Line data Source code
1 : /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
2 : Copyright (c) 2017-2020 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 "core/PlumedMain.h"
29 : #include "tools/Random.h"
30 : #include "tools/OpenMP.h"
31 :
32 : #define PLUMED_METAINF_INIT(ao) Action(ao),MetainferenceBase(ao)
33 :
34 : namespace PLMD {
35 : namespace isdb {
36 :
37 : /**
38 : \ingroup INHERIT
39 : This is the abstract base class to use for implementing new ISDB Metainference actions, within it there is
40 : information as to how to go about implementing a new Metainference action.
41 : */
42 :
43 : class MetainferenceBase :
44 : public ActionAtomistic,
45 : public ActionWithArguments,
46 : public ActionWithValue
47 : {
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 : unsigned MCstride_;
107 : long unsigned MCaccept_;
108 : long unsigned MCacceptScale_;
109 : long unsigned MCacceptFT_;
110 : long unsigned MCtrial_;
111 : unsigned MCchunksize_;
112 :
113 : // output
114 : Value* valueScore;
115 : Value* valueScale;
116 : Value* valueOffset;
117 : Value* valueAccept;
118 : Value* valueAcceptScale;
119 : Value* valueAcceptFT;
120 : std::vector<Value*> valueSigma;
121 : std::vector<Value*> valueSigmaMean;
122 : std::vector<Value*> valueFtilde;
123 :
124 : // restart
125 : std::string status_file_name_;
126 : OFile sfile_;
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 :
146 : // average weights
147 : double decay_w_;
148 : std::vector< std::vector <double> > average_weights_;
149 :
150 : double getEnergyMIGEN(const std::vector<double> &mean, const std::vector<double> &ftilde, const std::vector<double> &sigma,
151 : const double scale, const double offset);
152 : double getEnergySP(const std::vector<double> &mean, const std::vector<double> &sigma,
153 : const double scale, const double offset);
154 : double getEnergySPE(const std::vector<double> &mean, const std::vector<double> &sigma,
155 : const double scale, const double offset);
156 : double getEnergyGJ(const std::vector<double> &mean, const std::vector<double> &sigma,
157 : const double scale, const double offset);
158 : double getEnergyGJE(const std::vector<double> &mean, const std::vector<double> &sigma,
159 : const double scale, const double offset);
160 : void setMetaDer(const unsigned index, const double der);
161 : double getEnergyForceSP(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
162 : double getEnergyForceSPE(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
163 : double getEnergyForceGJ(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
164 : double getEnergyForceGJE(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
165 : double getEnergyForceMIGEN(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
166 : double getCalcData(const unsigned index);
167 : void get_weights(double &fact, double &var_fact);
168 : void replica_averaging(const double fact, std::vector<double> &mean, std::vector<double> &dmean_b);
169 : void get_sigma_mean(const double fact, const double var_fact, const std::vector<double> &mean);
170 : void do_regression_zero(const std::vector<double> &mean);
171 : void doMonteCarlo(const std::vector<double> &mean);
172 :
173 :
174 : public:
175 : static void registerKeywords( Keywords& keys );
176 : explicit MetainferenceBase(const ActionOptions&);
177 : ~MetainferenceBase();
178 : void Initialise(const unsigned input);
179 : void Selector();
180 : unsigned getNarg();
181 : void setNarg(const unsigned input);
182 : void setParameters(const std::vector<double>& input);
183 : void setParameter(const double input);
184 : void setCalcData(const unsigned index, const double datum);
185 : void setCalcData(const std::vector<double>& data);
186 : bool getDoScore();
187 : unsigned getWstride();
188 : double getScore();
189 : void setScore(const double score);
190 : void setDerivatives();
191 : double getMetaDer(const unsigned index);
192 : void writeStatus();
193 : void turnOnDerivatives();
194 : unsigned getNumberOfDerivatives();
195 : void lockRequests();
196 : void unlockRequests();
197 : void calculateNumericalDerivatives( ActionWithValue* a );
198 : void apply();
199 : void setArgDerivatives(Value *v, const double &d);
200 : void setAtomsDerivatives(Value*v, const unsigned i, const Vector&d);
201 : void setBoxDerivatives(Value*v, const Tensor&d);
202 : };
203 :
204 : inline
205 : void MetainferenceBase::setNarg(const unsigned input)
206 : {
207 31 : narg = input;
208 : }
209 :
210 : inline
211 : bool MetainferenceBase::getDoScore()
212 : {
213 35563 : return doscore_;
214 : }
215 :
216 : inline
217 : unsigned MetainferenceBase::getWstride()
218 : {
219 1382 : return write_stride_;
220 : }
221 :
222 : inline
223 : unsigned MetainferenceBase::getNarg()
224 : {
225 6675 : return narg;
226 : }
227 :
228 : inline
229 : void MetainferenceBase::setMetaDer(const unsigned index, const double der)
230 : {
231 11827 : metader_[index] = der;
232 : }
233 :
234 : inline
235 : double MetainferenceBase::getMetaDer(const unsigned index)
236 : {
