print("Tamanho do conjunto de treinamento:", data_train.shape[0]) print("Tamanho do conjunto de teste:", data_test.shape[0]) print("Dados coletados") print("Treinando AdaBoost") model = AdaBoostClassifier(n_estimators=200, random_state=0) model.fit(data_train, label_train) pred = model.predict(data_test) print("Erro teste:", np.abs(pred - label_test).mean()) pred = model.predict(data_train) print("Erro treinamento:", np.abs(pred - label_train).mean()) print("Treinando SVM") model = cv2.ml.SVM_create() model.setKernel(cv2.ml.SVM_RBF) model.setType(cv2.ml.SVM_C_SVC) model.setC(2.5) model.setGamma(0.03375) # model.trainAuto(data_train, cv2.ml.ROW_SAMPLE, label_train) model.train(data_train, cv2.ml.ROW_SAMPLE, label_train) print("Numero de vetores suporte:", model.getSupportVectors().shape[0]) pred = model.predict(data_test)[1] print("Erro teste:", np.abs(pred - label_test).mean()) pred = model.predict(data_train)[1] print("Erro treinamento:", np.abs(pred - label_train).mean())