Line data Source code
1 : /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
2 : Copyright (c) 2017-2023 The plumed team
3 : (see the PEOPLE file at the root of the distribution for a list of names)
4 :
5 : See http://www.plumed.org for more information.
6 :
7 : This file is part of plumed, version 2.
8 :
9 : plumed is free software: you can redistribute it and/or modify
10 : it under the terms of the GNU Lesser General Public License as published by
11 : the Free Software Foundation, either version 3 of the License, or
12 : (at your option) any later version.
13 :
14 : plumed is distributed in the hope that it will be useful,
15 : but WITHOUT ANY WARRANTY; without even the implied warranty of
16 : MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17 : GNU Lesser General Public License for more details.
18 :
19 : You should have received a copy of the GNU Lesser General Public License
20 : along with plumed. If not, see <http://www.gnu.org/licenses/>.
21 : +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
22 : #ifndef __PLUMED_isdb_MetainferenceBase_h
23 : #define __PLUMED_isdb_MetainferenceBase_h
24 :
25 : #include "core/ActionWithValue.h"
26 : #include "core/ActionAtomistic.h"
27 : #include "core/ActionWithArguments.h"
28 : #include "tools/Communicator.h"
29 : #include "core/PlumedMain.h"
30 : #include "tools/Random.h"
31 : #include "tools/OpenMP.h"
32 :
33 : #define PLUMED_METAINF_INIT(ao) Action(ao),MetainferenceBase(ao)
34 :
35 : namespace PLMD {
36 : namespace isdb {
37 :
38 : /**
39 : \ingroup INHERIT
40 : This is the abstract base class to use for implementing new ISDB Metainference actions, within it there is
41 : information as to how to go about implementing a new Metainference action.
42 : */
43 :
44 : class MetainferenceBase :
45 : public ActionAtomistic,
46 : public ActionWithArguments,
47 : public ActionWithValue {
48 : private:
49 : std::vector<double> forces;
50 : std::vector<double> forcesToApply;
51 :
52 : // activate metainference
53 : bool doscore_;
54 : unsigned write_stride_;
55 : // number of experimental data
56 : unsigned narg;
57 : // experimental data
58 : std::vector<double> parameters;
59 : // metainference derivatives
60 : std::vector<double> metader_;
61 : // vector of back-calculated experimental data
62 : std::vector<double> calc_data_;
63 :
64 : // noise type
65 : unsigned noise_type_;
66 : enum { GAUSS, MGAUSS, OUTLIERS, MOUTLIERS, GENERIC };
67 : unsigned gen_likelihood_;
68 : enum { LIKE_GAUSS, LIKE_LOGN };
69 : bool doscale_;
70 : unsigned scale_prior_;
71 : enum { SC_GAUSS, SC_FLAT };
72 : double scale_;
73 : double scale_mu_;
74 : double scale_min_;
75 : double scale_max_;
76 : double Dscale_;
77 : // scale is data scaling factor
78 : // noise type
79 : unsigned offset_prior_;
80 : bool dooffset_;
81 : double offset_;
82 : double offset_mu_;
83 : double offset_min_;
84 : double offset_max_;
85 : double Doffset_;
86 : // scale and offset regression
87 : bool doregres_zero_;
88 : int nregres_zero_;
89 : // sigma is data uncertainty
90 : std::vector<double> sigma_;
91 : std::vector<double> sigma_min_;
92 : std::vector<double> sigma_max_;
93 : std::vector<double> Dsigma_;
94 : // sigma_mean is uncertainty in the mean estimate
95 : std::vector<double> sigma_mean2_;
96 : // this is the estimator of the mean value per replica for generic metainference
97 : std::vector<double> ftilde_;
98 : double Dftilde_;
99 :
100 : // temperature in kbt
101 : double kbt_;
102 :
103 : // Monte Carlo stuff
