFileName << endl;
|
|---|
| 288 | return kFALSE;
|
|---|
| 289 | }
|
|---|
| 290 |
|
|---|
| 291 | return kTRUE;
|
|---|
| 292 | }
|
|---|
| 293 |
|
|---|
| 294 | Int_t MRanForestCalc::PreProcess(MParList *plist)
|
|---|
| 295 | {
|
|---|
| 296 | fRFOut = (MParameterD*)plist->FindCreateObj("MParameterD", fNameOutput);
|
|---|
| 297 | if (!fRFOut)
|
|---|
| 298 | return kFALSE;
|
|---|
| 299 |
|
|---|
| 300 | fData = (MDataArray*)plist->FindCreateObj("MDataArray");
|
|---|
| 301 | if (!fData)
|
|---|
| 302 | return kFALSE;
|
|---|
| 303 |
|
|---|
| 304 | if (!ReadForests(*plist))
|
|---|
| 305 | {
|
|---|
| 306 | *fLog << err << "Reading RFs failed... aborting." << endl;
|
|---|
| 307 | return kFALSE;
|
|---|
| 308 | }
|
|---|
| 309 |
|
|---|
| 310 | *fLog << inf << "RF read from " << fFileName << endl;
|
|---|
| 311 |
|
|---|
| 312 | if (fTestMatrix)
|
|---|
| 313 | return kTRUE;
|
|---|
| 314 |
|
|---|
| 315 | fData->Print();
|
|---|
| 316 |
|
|---|
| 317 | if (!fData->PreProcess(plist))
|
|---|
| 318 | {
|
|---|
| 319 | *fLog << err << "PreProcessing of the MDataArray failed... aborting." << endl;
|
|---|
| 320 | return kFALSE;
|
|---|
| 321 | }
|
|---|
| 322 |
|
|---|
| 323 | return kTRUE;
|
|---|
| 324 | }
|
|---|
| 325 |
|
|---|
| 326 | Int_t MRanForestCalc::Process()
|
|---|
| 327 | {
|
|---|
| 328 | TVector event;
|
|---|
| 329 | if (fTestMatrix)
|
|---|
| 330 | *fTestMatrix >> event;
|
|---|
| 331 | else
|
|---|
| 332 | *fData >> event;
|
|---|
| 333 |
|
|---|
| 334 | // --------------- Single Tree RF -------------------
|
|---|
| 335 | if (fEForests.GetEntriesFast()==1)
|
|---|
| 336 | {
|
|---|
| 337 | MRanForest *rf = static_cast<MRanForest*>(fEForests.UncheckedAt(0));
|
|---|
| 338 | fRFOut->SetVal(rf->CalcHadroness(event));
|
|---|
| 339 | fRFOut->SetReadyToSave();
|
|---|
| 340 |
|
|---|
| 341 | return kTRUE;
|
|---|
| 342 | }
|
|---|
| 343 |
|
|---|
| 344 | // --------------- Multi Tree RF -------------------
|
|---|
| 345 | static TF1 f1("f1", "gaus");
|
|---|
| 346 |
|
|---|
| 347 | Double_t sume = 0;
|
|---|
| 348 | Double_t sumh = 0;
|
|---|
| 349 | Double_t maxh = 0;
|
|---|
| 350 | Double_t maxe = 0;
|
|---|
| 351 |
|
|---|
| 352 | Double_t max = -1e10;
|
|---|
| 353 | Double_t min = 1e10;
|
|---|
| 354 |
|
|---|
| 355 | TIter Next(&fEForests);
|
|---|
| 356 | MRanForest *rf = 0;
|
|---|
| 357 |
|
|---|
| 358 | TGraph g;
|
|---|
| 359 | while ((rf=(MRanForest*)Next()))
|
|---|
| 360 | {
|
|---|
| 361 | const Double_t h = rf->CalcHadroness(event);
|
|---|
| 362 | const Double_t e = rf->GetUserVal();
|
|---|
| 363 |
|
|---|
| 364 | g.SetPoint(g.GetN(), e, h);
|
|---|
| 365 |
|
|---|
| 366 | sume += e*h;
|
|---|
| 367 | sumh += h;
|
|---|
| 368 |
|
|---|
| 369 | if (h>maxh)
|
|---|
| 370 | {
|
|---|
| 371 | maxh = h;
|
|---|
| 372 | maxe = e;
|
|---|
| 373 | }
|
|---|
| 374 | if (e>max)
|
|---|
| 375 | max = e;
|
|---|
| 376 | if (e<min)
|
|---|
| 377 | min = e;
|
|---|
| 378 | }
|
|---|
| 379 |
|
|---|
| 380 | switch (fEstimationMode)
|
|---|
| 381 | {
|
|---|
| 382 | case kMean:
|
