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cec46807 | 1 | // --------------------------------------------------------------------------------- |
2 | // AliITStrackerANN | |
3 | // --------------------------------------------------------------------------------- | |
4 | // Neural Network-based algorithm for track reconstruction in the ALICE ITS. | |
5 | // The class contains all that is necessary in order to perform the tracking | |
6 | // using the ITS clusters in the V2 format. | |
7 | // The class is organized with some embedded objects which serve for | |
8 | // data management, and all setters and getters for all parameters. | |
9 | // The usage of the class from a tracking macro should be based essentially | |
10 | // in the initialization (constructor) and the call of a method which | |
11 | // performs in the right sequence all the operations. | |
12 | // Temporarily (and maybe definitively) all the intermediate steps are | |
13 | // public functions which could be called independently, but they do not | |
14 | // produce any result if all the preventively required steps have not been | |
15 | // performed. | |
16 | // --------------------------------------------------------------------------------- | |
17 | // Author: Alberto Pulvirenti (university of Catania) | |
18 | // Email : alberto.pulvirenti@ct.infn.it | |
19 | // --------------------------------------------------------------------------------- | |
20 | ||
0db9364f | 21 | #include <Riostream.h> |
cec46807 | 22 | #include <TMath.h> |
23 | #include <TString.h> | |
24 | #include <TObjArray.h> | |
25 | #include <TVector3.h> | |
26 | #include <TFile.h> | |
27 | #include <TTree.h> | |
28 | #include <TRandom.h> | |
29 | #include <TMatrixD.h> | |
30 | ||
31 | #include "AliITSgeom.h" | |
32 | #include "AliITStrackSA.h" | |
33 | #include "AliITSclusterV2.h" | |
34 | #include "AliITStrackV2.h" | |
35 | ||
36 | #include "AliITStrackerANN.h" | |
37 | ||
0db9364f | 38 | const Double_t AliITStrackerANN::fgkPi = 3.141592653; // pi |
39 | const Double_t AliITStrackerANN::fgkHalfPi = 1.570796327; // pi / 2 | |
40 | const Double_t AliITStrackerANN::fgkTwoPi = 6.283185307; // 2 * pi | |
cec46807 | 41 | |
42 | ClassImp(AliITStrackerANN) | |
43 | ||
44 | //__________________________________________________________________________________ | |
45 | AliITStrackerANN::AliITStrackerANN(const AliITSgeom *geom, Int_t msglev) | |
46 | : AliITStrackerV2(geom), fMsgLevel(msglev) | |
47 | { | |
48 | /************************************************************************** | |
49 | ||
50 | CONSTRUCTOR (standard-to-use version) | |
51 | ||
52 | Arguments: | |
53 | 1) the ITS geometry used in the generated event | |
54 | 2) the flag for log-messages writing | |
55 | ||
56 | The AliITSgeometry is used along the class, | |
57 | in order to translate the local AliITSclusterV2 coordinates | |
58 | into the Global reference frame, which is necessary for the | |
59 | Neural Network algorithm to operate. | |
60 | In case of serialized use, the log messages should be excluded, | |
61 | in order to save real execution time. | |
62 | ||
63 | Operations: | |
64 | - all pointer data members are initialized | |
65 | (if possible, otherwise are set to NULL) | |
66 | - all numeric data members are initialized to | |
67 | values which allow the Neural Network to operate | |
68 | even if nothing is externally set. | |
69 | ||
70 | NOTE: it is possible that tracking an event | |
71 | with these default values results in a non-sense. | |
72 | ||
73 | **************************************************************************/ | |
74 | ||
75 | Int_t i; | |
76 | ||
77 | // Get ITS geometry | |
78 | fGeom = (AliITSgeom*)geom; | |
79 | ||
80 | // Initialize the array of first module indexes per layer | |
81 | fNLayers = geom->GetNlayers(); | |
82 | fFirstModInLayer = new Int_t[fNLayers + 1]; | |
83 | for (i = 0; i < fNLayers; i++) { | |
84 | fFirstModInLayer[i] = fGeom->GetModuleIndex(i + 1, 1, 1); | |
85 | } | |
86 | fFirstModInLayer[fNLayers] = geom->GetIndexMax(); | |
87 | ||
88 | // initialization: no curvature cut steps | |
89 | fCurvNum = 0; | |
90 | fCurvCut = 0; | |
91 | ||
92 | // initialization: 4 sectors (one for each quadrant) | |
93 | fSectorNum = 4; | |
94 | fSectorWidth = fgkHalfPi; | |
95 | ||
96 | // initialization: theta offset of 20 degrees | |
97 | fPolarInterval = 20.0; | |
98 | ||
99 | // initialization: array structure not defined | |
100 | fStructureOK = kFALSE; | |
101 | ||
102 | // initialization: vertex in the origin | |
103 | fVertexX = 0.0; | |
104 | fVertexY = 0.0; | |
105 | fVertexZ = 0.0; | |
106 | ||
107 | // initialization: uninitialized point array | |
108 | fNodes = 0; | |
109 | ||
110 | // initialization: very large (unuseful) cut values | |
111 | Int_t ilayer; | |
112 | for (ilayer = 0; ilayer < 6; ilayer++) { | |
113 | fThetaCut2D[ilayer] = TMath::Pi(); | |
114 | fThetaCut3D[ilayer] = TMath::Pi(); | |
115 | fHelixMatchCut[ilayer] = 1.0; | |
116 | } | |
117 | ||
118 | // initialization: inictial activation range between 0.3 and 0.7 | |
119 | fEdge1 = 0.3; | |
120 | fEdge2 = 0.7; | |
121 | ||
122 | // initialization: neural network operation & weights | |
123 | fTemperature = 1.0; | |
124 | fStabThreshold = 0.001; | |
125 | fGain2CostRatio = 1.0; | |
126 | fExponent = 1.0; | |
127 | fActMinimum = 0.5; | |
128 | ||
129 | // initialization: uninitialized array of neurons | |
130 | fNeurons = 0; | |
131 | ||
132 | // initialization: uninitialized array of tracks | |
133 | fFoundTracks = 0; | |
134 | } | |
135 | ||
136 | //__________________________________________________________________________________ | |
137 | void AliITStrackerANN::SetCuts | |
138 | (Int_t ncurv, Double_t *curv, Double_t *theta2D, Double_t *theta3D, Double_t *helix) | |
139 | ||
140 | /************************************************************************** | |
141 | ||
142 | CUT SETTER | |
143 | ||
144 | Allows for the definition of all kind of geometric cuts | |
145 | which have been studied in for the creation of a neuron | |
146 | from a pair of clusters C1 and C2 on consecutive layers. | |
147 | Neuron will be created only if the pair passes ALL of these cuts. | |
148 | ||
149 | At the moment, we define 4 kinds of geometrical cuts: | |
150 | a) cut on the difference of the polar 'theta' angle; | |
151 | b) cut on the angle between origin->C1 and C1->C2 in space; | |
152 | c) cut on the curvature of the circle passing | |
153 | through C1, C2 and the primary vertex; | |
154 | d) cut on heli-matching of the same three points. | |
155 | ||
156 | Arguments: | |
157 | 1) the number of curvature cut steps | |
158 | 2) the array of curvature cuts for each step | |
159 | (its dimension is given by the argument 1) | |
160 | 3) array of 5 cut values (one for each consecutive lauer pair) | |
161 | related to cut a) | |
162 | 4) array of 5 cut values (one for each consecutive lauer pair) | |
163 | related to cut b) | |
164 | 5) array of 5 cut values (one for each consecutive lauer pair) | |
165 | related to cut c) | |
166 | ||
167 | Operations: | |
168 | - gets the values for each cut and stores them in data members | |
169 | - in the case of curvature cuts, the cut array | |
170 | (whose size is not fixed) is allocated | |
171 | ||
172 | NOTE: in case that the user wants to set onyl ONE of the 4 cuts array, | |
173 | he can simply pass NULL arguments for other cuts, and (eventually) | |
174 | a ZERO as the first argument (if curvature cuts have not to be set). | |
175 | ||
176 | Anyway, all the cuts have to be set at least once. | |
177 | ||
178 | **************************************************************************/ | |
179 | { | |
180 | // counter | |
181 | Int_t i; | |
182 | ||
183 | /*** Curvature cut setting ***/ | |
184 | ||
185 | // first of all, the curvature cuts are sorted in increasing order | |
186 | // (from the smallest to the largest one) | |
187 | Int_t *ind = new Int_t[ncurv]; | |
188 | TMath::Sort(ncurv, curv, ind, kFALSE); | |
189 | // then, the curvature cut array is allocated and filled | |
190 | // (a message with the list of defined cuts can be optionally shown) | |
191 | fCurvCut = new Double_t[ncurv]; | |
192 | if (fMsgLevel >= 1) cout << "Number of curvature cuts: " << ncurv << endl; | |
193 | for (i = 0; i < ncurv; i++) { | |
194 | fCurvCut[i] = curv[ind[i]]; | |
195 | if (fMsgLevel >= 1) cout << " - " << fCurvCut[i] << endl; | |
196 | } | |
197 | fCurvNum = ncurv; | |
198 | ||
199 | /*** 'Fixed' cuts setting ***/ | |
200 | ||
201 | // checks what cuts have to be set | |
202 | Bool_t doTheta2D = (theta2D != 0); | |
203 | Bool_t doTheta3D = (theta3D != 0); | |
204 | Bool_t doHelix = (helix != 0); | |
205 | // sets the cuts for all layer pairs | |
206 | for (i = 0; i < fNLayers - 1; i++) { | |
207 | if (doTheta2D) fThetaCut2D[i] = theta2D[i]; | |
208 | if (doTheta3D) fThetaCut3D[i] = theta3D[i]; | |
209 | if (doHelix) fHelixMatchCut[i] = helix[i]; | |
210 | } | |
211 | // if required, lists the cuts | |
212 | if (!fMsgLevel < 2) return; | |
213 | cout << "Theta 2D cuts: "; | |
214 | if (!doTheta2D) { | |
215 | cout << "<not set>" << endl; | |
216 | } | |
217 | else { | |
218 | cout << endl; | |
219 | for (i = 0; i < fNLayers - 1; i++) { | |
220 | cout << "For layers " << i << " --> " << i + 1; | |
221 | cout << " cut = " << fThetaCut2D[i] << endl; | |
222 | } | |
223 | } | |
224 | cout << "---" << endl; | |
225 | cout << "Theta 3D cuts: "; | |
226 | if (!doTheta3D) { | |
227 | cout << "<not set>" << endl; | |
228 | } | |
229 | else { | |
230 | cout << endl; | |
231 | for (i = 0; i < fNLayers - 1; i++) { | |
232 | cout << "For layers " << i << " --> " << i + 1; | |
233 | cout << " cut = " << fThetaCut3D[i] << endl; | |
234 | } | |
235 | } | |
236 | cout << "---" << endl; | |
237 | cout << "Helix-match cuts: "; | |
238 | if (!doHelix) { | |
239 | cout << "<not set>" << endl; | |
240 | } | |
241 | else { | |
242 | cout << endl; | |
243 | for (i = 0; i < fNLayers - 1; i++) { | |
244 | cout << "For layers " << i << " --> " << i + 1; | |
245 | cout << " cut = " << fHelixMatchCut[i] << endl; | |
246 | } | |
247 | } | |
248 | cout << "---" << endl; | |
249 | } | |
250 | ||
251 | //__________________________________________________________________________________ | |
252 | Bool_t AliITStrackerANN::GetGlobalXYZ | |
253 | (Int_t refIndex, | |
254 | Double_t &x, Double_t &y, Double_t &z, Double_t &ex2, Double_t &ey2, Double_t &ez2) | |
255 | ||
256 | /************************************************************************** | |
257 | ||
258 | LOCAL TO GLOBAL TRANSLATOR | |
259 | ||
260 | Taking information from the ITS geometry stored in the class, | |
261 | gets a stored AliITScluster and calculates it coordinates | |
262 | and errors in the global reference frame. | |
263 | These values are stored in the variables, | |
264 | which are passed by reference. | |
265 | ||
266 | Arguments: | |
267 | 1) reference index for the cluster to use | |
268 | 2) (by reference) place to store the global X-coord into | |
269 | 3) (by reference) place to store the global Y-coord into | |