104 : std::vector<Random> random;
105 : unsigned MCsteps_;
106 : long long unsigned MCaccept_;
107 : long long unsigned MCacceptScale_;
108 : long long unsigned MCacceptFT_;
109 : long long unsigned MCtrial_;
110 : unsigned MCchunksize_;
111 :
112 : // output
113 : Value* valueScore;
114 : Value* valueScale;
115 : Value* valueOffset;
116 : Value* valueAccept;
117 : Value* valueAcceptScale;
118 : Value* valueAcceptFT;
119 : std::vector<Value*> valueSigma;
120 : std::vector<Value*> valueSigmaMean;
121 : std::vector<Value*> valueFtilde;
122 :
123 : // restart
124 : std::string status_file_name_;
125 : OFile sfile_;
126 : std::string fmt_;
127 :
128 : // others
129 : bool firstTime;
130 : std::vector<bool> firstTimeW;
131 : bool master;
132 : bool do_reweight_;
133 : unsigned do_optsigmamean_;
134 : unsigned nrep_;
135 : unsigned replica_;
136 :
137 : // selector
138 : unsigned nsel_;
139 : std::string selector_;
140 : unsigned iselect;
141 :
142 : // optimize sigma mean
143 : std::vector< std::vector < std::vector <double> > > sigma_mean2_last_;
144 : unsigned optsigmamean_stride_;
145 : // optimize sigma max
146 : unsigned N_optimized_step_;
147 : unsigned optimized_step_;
148 : bool sigmamax_opt_done_;
149 : std::vector<double> sigma_max_est_;
150 :
151 : // average weights
152 : double decay_w_;
153 : std::vector< std::vector <double> > average_weights_;
154 :
155 : double getEnergyMIGEN(const std::vector<double> &mean, const std::vector<double> &ftilde, const std::vector<double> &sigma,
156 : const double scale, const double offset);
157 : double getEnergySP(const std::vector<double> &mean, const std::vector<double> &sigma,
158 : const double scale, const double offset);
159 : double getEnergySPE(const std::vector<double> &mean, const std::vector<double> &sigma,
160 : const double scale, const double offset);
161 : double getEnergyGJ(const std::vector<double> &mean, const std::vector<double> &sigma,
162 : const double scale, const double offset);
163 : double getEnergyGJE(const std::vector<double> &mean, const std::vector<double> &sigma,
164 : const double scale, const double offset);
165 : void setMetaDer(const unsigned index, const double der);
166 : void getEnergyForceSP(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
167 : void getEnergyForceSPE(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
168 : void getEnergyForceGJ(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
169 : void getEnergyForceGJE(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
170 : void getEnergyForceMIGEN(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
171 : double getCalcData(const unsigned index);
172 : void get_weights(double &weight, double &norm, double &neff);
173 : void replica_averaging(const double weight, const double norm, std::vector<double> &mean, std::vector<double> &dmean_b);
174 : void get_sigma_mean(const double weight, const double norm, const double neff, const std::vector<double> &mean);
175 : void do_regression_zero(const std::vector<double> &mean);
176 : void moveTilde(const std::vector<double> &mean_, double &old_energy);
177 : void moveScaleOffset(const std::vector<double> &mean_, double &old_energy);
178 : void moveSigmas(const std::vector<double> &mean_, double &old_energy, const unsigned i, const std::vector<unsigned> &indices, bool &breaknow);
179 : double doMonteCarlo(const std::vector<double> &mean);
180 :
181 : public:
182 : static void registerKeywords( Keywords& keys );
183 : explicit MetainferenceBase(const ActionOptions&);
