def main():
data_in = []
feed_id = []
print('start reading data')
#read_json.read_json(config.path_in,data_in,config.stop_word_path,feed_id,config.data_lines)
#read_json.test_alignment_py('../data/cpp/small.txt','../data/cpp/python_alignment_extraction1.txt','stop_words.utf8',True,True,5,data_in)
#print('finish reading data')
#term_id = []
id_url = []
#read_liulanqi_data.read_data(config.path_in, data_in, id_url,50)
read_weishi_data.read_json(config.path_in, data_in, None, feed_id,
config.data_lines, None, config.topk)
if config.mode == 'Training':
if config.model_name == 'Counter':
model = Vectorizer.CounterVector(config.model_name)
elif config.model_name == 'TfIdf':
model = Vectorizer.TfIdfVector(config.model_name)
print('finish initilizing model')
elif config.model_name == 'FeatureHasher':
model = Vectorizer.FeatureHasherVector(config.model_name,
config.n_features)
model.feature_transform(data_in)
print(len(model.vectorizer.vocabulary_))
model.serilize_model()
if config.algo_name == 'KMeans':
algo_instance = KMeans.KMeansClustering(config.algo_name)
print('start training model')
algo_instance.fit(model.feature)
algo_instance.serilize_model()
algo_instance.output_cluster_info(data_in, model, feed_id)
else:
print('loading vectorizer')
model = BaseModel.BaseModel(config.model_name)
model.de_serilize_model()
print('finish loading vector')
if config.algo_name == 'KMeans':
algo_instance = Algorithm.Base_Algorithm(config.algo_name)
algo_instance.de_serilize_model()
print('finish desirialization')
features = model.transform(data_in)
labels = algo_instance.predict(features)
print(labels)
#algo_instance.get_centroids()
#algo_instance.output_cluster_info(data_in, model, feed_id)
print('finish all')
def main():
data_in = []
feed_id = []
print('start reading data')
path = 'E:\\QQ_Browser_data\\ruyizhuan.csv'
path2 = 'E:\\QQ_Browser_data\\yanxigonglue.csv'
tv_show.process_data(path, feed_id, data_in)
tv_show.process_data(path2, feed_id, data_in)
if config.mode == 'Training':
if config.model_name == 'Counter':
model = Vectorizer.CounterVector(config.model_name)
elif config.model_name == 'TfIdf':
model = Vectorizer.TfIdfVector(config.model_name)
print('finish initilizing model')
elif config.model_name == 'FeatureHasher':
model = Vectorizer.FeatureHasherVector(config.model_name,
config.n_features)
model.feature_transform(data_in)
print(len(model.vectorizer.vocabulary_))
if config.algo_name == 'KMeans':
algo_instance = KMeans.KMeansClustering(config.algo_name)
print('start training model')
algo_instance.fit(model.feature)
algo_instance.serilize_model()
print('finish serilizing model')
algo_instance.output_cluster_info(data_in, model, feed_id)
else:
print('loading vectorizer')
model = BaseModel.BaseModel(config.model_name)
model.de_serilize_model()
print('finish loading vector')
if config.algo_name == 'KMeans':
algo_instance = Algorithm.Base_Algorithm(config.algo_name)
algo_instance.de_serilize_model()
print('finish desirialization')
features = model.transform(data_in)
labels = algo_instance.predict(features)
print(labels)
#algo_instance.get_centroids()
#algo_instance.output_cluster_info(data_in, model, feed_id)
print('finish all')
