def RandomTrees_LBGLCM(self): global accuracies, all_classifiers, labels lbglcm_feat = self.compute_LBGLCM() n_trees = self.notreesRF.text() max_feats = self.FeaturesRF.currentText() clf, x_rf2, y_rf2, dict2 = Classifiers.RF_train( lbglcm_feat, n_trees, max_feats ) #Collecting the trained classifier, x_test, y_test and labels Y_pred_rf2 = Classifiers.pred(clf, x_rf2) acc_test = Classifiers.display_results(Y_pred_rf2, y_rf2) all_classifiers.append(clf) accuracies.append(acc_test) labels.append(dict2)
def RandomTrees_GLCM(self): global accuracies, all_classifiers, labels glcm_feat = self.compute_GLCM() n_trees = self.notreesRF.text() max_feats = self.FeaturesRF.currentText() clf, x_rf1, y_rf1, dict1 = Classifiers.RF_train( glcm_feat, n_trees, max_feats ) #Collecting the trained classifier, x_test, y_test and labels Y_pred_rf1 = Classifiers.pred(clf, x_rf1) #Predicting the x_test labels acc_test = Classifiers.display_results(Y_pred_rf1, y_rf1) #accuracy of prediction all_classifiers.append(clf) accuracies.append(acc_test) labels.append(dict1)