Line data    Source code

       1             : /* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
       2             :    Copyright (c) 2015-2023 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             : #include "DimensionalityReductionBase.h"
      23             : #include "reference/ReferenceConfiguration.h"
      24             : #include "core/PlumedMain.h"
      25             : #include "core/Atoms.h"
      26             : 
      27             : namespace PLMD {
      28             : namespace dimred {
      29             : 
      30          23 : void DimensionalityReductionBase::registerKeywords( Keywords& keys ) {
      31          23 :   analysis::AnalysisBase::registerKeywords( keys );
      32          46 :   keys.add("compulsory","NLOW_DIM","number of low-dimensional coordinates required");
      33          46 :   keys.addOutputComponent("coord","default","the low-dimensional projections of the various input configurations");
      34          23 : }
      35             : 
      36          16 : DimensionalityReductionBase::DimensionalityReductionBase( const ActionOptions& ao ):
      37             :   Action(ao),
      38             :   analysis::AnalysisBase(ao),
      39          16 :   dimredbase(NULL)
      40             : {
      41             :   // Check that some dissimilarity information is available
      42          16 :   if( my_input_data ) {
      43          27 :     if( getName()!="PCA" && !dissimilaritiesWereSet() ) error("dissimilarities have not been calcualted in input actions");
      44             :     // Now we check if the input was a dimensionality reduction object
      45          15 :     dimredbase = dynamic_cast<DimensionalityReductionBase*>( my_input_data );
      46             :   }
      47             : 
      48             :   // Retrieve the dimension in the low dimensionality space
      49          16 :   nlow=0;
      50          16 :   if( dimredbase ) {
      51           5 :     nlow=dimredbase->nlow;
      52           5 :     log.printf("  projecting in %u dimensional space \n",nlow);
      53          22 :   } else if( keywords.exists("NLOW_DIM") ) {
      54          10 :     parse("NLOW_DIM",nlow);
      55          10 :     if( nlow<1 ) error("dimensionality of low dimensional space must be at least one");
      56          10 :     log.printf("  projecting in %u dimensional space \n",nlow);
      57             :   }
      58             :   // Now add fake components to the underlying ActionWithValue for the arguments
      59             :   std::string num;
      60          45 :   for(unsigned i=0; i<nlow; ++i) {
      61          87 :     Tools::convert(i+1,num); addComponent( "coord-" + num ); componentIsNotPeriodic( "coord-" + num );
      62             :   }
      63          16 : }
      64             : 
      65           2 : std::vector<Value*> DimensionalityReductionBase::getArgumentList() {
      66             :   std::vector<Value*> arglist( analysis::AnalysisBase::getArgumentList() );
      67           6 :   for(unsigned i=0; i<nlow; ++i) arglist.push_back( getPntrToComponent(i) );
      68           2 :   return arglist;
      69             : }
      70             : 
      71        1050 : void DimensionalityReductionBase::getProjection( const unsigned& idata, std::vector<double>& point, double& weight ) {
      72        1050 :   if( point.size()!=nlow ) point.resize( nlow );
      73        3150 :   weight = getWeight(idata); for(unsigned i=0; i<nlow; ++i) point[i]=projections(idata,i);
      74        1050 : }
      75             : 
      76          19 : void DimensionalityReductionBase::performAnalysis() {
      77          19 :   log.printf("Generating projections required by action %s \n",getLabel().c_str() );
      78             :   // Resize the tempory array (this is used for out of sample)
      79          19 :   dtargets.resize( getNumberOfDataPoints() );
      80             :   // Resize the projections array
      81          19 :   projections.resize( getNumberOfDataPoints(), nlow );
      82             :   // Retrieve the projections from the previous calculation
      83          19 :   if( dimredbase ) {
      84           6 :     std::vector<double> newp( nlow ); double w;
      85        1056 :     for(unsigned i=0; i<getNumberOfDataPoints(); ++i) {
      86        1050 :       dimredbase->getProjection( i, newp, w ); plumed_dbg_assert( newp.size()==nlow );
      87        3150 :       for(unsigned j=0; j<nlow; ++j) projections(i,j)=newp[j];
      88             :     }
      89             :   }
      90             :   // Calculate matrix of dissimilarities
      91          19 :   Matrix<double> targets( getNumberOfDataPoints(), getNumberOfDataPoints() ); targets=0;
      92        2686 :   for(unsigned i=1; i<getNumberOfDataPoints(); ++i) {
      93      367354 :     for(unsigned j=0; j<i; ++j) targets(i,j)=targets(j,i)=getDissimilarity( i, j );
      94             :   }
      95             :   // This calculates the projections of the points
      96          19 :   calculateProjections( targets, projections );
      97             :   // Now set the projection values in the underlying object
      98          19 :   if( my_input_data ) {
      99        2620 :     for(unsigned idat=0; idat<getNumberOfDataPoints(); ++idat) {
     100        2602 :       analysis::DataCollectionObject& myref=AnalysisBase::getStoredData(idat,false); std::string num;
     101        7804 :       for(unsigned i=0; i<nlow; ++i) { Tools::convert(i+1,num); myref.setArgument( getLabel() + ".coord-" + num, projections(idat,i) ); }
     102             :     }
     103             :   }
     104          19 :   log.printf("Generated projections required by action %s \n",getLabel().c_str() );
     105          19 : }
     106             : 
     107           0 : double DimensionalityReductionBase::calculateStress( const std::vector<double>& p, std::vector<double>& d ) {
     108             : 
     109             :   // Zero derivative and stress accumulators
     110           0 :   for(unsigned i=0; i<p.size(); ++i) d[i]=0.0;
     111           0 :   double stress=0; std::vector<double> dtmp( p.size() );
     112             : 
     113             :   // Now accumulate total stress on system
     114           0 :   for(unsigned i=0; i<dtargets.size(); ++i) {
     115           0 :     if( dtargets[i]<epsilon ) continue ;
     116             : 
     117             :     // Calculate distance in low dimensional space
     118             :     double dd=0;
     119           0 :     for(unsigned j=0; j<p.size(); ++j) { dtmp[j]=p[j]-projections(i,j); dd+=dtmp[j]*dtmp[j]; }
     120           0 :     dd = std::sqrt(dd);
     121             : 
     122             :     // Now do transformations and calculate differences
     123           0 :     double ddiff = dd - dtargets[i];
     124             : 
     125             :     // Calculate derivatives
     126           0 :     double pref = 2.*getWeight(i) / dd;
     127           0 :     for(unsigned j=0; j<p.size(); ++j) d[j] += pref*ddiff*dtmp[j];
     128             : 
     129             :     // Accumulate the total stress
     130           0 :     stress += getWeight(i)*ddiff*ddiff;
     131             :   }
     132           0 :   return stress;
     133             : }
     134             : 
     135             : }
     136             : }