|---|
| 383 | fRFOut->SetVal(pow(10, sume/sumh));
|
|---|
| 384 | break;
|
|---|
| 385 | case kMaximum:
|
|---|
| 386 | fRFOut->SetVal(pow(10, maxe));
|
|---|
| 387 | break;
|
|---|
| 388 | case kFit:
|
|---|
| 389 | f1.SetParameter(0, maxh);
|
|---|
| 390 | f1.SetParameter(1, maxe);
|
|---|
| 391 | f1.SetParameter(2, 0.125);
|
|---|
| 392 | g.Fit(&f1, "Q0N");
|
|---|
| 393 | fRFOut->SetVal(pow(10, f1.GetParameter(1)));
|
|---|
| 394 | break;
|
|---|
| 395 | }
|
|---|
| 396 |
|
|---|
| 397 | fRFOut->SetReadyToSave();
|
|---|
| 398 |
|
|---|
| 399 | return kTRUE;
|
|---|
| 400 | }
|
|---|
| 401 |
|
|---|
| 402 | void MRanForestCalc::Print(Option_t *o) const
|
|---|
| 403 | {
|
|---|
| 404 | *fLog << all;
|
|---|
| 405 | *fLog << GetDescriptor() << ":" << endl;
|
|---|
| 406 | *fLog << " - Forest ";
|
|---|
| 407 | switch (fEForests.GetEntries())
|
|---|
| 408 | {
|
|---|
| 409 | case 0: *fLog << "not yet initialized." << endl; break;
|
|---|
| 410 | case 1: *fLog << "is a single tree forest." << endl; break;
|
|---|
| 411 | default: *fLog << "is a multi tree forest." << endl; break;
|
|---|
| 412 | }
|
|---|
| 413 | /*
|
|---|
| 414 | *fLog << " - Trees: " << fNumTrees << endl;
|
|---|
| 415 | *fLog << " - Trys: " << fNumTry << endl;
|
|---|
| 416 | *fLog << " - Node Size: " << fNdSize << endl;
|
|---|
| 417 | *fLog << " - Node Size: " << fNdSize << endl;
|
|---|
| 418 | */
|
|---|
| 419 | *fLog << " - FileName: " << fFileName << endl;
|
|---|
| 420 | *fLog << " - NameOutput: " << fNameOutput << endl;
|
|---|
| 421 | }
|
|---|
| 422 |
|
|---|
| 423 | // --------------------------------------------------------------------------
|
|---|
| 424 | //
|
|---|
| 425 | //
|
|---|
| 426 | Int_t MRanForestCalc::ReadEnv(const TEnv &env, TString prefix, Bool_t print)
|
|---|
| 427 | {
|
|---|
| 428 | Bool_t rc = kFALSE;
|
|---|
| 429 | if (IsEnvDefined(env, prefix, "FileName", print))
|
|---|
| 430 | {
|
|---|
| 431 | rc = kTRUE;
|
|---|
| 432 | SetFileName(GetEnvValue(env, prefix, "FileName", fFileName));
|
|---|
| 433 | }
|
|---|
| 434 | if (IsEnvDefined(env, prefix, "Debug", print))
|
|---|
| 435 | {
|
|---|
| 436 | rc = kTRUE;
|
|---|
| 437 | SetDebug(GetEnvValue(env, prefix, "Debug", fDebug));
|
|---|
| 438 | }
|
|---|
| 439 | if (IsEnvDefined(env, prefix, "NameOutput", print))
|
|---|
| 440 | {
|
|---|
| 441 | rc = kTRUE;
|
|---|
| 442 | SetNameOutput(GetEnvValue(env, prefix, "NameOutput", fNameOutput));
|
|---|
| 443 | }
|
|---|
| 444 | if (IsEnvDefined(env, prefix, "EstimationMode", print))
|
|---|
| 445 | {
|
|---|
| 446 | TString txt = GetEnvValue(env, prefix, "EstimationMode", "");
|
|---|
| 447 | txt = txt.Strip(TString::kBoth);
|
|---|
| 448 | txt.ToLower();
|
|---|
| 449 | if (txt==(TString)"mean")
|
|---|
| 450 | fEstimationMode = kMean;
|
|---|
| 451 | if (txt==(TString)"maximum")
|
|---|
| 452 | fEstimationMode = kMaximum;
|
|---|
| 453 | if (txt==(TString)"fit")
|
|---|
| 454 | fEstimationMode = kFit;
|
|---|
| 455 | rc = kTRUE;
|
|---|
| 456 | }
|
|---|
| 457 | return rc;
|
|---|
| 458 | }
|
|---|