237 1366796 : return metader_[index];
238 : }
239 :
240 : inline
241 : double MetainferenceBase::getCalcData(const unsigned index)
242 : {
243 1260 : return calc_data_[index];
244 : }
245 :
246 : inline
247 : void MetainferenceBase::setCalcData(const unsigned index, const double datum)
248 : {
249 11827 : calc_data_[index] = datum;
250 : }
251 :
252 : inline
253 : void MetainferenceBase::setCalcData(const std::vector<double>& data)
254 : {
255 : for(unsigned i=0; i<data.size(); i++) calc_data_[i] = data[i];
256 : }
257 :
258 : inline
259 27 : void MetainferenceBase::setParameters(const std::vector<double>& input) {
260 477 : for(unsigned i=0; i<input.size(); i++) parameters.push_back(input[i]);
261 27 : }
262 :
263 : inline
264 : void MetainferenceBase::setParameter(const double input) {
265 2356 : parameters.push_back(input);
266 : }
267 :
268 : inline
269 : void MetainferenceBase::setScore(const double score) {
270 2225 : valueScore->set(score);
271 : }
272 :
273 : inline
274 86 : void MetainferenceBase::setDerivatives() {
275 : // Get appropriate number of derivatives
276 : // Derivatives are first for arguments and then for atoms
277 : unsigned nder;
278 86 : if( getNumberOfAtoms()>0 ) {
279 86 : nder = 3*getNumberOfAtoms() + 9 + getNumberOfArguments();
280 : } else {
281 0 : nder = getNumberOfArguments();
282 : }
283 :
284 : // Resize all derivative arrays
285 86 : forces.resize( nder ); forcesToApply.resize( nder );
286 42252 : for(int i=0; i<getNumberOfComponents(); ++i) getPntrToComponent(i)->resizeDerivatives(nder);
287 86 : }
288 :
289 : inline
290 3480171 : void MetainferenceBase::turnOnDerivatives() {
291 3480171 : ActionWithValue::turnOnDerivatives();
292 3480171 : }
293 :
294 : inline
295 4099242757 : unsigned MetainferenceBase::getNumberOfDerivatives() {
296 4099242757 : if( getNumberOfAtoms()>0 ) {
297 4099242757 : return 3*getNumberOfAtoms() + 9 + getNumberOfArguments();
298 : }
299 0 : return getNumberOfArguments();
300 : }
301 :
302 : inline
303 691 : void MetainferenceBase::lockRequests() {
304 : ActionAtomistic::lockRequests();
305 : ActionWithArguments::lockRequests();
306 691 : }
307 :
308 : inline
309 691 : void MetainferenceBase::unlockRequests() {
310 : ActionAtomistic::unlockRequests();
311 : ActionWithArguments::unlockRequests();
312 691 : }
313 :
314 : inline
315 75 : void MetainferenceBase::calculateNumericalDerivatives( ActionWithValue* a=NULL ) {
316 75 : if( getNumberOfArguments()>0 ) {
317 48 : ActionWithArguments::calculateNumericalDerivatives( a );
318 : }
319 75 : if( getNumberOfAtoms()>0 ) {
320 150 : Matrix<double> save_derivatives( getNumberOfComponents(), getNumberOfArguments() );
321 2405 : for(int j=0; j<getNumberOfComponents(); ++j) {
322 3517 : for(unsigned i=0; i<getNumberOfArguments(); ++i) if(getPntrToComponent(j)->hasDerivatives()) save_derivatives(j,i)=getPntrToComponent(j)->getDerivative(i);
323 : }
324 75 : calculateAtomicNumericalDerivatives( a, getNumberOfArguments() );
325 2405 : for(int j=0; j<getNumberOfComponents(); ++j) {
326 4573 : for(unsigned i=0; i<getNumberOfArguments(); ++i) if(getPntrToComponent(j)->hasDerivatives()) getPntrToComponent(j)->addDerivative( i, save_derivatives(j,i) );
327 : }
328 : }
329 75 : }
330 :
331 : inline
332 691 : void MetainferenceBase::apply() {
333 1382 : bool wasforced=false; forcesToApply.assign(forcesToApply.size(),0.0);
334 63993 : for(int i=0; i<getNumberOfComponents(); ++i) {
335 31651 : if( getPntrToComponent(i)->applyForce( forces ) ) {
336 : wasforced=true;
337 166645292 : for(unsigned i=0; i<forces.size(); ++i) forcesToApply[i]+=forces[i];
338 : }
339 : }
340 691 : if( wasforced ) {
341 350 : addForcesOnArguments( forcesToApply );
342 350 : if( getNumberOfAtoms()>0 ) setForcesOnAtoms( forcesToApply, getNumberOfArguments() );
343 : }
344 691 : }
345 :
346 : inline
347 : void MetainferenceBase::setArgDerivatives(Value *v, const double &d) {
348 160 : v->addDerivative(0,d);
349 : }
350 :
351 : inline
352 1865108 : void MetainferenceBase::setAtomsDerivatives(Value*v, const unsigned i, const Vector&d) {
353 1865108 : const unsigned noa=getNumberOfArguments();
354 1865108 : v->addDerivative(noa+3*i+0,d[0]);
355 1865108 : v->addDerivative(noa+3*i+1,d[1]);
356 1865108 : v->addDerivative(noa+3*i+2,d[2]);
357 1865108 : }
358 :
359 : inline
360 11782 : void MetainferenceBase::setBoxDerivatives(Value* v,const Tensor&d) {
361 11782 : const unsigned noa=getNumberOfArguments();
362 : const unsigned nat=getNumberOfAtoms();
363 11782 : v->addDerivative(noa+3*nat+0,d(0,0));
364 11782 : v->addDerivative(noa+3*nat+1,d(0,1));
365 11782 : v->addDerivative(noa+3*nat+2,d(0,2));
366 11782 : v->addDerivative(noa+3*nat+3,d(1,0));
367 11782 : v->addDerivative(noa+3*nat+4,d(1,1));
368 11782 : v->addDerivative(noa+3*nat+5,d(1,2));
369 11782 : v->addDerivative(noa+3*nat+6,d(2,0));
370 11782 : v->addDerivative(noa+3*nat+7,d(2,1));
371 11782 : v->addDerivative(noa+3*nat+8,d(2,2));
372 11782 : }
373 :
374 :
375 : }
376 : }
377 :
378 : #endif
379 :
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