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);
}
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);
}
int main (int argc, char *argv[])
{
int i,j,k,count;
struct vertex* G; // graph represented by adjacency list
int n = 8; // number of vertices in G
double vm = 4; // numero medio de vizinhos
int no_viz, *vizs, flag;
struct neighbor *aux, *viz;
// Inicializacao
if (argc > 1) {
n = atoi(argv[1]);
vm = atoi(argv[2]);
}
srand((unsigned)time(NULL));
vizs = (int *) malloc (sizeof (int) * n);
G = (struct vertex *) malloc (sizeof (struct vertex) * n);
for ( i = 0; i < n; i++ ) {
G[i].id = i+1;
G[i].adj_list = NULL;
}
// Geracao aleatoria do grafo G
for ( i = 0; i < n; i++ ) {
no_viz = geometrica(vm);
for ( j = 0; j < no_viz; j++ ) {
viz = (struct neighbor *) malloc (sizeof(struct neighbor));
do{
viz->id = uniforme (1,n); // verifica repeticoes
flag = 0;
for (k=0;k<j;k++) if (vizs[k] == viz->id) flag = 1;
} while(flag == 1);
vizs[j] = viz->id;
viz->weight = geometrica((int)n/vm);
aux = G[i].adj_list;
viz->next = aux;
G[i].adj_list = viz;
}
}
// Imprime o grafo G
printf ("%d\n",n);
for ( i = 0; i < n; i++ ) {
printf ("v%d,", G[i].id);
aux = G[i].adj_list;
while (aux != NULL) {
printf ("v%d", G[aux->id].id);
aux = aux->next;
if (aux != NULL)
printf (",");
}
aux = G[i].adj_list;
if (aux != NULL)
printf ("/");
while (aux != NULL) {
printf ("%d", aux->weight);
aux = aux->next;
if (aux != NULL)
printf (",");
}
printf ("\n");
}
}