270 | 4) (by reference) place to store the global Z-coord into | |
271 | 5) (by reference) place to store the global X-coord error into | |
272 | 6) (by reference) place to store the global Y-coord error into | |
273 | 7) (by reference) place to store the global Z-coord error into | |
274 | ||
275 | Operations: | |
276 | essentially, determines the ITS module index from the | |
277 | detector index of the AliITSclusterV2 object, and extracts | |
278 | the roto-translation from the ITS geometry, to convert | |
279 | the local module coordinates into the global ones. | |
280 | ||
281 | Return value: | |
282 | - kFALSE if the given cluster index points to a non-existing | |
283 | cluster, or if the layer number makes no sense (< 0 or > 6). | |
284 | - otherwise, kTRUE (meaning a successful operation). | |
285 | ||
286 | **************************************************************************/ | |
287 | { | |
288 | // checks if the layer is correct | |
289 | Int_t ilayer = (refIndex & 0xf0000000) >> 28; | |
290 | if (ilayer < 0 || ilayer >= fNLayers) { | |
291 | Error("GetGlobalXYZ", "Wrong layer number: %d [range: %d - %d]", ilayer, 0, fNLayers); | |
292 | return kFALSE; | |
293 | } | |
294 | // checks if the referenced cluster exists and corresponds to the passed reference | |
295 | AliITSclusterV2 *refCluster = (AliITSclusterV2*) GetCluster(refIndex); | |
296 | if (!refCluster) { | |
297 | Error("GetGlobalXYZ", "Cluster not found for index %d", refIndex); | |
298 | return kFALSE; | |
299 | } | |
300 | ||
301 | // determine the detector number | |
302 | Int_t detID = refCluster->GetDetectorIndex() + fFirstModInLayer[ilayer]; | |
303 | ||
304 | // get rotation matrix | |
305 | Double_t rot[9]; | |
306 | fGeom->GetRotMatrix(detID, rot); | |
307 | ||
308 | // get translation vector | |
309 | Float_t tx,ty,tz; | |
310 | fGeom->GetTrans(detID, tx, ty, tz); | |
311 | ||
312 | // determine r and phi for the reference conversion | |
313 | Double_t r = -(Double_t)tx * rot[1] + (Double_t)ty * rot[0]; | |
314 | if (ilayer == 0) r = -r; | |
315 | Double_t phi = TMath::ATan2(rot[1],rot[0]); | |
316 | if (ilayer == 0) phi -= fgkPi; | |
317 | ||
318 | // sets values for X, Y, Z in global coordinates and their errors | |
319 | Double_t cosPhi = TMath::Cos(phi); | |
320 | Double_t sinPhi = TMath::Sin(phi); | |
321 | x = r*cosPhi + refCluster->GetY()*sinPhi; | |
322 | y = -r*sinPhi + refCluster->GetY()*cosPhi; | |
323 | z = refCluster->GetZ(); | |
324 | ex2 = refCluster->GetSigmaY2()*sinPhi*sinPhi; | |
325 | ey2 = refCluster->GetSigmaY2()*cosPhi*cosPhi; | |
326 | ez2 = refCluster->GetSigmaZ2(); | |
327 | ||
328 | return kTRUE; | |
329 | } | |
330 | ||
331 | //__________________________________________________________________________________ | |
332 | AliITStrackerANN::AliITSnode* AliITStrackerANN::AddNode(Int_t refIndex) | |
333 | ||
334 | /************************************************************************** | |
335 | ||
336 | GENERATOR OF NEURAL NODES | |
337 | ||
338 | Fills the array of neural 'nodes', which are the ITS clusters | |
339 | translated in the global reference frame. | |
340 | Given that the global coordinates are used many times, they are | |
341 | stored in a well-defined structure, in the form of an embedded class. | |
342 | Moreover, this class allows a faster navigation among points | |
343 | and neurons, by means of some object arrays, storing only the | |
344 | neurons which start from, or end to, the given node. | |
345 | Finally, each node contains all the other nodes which match it | |
346 | with respect to the fixed walues, in order to perform a faster | |
347 | neuron-creation phase. | |
348 | ||
349 | Arguments: | |
350 | 1) reference index of the correlated AliITSclusterV2 object | |
351 | ||
352 | Operations: | |
353 | - allocates the new AliITSnode objects | |
354 | - initializes its object arrays | |
355 | - from the global coordinates, calculates the | |
356 | 'phi' and 'theta' coordinates, in order to store it | |
357 | into the correct theta-slot and azimutal sector. | |
358 | ||
359 | REturn values: | |
360 | - the pointer of the creater AliITSnode object | |
361 | - in case of errors, a waring is given and a NULL is returned | |
362 | ||
363 | **************************************************************************/ | |
364 | { | |
365 | // create object and set the reference | |
366 | AliITSnode *node = new AliITSnode; | |
367 | if (!node) { | |
368 | Warning("AddNode", "Error occurred when allocating AliITSnode"); | |
369 | return 0; | |
370 | } | |
371 | node->ClusterRef() = refIndex; | |
372 | ||
373 | // calls the conversion function, which makes also some checks | |
374 | // (layer number within range, existence of referenced cluster) | |
375 | if ( !GetGlobalXYZ ( | |
376 | refIndex, | |
377 | node->X(), node->Y(), node->Z(), | |
378 | node->ErrX2(), node->ErrY2(), node->ErrZ2() | |
379 | ) ) {return 0;} | |
380 | ||
381 | // initializes the object arrays | |
382 | node->Matches() = new TObjArray; | |
383 | node->InnerOf() = new TObjArray; | |
384 | node->OuterOf() = new TObjArray; | |
385 | ||
386 | // finds azimutal and polar sector (in degrees) | |
387 | Double_t phi = node->GetPhi() * 180.0 / fgkPi; | |
388 | Double_t theta = node->GetTheta() * 180.0 / fgkPi; | |
389 | Int_t isector = (Int_t)(phi / fSectorWidth); | |
390 | Int_t itheta = (Int_t)theta; | |
391 | Int_t ilayer = (refIndex & 0xf0000000) >> 28; | |
392 | ||
393 | // selects the right TObjArray to store object into | |
394 | TObjArray *sector = (TObjArray*)fNodes[ilayer][itheta]->At(isector); | |
395 | sector->AddLast(node); | |
396 | ||
397 | return node; | |
398 | } | |
399 | ||
400 | //__________________________________________________________________________________ | |
401 | void AliITStrackerANN::CreateArrayStructure(Int_t nsectors) | |
402 | ||
403 | /************************************************************************** | |
404 | ||
405 | ARRAY STRUCTURE CREATOR | |
406 | ||
407 | Creates a structure of nested TObjArray's where the AliITSnode's | |
408 | have to be stored: | |
409 | - the first level is made by 6 arrays (one for each layer) | |
410 | - the second level is made by 180 arrays (one for each int part of theta) | |
411 | - the third level is made by a variable number of arrays | |
412 | (one for each azimutal sector) | |
413 | ||
414 | Arguments: | |
415 | 1) the number of azimutal sectors | |
416 | ||
417 | Operations: | |
418 | - calculates the width of each sector, from the argument | |
419 | - allocates and initializes all array levels | |
420 | - sets a flag which tells the user if this NECESSARY operation | |
421 | has been performed (it is needed BEFORE performing tracking) | |
422 | ||
423 | **************************************************************************/ | |
424 | { | |
425 | // Set the number of sectors and their width. | |
426 | fSectorNum = nsectors; | |
427 | fSectorWidth = 360.0 / (Double_t)fSectorNum; | |
428 | if (fMsgLevel >= 2) { | |
429 | cout << fSectorNum << " sectors --> sector width (degrees) = " << fSectorWidth << endl; | |
430 | } | |
431 | ||
432 | // Meaningful indexes | |
433 | Int_t ilayer, isector, itheta; | |
434 | ||
435 | // Mark for the created objects | |
436 | TObjArray *sector = 0; | |
437 | ||
438 | // First index: layer | |
439 | fNodes = new TObjArray**[fNLayers]; | |
440 | for (ilayer = 0; ilayer < fNLayers; ilayer++) { | |
441 | fNodes[ilayer] = new TObjArray*[180]; | |
442 | for (itheta = 0; itheta < 180; itheta++) fNodes[ilayer][itheta] = 0; | |
443 | for (itheta = 0; itheta < 180; itheta++) { | |
444 | fNodes[ilayer][itheta] = new TObjArray(nsectors); | |
445 | for (isector = 0; isector < nsectors; isector++) { | |
446 | sector = new TObjArray; | |
447 | sector->SetOwner(); | |
448 | fNodes[ilayer][itheta]->AddAt(sector, isector); | |
449 | } | |
450 | } | |
451 | } | |
452 | ||
453 | // Sets a checking flag to TRUE. | |
454 | // This flag is checked before filling up the arrays with the points. | |
455 | fStructureOK = kTRUE; | |
456 | } | |
457 | ||
458 | //__________________________________________________________________________________ | |
459 | Int_t AliITStrackerANN::ArrangePoints(char *exportFile) | |
460 | ||
461 | /************************************************************************** | |
462 | ||
463 | POINTS LOCATOR | |
464 | ||
465 | This function assembles the operation from the other above methods, | |
466 | and fills the arrays with the clusters already stored in the | |
467 | layers of the tracker. | |
468 | Then, in order to use this method, the user MUSTs call LoadClusters() | |
469 | before. | |
470 | ||
471 | Arguments: | |
472 | 1) string for a file name where the global ccordinates | |
473 | of all points can be exported (optional). | |
474 | If this file must not be created, simply pass a NULL argument | |
475 | ||
476 | Operations: | |
477 | - for each AliITSclusterV2 in each AliITSlayer, a ne AliITSnode | |
478 | is created and stored in the correct location. | |
479 | ||
480 | Return values: | |
481 | - the number of stored points | |
482 | - when errors occur, or no points are found, 0 is returned | |
483 | ||
484 | **************************************************************************/ | |
485 | { | |
486 | // Check if the array structure has been created | |
487 | if (!fStructureOK) { | |
488 | Error("ArrangePoints", "Structure NOT defined. Call CreateArrayStructure() first"); | |
489 | return 0; | |
490 | } | |
491 | ||
492 | // meaningful indexes | |
493 | Int_t ientry, ilayer, nentries = 0, index; | |
494 | Int_t nPtsLayer = 0; | |
495 | ||
496 | // if the argument is not NULL, a file is opened | |
497 | fstream file(exportFile, ios::out); | |
498 | if (!exportFile || file.fail()) { | |
499 | file.close(); | |
500 | exportFile = 0; | |
501 | } | |
502 | ||
503 | // scan all layers for node creation | |
504 | for (ilayer = 0; ilayer < fNLayers; ilayer++) { | |
505 | nPtsLayer = GetNumberOfClustersLayer(ilayer); | |
506 | if (fMsgLevel >= 1) { | |
507 | cout << "Layer " << ilayer << " --> " << nPtsLayer << " clusters" << endl; | |
508 | } | |
509 | for (ientry = 0; ientry < nPtsLayer; ientry++) { | |
510 | // calculation of cluster index : (Bit mask LLLLIIIIIIIIIIII) | |
511 | // (L = bits used for layer) | |
512 | // (I = bits used for position in layer) | |
513 | index = ilayer << 28; | |
514 | index += ientry; | |
515 | // add new AliITSnode object | |
516 | AliITSnode *n = AddNode(index); | |
517 | if ( (n != NULL) && exportFile ) { | |
518 | file << index << ' ' << n->X() << ' ' << n->Y() << ' ' << n->Z() << endl; | |
519 | } | |
520 | } | |
521 | nentries += nPtsLayer; | |
522 | } | |
523 | ||
524 | // a conventional final message is put at the end of file | |
525 | if (exportFile) { | |
526 | file << "-1 0.0 0.0 0.0" << endl; | |
527 | file.close(); | |
528 | } | |
529 | ||
530 | // returns the number of points processed | |
531 | return nentries; | |
532 | } | |
533 | ||
534 | //__________________________________________________________________________________ | |
