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TMVAClassification_LikelihoodPCA.class.C
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1// Class: ReadLikelihoodPCA
2// Automatically generated by MethodBase::MakeClass
3//
4
5/* configuration options =====================================================
6
7#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
8
9Method : Likelihood::LikelihoodPCA
10TMVA Release : 4.2.1 [262657]
11ROOT Release : 6.16/01 [397313]
12Creator : sftnight
13Date : Sun Dec 19 22:13:31 2021
14Host : Linux root-ubuntu-2004-3 5.4.0-73-generic #82-Ubuntu SMP Wed Apr 14 17:39:42 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
15Dir : /home/sftnight/build/workspace/root-makedoc-v616/rootspi/rdoc/src/v6-16-00-patches/documentation/doxygen
16Training events: 2000
17Analysis type : [Classification]
18
19
20#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-
21
22# Set by User:
23V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
24VarTransform: "PCA" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
25H: "False" [Print method-specific help message]
26TransformOutput: "False" [Transform likelihood output by inverse sigmoid function]
27# Default:
28VerbosityLevel: "Default" [Verbosity level]
29CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
30IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
31##
32
33
34#VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-
35
36NVar 4
37var1+var2 myvar1 myvar1 myvar1 'F' [-9.33803939819,7.69307804108]
38var1-var2 myvar2 myvar2 Expression 2 'F' [-3.25508260727,4.02912044525]
39var3 var3 var3 Variable 3 units 'F' [-5.2777428627,4.64297914505]
40var4 var4 var4 Variable 4 units 'F' [-5.6007027626,4.67435789108]
41NSpec 2
42var1*2 spec1 spec1 Spectator 1 units 'F' [-9.91655540466,8.7030172348]
43var1*3 spec2 spec2 Spectator 2 units 'F' [-14.874833107,13.0545253754]
44
45
46============================================================================ */
47
48#include <array>
49#include <vector>
50#include <cmath>
51#include <string>
52#include <iostream>
53
54#include <math.h>
55#include <cstdlib>
56#ifndef IClassifierReader__def
57#define IClassifierReader__def
58
59class IClassifierReader {
60
61 public:
62
63 // constructor
64 IClassifierReader() : fStatusIsClean( true ) {}
65 virtual ~IClassifierReader() {}
66
67 // return classifier response
68 virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
69
70 // returns classifier status
71 bool IsStatusClean() const { return fStatusIsClean; }
72
73 protected:
74
75 bool fStatusIsClean;
76};
77
78#endif
79
80class ReadLikelihoodPCA : public IClassifierReader {
81
82 public:
83
84 // constructor
85 ReadLikelihoodPCA( std::vector<std::string>& theInputVars )
86 : IClassifierReader(),
87 fClassName( "ReadLikelihoodPCA" ),
88 fNvars( 4 )
89 {
90 // the training input variables
91 const char* inputVars[] = { "var1+var2", "var1-var2", "var3", "var4" };
92
93 // sanity checks
94 if (theInputVars.size() <= 0) {
95 std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
96 fStatusIsClean = false;
97 }
98
99 if (theInputVars.size() != fNvars) {
100 std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
101 << theInputVars.size() << " != " << fNvars << std::endl;
102 fStatusIsClean = false;
103 }
104
105 // validate input variables
106 for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
107 if (theInputVars[ivar] != inputVars[ivar]) {
108 std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
109 << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
110 fStatusIsClean = false;
111 }
112 }
113
114 // initialize min and max vectors (for normalisation)
115 fVmin[0] = -11.2719573974609;
116 fVmax[0] = 9.09155368804932;
117 fVmin[1] = -3.98751449584961;
118 fVmax[1] = 3.38363432884216;
119 fVmin[2] = -2.01709818840027;
120 fVmax[2] = 2.19576525688171;
121 fVmin[3] = -1.01762998104095;
122 fVmax[3] = 1.061687707901;
123
124 // initialize input variable types
125 fType[0] = 'F';
126 fType[1] = 'F';
127 fType[2] = 'F';
128 fType[3] = 'F';
129
130 // initialize constants
131 Initialize();
132
133 // initialize transformation
134 InitTransform();
135 }
136
137 // destructor
138 virtual ~ReadLikelihoodPCA() {
139 Clear(); // method-specific
140 }
141
142 // the classifier response
143 // "inputValues" is a vector of input values in the same order as the
144 // variables given to the constructor
