parameters::parameters(datafile dat, model nv_mod, model ref_mod,parameters ref_param,int compo,int iter){
const MatrixXi & omega=nv_mod.Get_model(),ref_omega=ref_mod.Get_model(),mat=dat.Get_mat_datafile();
const int g=omega.rows(),unique=mat.rows();
m_proba=ref_param.m_proba;
m_proba_block.resize(g);
m_param.resize(g);
for (int k=0;k<g;k++){
if (k!=compo){
m_param[k].resize(omega.rowwise().maxCoeff()(k)+1);
m_param[k]=ref_param.m_param[k];
m_proba_block[k].resize(unique,omega.rowwise().maxCoeff()(k)+1);
m_proba_block[k]=ref_param.m_proba_block[k];
for (int b=0;b<(omega.rowwise().maxCoeff()(k)+1) ;b++){
if ((omega.row(k).array()==b).any()){
m_param[k][b]=ref_param.m_param[k][b];
}
}
}else{
m_param[k].resize(omega.rowwise().maxCoeff()(k)+1);
m_proba_block[k].resize(unique,omega.rowwise().maxCoeff()(k)+1);
for (int b=0;b<(omega.rowwise().maxCoeff()(k)+1) ;b++){
if ((omega.row(k).array()==b).any()){
if ((((omega.row(k).array()==b)==(ref_omega.row(k).array()==b)).prod())==1){
m_param[k][b]=ref_param.m_param[k][b];
}else{
m_param[k][b]=param_block(k,b,dat,nv_mod,m_proba.col(k).array()/m_proba.rowwise().sum().array(),1);
if ((omega.row(k).array()==b).count()>1){
int prem=0;
while(omega(k,prem)!=b){prem++;}
if (mat.col(prem).maxCoeff()>5){
m_param[k][b]=m_param[k][b].Optimise_gamma(k,b,dat,nv_mod,5,m_proba.col(k).array()/m_proba.rowwise().sum().array(),dat.Get_eff_datafile());
}
}
}
}
}
}
}
m_propor=uniforme(g);
Probapost( nv_mod , mat );
Compte_nbparam(dat,nv_mod);
Likelihood(dat.Get_eff_datafile());
Estimation(1,0,iter,dat,nv_mod);
}
void parameters::Estimation_discrete(int nbtent, datafile dat, model mod){
const MatrixXi & omega=mod.Get_model();
const int g=omega.rows();
for (int k=0;k<g;k++){
for (int b=0;b<(omega.row(k).maxCoeff()+1);b++){
if ((omega.row(k).array()==b).count()>1){
m_param[k][b]=m_param[k][b].Optimise_gamma(k,b,dat,mod,nbtent, m_proba.col(k).array()/m_proba.rowwise().sum().array(),dat.Get_eff_datafile());
}
}
}
}
void parameters::Mstep(datafile dat, model mod){
const MatrixXi & omega=mod.Get_model(),mat=dat.Get_mat_datafile();
const VectorXd & eff=dat.Get_eff_datafile();
for (int k=0;k<m_proba.cols();k++){
m_propor(k)= (eff.array()*(m_proba.col(k)).array()/m_proba.rowwise().sum().array()).sum() / eff.sum();
for (int b=0;b<mat.cols();b++){
if ((omega.row(k).array()==b).any()){
const VectorXi & who=mod.Get_var_block(k,b);
m_param[k][b].Mstep(who,mat,m_proba_block[k].col(b),m_proba.col(k).array()/m_proba.rowwise().sum().array(),eff);
}
}
}
}
void parameters::Probapost(model mod, const MatrixXi & mat){
const MatrixXi & omega=mod.Get_model();
m_proba=MatrixXd::Ones(mat.rows(),omega.rows());
for (int k = 0; k < omega.rows(); ++k) {
m_proba_block[k]=m_proba_block[k].Ones(mat.rows(),(omega.rowwise().maxCoeff()(k)+1));
for (int b = 0; b < (omega.rowwise().maxCoeff()(k)+1); ++b){
if ((omega.row(k).array()==b).any()){
const VectorXi & who=mod.Get_var_block(k,b);
if (who.rows()>1){
m_proba_block[k].col(b)=m_param[k][b].proba_indpt(who,mat)+m_param[k][b].proba_dpt(who,mat);
}else{
m_proba_block[k].col(b)=m_param[k][b].proba_indpt(who,mat);
}
}
}
m_proba.col(k)=m_propor(k)* (m_proba_block[k].rowwise().prod()).array();
}
}
void parameters::Compte_nbparam(datafile dat,model mod){
const MatrixXi & omega=mod.Get_model();
int g=omega.rows();
m_nbparam=g-1;
for (int k=0;k<g;k++){
for (int b=0;b<(omega.rowwise().maxCoeff()(k)+1) ;b++){
if ((omega.row(k).array()==b).any()){
const VectorXi & who=mod.Get_var_block(k,b);
const VectorXi modalite=dat.Get_modalite(who);
if (who.rows()==1){
m_nbparam+=modalite(0)-1;
}else{
for (int h=0;h<modalite.rows();h++){
m_nbparam+=modalite(h)-1;
}
m_nbparam+=modalite(0);
if ((modalite(1)==2)&&(who.rows()==2)){m_nbparam--;}
}
}
}
}
}
parameters::parameters(datafile dat, model mod){
// TODO Auto-generated constructor stub
const MatrixXi & omega=mod.Get_model(),mat=dat.Get_mat_datafile();
const int g=omega.rows(),unique=mat.rows();
m_proba=MatrixXd::Ones(unique,g);
m_proba_block.resize(g);
m_param.resize(g);
for (int k=0;k<g;k++){
m_param[k].resize(omega.rowwise().maxCoeff()(k)+1);
m_proba_block[k].resize(unique,omega.rowwise().maxCoeff()(k)+1);
for (int b=0;b<(omega.rowwise().maxCoeff()(k)+1) ;b++){
if ((omega.row(k).array()==b).any()){
m_param[k][b]=param_block(k,b,dat,mod,VectorXd::Ones(mat.rows()),1);
}
}
}
m_propor=uniforme(g);
Probapost( mod , mat );
Compte_nbparam(dat,mod);
Likelihood(dat.Get_eff_datafile());
Estimation(1,0,6,dat,mod);
}
