Line data Source code
1 : /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
2 : Copyright (c) 2017-2019 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 : long unsigned MCaccept_;
107 : long unsigned MCacceptScale_;
108 : long unsigned MCacceptFT_;
109 : 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 :
127 : // others
128 : bool firstTime;
129 : std::vector<bool> firstTimeW;
130 : bool master;
131 : bool do_reweight_;
132 : unsigned do_optsigmamean_;
133 : unsigned nrep_;
134 : unsigned replica_;
135 :
136 : // selector
137 : unsigned nsel_;
138 : std::string selector_;
139 : unsigned iselect;
140 :
141 : // optimize sigma mean
142 : std::vector< std::vector < std::vector <double> > > sigma_mean2_last_;
143 : unsigned optsigmamean_stride_;
144 :
145 : // average weights
146 : double decay_w_;
147 : std::vector< std::vector <double> > average_weights_;
148 :
149 : double getEnergyMIGEN(const std::vector<double> &mean, const std::vector<double> &ftilde, const std::vector<double> &sigma,
150 : const double scale, const double offset);
151 : double getEnergySP(const std::vector<double> &mean, const std::vector<double> &sigma,
152 : const double scale, const double offset);
153 : double getEnergySPE(const std::vector<double> &mean, const std::vector<double> &sigma,
154 : const double scale, const double offset);
155 : double getEnergyGJ(const std::vector<double> &mean, const std::vector<double> &sigma,
156 : const double scale, const double offset);
157 : double getEnergyGJE(const std::vector<double> &mean, const std::vector<double> &sigma,
158 : const double scale, const double offset);
159 : void setMetaDer(const unsigned index, const double der);
160 : void getEnergyForceSP(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
161 : void getEnergyForceSPE(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
162 : void getEnergyForceGJ(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
163 : void getEnergyForceGJE(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
164 : void getEnergyForceMIGEN(const std::vector<double> &mean, const std::vector<double> &dmean_x, const std::vector<double> &dmean_b);
165 : double getCalcData(const unsigned index);
166 : void get_weights(double &fact, double &var_fact);
167 : void replica_averaging(const double fact, std::vector<double> &mean, std::vector<double> &dmean_b);
168 : void get_sigma_mean(const double fact, const double var_fact, const std::vector<double> &mean);
169 : void do_regression_zero(const std::vector<double> &mean);
170 : double doMonteCarlo(const std::vector<double> &mean);
171 :
172 :
173 : public:
174 : static void registerKeywords( Keywords& keys );
175 : explicit MetainferenceBase(const ActionOptions&);
176 : ~MetainferenceBase();
177 : void Initialise(const unsigned input);
178 : void Selector();
179 : unsigned getNarg();
180 : void setNarg(const unsigned input);
181 : void setParameters(const std::vector<double>& input);
182 : void setParameter(const double input);
183 : void setCalcData(const unsigned index, const double datum);
184 : void setCalcData(const std::vector<double>& data);
185 : bool getDoScore();
186 : unsigned getWstride();
187 : double getScore();
188 : void setScore(const double score);
189 : void setDerivatives();
190 : double getMetaDer(const unsigned index);
191 : void writeStatus();
192 : void turnOnDerivatives() override;
193 : unsigned getNumberOfDerivatives() override;
194 : void lockRequests() override;
195 : void unlockRequests() override;
196 : void calculateNumericalDerivatives( ActionWithValue* a ) override;
197 : void apply() override;
198 : void setArgDerivatives(Value *v, const double &d);
199 : void setAtomsDerivatives(Value*v, const unsigned i, const Vector&d);
200 : void setBoxDerivatives(Value*v, const Tensor&d);
201 : };
202 :
203 : inline
204 : void MetainferenceBase::setNarg(const unsigned input)
205 : {
206 31 : narg = input;
207 : }
208 :
209 : inline
210 : bool MetainferenceBase::getDoScore()
211 : {
212 35521 : return doscore_;
213 : }
214 :
215 : inline
216 : unsigned MetainferenceBase::getWstride()
217 : {
218 1382 : return write_stride_;
219 : }
220 :
221 : inline
222 : unsigned MetainferenceBase::getNarg()
223 : {
224 6675 : return narg;
225 : }
226 :
227 : inline
228 : void MetainferenceBase::setMetaDer(const unsigned index, const double der)
229 : {
230 11825 : metader_[index] = der;
231 : }
232 :
233 : inline
234 : double MetainferenceBase::getMetaDer(const unsigned index)
235 : {
236 1366796 : return metader_[index];
237 : }
238 :
239 : inline
240 : double MetainferenceBase::getCalcData(const unsigned index)
241 : {
242 1260 : return calc_data_[index];
243 : }
244 :
245 : inline
246 : void MetainferenceBase::setCalcData(const unsigned index, const double datum)
