2021-1-capstone-design1 / HCG_Project1

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Merge branch 'master' into 'master' 

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import warnings
#########5/23일작성중 #########
warnings.simplefilter(("ignore"))
import openpyxl
import pandas as pd
####### [날짜, 뉴스단어 한개] 구성 만드는 파일 차트 분석 시 count함수 이욜 할 때 참조 자료
# ex)hmm뉴스키워드날짜뉴스모으고특수삭제.xlsx 파일 넣음<- DayNewsMerge.py 중간에 주석처리 된 부분 해제하고 결과 얻기
Stockfilename = input("키워드파일이름입력:")
fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/샘플/" + Stockfilename + ".xlsx"
Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
stock_ws = Stockfile.active
Stock_data = [] # list 타입
date=[]
i = 0
for row in stock_ws.rows:
Stock_data.append([])
date.append(row[1].value)
for cell in row:
if cell.value != None :
Stock_data[i].append(cell.value)
i += 1
del Stock_data[0]
del date[0]
for i in range(len(Stock_data)):
del Stock_data[i][0] #각 열의 첫번째 행 삭제
for i in range(len(Stock_data)):
del Stock_data[i][0] #각 열의 첫번째 행 삭제
print(Stock_data)
print(date)
a=[] #
print(len(date),len(Stock_data))
for j in range(len(Stock_data)):
for k in range(len(Stock_data[j])):
a.append([date[j],Stock_data[j][k]])
print(a)
df_SourTar = pd.DataFrame(a)
df_SourTar.to_excel(Stockfilename+'countif.xlsx',sheet_name='sheet1')
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import pandas as pd
source = {
'학년': [1, 2, 1, 3, 4],
'성별': ['남자', '여자', '남자', '여자', '남자'],
'국어': [98, 88, 92, 63, 120],
'영어': [88, 90, 70, 60, 50],
'수학': [64, 62, None, 31, None],
'과학': [None, 72, None, 70, 88]
}
df = pd.DataFrame(source)
df1=df[(df['성별']!='남자')].index
df2=df.drop(df1)
print(df)
print("---------")
print(df2)
import warnings
warnings.simplefilter(("ignore"))
import openpyxl
import pandas as pd
# 000_KNU_New_Vdic2.xlsx 파일 넣기
Stockfilefolder = input("종목시세폴더입력: ")
Stockfilename = input("시세파일이름입력:")
fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스키워드/" + Stockfilefolder + "/" + Stockfilename + ".xlsx"
Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
stock_ws = Stockfile.active
Stock_data = [] # list 타입
i = 0
for row in stock_ws.rows:
Stock_data.append([])
for cell in row:
if cell.value != None:
Stock_data[i].append(cell.value)
i += 1
del Stock_data[0]
for i in range(len(Stock_data)):
del Stock_data[i][0]
#print(Stock_data)
vert_p = [] # 수직 중복 삭제
for i in range(len(Stock_data)):
vert_p.append([])
for j in range(len(Stock_data[i])):
vert_p[i].append(Stock_data[i][j]) # 단어만 넣기
print(vert_p)
vert_p.sort(key=lambda x: x[0]) # 단어 기준으로 정렬
for i in range(len(vert_p) - 2): # 단어 비교해서 같으면 누적, 다르면 값 바꾸기
for j in range(i + 1, len(vert_p)):
if vert_p[i][0] == vert_p[j][0] :
vert_p[i][1] += vert_p[j][1]
vert_p[j] = ['0', 0]
if str.isalnum(vert_p[i][0]) == False:
vert_p[i] =['0', 0]
vert_p = [i for i in vert_p if not '0' in i] # '0'들어간 열 제거
df_ver = pd.DataFrame(vert_p)
df_ver.to_excel(Stockfilename + ' Stock_dictionary2.xlsx', sheet_name='sheet1')
####사전 완성####
File mode changed
