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"))