247 : {
248 11798 : calc_data_[index] = datum;
249 : }
250 :
251 : inline
252 : void MetainferenceBase::setCalcData(const std::vector<double>& data)
253 : {
254 : for(unsigned i=0; i<data.size(); i++) calc_data_[i] = data[i];
255 : }
256 :
257 : inline
258 27 : void MetainferenceBase::setParameters(const std::vector<double>& input) {
259 336 : for(unsigned i=0; i<input.size(); i++) parameters.push_back(input[i]);
260 27 : }
261 :
262 : inline
263 : void MetainferenceBase::setParameter(const double input) {
264 2356 : parameters.push_back(input);
265 : }
266 :
267 : inline
268 : void MetainferenceBase::setScore(const double score) {
269 2225 : valueScore->set(score);
270 : }
271 :
272 : inline
273 86 : void MetainferenceBase::setDerivatives() {
274 : // Get appropriate number of derivatives
275 : // Derivatives are first for arguments and then for atoms
276 : unsigned nder;
277 86 : if( getNumberOfAtoms()>0 ) {
278 86 : nder = 3*getNumberOfAtoms() + 9 + getNumberOfArguments();
279 : } else {
280 0 : nder = getNumberOfArguments();
281 : }
282 :
283 : // Resize all derivative arrays
284 86 : forces.resize( nder ); forcesToApply.resize( nder );
285 42338 : for(int i=0; i<getNumberOfComponents(); ++i) getPntrToComponent(i)->resizeDerivatives(nder);
286 86 : }
287 :
288 : inline
289 3480171 : void MetainferenceBase::turnOnDerivatives() {
290 3480171 : ActionWithValue::turnOnDerivatives();
291 3480171 : }
292 :
293 : inline
294 4099242757 : unsigned MetainferenceBase::getNumberOfDerivatives() {
295 4099242757 : if( getNumberOfAtoms()>0 ) {
296 4099242757 : return 3*getNumberOfAtoms() + 9 + getNumberOfArguments();
297 : }
298 0 : return getNumberOfArguments();
299 : }
300 :
301 : inline
302 691 : void MetainferenceBase::lockRequests() {
303 : ActionAtomistic::lockRequests();
304 : ActionWithArguments::lockRequests();
305 691 : }
306 :
307 : inline
308 691 : void MetainferenceBase::unlockRequests() {
309 : ActionAtomistic::unlockRequests();
310 : ActionWithArguments::unlockRequests();
311 691 : }
312 :
313 : inline
314 75 : void MetainferenceBase::calculateNumericalDerivatives( ActionWithValue* a=NULL ) {
315 75 : if( getNumberOfArguments()>0 ) {
316 48 : ActionWithArguments::calculateNumericalDerivatives( a );
317 : }
318 75 : if( getNumberOfAtoms()>0 ) {
319 150 : Matrix<double> save_derivatives( getNumberOfComponents(), getNumberOfArguments() );
320 2480 : for(int j=0; j<getNumberOfComponents(); ++j) {
321 2592 : for(unsigned i=0; i<getNumberOfArguments(); ++i) if(getPntrToComponent(j)->hasDerivatives()) save_derivatives(j,i)=getPntrToComponent(j)->getDerivative(i);
322 : }
323 75 : calculateAtomicNumericalDerivatives( a, getNumberOfArguments() );
324 2480 : for(int j=0; j<getNumberOfComponents(); ++j) {
325 3408 : for(unsigned i=0; i<getNumberOfArguments(); ++i) if(getPntrToComponent(j)->hasDerivatives()) getPntrToComponent(j)->addDerivative( i, save_derivatives(j,i) );
326 : }
327 : }
328 75 : }
329 :
330 : inline
331 691 : void MetainferenceBase::apply() {
332 2073 : bool wasforced=false; forcesToApply.assign(forcesToApply.size(),0.0);
333 64684 : for(int i=0; i<getNumberOfComponents(); ++i) {
334 31651 : if( getPntrToComponent(i)->applyForce( forces ) ) {
335 : wasforced=true;
336 124980884 : for(unsigned i=0; i<forces.size(); ++i) forcesToApply[i]+=forces[i];
337 : }
338 : }
339 691 : if( wasforced ) {
340 350 : addForcesOnArguments( forcesToApply );
341 350 : if( getNumberOfAtoms()>0 ) setForcesOnAtoms( forcesToApply, getNumberOfArguments() );
342 : }
343 691 : }
344 :
345 : inline
346 : void MetainferenceBase::setArgDerivatives(Value *v, const double &d) {
347 160 : v->addDerivative(0,d);
348 : }
349 :
350 : inline
351 1865049 : void MetainferenceBase::setAtomsDerivatives(Value*v, const unsigned i, const Vector&d) {
352 1865049 : const unsigned noa=getNumberOfArguments();
353 1865073 : v->addDerivative(noa+3*i+0,d[0]);
354 1865079 : v->addDerivative(noa+3*i+1,d[1]);
355 1865098 : v->addDerivative(noa+3*i+2,d[2]);
356 1865096 : }
357 :
358 : inline
359 11757 : void MetainferenceBase::setBoxDerivatives(Value* v,const Tensor&d) {
360 11757 : const unsigned noa=getNumberOfArguments();
361 : const unsigned nat=getNumberOfAtoms();
362 11770 : v->addDerivative(noa+3*nat+0,d(0,0));
363 11765 : v->addDerivative(noa+3*nat+1,d(0,1));
364 11774 : v->addDerivative(noa+3*nat+2,d(0,2));
365 11773 : v->addDerivative(noa+3*nat+3,d(1,0));
366 11776 : v->addDerivative(noa+3*nat+4,d(1,1));
367 11776 : v->addDerivative(noa+3*nat+5,d(1,2));
368 11781 : v->addDerivative(noa+3*nat+6,d(2,0));
369 11782 : v->addDerivative(noa+3*nat+7,d(2,1));
370 11782 : v->addDerivative(noa+3*nat+8,d(2,2));
371 11782 : }
372 :
373 :
374 : }
375 : }
376 :
377 : #endif
378 :
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