535 | void AliITStrackerANN::StoreOverallMatches() | |
536 | ||
537 | /************************************************************************** | |
538 | ||
539 | NODE-MATCH ANALYSIS | |
540 | ||
541 | Once the nodes have been created, a firs analysis is to check | |
542 | what pairs will satisfy at least the 'fixed' cuts (theta, helix-match) | |
543 | and the most permissive (= larger) curvature cut. | |
544 | All these node pairs are suitable for neuron creation. | |
545 | In fact, when performing a Neural Tracking step, the only further check | |
546 | will be a check against the current curvature step, while the other | |
547 | are always the same. | |
548 | For thi purpose, each AliITSnode has a data member, named 'fMatches' | |
549 | which contains references to all other AliITSnodes in the successive layer | |
550 | that form, with it, a 'good' pair, with respect to the above cited cuts. | |
551 | Then, in each step for neuron creation, the possible neurons starting from | |
552 | each node will be searched ONLY within the nodes referenced in fMatches. | |
553 | This, of course, speeds up a lot the neuron creation procedure, at the | |
554 | cost of some memory occupation, which results not to be critical. | |
555 | ||
556 | Operations: | |
557 | - for each AliITSnode, matches are found according to the criteria | |
558 | expressed above, and stored in the node->fMatches array | |
559 | ||
560 | **************************************************************************/ | |
561 | { | |
562 | // meaningful counters | |
563 | Int_t ilayer, isector, itheta1, itheta2, check; | |
564 | TObjArray *list1 = 0, *list2 = 0; | |
565 | AliITSnode *node1 = 0, *node2 = 0; | |
566 | Double_t thetaMin, thetaMax; | |
567 | Int_t imin, imax; | |
568 | ||
569 | // Scan for each sector | |
570 | for (isector = 0; isector < fSectorNum; isector++) { | |
571 | // sector is chosen once for both lists | |
572 | for (ilayer = 0; ilayer < fNLayers - 1; ilayer++) { | |
573 | for (itheta1 = 0; itheta1 < 180; itheta1++) { | |
574 | list1 = (TObjArray*)fNodes[ilayer][itheta1]->At(isector); | |
575 | TObjArrayIter iter1(list1); | |
576 | while ( (node1 = (AliITSnode*)iter1.Next()) ) { | |
577 | if (node1->IsUsed()) continue; | |
578 | // clear an eventually already present array | |
579 | // node1->Matches()->Clear(); | |
580 | // get the global coordinates and defines the theta interval from cut | |
581 | thetaMin = (node1->GetTheta() * 180.0 / fgkPi) - fPolarInterval; | |
582 | thetaMax = (node1->GetTheta() * 180.0 / fgkPi) + fPolarInterval; | |
583 | imin = (Int_t)thetaMin; | |
584 | imax = (Int_t)thetaMax; | |
585 | if (imin < 0) imin = 0; | |
586 | if (imax > 179) imax = 179; | |
587 | // loop on the selected theta slots | |
588 | for (itheta2 = imin; itheta2 <= imax; itheta2++) { | |
589 | list2 = (TObjArray*)fNodes[ilayer + 1][itheta2]->At(isector); | |
590 | TObjArrayIter iter2(list2); | |
591 | while ( (node2 = (AliITSnode*)iter2.Next()) ) { | |
592 | check = PassAllCuts(node1, node2, fCurvNum - 1, fVertexX, fVertexY, fVertexZ); | |
593 | if (check == 0) { | |
594 | node1->Matches()->AddLast(node2); | |
595 | } | |
596 | } // while (node2...) | |
597 | } // for (itheta2...) | |
598 | } // while (node1...) | |
599 | } // for (itheta...) | |
600 | } // for (ilayer...) | |
601 | } // for (isector...) | |
602 | } | |
603 | ||
604 | //__________________________________________________________________________________ | |
605 | Int_t AliITStrackerANN::PassAllCuts | |
606 | (AliITSnode *inner, AliITSnode *outer, Int_t curvStep, | |
607 | Double_t vx, Double_t vy, Double_t vz) | |
608 | { | |
609 | // *********************************************************************************** | |
610 | // | |
611 | // This check is called in the above method for finding the matches of each node | |
612 | // It check the passed point pair against all the fixed cuts and a specified | |
613 | // curvature cut, among all the ones which have been defined. | |
614 | // The cuts need a vertex-constraint, which is not absolute, but it is passed | |
615 | // as argument. | |
616 | // | |
617 | // Arguments: | |
618 | // 1) the point in the inner layer | |
619 | // 2) the point in the outer layer | |
620 | // 3) curvature step for the curvature cut check (preferably the last) | |
621 | // 4) X of the used vertex | |
622 | // 5) Y of the used vertex | |
623 | // 6) Z of the used vertex | |
624 | // | |
625 | // Operations: | |
626 | // - if necessary, swaps the two points | |
627 | // (the first must be in the innermost of the two layers) | |
628 | // - checks for the theta cuts | |
629 | // - calculates the circle passing through the vertex | |
630 | // and the given points and checks for the curvature cut | |
631 | // - using the radius calculated there, checks for the helix-math cut | |
632 | // | |
633 | // Return values: | |
634 | // 0 - All cuts passed | |
635 | // 1 - theta 2D cut not passed | |
636 | // 2 - theta 3D cut not passed | |
637 | // 3 - curvature calculated but cut not passed | |
638 | // 4 - curvature not calculated (division by zero) | |
639 | // 5 - helix cut not passed | |
640 | // 6 - curvature inxed out of range | |
641 | // | |
642 | // *********************************************************************************** | |
643 | ||
644 | // Check for curvature index | |
645 | if (curvStep < 0 || curvStep >= fCurvNum) return 6; | |
646 | ||
647 | // Swap points in order that r1 < r2 | |
648 | AliITSnode *temp = 0; | |
649 | if (outer->GetLayer() < inner->GetLayer()) { | |
650 | temp = outer; | |
651 | outer = inner; | |
652 | inner = temp; | |
653 | } | |
654 | ||
655 | // All cuts are variable according to the layer of the | |
656 | // innermost point (the other point will surely be | |
657 | // in the further one, because we don't check poin pairs | |
658 | // which are not in adjacent layers) | |
659 | // The reference is given by the innermost point. | |
660 | Int_t layRef = inner->GetLayer(); | |
661 | ||
662 | // The calculations in the transverse plane are made in | |
663 | // a shifted reference frame, whose origin corresponds to | |
664 | // the reference point passed in the argument. | |
665 | Double_t xIn = inner->X() - vx; | |
666 | Double_t xOut = outer->X() - vx; | |
667 | Double_t yIn = inner->Y() - vy; | |
668 | Double_t yOut = outer->Y() - vy; | |
669 | Double_t zIn = inner->Z() - vz; | |
670 | Double_t zOut = outer->Z() - vz; | |
671 | Double_t rIn = TMath::Sqrt(xIn*xIn + yIn*yIn); | |
672 | Double_t rOut = TMath::Sqrt(xOut*xOut + yOut*yOut); | |
673 | ||
674 | // Check for theta cut. | |
675 | // There are two different angular cuts: | |
676 | // one w.r. to the angle in the 2-dimensional r-z plane... | |
677 | Double_t dthetaRZ; | |
678 | TVector3 origin2innerRZ(zIn, rIn, 0.0); | |
679 | TVector3 inner2outerRZ(zOut - zIn, rOut - rIn, 0.0); | |
680 | dthetaRZ = origin2innerRZ.Angle(inner2outerRZ) * 180.0 / fgkPi; | |
681 | if (dthetaRZ > fThetaCut2D[layRef]) { | |
682 | return 1; | |
683 | // r-z theta cut not passed ---> 1 | |
684 | } | |
685 | // ...and another w.r. to the angle in the 3-dimensional x-y-z space | |
686 | Double_t dthetaXYZ; | |
687 | TVector3 origin2innerXYZ(xIn, yIn, zIn); | |
688 | TVector3 inner2outerXYZ(xOut - xIn, yOut - yIn, zOut - zIn); | |
689 | dthetaXYZ = origin2innerXYZ.Angle(inner2outerXYZ) * 180.0 / fgkPi; | |
690 | if (dthetaXYZ > fThetaCut3D[layRef]) { | |
691 | return 2; | |
692 | // x-y-z theta cut not passed ---> 2 | |
693 | } | |
694 | ||
695 | // Calculation & check of curvature | |
696 | Double_t dx = xIn - xOut; | |
697 | Double_t dy = yIn - yOut; | |
698 | Double_t num = 2.0 * (xIn*yOut - xOut*yIn); | |
699 | Double_t den = rIn*rOut*sqrt(dx*dx + dy*dy); | |
700 | Double_t curv = 0.; | |
701 | if (den != 0.) { | |
702 | curv = TMath::Abs(num / den); | |
703 | if (curv > fCurvCut[curvStep]) { | |
704 | return 3; | |
705 | // curvature too large for cut ---> 3 | |
706 | } | |
707 | } | |
708 | else { | |
709 | Error("PassAllCuts", "Curvature calculations gives zero denominator"); | |
710 | return 4; | |
711 | // error: denominator = 0 ---> 4 | |
712 | } | |
713 | ||
714 | // Calculation & check of helix matching | |
715 | Double_t helMatch = 0.0; | |
716 | Double_t arcIn = 2.0 * rIn * curv; | |
717 | Double_t arcOut = 2.0 * rOut * curv; | |
718 | if (arcIn > -1.0 && arcIn < 1.0) | |
719 | arcIn = TMath::ASin(arcIn); | |
720 | else | |
721 | arcIn = ((arcIn > 0.0) ? 0.5 : 1.5) * TMath::Pi(); | |
722 | if (arcOut > -1.0 && arcOut < 1.0) | |
723 | arcOut = TMath::ASin(arcOut); | |
724 | else | |
725 | arcOut = ((arcOut > 0.0) ? 0.5 : 1.5) * TMath::Pi(); | |
726 | arcIn /= 2.0 * curv; | |
727 | arcOut /= 2.0 * curv; | |
728 | if (arcIn == 0.0 || arcOut == 0.0) { | |
729 | Error("PassAllCuts", "Calculation returns zero-length arcs: l1=%f, l2=%f", arcIn, arcOut); | |
730 | return 4; | |
731 | // error: circumference arcs seem to equal zero ---> 4 | |
732 | } | |
733 | helMatch = TMath::Abs(zIn / arcIn - zOut / arcOut); | |
734 | if (helMatch > fHelixMatchCut[layRef]) { | |
735 | return 5; | |
736 | // helix match cut not passed ---> 5 | |
737 | } | |
738 | ||
739 | // ALL CUTS PASSED ---> 0 | |
740 | return 0; | |
741 | } | |
742 | ||
743 | //__________________________________________________________________________________ | |
744 | Bool_t AliITStrackerANN::PassCurvCut | |
745 | (AliITSnode *inner, AliITSnode *outer, | |
746 | Int_t curvStep, | |
747 | Double_t vx, Double_t vy, Double_t vz) | |
748 | { | |
749 | //*********************************************************************************** | |
750 | // | |
751 | // This method operates essentially like the above one, but it is used | |
752 | // during a single step of Neural Tracking, where the curvature cut | |
753 | // changes. | |
754 | // Then, not necessaryly all the nodes stored in the fMatches array | |
755 | // will be suitable for neuron creation in an intermediate step. | |
756 | // | |
757 | // It has the same arguments of the PassAllCuts() method, but | |
758 | // the theta cut is not checked. | |
759 | // Moreover, it has a boolean return value. | |
760 | // | |
761 | //*********************************************************************************** | |
762 | ||
763 | // Check for curvature index | |
764 | if (curvStep < 0 || curvStep >= fCurvNum) return 6; | |
765 | ||
766 | // Find the reference layer | |
767 | Int_t layIn = inner->GetLayer(); | |
768 | Int_t layOut = outer->GetLayer(); | |
769 | Int_t layRef = (layIn < layOut) ? layIn : layOut; | |
770 | ||
771 | // The calculations in the transverse plane are made in | |
772 | // a shifted reference frame, whose origin corresponds to | |
773 | // the reference point passed in the argument. | |
774 | Double_t xIn = inner->X() - vx; | |
775 | Double_t xOut = outer->X() - vx; | |
776 | Double_t yIn = inner->Y() - vy; | |
777 | Double_t yOut = outer->Y() - vy; | |
778 | Double_t zIn = inner->Z() - vz; | |
779 | Double_t zOut = outer->Z() - vz; | |
780 | Double_t rIn = TMath::Sqrt(xIn*xIn + yIn*yIn); | |
