# scikit learn
from sklearn import svm
from sklearn.externals import joblib

# nltk package
from nltk.classify import NaiveBayesClassifier as nbc
import nltk.classify.util

class Classifier(object):
	"""docstring for Classifier"""
	def __init__(self):
		super(Classifier, self).__init__()
		
	def svm_process_train(self, training_data, word_vector_base):
		# training_data 為tweet元件，因此可以從Feature 與 SA取得你的特徵與正確答案

	def svm_trainModel_test(self, model, testing_data):
		if type(model) is str:
			# 讀取外部以訓練好的分類氣
			svm_clf = joblib.load(model_pkl)
		else:
			# 讀取已經訓練好的分類器模型
			svm_clf = model

		# 載入testing data進行預測
		testing_result = svm_clf.predict(testing_data)
		
		return testing_result


	def nbc_process_train(self, training_data, word_vector_base):
		# training_data 為tweet元件，因此可以從Feature 與 SA取得你的特徵與正確答案
		
	def nbc_trainModel_test(self, model_file, testing_data):
19a7bc7c7931a351c191b1b4f29a9d6d2b353190
# scikit learn
from sklearn import svm
from sklearn.externals import joblib

# nltk package
from nltk.classify import NaiveBayesClassifier as nbc
import nltk.classify.util

class Classifier(object):
	"""docstring for Classifier"""
	def __init__(self):
		super(Classifier, self).__init__()
		
	def svm_process_train(self, training_data, word_vector_base):
		# training_data 為tweet元件，因此可以從Feature 與 SA取得你的特徵與正確答案

	def svm_trainModel_test(self, model, testing_data):
		if type(model) is str:
			# 讀取外部以訓練好的分類氣
			svm_clf = joblib.load(model_pkl)
		else:
			# 讀取已經訓練好的分類器模型
			svm_clf = model

		# 載入testing data進行預測
		testing_result = svm_clf.predict(testing_data)
		
		return testing_result


	def nbc_process_train(self, training_data, word_vector_base):
		# training_data 為tweet元件，因此可以從Feature 與 SA取得你的特徵與正確答案
		
	def nbc_trainModel_test(self, model_file, testing_data):