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BiasRepresentation.cpp
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BiasRepresentation.cpp
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/* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Copyright (c) 2012-2016 The plumed team
(see the PEOPLE file at the root of the distribution for a list of names)
See http://www.plumed.org for more information.
This file is part of plumed, version 2.
plumed is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
plumed is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with plumed. If not, see <http://www.gnu.org/licenses/>.
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
#include "core/Value.h"
#include "Communicator.h"
#include "BiasRepresentation.h"
#include <iostream>
namespace PLMD {
/// the constructor here
BiasRepresentation::BiasRepresentation(vector<Value*> tmpvalues, Communicator &cc ):hasgrid(false),rescaledToBias(false),mycomm(cc),BiasGrid_(NULL){
lowI_=0.0;
uppI_=0.0;
doInt_=false;
ndim=tmpvalues.size();
for(int i=0;i<ndim;i++){
values.push_back(tmpvalues[i]);
names.push_back(values[i]->getName());
}
}
/// overload the constructor: add the sigma at constructor time
BiasRepresentation::BiasRepresentation(vector<Value*> tmpvalues, Communicator &cc, vector<double> sigma ):hasgrid(false), rescaledToBias(false), histosigma(sigma),mycomm(cc),BiasGrid_(NULL){
lowI_=0.0;
uppI_=0.0;
doInt_=false;
ndim=tmpvalues.size();
for(int i=0;i<ndim;i++){
values.push_back(tmpvalues[i]);
names.push_back(values[i]->getName());
}
}
/// overload the constructor: add the grid at constructor time
BiasRepresentation::BiasRepresentation(vector<Value*> tmpvalues, Communicator &cc , vector<string> gmin, vector<string> gmax,
vector<unsigned> nbin, bool doInt, double lowI, double uppI ):hasgrid(false), rescaledToBias(false), mycomm(cc), BiasGrid_(NULL){
ndim=tmpvalues.size();
for(int i=0;i<ndim;i++){
values.push_back(tmpvalues[i]);
names.push_back(values[i]->getName());
}
doInt_=doInt;
lowI_=lowI;
uppI_=uppI;
// initialize the grid
addGrid(gmin,gmax,nbin);
}
/// overload the constructor with some external sigmas: needed for histogram
BiasRepresentation::BiasRepresentation(vector<Value*> tmpvalues, Communicator &cc , vector<string> gmin, vector<string> gmax, vector<unsigned> nbin , vector<double> sigma):hasgrid(false), rescaledToBias(false),histosigma(sigma),mycomm(cc),BiasGrid_(NULL){
lowI_=0.0;
uppI_=0.0;
doInt_=false;
ndim=tmpvalues.size();
for(int i=0;i<ndim;i++){
values.push_back(tmpvalues[i]);
names.push_back(values[i]->getName());
}
// initialize the grid
addGrid(gmin,gmax,nbin);
}
BiasRepresentation::~BiasRepresentation(){
if(BiasGrid_) delete BiasGrid_;
for(unsigned i=0;i<hills.size();i++) delete hills[i];
}
void BiasRepresentation::addGrid( vector<string> gmin, vector<string> gmax, vector<unsigned> nbin ){
plumed_massert(hills.size()==0,"you can set the grid before loading the hills");
plumed_massert(hasgrid==false,"to build the grid you should not having the grid in this bias representation");
string ss; ss="file.free";
vector<Value*> vv;for(unsigned i=0;i<values.size();i++)vv.push_back(values[i]);
//cerr<<" initializing grid "<<endl;
BiasGrid_=new Grid(ss,vv,gmin,gmax,nbin,false,true);
hasgrid=true;
}
bool BiasRepresentation::hasSigmaInInput(){
if(histosigma.size()==0){return false;}else{return true;}
}
void BiasRepresentation::setRescaledToBias(bool rescaled){
plumed_massert(hills.size()==0,"you can set the rescaling function only before loading hills");
rescaledToBias=rescaled;
}
const bool & BiasRepresentation::isRescaledToBias(){
return rescaledToBias;
}
unsigned BiasRepresentation::getNumberOfDimensions(){
return values.size();
}
vector<string> BiasRepresentation::getNames(){
return names;
}
const string & BiasRepresentation::getName(unsigned i){
return names[i];
}
const vector<Value*>& BiasRepresentation::getPtrToValues(){
return values;
}
Value* BiasRepresentation::getPtrToValue(unsigned i){
return values[i];
}
KernelFunctions* BiasRepresentation::readFromPoint(IFile *ifile){
vector<double> cc( names.size() );
for(unsigned i=0;i<names.size();++i){
ifile->scanField(names[i],cc[i]);
}
double h=1.0;
return new KernelFunctions(cc,histosigma,"gaussian",false,h,false);
}
void BiasRepresentation::pushKernel( IFile *ifile ){
KernelFunctions *kk=NULL;
// here below the reading of the kernel is completely hidden
if(histosigma.size()==0){
ifile->allowIgnoredFields();
kk=KernelFunctions::read(ifile,names) ;