import warnings
warnings.simplefilter(("ignore"))
import openpyxl
import pandas as pd
Stockfilefolder = input("종목시세폴더입력: ")
Stockfilename = input("시세파일이름입력:")
fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/종목별시세/" + Stockfilefolder + "/" + Stockfilename + ".xlsx"
Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
stock_ws = Stockfile.active
Stock_data = [] # list 타입
i = 0
for row in stock_ws.rows:
Stock_data.append([])
for cell in row:
if cell.value != None:
Stock_data[i].append(cell.value)
i += 1
del Stock_data[0]
for i in range(len(Stock_data)):
del Stock_data[i][0] # 대비 삭제
#print(Stock_data)
vert_p = [] # 수직 중복 삭제
for i in range(len(Stock_data)):
vert_p.append([])
for j in range(len(Stock_data[i])):
vert_p[i].append(Stock_data[i][j]) # 단어만 넣기
print(vert_p)
vert_p.sort(key=lambda x: x[0]) # 단어 기준으로 정렬
for i in range(len(vert_p) - 2): # 단어 비교해서 같으면 누적 다르면 값 바꾸기
for j in range(i + 1, len(vert_p)):
if vert_p[i][0] == vert_p[j][0] :
vert_p[i][1] += vert_p[j][1]
vert_p[j] = ['0', 0]
if str.isalnum(vert_p[i][0]) == False:
vert_p[i] =['0', 0]
vert_p = [i for i in vert_p if not '0' in i] # '0'들어간 열 제거
df_ver = pd.DataFrame(vert_p)
df_ver.to_excel(Stockfilename + ' Stock_dictionary2.xlsx', sheet_name='sheet1')
# **뉴스 키워드 노출 빈도수에 따른 기업 주가 영향 분석**
---------------------------------------------------
----------------------------------------------------
## **지도교수님**
* 한치근 교수님
## **팀원**
* 2017104003 컴퓨터공학과 양지수
* 2017104039 컴퓨터공학과 한서흔
---
## **개발일정**
* 2021.03.09~2021.03.22 데이터 수집
* 2021.03.23~2021.04.19 자연어 처리,중간보고서 작성
* 2021.04.20~2021.05.04 감성사전 분석 및 특화 사전 제작
* 2021.05.05~ 중심성 분석 및 최종 결과 제작
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import warnings
warnings.simplefilter(("ignore"))
import konlpy
from konlpy.tag import *
import openpyxl
import pandas as pd
from math import log10
import numpy as np
#형태소분석라이브러리
#okt = Okt()
hannanum = Hannanum()
#filename= input("분석할 파일이름 입력:") #파일명
filefolder = input("종목폴더입력: ")
filename=input("파일이름입력:")
filepos = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스크롤링/"+filefolder+"/" + filename + ".xlsx"
kfile = openpyxl.load_workbook(filepos)#파일이름입력
sheet=kfile.worksheets[0]#sheet1에 있는 데이터 가죠오기
#print(sheet)
data=[]
for row in sheet.rows: #data에 크롤링한 뉴스 제목들 저장
data.append(
row[1].value
)
#print(data)
#print(type(data[1])) #str
newData2=[]
#print(newData)
for i in range(len(data)-1):
newData2.append(hannanum.nouns(data[i+1])) #명사만 추출hannanum가 okt보다 성능좋음
#print(newData2)
newData3=[]
for i in range(len(newData2)):
newData3.append([])
for j in newData2[i]:
if any(map(str.isdigit,j))==False and len(j)>1: #추출한 결과가 숫자포함이거나 한글자 인것 제외
newData3[i].append(j)
#print(newData3)
#print(type(newData2))#newData2 데이터 형식은 list
#df= pd.DataFrame.from_records(newData3)#newData3 dataframe으로 변환
#df.to_excel(filename+'_명사추출_숫자제외'+'.xlsx') #파일명의 엑셀로 변환
#TF-IDF함수 시작
def f(t, d): # 엑셀 d 안에 있는 t 빈도 세기
return d.count(t)
def tf(t, d): #tf(t,d)증가빈도 공식 적용
return 0.5 + 0.5*f(t,d)/max([f(w,d) for w in d])
