/*
* The hash table is to keep track of leaves which have already been displayed
* to the user.
*/
int main(int argc, char **argv)
{
int i;
/* Initialize space for blacklist hash table */
if(hcreate(10000)==0) {
fputs("Cannot create hash table.\n",stderr);
return(EX_UNAVAILABLE);
}
i=0;
do {
if(read_pkglist(i++))
break;
if(display_menu())
break;
if(remove_packages())
break;
free_menu();
} while(keep_going());
hdestroy();
fputs("\nProgram Terminated Successfully\n",stderr);
return(0);
}
/**
* Build the ADTree, assuming no covariates.
*
*/
void ADTree::processNoCov(){
int temp_precon=0; long temp_attr=0; short temp_val=0;
Condition::comparison temp_test = Condition::GE;
double new_score_t, new_score_f;
double *temp_weights = new double[5];
AD_Rule *temp_r;
Condition *c;
//
// READY TO RUN
//
while(keep_going()){
double z = 0;
// Get minimum score
//z = minimize(&temp_precon, &temp_attr, &temp_test, &temp_val);
z = minimize_ordinal(temp_precon, temp_attr, temp_test, temp_val);
// Calc new scores
weights(temp_weights, temp_precon, temp_attr, temp_test, temp_val);
if(param_reader->get_verbosity() > 2){
cout << "Num nodes: " << tree.node_size() << " score " << z << endl;
cout << "Attribute: " << temp_attr << endl;
}
// Using 0.0005 for now, will change later to be 1/size
new_score_t = .5 * log((temp_weights[0]+this->fudge) / (temp_weights[1]+this->fudge));
new_score_f = .5 * log((temp_weights[2]+this->fudge) / (temp_weights[3]+this->fudge));
// Make new rule and make two new preconditions.
c = new Condition;
c->attribute_index = temp_attr;
c->genotype_conditional = temp_test;
c->genotype_reference = temp_val;
vector<Condition> condi_v = tree.precon_at(temp_precon)->conditions;
Precondition *p1 = new Precondition;
p1->conditions = condi_v;
temp_r = new AD_Rule(*p1,*c, new_score_t, new_score_f);
(*p1).conditions.push_back(*c);
tree.push_node(*temp_r, (temp_precon+1)/2);
tree.push_precondition(*p1);
// Make a precondition with this set.
c->inverse();
condi_v = tree.precon_at(temp_precon)->conditions;
Precondition *p2 = new Precondition;
p2->conditions = condi_v;
p2->conditions.push_back(*c);
tree.push_precondition(*p2);
delete c;
delete p1;
delete p2;
delete temp_r;
// Reweight data.
for(unsigned int i=0; i < weight_vec.size(); i++){
weight_vec.at(i) *= exp(-1.0 * tree.evaluate_on_last(data->get_data(i)) * data->get_phenotype(i));
}
}
delete[] (temp_weights);
}
