def __init__(self):
Processor.__init__(self)
self.windowSize = 0
self.conservationLimit = 0.0
self.desiredSpeciesList = []
self.algorithm = BlockProcessor.ALGORITHM_OCCURENCE_RATIO_VALUE
def __init__(self):
Processor.__init__(self)
self.mapping = {0: 'irrelevant', 1: 'related'}
self.path = root_path
self.nb_model = irr_naive_bayes
self.tf_idf_vec = pickle.load(
open(os.path.join(self.path, "model/NB_relevant/tf_idf_vec.pkl"),
"rb"))
def __init__(self):
Processor.__init__(self)
self.desiredSpeciesList = []
self.referenceSpecies = None
self.bedSequencesDict = None
self.mafBlockDic = {}
self.parsedSpeciesList = []
self.threadLock = None
def __init__(self,
index_elasic,
num_of_fields=3,
jaccard_measure=0.8,
similarity_threshold=0.9):
Processor.__init__(self)
self.num_of_fields = num_of_fields
self.jaccard_measure = jaccard_measure
self.similarity_threshold = similarity_threshold
self.clean_news_normalize = []
self.clean_news_index = []
self.soft_tf_idf = []
self.index = index_elasic
self.last_size = -1
def __init__(self):
Processor.__init__(self)
self.mapping = {
0: 'Công nghệ',
1: 'Giáo dục',
2: 'Giải trí',
3: 'Khoa học',
4: 'Kinh tế',
5: 'Pháp luật',
6: 'Thế giới',
7: 'Thể thao',
8: 'Văn hóa',
9: 'Xã hội',
10: 'Y tế'
}
self.path = root_path
self.bi_rnn_model = bi_rnn
self.tf_idf_vec = pickle.load(
open(os.path.join(self.path, "model/tf_idf_vec.pkl"), "rb"))
def __init__( self):
Processor.__init__( self)
self.outPath = ""
def __init__(self):
Processor.__init__(self)
def __init__(self):
Processor.__init__(self)
self.name = "ProcessorByKeyAndPattern_TalentInfo"
self.keywords = ["Salary", "Environment", "Regime"]
def __init__(self):
Processor.__init__(self)
self.aho_cora_dict = ahocorasick.Automaton()
self.keyword = []
self.initialization()
def __init__(self):
Processor.__init__( self)
self.referenceSpecies = ""
def __init__(self):
Processor.__init__(self)
self.path = root_path
self.svm_model = cate_svm
self.tf_idf_vec = pickle.load(
open(os.path.join(self.path, "model/tf_idf_vec.pkl"), "rb"))