781 | Double_t rOut = TMath::Sqrt(xOut*xOut + yOut*yOut); | |
782 | ||
783 | // Calculation & check of curvature | |
784 | Double_t dx = xIn - xOut; | |
785 | Double_t dy = yIn - yOut; | |
786 | Double_t num = 2.0 * (xIn*yOut - xOut*yIn); | |
787 | Double_t den = rIn*rOut*sqrt(dx*dx + dy*dy); | |
788 | Double_t curv = 0.; | |
789 | /* OLD VERSION | |
790 | if (den != 0.) { | |
791 | curv = TMath::Abs(num / den); | |
792 | if (curv > fCurvCut[curvStep]) return kFALSE; | |
793 | return kTRUE; | |
794 | } | |
795 | else | |
796 | return kFALSE; | |
797 | */ | |
798 | // NEW VERSION | |
799 | if (den != 0.) { | |
800 | curv = TMath::Abs(num / den); | |
801 | if (curv > fCurvCut[curvStep]) { | |
802 | return kFALSE; | |
803 | } | |
804 | } | |
805 | else { | |
806 | Error("PassAllCuts", "Curvature calculations gives zero denominator"); | |
807 | return kFALSE; | |
808 | } | |
809 | ||
810 | // Calculation & check of helix matching | |
811 | Double_t helMatch = 0.0; | |
812 | Double_t arcIn = 2.0 * rIn * curv; | |
813 | Double_t arcOut = 2.0 * rOut * curv; | |
814 | if (arcIn > -1.0 && arcIn < 1.0) | |
815 | arcIn = TMath::ASin(arcIn); | |
816 | else | |
817 | arcIn = ((arcIn > 0.0) ? 0.5 : 1.5) * TMath::Pi(); | |
818 | if (arcOut > -1.0 && arcOut < 1.0) | |
819 | arcOut = TMath::ASin(arcOut); | |
820 | else | |
821 | arcOut = ((arcOut > 0.0) ? 0.5 : 1.5) * TMath::Pi(); | |
822 | arcIn /= 2.0 * curv; | |
823 | arcOut /= 2.0 * curv; | |
824 | if (arcIn == 0.0 || arcOut == 0.0) { | |
825 | Error("PassAllCuts", "Calculation returns zero-length arcs: l1=%f, l2=%f", arcIn, arcOut); | |
826 | return 4; | |
827 | // error: circumference arcs seem to equal zero ---> 4 | |
828 | } | |
829 | helMatch = TMath::Abs(zIn / arcIn - zOut / arcOut); | |
830 | return (helMatch <= fHelixMatchCut[layRef]); | |
831 | } | |
832 | ||
833 | //__________________________________________________________________________________ | |
834 | void AliITStrackerANN::PrintMatches(Bool_t stop) | |
835 | { | |
836 | // Prints the list of points which appear to match | |
837 | // each one of them, according to the preliminary | |
838 | // overall cuts. | |
839 | // The arguments states if a pause is required after printing | |
840 | // the matches for each one. In this case, a keypress is needed. | |
841 | ||
842 | TObjArray *sector = 0; | |
843 | Int_t ilayer, isector, itheta, nF; | |
844 | AliITSnode *node1 = 0, *node2 = 0; | |
845 | //AliITSclusterV2 *cluster1 = 0, *cluster2 = 0; | |
846 | ||
847 | for (ilayer = 0; ilayer < 6; ilayer++) { | |
848 | for (isector = 0; isector < fSectorNum; isector++) { | |
849 | for (itheta = 0; itheta < 180; itheta++) { | |
850 | sector = (TObjArray*)fNodes[ilayer][itheta]->At(isector); | |
851 | TObjArrayIter points(sector); | |
852 | while ( (node1 = (AliITSnode*)points.Next()) ) { | |
853 | nF = (Int_t)node1->Matches()->GetEntries(); | |
854 | cout << "Node layer: " << node1->GetLayer() << " --> "; | |
855 | if (!nF) { | |
856 | cout << "NO Matches!!!" << endl; | |
857 | continue; | |
858 | } | |
859 | cout << nF << " Matches" << endl; | |
860 | cout << "Reference cluster: " << hex << node1->ClusterRef() << endl; | |
861 | TObjArrayIter matches(node1->Matches()); | |
862 | while ( (node2 = (AliITSnode*)matches.Next()) ) { | |
863 | cout << "Match with " << hex << node2->ClusterRef() << endl; | |
864 | } | |
865 | if (stop) { | |
866 | cout << "Press a key" << endl; | |
867 | cin.get(); | |
868 | } | |
869 | } | |
870 | } | |
871 | } | |
872 | } | |
873 | } | |
874 | ||
875 | //__________________________________________________________________________________ | |
876 | void AliITStrackerANN::ResetNodes(Int_t isector) | |
877 | { | |
878 | /*********************************************************************************** | |
879 | ||
880 | NODE NEURON ARRAY CLEANER | |
881 | ||
882 | After a neural tracking step, this method | |
883 | clears the arrays 'fInnerOf' and 'fOuterOf' of each AliITSnode | |
884 | ||
885 | Arguments: | |
886 | - the sector where the operation is being executed | |
887 | ||
888 | ***********************************************************************************/ | |
889 | ||
890 | Int_t ilayer, itheta; | |
891 | TObjArray *sector = 0; | |
892 | AliITSnode *node = 0; | |
893 | for (ilayer = 0; ilayer < fNLayers; ilayer++) { | |
894 | for (itheta = 0; itheta < 180; itheta++) { | |
895 | sector = (TObjArray*)fNodes[ilayer][itheta]->At(isector); | |
896 | TObjArrayIter iter(sector); | |
897 | for (;;) { | |
898 | node = (AliITSnode*)iter.Next(); | |
899 | if (!node) break; | |
900 | node->InnerOf()->Clear(); | |
901 | node->OuterOf()->Clear(); | |
902 | /* | |
903 | delete node->InnerOf(); | |
904 | delete node->OuterOf(); | |
905 | node->InnerOf() = new TObjArray; | |
906 | node->OuterOf() = new TObjArray; | |
907 | */ | |
908 | } | |
909 | } | |
910 | } | |
911 | } | |
912 | ||
913 | //__________________________________________________________________________________ | |
914 | Int_t AliITStrackerANN::CreateNeurons | |
915 | (AliITSnode *node, Int_t curvStep, Double_t vx, Double_t vy, Double_t vz) | |
916 | { | |
917 | // This method is used to create alle suitable neurons starting from | |
918 | // a single AliITSnode. Each unit is also stored in the fInnerOf array | |
919 | // of the passed node, and in the fOuterOf array of the other neuron edge. | |
920 | // In the new implementation of the intermediate check steps, a further one | |
921 | // is made, which chechs how well a helix is matched by three points | |
922 | // in three consecutive layers. | |
923 | // Then, a vertex-constrained check is made with vertex located | |
924 | // in a layer L, for points in layers L+1 and L+2. | |
925 | // | |
926 | // In order to do this, the creator works recursively, in a tree-visit like operation. | |
927 | // The neurons are effectively created only if the node argument passed is in | |
928 | // the 5th layer (they are created between point of 5th and 6th layer). | |
929 | // If the node is in an inner layer, its coordinates are passet as vertex for a nested | |
930 | // call of the same function in the next two layers. | |
931 | // | |
932 | // Arguments: | |
933 | // 1) reference node | |
934 | // 2) current curvature cut step | |
935 | // 3) X of referenced temporary (not primary) vertex | |
936 | // 4) Y of referenced temporary (not primary) vertex | |
937 | // 5) Z of referenced temporary (not primary) vertex | |
938 | // | |
939 | // Operations: | |
940 | // - if the layer is the 5th, neurons are created with nodes | |
941 | // in the fMatches array of the passed node | |
942 | // - otherwise, the X, Y, Z of the passed node are given as | |
943 | // vertex and the same procedure is recursively called for all | |
944 | // nodes in the fMatches array of the passed one. | |
945 | // | |
946 | // Return values: | |
947 | // - the total number of neurons created from the passed one | |
948 | // summed with all neurons created from all nodes well matched with it | |
949 | // (assumes a meaning only for nodes in the first layer) | |
950 | ||
951 | // local variables | |
952 | Int_t made = 0; // counter | |
953 | Bool_t found = 0; // flag | |
954 | AliITSnode *match = 0; // cursor for a AliITSnode array | |
955 | AliITSneuron *unit = 0; // cursor for a AliITSneuron array | |
956 | ||
957 | // --> Case 0: the passed node has already been used | |
958 | // as member of a track found in a previous step. | |
959 | // In this case, of course, the function exits. | |
960 | if (node->IsUsed()) return 0; | |
961 | ||
962 | // --> Case 1: there are NO well-matched points. | |
963 | // This can happen in all ITS layers, but it happens **for sure** | |
964 | // for a node in the 'last' layer. | |
965 | // Even in this case, the function exits. | |
966 | if (node->Matches()->IsEmpty()) return 0; | |
967 | ||
968 | // --> Case 2: there are well-matched points. | |
969 | // In this case, the function creates a neuron for each | |
970 | // well-matched pair (according to the cuts for the current step) | |
971 | // Moreover, before storing the neuron, a check is necessary | |
972 | // to avoid the duplicate creation of the same neuron twice. | |
973 | // (This could happen if the 3 last arguments of the function | |
974 | // are close enough to cause a good match for the current step | |
975 | // between two points, independently of their difference). | |
976 | // Finally, a node is skipped if it has already been used. | |
977 | // For each matched point for which a neuron is created, the procedure is | |
978 | // recursively called. | |
979 | TObjArrayIter matches(node->Matches()); | |
980 | while ( (match = (AliITSnode*)matches.Next()) ) { | |
981 | if (match->IsUsed()) continue; | |
982 | if (!PassCurvCut(node, match, curvStep, vx, vy, vz)) continue; | |
983 | found = kFALSE; | |
984 | if (!node->InnerOf()->IsEmpty()) { | |
985 | TObjArrayIter presentUnits(node->InnerOf()); | |
986 | while ( (unit = (AliITSneuron*)presentUnits.Next()) ) { | |
987 | if (unit->Inner() == node && unit->Outer() == match) { | |
988 | found = kTRUE; | |
989 | break; | |
990 | } | |
991 | } | |
992 | } | |
993 | if (found) continue; | |
994 | AliITSneuron *unit = new AliITSneuron(node, match, fEdge2, fEdge1); | |
995 | fNeurons->AddLast(unit); | |
996 | node->InnerOf()->AddLast(unit); | |
997 | match->OuterOf()->AddLast(unit); | |
998 | made += CreateNeurons(match, curvStep, node->X(), node->Y(), node->Z()); | |
999 | made++; | |
1000 | } | |
1001 | ||
1002 | // Of course, the return value contains the number of neurons | |
1003 | // counting in also the oned created in all levels of recursive calls. | |
1004 | return made; | |
1005 | } | |
1006 | ||
1007 | //__________________________________________________________________________________ | |
1008 | Int_t AliITStrackerANN::CreateNetwork(Int_t sector, Int_t curvStep) | |
1009 | { | |
1010 | // This function simply recalls the CreateNeurons() method for each node | |
1011 | // in the first layer, for the current sector. | |
1012 | // This generates the whole network, thanks to the recursive calls. | |
1013 | // | |
1014 | // Arguments: | |
1015 | // 1) current sector | |
1016 | // 2) current curvature step | |
1017 | // | |
1018 | // Operations: | |
1019 | // - scans the nodes array for all theta's in the current sector | |
1020 | // and layer 0, and calls the CreateNeurons() function. | |
1021 | ||
1022 | // removes all eventually present neurons | |
1023 | if (fNeurons) delete fNeurons; | |
1024 | fNeurons = new TObjArray; | |
1025 | fNeurons->SetOwner(kTRUE); | |
1026 | ||
1027 | // calls the ResetNodes() function to free the AliITSnode arrays | |
1028 | if (fMsgLevel >= 2) { | |
1029 | cout << "Sector " << sector << " PHI = "; | |
1030 | cout << fSectorWidth * (Double_t)sector << " --> "; | |
1031 | cout << fSectorWidth * (Double_t)(sector + 1) << endl; | |
1032 | cout << "Curvature step " << curvStep << " [cut = " << fCurvCut[curvStep] << "]" << endl; | |
1033 | } | |
1034 | ResetNodes(sector); | |
1035 | ||
1036 | // meaningful counters | |