145 double GetMvaValue( const std::vector<double>& inputValues ) const override;
146
147 private:
148
149 // method-specific destructor
150 void Clear();
151
152 // input variable transformation
153
154 void X2P_1( const double*, double*, int ) const;
155 double fMeanValues_1[3][4];
156 double fEigenVectors_1[3][4][4];
157
158 void InitTransform_1();
159 void Transform_1( std::vector<double> & iv, int sigOrBgd ) const;
160 void InitTransform();
161 void Transform( std::vector<double> & iv, int sigOrBgd ) const;
162
163 // common member variables
164 const char* fClassName;
165
166 const size_t fNvars;
167 size_t GetNvar() const { return fNvars; }
168 char GetType( int ivar ) const { return fType[ivar]; }
169
170 // normalisation of input variables
171 double fVmin[4];
172 double fVmax[4];
173 double NormVariable( double x, double xmin, double xmax ) const {
174 // normalise to output range: [-1, 1]
175 return 2*(x - xmin)/(xmax - xmin) - 1.0;
176 }
177
178 // type of input variable: 'F' or 'I'
179 char fType[4];
180
181 // initialize internal variables
182 void Initialize();
183 double GetMvaValue__( const std::vector<double>& inputValues ) const;
184
185 // private members (method specific)
186 double fEpsilon;
187 static float fRefS[][10000]; // signal reference vector [nvars][max_nbins]
188 static float fRefB[][10000]; // backgr reference vector [nvars][max_nbins]
189
190// if a variable has its PDF encoded as a spline0 --> treat it like an Integer valued one
191 bool fHasDiscretPDF[4];
192 int fNbin[4]; // number of bins (discrete variables may have less bins)
193 double fHistMin[4];
194 double fHistMax[4];
195 double TransformLikelihoodOutput( double, double ) const;
196};
197
198inline void ReadLikelihoodPCA::Initialize()
199{
200 fEpsilon = 2.2250738585072e-305;
201 fNbin[0] = 10000;
202 fHistMin[0] = -12.0934629440308;
203 fHistMax[0] = 9.88210773468018;
204 fNbin[1] = 10000;
205 fHistMin[1] = -3.54455327987671;
206 fHistMax[1] = 4.11745309829712;
207 fNbin[2] = 10000;
208 fHistMin[2] = -2.29971814155579;
209 fHistMax[2] = 2.23682618141174;
210 fNbin[3] = 10000;
211 fHistMin[3] = -1.25347244739532;
212 fHistMax[3] = 1.25178468227386;
213 fHasDiscretPDF[0] = false;
214 fHasDiscretPDF[1] = false;
215 fHasDiscretPDF[2] = false;
216 fHasDiscretPDF[3] = false;
217}
218
219inline double ReadLikelihoodPCA::GetMvaValue__( const std::vector<double>& inputValues ) const
220{
221 double ps(1), pb(1);
222 std::vector<double> inputValuesSig = inputValues;
223 std::vector<double> inputValuesBgd = inputValues;
224 Transform(inputValuesSig,0);
225 Transform(inputValuesBgd,1);
226 for (size_t ivar = 0; ivar < GetNvar(); ivar++) {
227
228 // dummy at present... will be used for variable transforms
229 double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };
230
231 for (int itype=0; itype < 2; itype++) {
232
233 // interpolate linearly between adjacent bins
234 // this is not useful for discrete variables (or forced Spline0)
235 int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;
236
237 // since the test data sample is in general different from the training sample
238 // it can happen that the min/max of the training sample are trespassed --> correct this
239 if (bin < 0) {
240 bin = 0;
241 x[itype] = fHistMin[ivar];
242 }
243 else if (bin >= fNbin[ivar]) {
244 bin = fNbin[ivar]-1;
245 x[itype] = fHistMax[ivar];
246 }
247
248 // find corresponding histogram from cached indices
249 float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];
250
251 // sanity check
252 if (ref < 0) {
253 std::cout << "Fatal error in ReadLikelihoodPCA: bin entry < 0 ==> abort" << std::endl;
254 std::exit(1);
255 }
256
257 double p = ref;
258
259 if (GetType(ivar) != 'I' && !fHasDiscretPDF[ivar]) {
260 float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
261 int nextbin = bin;
262 if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0)
263 nextbin++;
264 else
265 nextbin--;
266
267 double refnext = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
268 float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
269
270 double dx = bincenter - nextbincenter;
271 double dy = ref - refnext;
272 p += (x[itype] - bincenter) * dy/dx;
273 }
274
275 if (p < fEpsilon) p = fEpsilon; // avoid zero response
276
277 if (itype == 0) ps *= p;
278 else pb *= p;
279 }
280 }
281
282 // the likelihood ratio (transform it ?)
