bool mutate(MutationCfg mcfg) { // delete some nodes; int i; BernoulliDistribution bd(0); bool b; // Delete nodes bd.setProbTrue(mcfg.nodeDeleteProb/nrVertices()); for (i=0;i<nrVertices();i++) { if (bd.draw(b) && b) { cout << "Removing node " << i << endl; removeVertex(i); } } // Add nodes bd.setProbTrue(mcfg.nodeAddProb); bool mustAdd; int nrV = nrVertices(); for (i=0;i<nrV;i++) { if (bd.draw(b) && b) { vector<int> edges; int j; BernoulliDistribution bdH(mcfg.edgeAddProbNewNode); for (j=0;j<nrV;j++) if (bdH.draw(b) && b) edges.push_back(j); if (!edges.empty()) { addVertex(); for (j=0;j<edges.size();j++) addEdge(mVertices.size()-1,edges[j]); } } } return true; }
/** * Description not yet available. * \param */ double do_gauss_hermite_block_diagonal_multi(const dvector& x, const dvector& u0,const dmatrix& Hess,const dvector& _xadjoint, const dvector& _uadjoint,const dmatrix& _Hessadjoint, function_minimizer * pmin) { ADUNCONST(dvector,xadjoint) ADUNCONST(dvector,uadjoint) //ADUNCONST(dmatrix,Hessadjoint) dvector & w= *(pmin->multinomial_weights); const int xs=x.size(); const int us=u0.size(); gradient_structure::set_NO_DERIVATIVES(); int nsc=pmin->lapprox->num_separable_calls; const ivector lrea = (*pmin->lapprox->num_local_re_array)(1,nsc); int hroom = sum(square(lrea)); int nvar=x.size()+u0.size()+hroom; independent_variables y(1,nvar); // need to set random effects active together with whatever // init parameters should be active in this phase initial_params::set_inactive_only_random_effects(); initial_params::set_active_random_effects(); /*int onvar=*/initial_params::nvarcalc(); initial_params::xinit(y); // get the initial values into the // do we need this next line? y(1,xs)=x; int i,j; // contribution for quadratic prior if (quadratic_prior::get_num_quadratic_prior()>0) { //Hess+=quadratic_prior::get_cHessian_contribution(); int & vxs = (int&)(xs); quadratic_prior::get_cHessian_contribution(Hess,vxs); } // Here need hooks for sparse matrix structures dvar3_array & block_diagonal_vhessian= *pmin->lapprox->block_diagonal_vhessian; block_diagonal_vhessian.initialize(); dvar3_array& block_diagonal_ch= *pmin->lapprox->block_diagonal_vch; //dvar3_array(*pmin->lapprox->block_diagonal_ch); int ii=xs+us+1; d3_array& bdH=(*pmin->lapprox->block_diagonal_hessian); int ic; for (ic=1;ic<=nsc;ic++) { int lus=lrea(ic); for (i=1;i<=lus;i++) for (j=1;j<=lus;j++) y(ii++)=bdH(ic)(i,j); } dvector g(1,nvar); gradcalc(0,g); gradient_structure::set_YES_DERIVATIVES(); dvar_vector vy=dvar_vector(y); //initial_params::stddev_vscale(d,vy); ii=xs+us+1; if (initial_df1b2params::have_bounded_random_effects) { cerr << "can't do importance sampling with bounded random effects" " at present" << endl; ad_exit(1); } else { for (int ic=1;ic<=nsc;ic++) { int lus=lrea(ic); if (lus>0) { for (i=1;i<=lus;i++) { for (j=1;j<=lus;j++) { block_diagonal_vhessian(ic,i,j)=vy(ii++); } } block_diagonal_ch(ic)= choleski_decomp(inv(block_diagonal_vhessian(ic))); } } } int nsamp=pmin->lapprox->use_gauss_hermite; pmin->lapprox->in_gauss_hermite_phase=1; dvar_vector sample_value(1,nsamp); sample_value.initialize(); dvar_vector tau(1,us);; // !!! This only works for one random efect in each separable call // at present. if (pmin->lapprox->gh->mi) { delete pmin->lapprox->gh->mi; pmin->lapprox->gh->mi=0; } pmin->lapprox->gh->mi=new multi_index(1,nsamp, pmin->lapprox->multi_random_effects); multi_index & mi = *(pmin->lapprox->gh->mi); //for (int is=1;is<=nsamp;is++) dvector& xx=pmin->lapprox->gh->x; do { int offset=0; pmin->lapprox->num_separable_calls=0; //pmin->lapprox->gh->is=is; for (ic=1;ic<=nsc;ic++) { int lus=lrea(ic); // will need vector stuff here when more than one random effect if (lus>0) { //tau(offset+1,offset+lus).shift(1)=block_diagonal_ch(ic)(1,1)* // pmin->lapprox->gh->x(is); dvector xv(1,lus); for (int iu=1;iu<=lus;iu++) { xv(iu)= xx(mi()(iu)); } tau(offset+1,offset+lus).shift(1)=block_diagonal_ch(ic)*xv; offset+=lus; } } // have to reorder the terms to match the block diagonal hessian imatrix & ls=*(pmin->lapprox->block_diagonal_re_list); int mmin=ls.indexmin(); int mmax=ls.indexmax(); int ii=1; int i; for (i=mmin;i<=mmax;i++) { int cmin=ls(i).indexmin(); int cmax=ls(i).indexmax(); for (int j=cmin;j<=cmax;j++) { vy(ls(i,j))+=tau(ii++); } } if (ii-1 != us) { cerr << "error in interface" << endl; ad_exit(1); } initial_params::reset(vy); // get the values into the model ii=1; for (i=mmin;i<=mmax;i++) { int cmin=ls(i).indexmin(); int cmax=ls(i).indexmax(); for (int j=cmin;j<=cmax;j++) { vy(ls(i,j))-=tau(ii++); } } *objective_function_value::pobjfun=0.0; pmin->AD_uf_outer(); ++mi; } while(mi.get_depth()<=pmin->lapprox->multi_random_effects); nsc=pmin->lapprox->num_separable_calls; dvariable vf=pmin->do_gauss_hermite_integration(); int sgn=0; dvariable ld=0.0; if (ad_comm::no_ln_det_choleski_flag) { for (int ic=1;ic<=nsc;ic++) { if (allocated(block_diagonal_vhessian(ic))) { ld+=w(2*ic)*ln_det(block_diagonal_vhessian(ic),sgn); } } ld*=0.5; } else { for (int ic=1;ic<=nsc;ic++) { if (allocated(block_diagonal_vhessian(ic))) { ld+=w(2*ic)*ln_det_choleski(block_diagonal_vhessian(ic)); } } ld*=0.5; } vf+=ld; //vf+=us*0.91893853320467241; double f=value(vf); gradcalc(nvar,g); // put uhat back into the model gradient_structure::set_NO_DERIVATIVES(); vy(xs+1,xs+us).shift(1)=u0; initial_params::reset(vy); // get the values into the model gradient_structure::set_YES_DERIVATIVES(); pmin->lapprox->in_gauss_hermite_phase=0; ii=1; for (i=1;i<=xs;i++) xadjoint(i)=g(ii++); for (i=1;i<=us;i++) uadjoint(i)=g(ii++); for (ic=1;ic<=nsc;ic++) { int lus=lrea(ic); for (i=1;i<=lus;i++) { for (j=1;j<=lus;j++) { (*pmin->lapprox->block_diagonal_vhessianadjoint)(ic)(i,j)=g(ii++); } } } return f; }