1037 | Int_t itheta, neurons = 0; | |
1038 | TObjArray *lstSector = 0; | |
1039 | ||
1040 | // NEW VERSION | |
1041 | Double_t vx[6], vy[6], vz[6]; | |
1042 | AliITSnode *p[6] = {0, 0, 0, 0, 0, 0}; | |
1043 | for (itheta = 0; itheta < 180; itheta++) { | |
1044 | lstSector = (TObjArray*)fNodes[0][itheta]->At(sector); | |
1045 | TObjArrayIter lay0(lstSector); | |
1046 | while ( (p[0] = (AliITSnode*)lay0.Next()) ) { | |
1047 | if (p[0]->IsUsed()) continue; | |
1048 | vx[0] = fVertexX; | |
1049 | vy[0] = fVertexY; | |
1050 | vz[0] = fVertexZ; | |
1051 | neurons += CreateNeurons(p[0], curvStep, fVertexX, fVertexY, fVertexZ); | |
1052 | /* | |
1053 | TObjArrayIter lay1(p[0]->Matches()); | |
1054 | while ( (p[1] = (AliITSnode*)lay1.Next()) ) { | |
1055 | if (p[1]->IsUsed()) continue; | |
1056 | if (!PassCurvCut(p[0], p[1], curvStep, vx[0], vy[0], vz[0])) continue; | |
1057 | unit = new AliITSneuron; | |
1058 | unit->Inner() = p[0]; | |
1059 | unit->Outer() = p[1]; | |
1060 | unit->Activation() = gRandom->Rndm() * (fEdge1 - fEdge2) + fEdge2; | |
1061 | unit->fGain = new TObjArray; | |
1062 | fNeurons->AddLast(unit); | |
1063 | p[0]->InnerOf()->AddLast(unit); | |
1064 | p[1]->OuterOf()->AddLast(unit); | |
1065 | neurons++; | |
1066 | vx[1] = p[0]->X(); | |
1067 | vy[1] = p[0]->Y(); | |
1068 | vz[1] = p[0]->Z(); | |
1069 | TObjArrayIter lay2(p[1]->Matches()); | |
1070 | while ( (p[2] = (AliITSnode*)lay2.Next()) ) { | |
1071 | if (p[2]->IsUsed()) continue; | |
1072 | if (!PassCurvCut(p[1], p[2], curvStep, vx[1], vy[1], vz[1])) continue; | |
1073 | unit = new AliITSneuron; | |
1074 | unit->Inner() = p[1]; | |
1075 | unit->Outer() = p[2]; | |
1076 | unit->Activation() = gRandom->Rndm() * (fEdge1 - fEdge2) + fEdge2; | |
1077 | unit->fGain = new TObjArray; | |
1078 | fNeurons->AddLast(unit); | |
1079 | p[1]->InnerOf()->AddLast(unit); | |
1080 | p[2]->OuterOf()->AddLast(unit); | |
1081 | neurons++; | |
1082 | vx[2] = p[1]->X(); | |
1083 | vy[2] = p[1]->Y(); | |
1084 | vz[2] = p[1]->Z(); | |
1085 | TObjArrayIter lay3(p[2]->Matches()); | |
1086 | while ( (p[3] = (AliITSnode*)lay3.Next()) ) { | |
1087 | if (p[3]->IsUsed()) continue; | |
1088 | if (!PassCurvCut(p[2], p[3], curvStep, vx[2], vy[2], vz[2])) continue; | |
1089 | unit = new AliITSneuron; | |
1090 | unit->Inner() = p[2]; | |
1091 | unit->Outer() = p[3]; | |
1092 | unit->Activation() = gRandom->Rndm() * (fEdge1 - fEdge2) + fEdge2; | |
1093 | unit->fGain = new TObjArray; | |
1094 | fNeurons->AddLast(unit); | |
1095 | p[2]->InnerOf()->AddLast(unit); | |
1096 | p[3]->OuterOf()->AddLast(unit); | |
1097 | neurons++; | |
1098 | vx[3] = p[2]->X(); | |
1099 | vy[3] = p[2]->Y(); | |
1100 | vz[3] = p[2]->Z(); | |
1101 | TObjArrayIter lay4(p[3]->Matches()); | |
1102 | while ( (p[4] = (AliITSnode*)lay4.Next()) ) { | |
1103 | if (p[4]->IsUsed()) continue; | |
1104 | if (!PassCurvCut(p[3], p[4], curvStep, vx[3], vy[3], vz[3])) continue; | |
1105 | unit = new AliITSneuron; | |
1106 | unit->Inner() = p[3]; | |
1107 | unit->Outer() = p[4]; | |
1108 | unit->Activation() = gRandom->Rndm() * (fEdge1 - fEdge2) + fEdge2; | |
1109 | unit->fGain = new TObjArray; | |
1110 | fNeurons->AddLast(unit); | |
1111 | p[3]->InnerOf()->AddLast(unit); | |
1112 | p[4]->OuterOf()->AddLast(unit); | |
1113 | neurons++; | |
1114 | vx[4] = p[3]->X(); | |
1115 | vy[4] = p[3]->Y(); | |
1116 | vz[4] = p[3]->Z(); | |
1117 | TObjArrayIter lay5(p[4]->Matches()); | |
1118 | while ( (p[5] = (AliITSnode*)lay5.Next()) ) { | |
1119 | if (p[5]->IsUsed()) continue; | |
1120 | if (!PassCurvCut(p[4], p[5], curvStep, vx[4], vy[4], vz[4])) continue; | |
1121 | unit = new AliITSneuron; | |
1122 | unit->Inner() = p[4]; | |
1123 | unit->Outer() = p[5]; | |
1124 | unit->Activation() = gRandom->Rndm() * (fEdge1 - fEdge2) + fEdge2; | |
1125 | unit->fGain = new TObjArray; | |
1126 | fNeurons->AddLast(unit); | |
1127 | p[4]->InnerOf()->AddLast(unit); | |
1128 | p[5]->OuterOf()->AddLast(unit); | |
1129 | neurons++; | |
1130 | } // while (p[5]) | |
1131 | } // while (p[4]) | |
1132 | } // while (p[3]) | |
1133 | } // while (p[2]) | |
1134 | } // while (p[1]) | |
1135 | */ | |
1136 | } // while (p[0]) | |
1137 | } // for (itheta...) | |
1138 | // END OF NEW VERSION | |
1139 | ||
1140 | /* OLD VERSION | |
1141 | for (ilayer = 0; ilayer < 6; ilayer++) { | |
1142 | for (itheta = 0; itheta < 180; itheta++) { | |
1143 | lstSector = (TObjArray*)fNodes[ilayer][itheta]->At(sector_idx); | |
1144 | TObjArrayIter inners(lstSector); | |
1145 | while ( (inner = (AliITSnode*)inners.Next()) ) { | |
1146 | if (inner->GetUser() >= 0) continue; | |
1147 | TObjArrayIter outers(inner->Matches()); | |
1148 | while ( (outer = (AliITSnode*)outers.Next()) ) { | |
1149 | if (outer->GetUser() >= 0) continue; | |
1150 | if (!PassCurvCut(inner, outer, curvStep, fVX, fVY, fVZ)) continue; | |
1151 | unit = new AliITSneuron; | |
1152 | unit->Inner() = inner; | |
1153 | unit->Outer() = outer; | |
1154 | unit->Activation() = gRandom->Rndm() * (fEdge1 - fEdge2) + fEdge2; | |
1155 | unit->fGain = new TObjArray; | |
1156 | fNeurons->AddLast(unit); | |
1157 | inner->InnerOf()->AddLast(unit); | |
1158 | outer->OuterOf()->AddLast(unit); | |
1159 | neurons++; | |
1160 | } // for (;;) | |
1161 | } // for (;;) | |
1162 | } // for (itheta...) | |
1163 | } // for (ilayer...) | |
1164 | */ | |
1165 | ||
1166 | fNeurons->SetOwner(); | |
1167 | return neurons; | |
1168 | } | |
1169 | ||
1170 | //__________________________________________________________________________________ | |
1171 | Int_t AliITStrackerANN::LinkNeurons() | |
1172 | { | |
1173 | /*********************************************************************************** | |
1174 | ||
1175 | SYNAPSIS GENERATOR | |
1176 | ||
1177 | Scans the whole neuron array, in order to find all neuron pairs | |
1178 | which are connected in sequence and share a positive weight. | |
1179 | For each of them, an AliITSlink is created, which stores | |
1180 | the weight value, and will allow for a faster calculation | |
1181 | of the total neural input for each updating cycle. | |
1182 | ||
1183 | Every neuron contains an object array which stores all AliITSlink | |
1184 | objects which point to sequenced units, with the respective weights. | |
1185 | ||
1186 | Return value: | |
1187 | - the number of link created for the neural network. | |
1188 | If they are 0, no updating can be done and the step is skipped. | |
1189 | ||
1190 | ***********************************************************************************/ | |
1191 | ||
1192 | // meaningful indexes | |
1193 | Int_t total = 0; | |
1194 | Double_t weight = 0.0; | |
1195 | TObjArrayIter neurons(fNeurons), *iter; | |
1196 | AliITSneuron *neuron = 0, *test = 0; | |
1197 | ||
1198 | // scan in the neuron array | |
1199 | for (;;) { | |
1200 | neuron = (AliITSneuron*)neurons.Next(); | |
1201 | if (!neuron) break; | |
1202 | // checks for neurons startin from its head ( -> ) | |
1203 | iter = (TObjArrayIter*)neuron->Inner()->OuterOf()->MakeIterator(); | |
1204 | for (;;) { | |
1205 | test = (AliITSneuron*)iter->Next(); | |
1206 | if (!test) break; | |
1207 | weight = Weight(test, neuron); | |
1208 | if (weight > 0.0) neuron->Gain()->AddLast(new AliITSlink(weight, test)); | |
1209 | total++; | |
1210 | } | |
1211 | delete iter; | |
1212 | // checks for neurons ending in its tail ( >- ) | |
1213 | iter = (TObjArrayIter*)neuron->Outer()->InnerOf()->MakeIterator(); | |
1214 | for (;;) { | |
1215 | test = (AliITSneuron*)iter->Next(); | |
1216 | if (!test) break; | |
1217 | weight = Weight(neuron, test); | |
1218 | if (weight > 0.0) neuron->Gain()->AddLast(new AliITSlink(weight, test)); | |
1219 | total++; | |
1220 | } | |
1221 | delete iter; | |
1222 | } | |
1223 | return total; | |
1224 | } | |
1225 | ||
1226 | //__________________________________________________________________________________ | |
1227 | Bool_t AliITStrackerANN::Update() | |
1228 | { | |
1229 | /*********************************************************************************** | |
1230 | ||
1231 | Performs a single updating cycle. | |
1232 | ||
1233 | Operations: | |
1234 | - for each neuron, gets the activation with the neuron Activate() method | |
1235 | - checks if stability has been reached (compare mean activation variation | |
1236 | with the stability threshold data member) | |
1237 | ||
1238 | Return values: | |
1239 | - kTRUE means that the neural network has stabilized | |
1240 | - kFALSE means that another updating cycle is needed | |
1241 | ||
1242 | ***********************************************************************************/ | |
1243 | ||
1244 | Double_t actVar = 0.0, totDiff = 0.0; | |
1245 | TObjArrayIter iter(fNeurons); | |
1246 | AliITSneuron *unit; | |
1247 | for (;;) { | |
1248 | unit = (AliITSneuron*)iter.Next(); | |
1249 | if (!unit) break; | |
1250 | actVar = unit->Activate(fTemperature); | |
1251 | // calculation the relative activation variation | |
1252 | totDiff += actVar; | |
1253 | } | |
1254 | totDiff /= fNeurons->GetSize(); | |
1255 | return (totDiff < fStabThreshold); | |
1256 | } | |
1257 | ||
1258 | //__________________________________________________________________________________ | |
1259 | void AliITStrackerANN::FollowChains(Int_t sector) | |
1260 | { | |
1261 | /*********************************************************************************** | |
1262 | ||
1263 | CHAINS CREATION | |
1264 | ||
1265 | After that the neural network has stabilized, | |
1266 | the final step is to create polygonal chains | |
1267 | of clusters, one in each layer, which represent | |
1268 | the tracks recognized by the neural algorithm. | |
1269 | This is made by means of a choice of the best | |
1270 | neuron among the ones starting from each point. | |
1271 | ||
1272 | Once that such neuron is selected, its inner point | |
1273 | will set the 'fNext' field to its outer point, and | |
1274 | similarly, its outer point will set the 'fPrev' field | |
1275 | to its inner point. | |
1276 | This defines a bi-directional sequence. | |
1277 | ||
1278 | In this procedure, it can happen that many neurons | |
1279 | which have the head of the arrow in a given node, will | |
1280 | all select as best following the neuron with the largest | |
1281 | activation starting in that point. | |
1282 | This results in MANY nodes which have the same 'fNext'. | |
1283 | But, this field will be set to NULL for all these points, | |
1284 | but the only one which is pointed by the 'fPrev' field | |
1285 | of this shared node. | |
1286 | ||
1287 | ***********************************************************************************/ | |
1288 | ||
1289 | // meaningful counters | |
1290 | Int_t itheta, ilayer; | |
1291 | TObjArray *lstSector = 0; | |
1292 | Double_t test = fActMinimum; | |
1293 | AliITSnode *p = 0; | |
1294 | AliITSneuron *n = 0; | |
1295 | ||
1296 | // scan the whole collection of nodes | |
1297 | for (ilayer = 0; ilayer < fNLayers; ilayer++) { | |
1298 | for (itheta = 0; itheta < 180; itheta++) { | |
1299 | // get the array of point in a given layer/theta-slot/sector | |