184 : ~MetainferenceBase();
185 : void Initialise(const unsigned input);
186 : void Selector();
187 : unsigned getNarg();
188 : void setNarg(const unsigned input);
189 : void setParameters(const std::vector<double>& input);
190 : void setParameter(const double input);
191 : void setCalcData(const unsigned index, const double datum);
192 : void setCalcData(const std::vector<double>& data);
193 : bool getDoScore();
194 : unsigned getWstride();
195 : double getScore();
196 : void setScore(const double score);
197 : void setDerivatives();
198 : double getMetaDer(const unsigned index);
199 : void writeStatus();
200 : void turnOnDerivatives() override;
201 : unsigned getNumberOfDerivatives() override;
202 : void lockRequests() override;
203 : void unlockRequests() override;
204 : void calculateNumericalDerivatives( ActionWithValue* a ) override;
205 : void apply() override;
206 : void setArgDerivatives(Value *v, const double &d);
207 : void setAtomsDerivatives(Value*v, const unsigned i, const Vector&d);
208 : void setBoxDerivatives(Value*v, const Tensor&d);
209 : };
210 :
211 : inline
212 : void MetainferenceBase::setNarg(const unsigned input) {
213 44 : narg = input;
214 : }
215 :
216 : inline
217 : bool MetainferenceBase::getDoScore() {
218 22895 : return doscore_;
219 : }
220 :
221 : inline
222 : unsigned MetainferenceBase::getWstride() {
223 1794 : return write_stride_;
224 : }
225 :
226 : inline
227 : unsigned MetainferenceBase::getNarg() {
228 2327 : return narg;
229 : }
230 :
231 : inline
232 : void MetainferenceBase::setMetaDer(const unsigned index, const double der) {
233 7524 : metader_[index] = der;
234 : }
235 :
236 : inline
237 : double MetainferenceBase::getMetaDer(const unsigned index) {
238 912722 : return metader_[index];
239 : }
240 :
241 : inline
242 : double MetainferenceBase::getCalcData(const unsigned index) {
243 : return calc_data_[index];
244 : }
245 :
246 : inline
247 : void MetainferenceBase::setCalcData(const unsigned index, const double datum) {
248 3868 : calc_data_[index] = datum;
249 3868 : }
250 :
251 : inline
252 : void MetainferenceBase::setCalcData(const std::vector<double>& data) {
253 : for(unsigned i=0; i<data.size(); i++) {
254 : calc_data_[i] = data[i];
255 : }
256 : }
257 :
258 : inline
259 40 : void MetainferenceBase::setParameters(const std::vector<double>& input) {
260 252 : for(unsigned i=0; i<input.size(); i++) {
261 212 : parameters.push_back(input[i]);
262 : }
263 40 : }
264 :
265 : inline
266 : void MetainferenceBase::setParameter(const double input) {
267 2356 : parameters.push_back(input);
268 2356 : }
269 :
270 : inline
271 2327 : void MetainferenceBase::setScore(const double score) {
272 2327 : valueScore->set(score);
273 2327 : }
274 :
275 : inline
276 136 : void MetainferenceBase::setDerivatives() {
277 : // Get appropriate number of derivatives
278 : // Derivatives are first for arguments and then for atoms
279 : unsigned nder;
280 136 : if( getNumberOfAtoms()>0 ) {
281 136 : nder = 3*getNumberOfAtoms() + 9 + getNumberOfArguments();
282 : } else {
283 0 : nder = getNumberOfArguments();
284 : }
285 :
286 : // Resize all derivative arrays
287 136 : forces.resize( nder );
288 136 : forcesToApply.resize( nder );
289 21777 : for(int i=0; i<getNumberOfComponents(); ++i) {
290 21641 : getPntrToComponent(i)->resizeDerivatives(nder);
291 : }
292 136 : }
293 :
294 : inline
295 2862 : void MetainferenceBase::turnOnDerivatives() {
296 2862 : ActionWithValue::turnOnDerivatives();
