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())