1300 | lstSector = (TObjArray*)fNodes[ilayer][itheta]->At(sector); | |
1301 | TObjArrayIter nodes(lstSector); | |
1302 | while ( (p = (AliITSnode*)nodes.Next()) ) { | |
1303 | // if the point is used, it is skipped | |
1304 | if (p->IsUsed()) continue; | |
1305 | // initially, fNext points to nothing, and | |
1306 | // the comparison value is set to the minimum activation | |
1307 | // which allows to say that a neuron is turned 'on' | |
1308 | // a node from which only 'off' neurons start is probably | |
1309 | // a noise point, which will be excluded from the reconstruction. | |
1310 | test = fActMinimum; | |
1311 | p->Next() = 0; | |
1312 | TObjArrayIter innerof(p->InnerOf()); | |
1313 | while ( (n = (AliITSneuron*)innerof.Next()) ) { | |
1314 | // if the examined neuron has not the largest activation | |
1315 | // it is skipped and removed from array of all neurons | |
1316 | // and of its outer point (its inner is the cursor p) | |
1317 | if (n->Activation() < test) { | |
1318 | p->InnerOf()->Remove(n); | |
1319 | n->Outer()->OuterOf()->Remove(n); | |
1320 | delete fNeurons->Remove(n); | |
1321 | continue; | |
1322 | } | |
1323 | // otherwise, its activation becomes the maximum reference | |
1324 | p->Next() = n->Outer(); | |
1325 | // at the exit of the while(), the fNext will point | |
1326 | // to the outer node of the neuron starting in p, whose | |
1327 | // activation is the largest. | |
1328 | } | |
1329 | // the same procedure is made now for all neurons | |
1330 | // for which p is the outer point | |
1331 | test = fActMinimum; | |
1332 | p->Prev() = 0; | |
1333 | TObjArrayIter outerof(p->OuterOf()); | |
1334 | while ( (n = (AliITSneuron*)outerof.Next()) ) { | |
1335 | // if the examined neuron has not the largest activation | |
1336 | // it is skipped and removed from array of all neurons | |
1337 | // and of its inner point (its outer is the cursor p) | |
1338 | if (n->Activation() < test) { | |
1339 | p->OuterOf()->Remove(n); | |
1340 | n->Inner()->InnerOf()->Remove(n); | |
1341 | delete fNeurons->Remove(n); | |
1342 | continue; | |
1343 | } | |
1344 | // otherwise, its activation becomes the maximum reference | |
1345 | p->Prev() = n->Inner(); | |
1346 | // at the exit of the while(), the fPrev will point | |
1347 | // to the inner node of the neuron ending in p, whose | |
1348 | // activation is the largest. | |
1349 | } | |
1350 | } // end while (p ...) | |
1351 | } // end for (itheta ...) | |
1352 | } // end for (ilayer ...) | |
1353 | ||
1354 | // now the mismatches are solved | |
1355 | Bool_t matchPrev, matchNext; | |
1356 | for (ilayer = 0; ilayer < fNLayers; ilayer++) { | |
1357 | for (itheta = 0; itheta < 180; itheta++) { | |
1358 | // get the array of point in a given layer/theta-slot/sector | |
1359 | lstSector = (TObjArray*)fNodes[ilayer][itheta]->At(sector); | |
1360 | TObjArrayIter nodes(lstSector); | |
1361 | while ( (p = (AliITSnode*)nodes.Next()) ) { | |
1362 | // now p will point to a fPrev and a fNext node. | |
1363 | // Ideally they are placed this way: fPrev --> P --> fNext | |
1364 | // A mismatch happens if the point addressed as fPrev does NOT | |
1365 | // point to p as its fNext. And the same for the point addressed | |
1366 | // as fNext. | |
1367 | // In this case, the fNext and fPrev pointers are set to NULL | |
1368 | // and p is excluded from the reconstruction | |
1369 | matchPrev = matchNext= kFALSE; | |
1370 | if (ilayer > 0 && p->Prev() != NULL) | |
1371 | if (p->Prev()->Next() == p) matchPrev = kTRUE; | |
1372 | if (ilayer < 5 && p->Next() != NULL) | |
1373 | if (p->Next()->Prev() == p) matchNext = kTRUE; | |
1374 | if (ilayer == 0) | |
1375 | matchPrev = kTRUE; | |
1376 | else if (ilayer == 5) | |
1377 | matchNext = kTRUE; | |
1378 | if (!matchNext || !matchPrev) { | |
1379 | p->Prev() = p->Next() = 0; | |
1380 | } | |
1381 | } // end while (p ...) | |
1382 | } // end for (itheta ...) | |
1383 | } // end for (ilayer ...) | |
1384 | } | |
1385 | ||
1386 | //__________________________________________________________________________________ | |
1387 | Int_t AliITStrackerANN::SaveTracks(Int_t sector) | |
1388 | { | |
1389 | /******************************************************************************** | |
1390 | ||
1391 | TRACK SAVING | |
1392 | ------------ | |
1393 | Using the fNext and fPrev pointers, the chain is followed | |
1394 | and the track is fitted and saved. | |
1395 | Of course, the track is followed as a chain with a point | |
1396 | for each layer, then the track following starts always | |
1397 | from the clusters in layer 0. | |
1398 | ||
1399 | ***********************************************************************************/ | |
1400 | ||
1401 | // if not initialized, the tracks TobjArray is initialized | |
1402 | if (!fFoundTracks) fFoundTracks = new TObjArray; | |
1403 | ||
1404 | // meaningful counters | |
1405 | Int_t itheta, ilayer, l; | |
1406 | TObjArray *lstSector = 0; | |
1407 | AliITSnode *p = 0, *q = 0, **node = new AliITSnode*[fNLayers]; | |
1408 | for (l = 0; l < fNLayers; l++) node[l] = 0; | |
1409 | ||
1410 | /* | |
1411 | array = new AliITSnode*[fNLayers + 1]; | |
1412 | for (l = 0; l <= fNLayers; l++) array[l] = 0; | |
1413 | array[0] = new AliITSnode(); | |
1414 | array[0]->X() = fVertexX; | |
1415 | array[0]->Y() = fVertexY; | |
1416 | array[0]->Z() = fVertexZ; | |
1417 | array[0]->ErrX2() = fVertexErrorX; | |
1418 | array[0]->ErrY2() = fVertexErrorY; | |
1419 | array[0]->ErrZ2() = fVertexErrorZ; | |
1420 | */ | |
1421 | Double_t *param = new Double_t[8]; | |
1422 | ||
1423 | // scan the whole collection of nodes | |
1424 | for (ilayer = 0; ilayer < 1; ilayer++) { | |
1425 | for (itheta = 0; itheta < 180; itheta++) { | |
1426 | // get the array of point in a given layer/theta-slot/sector | |
1427 | lstSector = (TObjArray*)fNodes[ilayer][itheta]->At(sector); | |
1428 | TObjArrayIter nodes(lstSector); | |
1429 | while ( (p = (AliITSnode*)nodes.Next()) ) { | |
1430 | for (q = p; q; q = q->Next()) { | |
1431 | l = q->GetLayer(); | |
1432 | node[l] = q; | |
1433 | } | |
1434 | //if (!RiemannFit(fNLayers, node, param)) continue; | |
1435 | // initialization of Kalman Filter Tracking | |
1436 | AliITSclusterV2 *cluster = (AliITSclusterV2*)GetCluster(node[0]->ClusterRef()); | |
1437 | Int_t mod = cluster->GetDetectorIndex(); | |
1438 | Int_t lay, lad, det; | |
1439 | fGeom->GetModuleId(mod, lay, lad, det); | |
1440 | Float_t y0 = cluster->GetY(); | |
1441 | Float_t z0 = cluster->GetZ(); | |
1442 | AliITStrackSA* trac = new AliITStrackSA(lay, lad, det, | |
1443 | y0, z0, | |
1444 | param[4], param[7], param[3], 1); | |
1445 | for (l = 0; l < fNLayers; l++) { | |
1446 | cluster = (AliITSclusterV2*)GetCluster(node[l]->ClusterRef()); | |
1447 | if (cluster) trac->AddClusterV2(l, (node[l]->ClusterRef() & 0x0fffffff)>>0); | |
1448 | } | |
1449 | AliITStrackV2* ot = new AliITStrackV2(*trac); | |
1450 | ot->ResetCovariance(); | |
1451 | ot->ResetClusters(); | |
1452 | if (RefitAt(49.,ot,trac)) { //fit from layer 1 to layer 6 | |
1453 | AliITStrackV2 *otrack2 = new AliITStrackV2(*ot); | |
1454 | otrack2->ResetCovariance(); | |
1455 | otrack2->ResetClusters(); | |
1456 | //fit from layer 6 to layer 1 | |
1457 | if (RefitAt(3.7,otrack2,ot)) fFoundTracks->AddLast(otrack2); | |
1458 | } | |
1459 | // end of Kalman Filter fit | |
1460 | } | |
1461 | } | |
1462 | } | |
1463 | ||
1464 | return 1; | |
1465 | } | |
1466 | ||
1467 | //__________________________________________________________________________________ | |
1468 | void AliITStrackerANN::ExportTracks(const char *filename) const | |
1469 | { | |
1470 | // Exports found tracks into a TTree of AliITStrackV2 objects | |
1471 | TFile *file = new TFile(filename, "RECREATE"); | |
1472 | TTree *tree = new TTree("TreeT-ANN", "Tracks found in ITS stand-alone with Neural Tracking"); | |
1473 | AliITStrackV2 *track = 0; | |
1474 | tree->Branch("Tracks", &track, "AliITStrackV2"); | |
1475 | TObjArrayIter tracks(fFoundTracks); | |
1476 | while ( (track = (AliITStrackV2*)tracks.Next()) ) { | |
1477 | tree->Fill(); | |
1478 | } | |
1479 | file->cd(); | |
1480 | tree->Write(); | |
1481 | file->Close(); | |
1482 | } | |
1483 | ||
1484 | ||
1485 | //__________________________________________________________________________________ | |
1486 | void AliITStrackerANN::CleanNetwork() | |
1487 | { | |
1488 | // Removes deactivated units from the network | |
1489 | ||
1490 | AliITSneuron *unit = 0; | |
1491 | TObjArrayIter neurons(fNeurons); | |
1492 | while ( (unit = (AliITSneuron*)neurons.Next()) ) { | |
1493 | if (unit->Activation() < fActMinimum) { | |
1494 | unit->Inner()->InnerOf()->Remove(unit); | |
1495 | unit->Outer()->OuterOf()->Remove(unit); | |
1496 | delete fNeurons->Remove(unit); | |
1497 | } | |
1498 | } | |
1499 | return; | |
1500 | Bool_t removed; | |
1501 | Int_t nIn, nOut; | |
1502 | AliITSneuron *enemy = 0; | |
1503 | neurons.Reset(); | |
1504 | while ( (unit = (AliITSneuron*)neurons.Next()) ) { | |
1505 | nIn = (Int_t)unit->Inner()->InnerOf()->GetSize(); | |
1506 | nOut = (Int_t)unit->Outer()->OuterOf()->GetSize(); | |
1507 | if (nIn < 2 && nOut < 2) continue; | |
1508 | removed = kFALSE; | |
1509 | if (nIn > 1) { | |
1510 | TObjArrayIter competing(unit->Inner()->InnerOf()); | |
1511 | while ( (enemy = (AliITSneuron*)competing.Next()) ) { | |
1512 | if (unit->Activation() > enemy->Activation()) { | |
1513 | enemy->Inner()->InnerOf()->Remove(enemy); | |
1514 | enemy->Outer()->OuterOf()->Remove(enemy); | |
1515 | delete fNeurons->Remove(enemy); | |
1516 | } | |
1517 | else { | |
1518 | unit->Inner()->InnerOf()->Remove(unit); | |
1519 | unit->Outer()->OuterOf()->Remove(unit); | |
1520 | delete fNeurons->Remove(unit); | |
1521 | removed = kTRUE; | |
1522 | break; | |
1523 | } | |
1524 | } | |
1525 | if (removed) continue; | |
1526 | } | |
1527 | if (nOut > 1) { | |
1528 | TObjArrayIter competing(unit->Outer()->OuterOf()); | |
1529 | while ( (enemy = (AliITSneuron*)competing.Next()) ) { | |
1530 | if (unit->Activation() > enemy->Activation()) { | |
1531 | enemy->Inner()->InnerOf()->Remove(enemy); | |
1532 | enemy->Outer()->OuterOf()->Remove(enemy); | |
1533 | delete fNeurons->Remove(enemy); | |
1534 | } | |
1535 | else { | |
1536 | unit->Inner()->InnerOf()->Remove(unit); | |
1537 | unit->Outer()->OuterOf()->Remove(unit); | |
1538 | delete fNeurons->Remove(unit); | |
1539 | removed = kTRUE; | |
1540 | break; | |
1541 | } | |
1542 | } | |
1543 | } | |
1544 | } | |
1545 | } | |
1546 | ||
1547 | //__________________________________________________________________________________ | |
1548 | Int_t AliITStrackerANN::StoreTracks() | |
1549 | { | |
1550 | // Stores the tracks found in a single neural tracking step. | |
1551 | // In order to do this, it sects each neuron which has a point | |
1552 | // in the first layer. | |
1553 | // Then | |
1554 | ||
1555 | // if not initialized, the tracks TobjArray is initialized | |
1556 | if (!fFoundTracks) fFoundTracks = new TObjArray; | |
1557 | ||
1558 | Int_t i, check, stored = 0; | |
1559 | Double_t testAct = 0; | |