283 return TransformLikelihoodOutput( ps, pb );
284}
285
286inline double ReadLikelihoodPCA::TransformLikelihoodOutput( double ps, double pb ) const
287{
288 // returns transformed or non-transformed output
289 if (ps < fEpsilon) ps = fEpsilon;
290 if (pb < fEpsilon) pb = fEpsilon;
291 double r = ps/(ps + pb);
292 if (r >= 1.0) r = 1. - 1.e-15;
293
294 if (false) {
295 // inverse Fermi function
296
297 // sanity check
298 if (r <= 0.0) r = fEpsilon;
299 else if (r >= 1.0) r = 1. - 1.e-15;
300
301 double tau = 15.0;
302 r = - log(1.0/r - 1.0)/tau;
303 }
304
305 return r;
306}
307
308// Clean up
309inline void ReadLikelihoodPCA::Clear()
310{
311 // nothing to clear
312}
313
314// signal map
315float ReadLikelihoodPCA::fRefS[][10000] =
316{
317 { 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 9.26878719785464e-16, 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321};
322
323// background map
324float ReadLikelihoodPCA::fRefB[][10000] =
325{
326 { 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 8.9423238e-16, 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0.0007404738, 0.00074652088, 0.00075263006, 0.00075869256, 0.00076481735, 0.00077094208, 0.000777098, 0.00078325381, 0.00078940968, 0.00079559663, 0.00080181466, 0.0008080171, 0.00081423513, 0.0008204843, 0.00082676444, 0.00083304464, 0.00083935593, 0.00084562058, 0.00085196301, 0.00085828989, 0.00086466339, 0.00087099016, 0.00087739481, 0.00088379934, 0.00089021941, 0.00089665508, 0.00090307521, 0.00090954197, 0.00091602426, 0.00092250656, 0.0009290044, 0.00093551778, 0.00094207784, 0.0009486223, 0.00095519779, 0.00096174225, 0.00096836447, 0.00097495562, 0.00098160887, 0.00098821556, 0.00099488441, 0.0010015531, 0.0010082531, 0.001014953, 0.0010216529, 0.001028384, 0.0010351462, 0.0010419083, 0.0010486703, 0.001055448, 0.0010622878, 0.0010690966, 0.0010759518, 0.0010827606, 0.0010896472, 0.0010965492, 0.0011034511, 0.001110322, 0.0011172707, 0.0011242194, 0.0011311835, 0.0011381789, 0.0011451275, 0.0011521538, 0.0011591646, 0.0011662222, 0.001173233, 0.001180306, 0.0011873945, 0.0011944986, 0.0012016026, 0.0012087069, 0.0012158576, 0.0012230237, 0.0012301902, 0.0012373563, 0.0012445537, 0.0012517978, 0.0012590106, 0.0012662858, 0.0012735142, 0.0012808049, 0.0012880955, 0.0012954017, 0.0013026923, 0.001310045, 0.0013173979, 0.0013247818, 0.0013321657, 0.0013395495, 0.0013469491, 0.0013543952, 0.0013618257, 0.0013692718, 0.0013767489, 0.0013842572, 0.0013917501, 0.0013992894, 0.0014067821, 0.0014143524 },
330};
331
332 inline double ReadLikelihoodPCA::GetMvaValue( const std::vector<double>& inputValues ) const
333 {
334 // classifier response value
335 double retval = 0;
336
337 // classifier response, sanity check first
338 if (!IsStatusClean()) {
339 std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
340 << " because status is dirty" << std::endl;
341 retval = 0;
342 }
343 else {
344 retval = GetMvaValue__( inputValues );
345 }
346
347 return retval;
348 }
349
350//_______________________________________________________________________
351inline void ReadLikelihoodPCA::X2P_1( const double* x, double* p, int index ) const
352{
353 // Calculate the principal components from the original data vector
354 // x, and return it in p (function extracted from TPrincipal::X2P)
355 // It's the users responsibility to make sure that both x and p are
356 // of the right size (i.e., memory must be allocated for p).
