Revision 1109 trunk/src/df1b2-separable/df1b2lme.cpp

df1b2lme.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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        ad_exit(1);
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      }
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      df1b2_gradlist::set_no_derivatives();
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      int nvar=initial_params::nvarcalc_all(); 
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      int nvar=initial_params::nvarcalc_all();
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      dvector x(1,nvar);
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      initial_params::xinit_all(x); 
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      initial_params::xinit_all(x);
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      initial_df1b2params::reset_all(x);
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      for (int i=1;i<=nvar;i++) y(i)=x(i);
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      step=get_newton_raphson_info_banded(pfmin);
......
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    ad_exit(1);
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  }
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  dvector df0=exp(-2.0*value(*variance_components_vector));
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  dvector vsave(mmin,mmax);
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  vsave=value(*variance_components_vector);
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  for(int ic=mmin;ic<=mmax;ic++)
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  {  
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  {
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    (*variance_components_vector)(ic)+=0.2;
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    // test newton raphson
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    switch(hesstype)
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    {
......
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    }
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    df1b2_gradlist::set_no_derivatives();
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    int nvar=initial_params::nvarcalc_all(); 
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    int nvar=initial_params::nvarcalc_all();
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    dvector x(1,nvar);
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    initial_params::xinit_all(x); 
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    initial_params::xinit_all(x);
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    initial_df1b2params::reset_all(x);
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    for (int i=1;i<=nvar;i++) y(i)=x(i);
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    step=get_newton_raphson_info_banded(pfmin);
......
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{
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  int mmin=variance_components_vector->indexmin();
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  int mmax=variance_components_vector->indexmax();
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  initial_params::set_inactive_only_random_effects(); 
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  initial_params::set_inactive_only_random_effects();
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  independent_variables xx(1,mmax-mmin+1);
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  initial_params::xinit(xx);    // get the initial values into the
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  dvar_vector vxx=dvar_vector(xx);
......
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  case 4:
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    {
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      for(int ic=mmin;ic<=mmax;ic++)
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      {  
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      {
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        if (var_flag==1)
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        {
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          vHess+= 
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          vHess+=
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            (*variance_components_vector)(ic)*((*Hess_components)(ic));
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        }
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        else
......
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  ADUNCONST(double,f)
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  //int i,j;
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  initial_params::set_inactive_only_random_effects(); 
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  initial_params::set_inactive_only_random_effects();
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  gradient_structure::set_NO_DERIVATIVES();
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  initial_params::reset(x);    // get current x values into the model
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  gradient_structure::set_YES_DERIVATIVES();
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  initial_params::set_active_only_random_effects(); 
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  initial_params::set_active_only_random_effects();
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  dvector g=get_gradient_lme(pfmin);
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  reset_gradient_stack();
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  // this is the main loop to do inner optimization
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  //for (i=1;i<=xsize;i++) { y(i)=x(i); }
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  //for (i=1;i<=usize;i++) { y(i+xsize)=uhat(i); }
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  dvar_matrix vHess=get_hessian_from_components_lme(pfmin);
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