1560 | AliITSneuron *unit = 0, *cursor = 0, *fwd = 0; | |
1561 | AliITSnode *node = 0; | |
1562 | TObjArrayIter iter(fNeurons), *fwdIter; | |
1563 | TObjArray *removedUnits = new TObjArray(0); | |
1564 | removedUnits->SetOwner(kFALSE); | |
1565 | AliITStrackANN annTrack(fNLayers); | |
1566 | ||
1567 | for (;;) { | |
1568 | unit = (AliITSneuron*)iter.Next(); | |
1569 | if (!unit) break; | |
1570 | if (unit->Inner()->GetLayer() > 0) continue; | |
1571 | annTrack.SetNode(unit->Inner()->GetLayer(), unit->Inner()); | |
1572 | annTrack.SetNode(unit->Outer()->GetLayer(), unit->Outer()); | |
1573 | node = unit->Outer(); | |
1574 | removedUnits->AddLast(unit); | |
1575 | while (node) { | |
1576 | testAct = fActMinimum; | |
1577 | fwdIter = (TObjArrayIter*)node->InnerOf()->MakeIterator(); | |
1578 | fwd = 0; | |
1579 | for (;;) { | |
1580 | cursor = (AliITSneuron*)fwdIter->Next(); | |
1581 | if (!cursor) break; | |
1582 | if (cursor->Used()) continue; | |
1583 | if (cursor->Activation() >= testAct) { | |
1584 | testAct = cursor->Activation(); | |
1585 | fwd = cursor; | |
1586 | } | |
1587 | } | |
1588 | if (!fwd) break; | |
1589 | removedUnits->AddLast(fwd); | |
1590 | node = fwd->Outer(); | |
1591 | annTrack.SetNode(node->GetLayer(), node); | |
1592 | } | |
1593 | check = annTrack.CheckOccupation(); | |
1594 | if (check >= 6) { | |
1595 | stored++; | |
1596 | // FIT | |
1597 | //if (!RiemannFit(fNLayers, trackitem, param)) continue; | |
1598 | if (!annTrack.RiemannFit()) continue; | |
1599 | // initialization of Kalman Filter Tracking | |
1600 | AliITSclusterV2 *cluster = (AliITSclusterV2*)GetCluster(annTrack[0]->ClusterRef()); | |
1601 | Int_t mod = cluster->GetDetectorIndex(); | |
1602 | Int_t lay, lad, det; | |
1603 | fGeom->GetModuleId(mod, lay, lad, det); | |
1604 | Float_t y0 = cluster->GetY(); | |
1605 | Float_t z0 = cluster->GetZ(); | |
1606 | AliITStrackSA* trac = new AliITStrackSA(lay, lad, det, y0, z0, | |
1607 | annTrack.Phi(), annTrack.TanLambda(), | |
1608 | annTrack.Curv(), 1); | |
1609 | for (Int_t l = 0; l < fNLayers; l++) { | |
1610 | if (!annTrack[l]) continue; | |
1611 | cluster = (AliITSclusterV2*)GetCluster(annTrack[l]->ClusterRef()); | |
1612 | if (cluster) trac->AddClusterV2(l, (annTrack[l]->ClusterRef() & 0x0fffffff)>>0); | |
1613 | } | |
1614 | AliITStrackV2* ot = new AliITStrackV2(*trac); | |
1615 | ot->ResetCovariance(); | |
1616 | ot->ResetClusters(); | |
1617 | if (RefitAt(49.,ot,trac)) { //fit from layer 1 to layer 6 | |
1618 | AliITStrackV2 *otrack2 = new AliITStrackV2(*ot); | |
1619 | otrack2->ResetCovariance(); | |
1620 | otrack2->ResetClusters(); | |
1621 | //fit from layer 6 to layer 1 | |
1622 | if (RefitAt(3.7,otrack2,ot)) fFoundTracks->AddLast(otrack2); | |
1623 | } | |
1624 | // end of Kalman Filter fit | |
1625 | // END FIT | |
1626 | for (i = 0; i < fNLayers; i++) { | |
1627 | //node = (AliITSnode*)removedPoints->At(i); | |
1628 | //node->Use(); | |
1629 | annTrack[i]->Use(); | |
1630 | } | |
1631 | fwdIter = (TObjArrayIter*)removedUnits->MakeIterator(); | |
1632 | for (;;) { | |
1633 | cursor = (AliITSneuron*)fwdIter->Next(); | |
1634 | if(!cursor) break; | |
1635 | cursor->Used() = 1; | |
1636 | } | |
1637 | } | |
1638 | } | |
1639 | ||
1640 | return stored; | |
1641 | } | |
1642 | ||
1643 | Double_t AliITStrackerANN::Weight(AliITSneuron *nAB, AliITSneuron *nBC) | |
1644 | { | |
1645 | /*********************************************************************************** | |
1646 | * Calculation of neural weight. | |
1647 | * The implementation of positive neural weight is set only in the case | |
1648 | * of connected units (e.g.: A->B with B->C). | |
1649 | * Given that B is the **common** point. care should be taken to pass | |
1650 | * as FIRST argument the neuron going "to" B, and | |
1651 | * as SECOND argument the neuron starting "from" B | |
1652 | * anyway, a check is put in order to return 0.0 when arguments are not well given. | |
1653 | ***********************************************************************************/ | |
1654 | ||
1655 | if (nAB->Outer() != nBC->Inner()) { | |
1656 | if (nBC->Outer() == nAB->Inner()) { | |
1657 | AliITSneuron *temp = nAB; | |
1658 | nAB = nBC; | |
1659 | nBC = temp; | |
1660 | temp = 0; | |
1661 | if (fMsgLevel >= 3) { | |
1662 | Info("Weight", "Switching wrongly ordered arguments."); | |
1663 | } | |
1664 | } | |
1665 | Warning("Weight", "Not connected segments. Returning 0.0"); | |
1666 | return 0.0; | |
1667 | } | |
1668 | ||
1669 | AliITSnode *pA = nAB->Inner(); | |
1670 | AliITSnode *pB = nAB->Outer(); | |
1671 | AliITSnode *pC = nBC->Outer(); | |
1672 | ||
1673 | TVector3 vAB(pB->X() - pA->X(), pB->Y() - pA->Y(), pB->Z() - pA->Z()); | |
1674 | TVector3 vBC(pC->X() - pB->X(), pC->Y() - pB->Y(), pC->Z() - pB->Z()); | |
1675 | ||
1676 | Double_t weight = 1.0 - sin(vAB.Angle(vBC)); | |
1677 | return fGain2CostRatio * TMath::Power(weight, fExponent); | |
1678 | } | |
1679 | ||
1680 | ||
1681 | ||
1682 | /****************************************** | |
1683 | ****************************************** | |
1684 | *** AliITStrackerANN::AliITSnode class *** | |
1685 | ****************************************** | |
1686 | ******************************************/ | |
1687 | ||
1688 | //__________________________________________________________________________________ | |
0db9364f | 1689 | AliITStrackerANN::AliITSnode::AliITSnode() |
cec46807 | 1690 | : fUsed(kFALSE), fClusterRef(-1), |
1691 | fMatches(NULL), fInnerOf(NULL), fOuterOf(NULL), | |
1692 | fNext(NULL), fPrev(NULL) | |
1693 | { | |
1694 | // Constructor for the embedded 'AliITSnode' class. | |
1695 | // It initializes all pointer-like objects. | |
1696 | ||
1697 | fX = fY = fZ = 0.0; | |
1698 | fEX2 = fEY2 = fEZ2 = 0.0; | |
1699 | } | |
1700 | ||
1701 | //__________________________________________________________________________________ | |
1702 | AliITStrackerANN::AliITSnode::~AliITSnode() | |
1703 | { | |
1704 | // Destructor for the embedded 'AliITSnode' class. | |
1705 | // It should clear the object arrays, but it is possible | |
1706 | // that some objects still are useful after the point deletion | |
1707 | // then the arrays are cleared but their objects are owed by | |
1708 | // another TCollection object, and not deleted. | |
1709 | // For safety reasons, all the pointers are set to zero. | |
1710 | ||
1711 | fInnerOf->SetOwner(kFALSE); | |
1712 | fInnerOf->Clear(); | |
1713 | delete fInnerOf; | |
1714 | fInnerOf = 0; | |
1715 | fOuterOf->SetOwner(kFALSE); | |
1716 | fOuterOf->Clear(); | |
1717 | delete fOuterOf; | |
1718 | fOuterOf = 0; | |
1719 | fMatches->SetOwner(kFALSE); | |
1720 | fMatches->Clear(); | |
1721 | delete fMatches; | |
1722 | fMatches = 0; | |
1723 | fNext = 0; | |
1724 | fPrev = 0; | |
1725 | } | |
1726 | ||
1727 | //__________________________________________________________________________________ | |
0db9364f | 1728 | Double_t AliITStrackerANN::AliITSnode::GetPhi() const |
cec46807 | 1729 | { |
1730 | // Calculates the 'phi' (azimutal) angle, and returns it | |
1731 | // in the range between 0 and 2Pi radians. | |
1732 | ||
1733 | Double_t q; | |
1734 | q = TMath::ATan2(fY,fX); | |
1735 | if (q >= 0.) | |
1736 | return q; | |
1737 | else | |
1738 | return q + TMath::TwoPi(); | |
1739 | } | |
1740 | ||
1741 | //__________________________________________________________________________________ | |
0db9364f | 1742 | Double_t AliITStrackerANN::AliITSnode::GetError(Option_t *option) |
cec46807 | 1743 | { |
1744 | // Returns the error or the square error of | |
1745 | // values related to the coordinates in different systems. | |
1746 | // The option argument specifies the coordinate error desired: | |
1747 | // | |
1748 | // "R2" --> error in transverse radius | |
1749 | // "R3" --> error in spherical radius | |
1750 | // "PHI" --> error in azimuthal angle | |
1751 | // "THETA" --> error in polar angle | |
1752 | // "SQ" --> get the square of error | |
1753 | // | |
1754 | // In order to get the error on the cartesian coordinates | |
1755 | // reference to the inline ErrX2(), ErrY2() adn ErrZ2() methods. | |
1756 | ||
1757 | TString opt(option); | |
1758 | Double_t errorSq = 0.0; | |
1759 | opt.ToUpper(); | |
1760 | ||
1761 | if (opt.Contains("R2")) { | |
1762 | errorSq = fX*fX*fEX2 + fY*fY*fEY2; | |
1763 | errorSq /= GetR2sq(); | |
1764 | } | |
1765 | else if (opt.Contains("R3")) { | |
1766 | errorSq = fX*fX*fEX2 + fY*fY*fEY2 + fZ*fZ*fEZ2; | |
1767 | errorSq /= GetR3sq(); | |
1768 | } | |
1769 | else if (opt.Contains("PHI")) { | |
1770 | errorSq = fY*fY*fEX2; | |
1771 | errorSq += fX*fX*fEY2; | |
1772 | errorSq /= GetR2sq() * GetR2sq(); | |
1773 | } | |
1774 | else if (opt.Contains("THETA")) { | |
1775 | errorSq = fZ*fZ * (fX*fX*fEX2 + fY*fY*fEY2); | |
1776 | errorSq += GetR2sq() * GetR2sq() * fEZ2; | |
1777 | errorSq /= GetR3sq() * GetR3sq() * GetR2() * GetR2(); | |
1778 | } | |
1779 | ||
1780 | if (!opt.Contains("SQ")) | |
1781 | return TMath::Sqrt(errorSq); | |
1782 | else | |
1783 | return errorSq; | |
1784 | } | |
1785 | ||
1786 | ||
1787 | ||
1788 | /******************************************** | |
1789 | ******************************************** | |
1790 | *** AliITStrackerANN::AliITSneuron class *** | |
1791 | ******************************************** | |
1792 | ********************************************/ | |
1793 | ||
1794 | //__________________________________________________________________________________ | |
1795 | AliITStrackerANN::AliITSneuron::AliITSneuron | |
1796 | (AliITSnode *inner, AliITSnode *outer, Double_t minAct, Double_t maxAct) | |
1797 | : fUsed(0), fInner(inner), fOuter(outer) | |
1798 | { | |
1799 | // Default neuron constructor | |
1800 | fActivation = gRandom->Rndm() * (maxAct-minAct) + minAct; | |
1801 | fGain = new TObjArray; | |
1802 | } | |
1803 | ||
1804 | //__________________________________________________________________________________ | |
0db9364f | 1805 | Double_t AliITStrackerANN::AliITSneuron::Activate(Double_t temperature) |
cec46807 | 1806 | { |
1807 | // This computes the new activation of a neuron, and returns | |
1808 | // its activation variation as a consequence of the updating. | |
1809 | // | |
1810 | // Arguments: | |
1811 | // - the 'temperature' parameter for the neural activation logistic function | |
1812 | // | |
1813 | // Operations: | |
1814 | // - collects the total gain, by summing the products | |
1815 | // of the activation of each sequenced unit by the relative weight. | |
1816 | // - collects the total cost, by summing the activations of | |
1817 | // all competing units | |
1818 | // - passes the sum of gain - cost to the activation function and | |
1819 | // calculates the new activation | |
1820 | // | |
1821 | // Return value: | |
1822 | // - the difference between the old activation and the new one | |
1823 | // (absolute value) | |
1824 | ||
1825 | // local variables | |
1826 | Double_t sumGain = 0.0; // total contribution from chained neurons | |
1827 | Double_t sumCost = 0.0; // total contribution from crossing neurons | |
1828 | Double_t input; // total input | |
