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)