357 const int nVar = 4;
358
359 for (int i = 0; i < nVar; i++) {
360 p[i] = 0;
361 for (int j = 0; j < nVar; j++) p[i] += (x[j] - fMeanValues_1[index][j]) * fEigenVectors_1[index][j][i];
362 }
363}
364
365//_______________________________________________________________________
366inline void ReadLikelihoodPCA::InitTransform_1()
367{
368 // PCA transformation, initialisation
369 // initialise vector of mean values
370 fMeanValues_1[0][0] = 0.464625579372;
371 fMeanValues_1[0][1] = -0.0639694689922;
372 fMeanValues_1[0][2] = 0.411290951946;
373 fMeanValues_1[0][3] = 0.7829579259;
374 fMeanValues_1[1][0] = -0.400829579487;
375 fMeanValues_1[1][1] = 0.169765598908;
376 fMeanValues_1[1][2] = -0.364625318032;
377 fMeanValues_1[1][3] = -0.419713574964;
378 fMeanValues_1[2][0] = 0.0318979999423;
379 fMeanValues_1[2][1] = 0.0528980649579;
380 fMeanValues_1[2][2] = 0.0233328169571;
381 fMeanValues_1[2][3] = 0.181622175468;
382
383 // initialise matrix of eigenvectors
384 fEigenVectors_1[0][0][0] = 0.768252604471;
385 fEigenVectors_1[0][0][1] = -0.028370894651;
386 fEigenVectors_1[0][0][2] = 0.525723457644;
387 fEigenVectors_1[0][0][3] = 0.364139910121;
388 fEigenVectors_1[0][1][0] = 0.0340781636064;
389 fEigenVectors_1[0][1][1] = 0.990577508276;
390 fEigenVectors_1[0][1][2] = -0.0730369150275;
391 fEigenVectors_1[0][1][3] = 0.110727087496;
392 fEigenVectors_1[0][2][0] = 0.392548727843;
393 fEigenVectors_1[0][2][1] = -0.116198174116;
394 fEigenVectors_1[0][2][2] = -0.83483442027;
395 fEigenVectors_1[0][2][3] = 0.368042078211;
396 fEigenVectors_1[0][3][0] = 0.504511754826;
397 fEigenVectors_1[0][3][1] = 0.0667028992291;
398 fEigenVectors_1[0][3][2] = -0.146054598898;
399 fEigenVectors_1[0][3][3] = -0.848343483866;
400 fEigenVectors_1[1][0][0] = 0.751070244153;
401 fEigenVectors_1[1][0][1] = -0.02967686386;
402 fEigenVectors_1[1][0][2] = 0.539733956366;
403 fEigenVectors_1[1][0][3] = 0.379077865938;
404 fEigenVectors_1[1][1][0] = 0.0381436516974;
405 fEigenVectors_1[1][1][1] = 0.989214418614;
406 fEigenVectors_1[1][1][2] = -0.0804003679249;
407 fEigenVectors_1[1][1][3] = 0.116342927068;
408 fEigenVectors_1[1][2][0] = 0.406388251313;
409 fEigenVectors_1[1][2][1] = -0.125857703267;
410 fEigenVectors_1[1][2][2] = -0.828377374267;
411 fEigenVectors_1[1][2][3] = 0.364416456168;
412 fEigenVectors_1[1][3][0] = 0.518928838452;
413 fEigenVectors_1[1][3][1] = 0.0688037519732;
414 fEigenVectors_1[1][3][2] = -0.126546287918;
415 fEigenVectors_1[1][3][3] = -0.84259417358;
416 fEigenVectors_1[2][0][0] = 0.74519049714;
417 fEigenVectors_1[2][0][1] = 0.00127983764507;
418 fEigenVectors_1[2][0][2] = -0.593203472111;
419 fEigenVectors_1[2][0][3] = 0.304629489155;