297 2862 : }
298 :
299 : inline
300 2829388 : unsigned MetainferenceBase::getNumberOfDerivatives() {
301 2829388 : if( getNumberOfAtoms()>0 ) {
302 2829388 : return 3*getNumberOfAtoms() + 9 + getNumberOfArguments();
303 : }
304 0 : return getNumberOfArguments();
305 : }
306 :
307 : inline
308 897 : void MetainferenceBase::lockRequests() {
309 : ActionAtomistic::lockRequests();
310 : ActionWithArguments::lockRequests();
311 897 : }
312 :
313 : inline
314 897 : void MetainferenceBase::unlockRequests() {
315 : ActionAtomistic::unlockRequests();
316 : ActionWithArguments::unlockRequests();
317 897 : }
318 :
319 : inline
320 75 : void MetainferenceBase::calculateNumericalDerivatives( ActionWithValue* a=NULL ) {
321 75 : if( getNumberOfArguments()>0 ) {
322 48 : ActionWithArguments::calculateNumericalDerivatives( a );
323 : }
324 75 : if( getNumberOfAtoms()>0 ) {
325 75 : Matrix<double> save_derivatives( getNumberOfComponents(), getNumberOfArguments() );
326 1293 : for(int j=0; j<getNumberOfComponents(); ++j) {
327 2322 : for(unsigned i=0; i<getNumberOfArguments(); ++i)
328 1104 : if(getPntrToComponent(j)->hasDerivatives()) {
329 240 : save_derivatives(j,i)=getPntrToComponent(j)->getDerivative(i);
330 : }
331 : }
332 75 : calculateAtomicNumericalDerivatives( a, getNumberOfArguments() );
333 1293 : for(int j=0; j<getNumberOfComponents(); ++j) {
334 2322 : for(unsigned i=0; i<getNumberOfArguments(); ++i)
335 1104 : if(getPntrToComponent(j)->hasDerivatives()) {
336 240 : getPntrToComponent(j)->addDerivative( i, save_derivatives(j,i) );
337 : }
338 : }
339 : }
340 75 : }
341 :
342 : inline
343 897 : void MetainferenceBase::apply() {
344 : bool wasforced=false;
345 897 : forcesToApply.assign(forcesToApply.size(),0.0);
346 35268 : for(int i=0; i<getNumberOfComponents(); ++i) {
347 34371 : if( getPntrToComponent(i)->applyForce( forces ) ) {
348 : wasforced=true;
349 41724504 : for(unsigned i=0; i<forces.size(); ++i) {
350 41717854 : forcesToApply[i]+=forces[i];
351 : }
352 : }
353 : }
354 897 : if( wasforced ) {
355 482 : unsigned ind=0;
356 482 : addForcesOnArguments( 0, forcesToApply, ind, getLabel() );
357 482 : if( getNumberOfAtoms()>0 ) {
358 482 : setForcesOnAtoms( forcesToApply, ind );
359 : }
360 : }
361 897 : }
362 :
363 : inline
364 : void MetainferenceBase::setArgDerivatives(Value *v, const double &d) {
365 160 : v->addDerivative(0,d);
366 : }
367 :
368 : inline
369 2776760 : void MetainferenceBase::setAtomsDerivatives(Value*v, const unsigned i, const Vector&d) {
370 2776760 : const unsigned noa=getNumberOfArguments();
371 2776760 : v->addDerivative(noa+3*i+0,d[0]);
372 2776760 : v->addDerivative(noa+3*i+1,d[1]);
373 2776760 : v->addDerivative(noa+3*i+2,d[2]);
374 2776760 : }
375 :
376 : inline
377 12544 : void MetainferenceBase::setBoxDerivatives(Value* v,const Tensor&d) {
378 12544 : const unsigned noa=getNumberOfArguments();
379 : const unsigned nat=getNumberOfAtoms();
380 12544 : v->addDerivative(noa+3*nat+0,d(0,0));
381 12544 : v->addDerivative(noa+3*nat+1,d(0,1));
382 12544 : v->addDerivative(noa+3*nat+2,d(0,2));
383 12544 : v->addDerivative(noa+3*nat+3,d(1,0));
384 12544 : v->addDerivative(noa+3*nat+4,d(1,1));
385 12544 : v->addDerivative(noa+3*nat+5,d(1,2));
386 12544 : v->addDerivative(noa+3*nat+6,d(2,0));
387 12544 : v->addDerivative(noa+3*nat+7,d(2,1));
388 12544 : v->addDerivative(noa+3*nat+8,d(2,2));
389 12544 : }
390 :
391 :
392 : }
393 : }
394 :
395 : #endif
396 :
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