}else{
// when doing histogram assume gaussian with a given diagonal sigma
// and neglect all the rest
kk=readFromPoint(ifile) ;
}
hills.push_back(kk);
// the bias factor is not something about the kernels but
// must be stored to keep the bias/free energy duality
string dummy; double dummyd;
if(ifile->FieldExist("biasf")){
ifile->scanField("biasf",dummy);
Tools::convert(dummy,dummyd);
}else{dummyd=1.0;}
biasf.push_back(dummyd);
// the domain does not pertain to the kernel but to the values here defined
string mins,maxs,minv,maxv,mini,maxi;mins="min_";maxs="max_";
for(int i=0 ; i<ndim; i++){
if(values[i]->isPeriodic()){
ifile->scanField(mins+names[i],minv);
ifile->scanField(maxs+names[i],maxv);
// verify that the domain is correct
values[i]->getDomain(mini,maxi);
plumed_massert(mini==minv,"the input periodicity in hills and in value definition does not match" );
plumed_massert(maxi==maxv,"the input periodicity in hills and in value definition does not match" );
}
}
// if grid is defined then it should be added on the grid
//cerr<<"now with "<<hills.size()<<endl;
if(hasgrid){
vector<unsigned> nneighb;
if(doInt_&&(kk->getCenter()[0]+kk->getContinuousSupport()[0] > uppI_ || kk->getCenter()[0]-kk->getContinuousSupport()[0] < lowI_ )) {
nneighb=BiasGrid_->getNbin();
} else nneighb=kk->getSupport(BiasGrid_->getDx());
vector<Grid::index_t> neighbors=BiasGrid_->getNeighbors(kk->getCenter(),nneighb);
vector<double> der(ndim);
vector<double> xx(ndim);
if(mycomm.Get_size()==1){
for(unsigned i=0;i<neighbors.size();++i){
Grid::index_t ineigh=neighbors[i];
for(int j=0;j<ndim;++j){der[j]=0.0;}
BiasGrid_->getPoint(ineigh,xx);
// assign xx to a new vector of values
for(int j=0;j<ndim;++j){values[j]->set(xx[j]);}
double bias;
if(doInt_) bias=kk->evaluate(values,der,true,doInt_,lowI_,uppI_);
else bias=kk->evaluate(values,der,true);
if(rescaledToBias){
double f=(biasf.back()-1.)/(biasf.back());
bias*=f;
for(int j=0;j<ndim;++j){der[j]*=f;}
}
BiasGrid_->addValueAndDerivatives(ineigh,bias,der);
}
} else {
unsigned stride=mycomm.Get_size();
unsigned rank=mycomm.Get_rank();
vector<double> allder(ndim*neighbors.size(),0.0);
vector<double> allbias(neighbors.size(),0.0);
vector<double> tmpder(ndim);
for(unsigned i=rank;i<neighbors.size();i+=stride){
Grid::index_t ineigh=neighbors[i];
BiasGrid_->getPoint(ineigh,xx);
for(int j=0;j<ndim;++j){values[j]->set(xx[j]);}
if(doInt_) allbias[i]=kk->evaluate(values,der,true,doInt_,lowI_,uppI_);
else allbias[i]=kk->evaluate(values,der,true);
if(rescaledToBias){
double f=(biasf.back()-1.)/(biasf.back());
allbias[i]*=f;
for(int j=0;j<ndim;++j){tmpder[j]*=f;}
}
// this solution with the temporary vector is rather bad, probably better to take
// a pointer of double as it was in old gaussian
for(int j=0;j<ndim;++j){ allder[ndim*i+j]=tmpder[j]; tmpder[j]=0.;}
}
mycomm.Sum(allbias);
mycomm.Sum(allder);
for(unsigned i=0;i<neighbors.size();++i){
Grid::index_t ineigh=neighbors[i];
for(int j=0;j<ndim;++j){der[j]=allder[ndim*i+j];}
BiasGrid_->addValueAndDerivatives(ineigh,allbias[i],der);
}
}
}
}
int BiasRepresentation::getNumberOfKernels(){
return hills.size();
}
Grid* BiasRepresentation::getGridPtr(){
plumed_massert(hasgrid,"if you want the grid pointer then you should have defined a grid before");
return BiasGrid_;
}
void BiasRepresentation::getMinMaxBin(vector<double> &vmin, vector<double> &vmax, vector<unsigned> &vbin){
vector<double> ss,cc,binsize;
vmin.clear();vmin.resize(ndim,10.e20);
vmax.clear();vmax.resize(ndim,-10.e20);
vbin.clear();vbin.resize(ndim);
binsize.clear();binsize.resize(ndim,10.e20);
int ndiv=10; // adjustable parameter: division per support
for(unsigned i=0;i<hills.size();i++){
if(histosigma.size()!=0){
ss=histosigma;
}else{
ss=hills[i]->getContinuousSupport();
}
cc=hills[i]->getCenter();
for(int j=0;j<ndim;j++){
double dmin=cc[j]-ss[j];
double dmax=cc[j]+ss[j];
double ddiv=ss[j]/double(ndiv);
if(dmin<vmin[j])vmin[j]=dmin;
if(dmax>vmax[j])vmax[j]=dmax;
if(ddiv<binsize[j])binsize[j]=ddiv;
}
}
for(int j=0;j<ndim;j++){
// reset to periodicity
if(values[j]->isPeriodic()){
double minv,maxv;
values[j]->getDomain(minv,maxv);
if(minv>vmin[j])vmin[j]=minv;
if(maxv<vmax[j])vmax[j]=maxv;
}
vbin[j]=static_cast<unsigned>(ceil((vmax[j]-vmin[j])/binsize[j]) );
}
}
void BiasRepresentation::clear(){
// clear the hills
for(vector<KernelFunctions*>::const_iterator it = hills.begin(); it != hills.end(); ++it)
{
delete *it;
}
hills.clear();
// clear the grid
if(hasgrid){
BiasGrid_->clear();
}
}
}