420 fEigenVectors_1[2][1][0] = 0.0300635881857;
421 fEigenVectors_1[2][1][1] = -0.990566953749;
422 fEigenVectors_1[2][1][2] = 0.0875254278685;
423 fEigenVectors_1[2][1][3] = 0.101057361342;
424 fEigenVectors_1[2][2][0] = 0.408169078132;
425 fEigenVectors_1[2][2][1] = 0.129262323181;
426 fEigenVectors_1[2][2][2] = 0.762290103097;
427 fEigenVectors_1[2][2][3] = 0.485389590106;
428 fEigenVectors_1[2][3][0] = 0.526483909815;
429 fEigenVectors_1[2][3][1] = -0.0454612358075;
430 fEigenVectors_1[2][3][2] = 0.243644698018;
431 fEigenVectors_1[2][3][3] = -0.813255943644;
432}
433
434//_______________________________________________________________________
435inline void ReadLikelihoodPCA::Transform_1( std::vector<double>& iv, int cls ) const
436{
437 // PCA transformation
438 const int nVar = 4;
439 double *dv = new double[nVar];
440 double *rv = new double[nVar];
441 if (cls < 0 || cls > 2) {
442 if (2 > 1 ) cls = 2;
443 else cls = 2;
444 }
445
446 // define the indices of the variables which are transformed by this transformation
447 static std::vector<int> indicesGet;
448 static std::vector<int> indicesPut;
449
450 if ( indicesGet.empty() ) {
451 indicesGet.reserve(fNvars);
452 indicesGet.push_back( 0);
453 indicesGet.push_back( 1);
454 indicesGet.push_back( 2);
455 indicesGet.push_back( 3);
456 }
457 if ( indicesPut.empty() ) {
458 indicesPut.reserve(fNvars);
459 indicesPut.push_back( 0);
460 indicesPut.push_back( 1);
461 indicesPut.push_back( 2);
462 indicesPut.push_back( 3);
463 }
464
465 for (int ivar=0; ivar<nVar; ivar++) dv[ivar] = iv[indicesGet.at(ivar)];
466
467 // Perform PCA and put it into PCAed events tree
468 this->X2P_1( dv, rv, cls );
469 for (int ivar=0; ivar<nVar; ivar++) iv[indicesPut.at(ivar)] = rv[ivar];
470
471 delete [] dv;
472 delete [] rv;
473}
474
475//_______________________________________________________________________
476inline void ReadLikelihoodPCA::InitTransform()
477{
478 InitTransform_1();
479}
480
481//_______________________________________________________________________
482inline void ReadLikelihoodPCA::Transform( std::vector<double>& iv, int sigOrBgd ) const
483{
484 Transform_1( iv, sigOrBgd );
485}
fType
PyObject * fType
Definition: MethodProxy.cxx:120
r
ROOT::R::TRInterface & r
Definition: Object.C:4
xmin
float xmin
Definition: THbookFile.cxx:93
xmax
float xmax
Definition: THbookFile.cxx:93
log
double log(double)
x
Double_t x[n]
Definition: legend1.C:17
RooStats::HistFactory::Constraint::GetType
Type GetType(const std::string &Name)
Definition: Systematics.cxx:34
TGeoUnit::ps
static constexpr double ps
Definition: TGeoSystemOfUnits.h:165
TMVA::TMVAGlob::Initialize
void Initialize(Bool_t useTMVAStyle=kTRUE)
Definition: tmvaglob.cxx:176