def setup_options_and_constraints(self):
options = option.Options()
options.add('test', 5)
options.add('test2', "test")
self.options = options
self.constraints = {}
def setup_options_and_constraints(self):
options = { 'sterics' : 1,
'verbose' : 0,
'frequency' : 10,
'max_node_level' : 20,
'max_steps' : 100000000,
'max_solutions' : 10,
'max_size' : 100000000,
'min_size' : 0,
'accept_score' : 10}
self.options = option.Options(options)
def __init__(self, mtype=None, mid=None, code=EMPTY, payload='', token=''):
if payload is None:
raise TypeError(
"Payload must not be None. Use empty string instead.")
self.version = 1
self.mtype = mtype
self.code = code
self.mid = mid
self.token = token
self.opt = option.Options()
self.payload = payload
self.remote = None
self.timeout = MAX_TRANSMIT_WAIT
def setup_options_and_constraints(self):
options = {'sterics': 1}
self.options = option.Options(options)
self.constraints = {}
import pandas as pd
import option as opt
import os
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras import preprocessing
op = opt.Options()
tokenizer = Tokenizer(num_words=1000)
def get_word_index(): # 단어 인덱스를 구축
for idx, dirnames in enumerate(op.clean_data_dir):
for filename in os.listdir(dirnames):
infilename = dirnames + "/" + filename
news = pd.read_csv(infilename, header=None, encoding="euc-kr")
tokenizer.fit_on_texts(news.iloc[:, 0])
#print("각 단어의 인덱스: \n", tokenizer.word_index)
return tokenizer
def make_onehot(t, data): # 원-핫 이진 벡터 표현
one_hot_results = t.texts_to_matrix(data)
return one_hot_results
def make_word_seq(t, data):
word_seq_results = t.texts_to_sequences(data)
word_seq_results = preprocessing.sequence.pad_sequences(word_seq_results,
maxlen=op.max_len)
return word_seq_results