1829 | Double_t actOld, actNew; // old and new values for the activation | |
1830 | AliITSneuron *linked = 0; // cursor for scanning the neuron arrays (for link check) | |
1831 | AliITSlink *link; // cursor for scanning the synapses arrays (for link check) | |
1832 | TObjArrayIter *iterator = 0; // pointer to the iterator along the neuron arrays | |
1833 | ||
1834 | // sum contributions from the correlated units | |
1835 | iterator = (TObjArrayIter*)fGain->MakeIterator(); | |
1836 | for(;;) { | |
1837 | link = (AliITSlink*)iterator->Next(); | |
1838 | if (!link) break; | |
1839 | sumGain += link->Contribution(); | |
1840 | } | |
1841 | delete iterator; | |
1842 | ||
1843 | // sum contributions from the competing units: | |
1844 | // the ones which have the same starting point... | |
1845 | iterator = (TObjArrayIter*)fInner->InnerOf()->MakeIterator(); | |
1846 | for (;;) { | |
1847 | linked = (AliITSneuron*)iterator->Next(); | |
1848 | if (!linked) break; | |
1849 | if (linked == this) continue; | |
1850 | sumCost += linked->fActivation; | |
1851 | } | |
1852 | delete iterator; | |
1853 | // ...and the ones which have the same ending point | |
1854 | iterator = (TObjArrayIter*)fOuter->OuterOf()->MakeIterator(); | |
1855 | for (;;) { | |
1856 | linked = (AliITSneuron*)iterator->Next(); | |
1857 | if (!linked) break; | |
1858 | if (linked == this) continue; | |
1859 | sumCost += linked->fActivation; | |
1860 | } | |
1861 | ||
1862 | // calculate the total input as the difference between gain and cost | |
1863 | input = (sumGain - sumCost) / temperature; | |
1864 | actOld = fActivation; | |
1865 | // calculate the final output | |
1866 | #ifdef NEURAL_LINEAR | |
1867 | if (input <= -2.0 * temperature) | |
1868 | actNew = 0.0; | |
1869 | else if (input >= 2.0 * temperature) | |
1870 | actNew = 1.0; | |
1871 | else | |
1872 | actNew = input / (4.0 * temperature) + 0.5; | |
1873 | #else | |
1874 | actNew = 1.0 / (1.0 + TMath::Exp(-input)); | |
1875 | #endif | |
1876 | fActivation = actNew; | |
1877 | ||
1878 | // return the activation variation | |
1879 | return TMath::Abs(actNew - actOld); | |
1880 | } | |
1881 | ||
1882 | ||
1883 | ||
1884 | /****************************************** | |
1885 | ****************************************** | |
1886 | *** AliITStrackerANN::AliITSlink class *** | |
1887 | ****************************************** | |
1888 | ******************************************/ | |
1889 | ||
1890 | // No methods defined non-inline | |
1891 | ||
1892 | ||
1893 | ||
1894 | /********************************************** | |
1895 | ********************************************** | |
1896 | *** AliITStrackerANN::AliITStrackANN class *** | |
1897 | ********************************************** | |
1898 | **********************************************/ | |
1899 | ||
1900 | //__________________________________________________________________________________ | |
1901 | AliITStrackerANN::AliITStrackANN::AliITStrackANN(Int_t dim) : fNPoints(dim) | |
1902 | { | |
1903 | // Default constructor for the AliITStrackANN class | |
1904 | ||
1905 | fXCenter = 0.0; | |
1906 | fYCenter = 0.0; | |
1907 | fRadius = 0.0; | |
1908 | fCurv = 0.0; | |
1909 | fDTrans = 0.0; | |
1910 | fDLong = 0.0; | |
1911 | fTanLambda = 0.0; | |
1912 | ||
1913 | if (! dim) { | |
1914 | fNode = 0; | |
1915 | } | |
1916 | else{ | |
1917 | Int_t i = 0; | |
1918 | fNode = new AliITSnode*[dim]; | |
1919 | for (i = 0; i < dim; i++) fNode[i] = 0; | |
1920 | } | |
1921 | } | |
1922 | ||
1923 | //__________________________________________________________________________________ | |
1924 | Int_t AliITStrackerANN::AliITStrackANN::CheckOccupation() const | |
1925 | { | |
1926 | // Returns the number of pointers fNode which are not NULL | |
1927 | ||
1928 | Int_t i; // cursor | |
1929 | Int_t count = 0; // counter for how many points are stored in the track | |
1930 | ||
1931 | for (i = 0; i < fNPoints; i++) { | |
1932 | if (fNode[i] != NULL) count++; | |
1933 | } | |
1934 | ||
1935 | return count; | |
1936 | } | |
1937 | ||
1938 | //__________________________________________________________________________________ | |
1939 | Bool_t AliITStrackerANN::AliITStrackANN::RiemannFit() | |
1940 | { | |
1941 | // Performs the Riemann Sphere fit for the given points to a circle | |
1942 | // and then uses the fit parameters to fit a helix in space. | |
1943 | // | |
1944 | // Return values: | |
1945 | // - kTRUE if all operations have been performed | |
1946 | // - kFALSE if the numbers risk to lead to an arithmetic violation | |
1947 | ||
1948 | Int_t i, j, count, dim = fNPoints; | |
1949 | ||
1950 | // First check for all points | |
1951 | count = CheckOccupation(); | |
1952 | if (count != fNPoints) { | |
1953 | Error ("AliITStrackANN::RiemannFit", "CheckOccupations returns %d, fNPoints = %d ==> MISMATCH", count, fNPoints); | |
1954 | return kFALSE; | |
1955 | } | |
1956 | ||
1957 | // matrix of ones | |
1958 | TMatrixD m1(dim,1); | |
1959 | for (i = 0; i < dim; i++) m1(i,0) = 1.0; | |
1960 | ||
1961 | // matrix of Rieman projection coordinates | |
1962 | TMatrixD coords(dim,3); | |
1963 | for (i = 0; i < dim; i++) { | |
1964 | coords(i,0) = fNode[i]->X(); | |
1965 | coords(i,1) = fNode[i]->Y(); | |
1966 | coords(i,2) = fNode[i]->GetR2sq(); | |
1967 | } | |
1968 | ||
1969 | // matrix of weights | |
1970 | Double_t xterm, yterm, ex, ey; | |
1971 | TMatrixD weights(dim,dim); | |
1972 | for (i = 0; i < dim; i++) { | |
1973 | xterm = fNode[i]->X() * fNode[i]->GetPhi() - fNode[i]->Y() / fNode[i]->GetR2(); | |
1974 | ex = fNode[i]->ErrX2(); | |
1975 | yterm = fNode[i]->Y() * fNode[i]->GetPhi() + fNode[i]->X() / fNode[i]->GetR2(); | |
1976 | ey = fNode[i]->ErrY2(); | |
1977 | weights(i,i) = fNode[i]->GetR2sq() / (xterm * xterm * ex + yterm * yterm * ey ); | |
1978 | } | |
1979 | ||
1980 | // weighted sample mean | |
1981 | Double_t meanX = 0.0, meanY = 0.0, meanW = 0.0, sw = 0.0; | |
1982 | for (i = 0; i < dim; i++) { | |
1983 | meanX += weights(i,i) * coords(i,0); | |
1984 | meanY += weights(i,i) * coords(i,1); | |
1985 | meanW += weights(i,i) * coords(i,2); | |
1986 | sw += weights(i,i); | |
1987 | } | |
1988 | meanX /= sw; | |
1989 | meanY /= sw; | |
1990 | meanW /= sw; | |
1991 | ||
1992 | // sample covariance matrix | |
1993 | for (i = 0; i < dim; i++) { | |
1994 | coords(i,0) -= meanX; | |
1995 | coords(i,1) -= meanY; | |
1996 | coords(i,2) -= meanW; | |
1997 | } | |
1998 | TMatrixD coordsT(TMatrixD::kTransposed, coords); | |
1999 | TMatrixD weights4coords(weights, TMatrixD::kMult, coords); | |
2000 | TMatrixD sampleCov(coordsT, TMatrixD::kMult, weights4coords); | |
2001 | for (i = 0; i < 3; i++) { | |
2002 | for (j = i + 1; j < 3; j++) { | |
2003 | sampleCov(i,j) = sampleCov(j,i) = (sampleCov(i,j) + sampleCov(j,i)) * 0.5; | |
2004 | } | |
2005 | } | |
2006 | ||
2007 | // Eigenvalue problem solving for V matrix | |
2008 | Int_t ileast = 0; | |
2009 | TVectorD eval(3), n(3); | |
2010 | TMatrixD evec = sampleCov.EigenVectors(eval); | |
2011 | if (eval(1) < eval(ileast)) ileast = 1; | |
2012 | if (eval(2) < eval(ileast)) ileast = 2; | |
2013 | n(0) = evec(0, ileast); | |
2014 | n(1) = evec(1, ileast); | |
2015 | n(2) = evec(2, ileast); | |
2016 | ||
2017 | // c - known term in the plane intersection with Riemann axes | |
2018 | Double_t c = -(meanX * n(0) + meanY * n(1) + meanW * n(2)); | |
2019 | ||
2020 | // center and radius of fitted circle | |
2021 | Double_t xc, yc, radius, curv; | |
2022 | xc = -n(0) / (2. * n(2)); | |
2023 | yc = -n(1) / (2. * n(2)); | |
2024 | radius = (1. - n(2)*n(2) - 4.*c*n(2)) / (4. * n(2) * n(2)); | |
2025 | ||
2026 | if (radius <= 0.E0) { | |
2027 | Error("RiemannFit", "Radius = %f less than zero!!!", radius); | |
2028 | return kFALSE; | |
2029 | } | |
2030 | radius = TMath::Sqrt(radius); | |
2031 | curv = 1.0 / radius; | |
2032 | ||
2033 | // evaluating signs for curvature and others | |
2034 | Double_t phi1 = 0.0, phi2, temp1, temp2, phi0, sumdphi = 0.0; | |
2035 | AliITSnode *p = fNode[0]; | |
2036 | phi1 = p->GetPhi(); | |
2037 | for (i = 1; i < dim; i++) { | |
2038 | p = (AliITSnode*)fNode[i]; | |
2039 | if (!p) break; | |
2040 | phi2 = p->GetPhi(); | |
2041 | temp1 = phi1; | |
2042 | temp2 = phi2; | |
2043 | if (temp1 > fgkPi && temp2 < fgkPi) | |
2044 | temp2 += fgkTwoPi; | |
2045 | else if (temp1 < fgkPi && temp2 > fgkPi) | |
2046 | temp1 += fgkTwoPi; | |
2047 | sumdphi += temp2 - temp1; | |
2048 | phi1 = phi2; | |
2049 | } | |
2050 | if (sumdphi < 0.E0) curv = -curv; | |
2051 | Double_t diff, angle = TMath::ATan2(yc, xc); | |
2052 | if (curv < 0.E0) | |
2053 | phi0 = angle + 0.5 * TMath::Pi(); | |
2054 | else | |
2055 | phi0 = angle - 0.5 * TMath::Pi(); | |
2056 | diff = angle - phi0; | |
2057 | ||
2058 | Double_t dt, temp = TMath::Sqrt(xc*xc + yc*yc) - radius; | |
2059 | if (curv >= 0.E0) | |
2060 | dt = temp; | |
2061 | else | |
2062 | dt = -temp; | |
2063 | //cout << "Dt = " << dt << endl; | |
2064 | ||
2065 | Double_t halfC = 0.5 * curv, test; | |
2066 | Double_t *s = new Double_t[dim], *zz = new Double_t[dim], *ws = new Double_t[dim]; | |
2067 | for (j = 0; j < 6; j++) { | |
2068 | p = fNode[j]; | |
2069 | if (!p) break; | |
2070 | //---- | |
2071 | s[j] = (p->GetR2sq() - dt * dt) / (1. + curv * dt); | |
2072 | if (s[j] < 0.) { | |
0db9364f | 2073 | if (TMath::Abs(s[j]) < 1.E-6) s[j] = 0.; |
cec46807 | 2074 | else { |
2075 | Error("RiemannFit", "Square root argument error: %17.15g < 0", s[j]); | |
2076 | return kFALSE; | |
2077 | } | |
2078 | } | |
2079 | s[j] = TMath::Sqrt(s[j]); | |
2080 | //cout << "Curv = " << halfC << " --- s[" << j << "] = " << s[j] << endl; | |
2081 | s[j] *= halfC; | |
2082 | test = TMath::Abs(s[j]); | |
2083 | if (test > 1.) { | |
2084 | if (test <= 1.1) | |
2085 | s[j] = ((s[j] > 0.) ? 0.99999999999 : -0.9999999999); | |
2086 | else { | |
2087 | Error("RiemannFit", "Value too large: %17.15g", s[j]); | |
2088 | return kFALSE; | |
2089 | } | |
2090 | } | |
2091 | //---- | |
2092 | zz[j] = p->Z(); | |
2093 | s[j] = TMath::ASin(s[j]) / halfC; | |
2094 | ws[j] = 1.0 / (p->ErrZ2()); | |
2095 | } | |
2096 | ||
2097 | // second tep final fit | |
2098 | Double_t s2Sum = 0.0, zSum = 0.0, szSum = 0.0, sSum = 0.0, sumw = 0.0; | |
2099 | for (i = 0; i < dim; i++) { | |
2100 | s2Sum += ws[i] * s[i] * s[i]; | |
2101 | zSum += ws[i] * zz[i]; | |
2102 | sSum += ws[i] * s[i]; | |
2103 | szSum += ws[i] * s[i] * zz[i]; | |
2104 | sumw += ws[i]; | |
2105 | } | |
2106 | s2Sum /= sumw; | |
2107 | zSum /= sumw; | |
2108 | sSum /= sumw; | |
2109 | szSum /= sumw; | |
2110 | temp = s2Sum - sSum*sSum; | |
2111 | ||
2112 | Double_t dz, tanL; | |
2113 | dz = (s2Sum*zSum - sSum*szSum) / temp; | |
2114 | tanL = (szSum - sSum*zSum) / temp; | |
2115 | ||
2116 | fXCenter = xc; | |
2117 | fYCenter = yc; | |
2118 | fRadius = radius; | |
2119 | fCurv = curv; | |
2120 | fPhi = phi0; | |
2121 | fDTrans = dt; | |
2122 | fDLong = dz; | |
2123 | fTanLambda = tanL; | |
2124 | ||
2125 | delete [] s; | |
2126 | delete [] zz; | |
2127 | delete [] ws; | |
2128 | ||
2129 | return kTRUE; | |
2130 | } |