EludeCaller::~EludeCaller() {
if (train_features_table_) {
DataManager::CleanUpTable(train_psms_, train_features_table_);
train_features_table_ = NULL;
}
if (test_features_table_) {
DataManager::CleanUpTable(test_psms_, test_features_table_);
test_features_table_ = NULL;
}
if (rt_model_) {
delete rt_model_;
rt_model_ = NULL;
}
if (lts != NULL) {
delete lts;
}
std::vector<PSMDescription*>::iterator it = train_psms_.begin();
for ( ; it != train_psms_.end(); ++it) {
PSMDescription::deletePtr(*it);
}
it = test_psms_.begin();
for ( ; it != test_psms_.end(); ++it) {
PSMDescription::deletePtr(*it);
}
DeleteRTModels();
}
EludeCaller::~EludeCaller() {
if (train_features_table_) {
DataManager::CleanUpTable(train_psms_, train_features_table_);
train_features_table_ = NULL;
}
if (test_features_table_) {
DataManager::CleanUpTable(test_psms_, test_features_table_);
test_features_table_ = NULL;
}
if (rt_model_) {
delete rt_model_;
rt_model_ = NULL;
}
if (lts != NULL) {
delete lts;
}
DeleteRTModels();
}
int EludeCaller::SelectTestModel(std::vector<PSMDescription*> &calibration_psms,
std::vector<PSMDescription*> &test_psms) {
train_psms_ = calibration_psms;
test_psms_ = test_psms;
train_aa_alphabet_ = GetAAAlphabet(calibration_psms);
test_aa_alphabet_ = GetAAAlphabet(test_psms);
AllocateRTFeatures(train_psms_);
AllocateRTFeatures(test_psms_);
pair<int, double> best_model = AutomaticModelSelection();
int index = best_model.first;
if (index < 0) {
cerr << "Error: No model available to predict rt. " << endl;
return 1;
}
rt_models_[index]->PredictRT(test_aa_alphabet_, false, "test psms", test_psms_);
rt_models_[index]->PredictRT(train_aa_alphabet_, false, "calibration psms",
train_psms_);
pair<vector<double> , vector<double> > rts = GetRTs(train_psms_);
lts = new LTSRegression();
lts->setData(rts.first, rts.second);
lts->runLTS();
AdjustLinearly(test_psms_);
calibration_psms = train_psms_;
test_psms = test_psms_;
// clean up the models
delete lts;
lts = NULL;
DeleteRTModels();
return 0;
}
