Revision 1109 trunk/src/df1b2-separable/df1b2im5.cpp

df1b2im5.cpp (revision 1109)
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 * $Id$
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 *
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 * Author: David Fournier
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 * Copyright (c) 2008-2012 Regents of the University of California 
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 * Copyright (c) 2008-2012 Regents of the University of California
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 */
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/**
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 * \file
......
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  int hroom =  sum(square(lrea));
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  int nvar=x.size()+u0.size()+hroom;
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  independent_variables y(1,nvar);
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  // need to set random effects active together with whatever
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  // init parameters should be active in this phase
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  initial_params::set_inactive_only_random_effects(); 
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  initial_params::set_active_random_effects(); 
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  /*int onvar=*/initial_params::nvarcalc(); 
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  initial_params::set_inactive_only_random_effects();
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  initial_params::set_active_random_effects();
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  /*int onvar=*/initial_params::nvarcalc();
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  initial_params::xinit(y);    // get the initial values into the
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  // do we need this next line?
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  y(1,xs)=x;
......
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    quadratic_prior::get_cHessian_contribution(Hess,vxs);
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  }
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 // Here need hooks for sparse matrix structures
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  dvar3_array & block_diagonal_vhessian=
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    *pmin->lapprox->block_diagonal_vhessian;
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  block_diagonal_vhessian.initialize();
......
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  dvector g(1,nvar);
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  gradcalc(0,g);
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  gradient_structure::set_YES_DERIVATIVES();
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  dvar_vector vy=dvar_vector(y); 
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  dvar_vector vy=dvar_vector(y);
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  //initial_params::stddev_vscale(d,vy);
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  ii=xs+us+1;
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  if (initial_df1b2params::have_bounded_random_effects)
......
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   int nsamp=pmin->lapprox->num_importance_samples;
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   dvariable vf=0.0;
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   dvar_vector sample_value(1,nsamp);
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   sample_value.initialize();
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......
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         (*pmin->lapprox->antiepsilon)(is);
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       offset+=lus;
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     }
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     // have to reorder the terms to match the block diagonal hessian
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     imatrix & ls=*(pmin->lapprox->block_diagonal_re_list);
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     int mmin=ls.indexmin();
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     int mmax=ls.indexmax();
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     int ii=1;
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     int i;
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     for (i=mmin;i<=mmax;i++)
......
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         neps-value(*objective_function_value::pobjfun);
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       sample_value(is)=*objective_function_value::pobjfun
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         -neps; 
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         -neps;
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     }
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   }
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......
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   //double ns=lcomp.indexmax()-lcomp.indexmin()+1;
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   //double min_vf=min(value(lcomp));
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   vf= sum(lcomp);
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   vf-=us*0.91893853320467241; 
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   vf-=us*0.91893853320467241;
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   int sgn=0;
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   dvariable ld=0.0;
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   if (ad_comm::no_ln_det_choleski_flag)
......
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   vy(xs+1,xs+us).shift(1)=u0;
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   initial_params::reset(vy);    // get the values into the model
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   gradient_structure::set_YES_DERIVATIVES();
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  ii=1;
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