def idf(t, D): #역문서 빈도 공식 적용
numerator = len(D) #문서 집합에 포함 된 문서 수
denominator = 1 + len([ True for d in D if t in d]) #1더해서 0되는 것 방지
return log10(numerator/denominator)
def tfidf(t, d, D):
return tf(t,d)*idf(t, D)
def tfidfScorer(D):
result = []
for d in D:
result.append([(t, tfidf(t, d, D)) for t in d] )
return result
#newData3는 명사추출을 통해 분리되어있음(이미 split상태)
if __name__ == '__main__':
corpus=[]
for i in range(len(newData3)):
corpus.append(newData3[i])
TfIf=[] #결과저장
for i, result in enumerate(tfidfScorer(corpus)):
#print('====== document[%d] ======' % i)
#print(result)
TfIf.append(result)
print(TfIf)#TFIF는 (단어,가중치) 조합으로 저장
for i in range(len(TfIf)-1):
TfIf[i].sort(key=lambda x:x[1], reverse=True) #가중치기준 내림차순 정렬
print(TfIf)
onlynouns = []
for i in range(len(TfIf)):
onlynouns.append([])
for j in range(len(TfIf[i])):
for k in range(len(TfIf[i][j])):
if k%2==0:
onlynouns[i].append(TfIf[i][j][k])
print(onlynouns)
#df= pd.DataFrame.from_records(TfIf)#TfIf dataframe으로 변환
#df.to_excel(filename+'_가중치추출_내림정렬'+'.xlsx')
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import warnings
warnings.simplefilter(("ignore"))
import openpyxl
import pandas as pd
#######Gephi에 사용할 edge파일 만들기 전에 필요한 자료 만드는 과정
####6개월치 키워드 합친 키워드 파일 넣기
Stockfilename = input("키워드파일이름입력:")
fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/샘플/" + Stockfilename + ".xlsx"
Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
stock_ws = Stockfile.active
Stock_data = [] # list 타입
date=[]
i = 0
for row in stock_ws.rows:
Stock_data.append([])
date.append(row[1].value)
for cell in row:
if cell.value != None:
Stock_data[i].append(cell.value)
i += 1
del Stock_data[0] #첫번째 의미없는 열 삭제
del date[0]
for i in range(len(Stock_data)):
del Stock_data[i][0] #각 열의 첫번째 행 삭제
for i in range(len(Stock_data)):
del Stock_data[i][0] #각 열의 날짜 행 삭제
Tdata=[]
for x in range(len(Stock_data)):
Tdata.append([])
for y in range(len(Stock_data[x])):
if str.isalnum(Stock_data[x][y]) == True:
Tdata[x].append(Stock_data[x][y])
result = { '날짜':date, '단어':Tdata }
df = pd.DataFrame(result)
#print(df)
list_df=df.values.tolist() #dataframe list로 변경
print(list_df)
#print(list_df[0][0]) 날짜 2021.01.01.
new_date = [] # 날짜 중복 삭제
for v in date:
if v not in new_date:
new_date.append(v)
#print(new_date)
Setlist =[]# 날짜별 키워드 넣기
for v in range(len(new_date)):
Setlist.append([])
Setlist[v].append(new_date[v])
for i in range(len(list_df)):
for j in range(len(list_df[i][1])):
if new_date[v] == list_df[i][0] :
Setlist[v].append(list_df[i][1][j])
print(Setlist)
df_ver= pd.DataFrame(Setlist)
#df_ver.to_excel(Stockfilename+' 날짜뉴스모으고특수삭제.xlsx',sheet_name='sheet1')
SourceTarget=[]
for i in range(len(list_df)):
SourceTarget.append([])
for j in range(len(list_df[i][1])-1):
SourceTarget.append([list_df[i][0],list_df[i][1][j],list_df[i][1][j+1],1])
print(SourceTarget)
SourceTarget = [v for v in SourceTarget if v]
df_SourTar = pd.DataFrame(SourceTarget)
df_SourTar.to_excel(Stockfilename+'Edge3.xlsx',sheet_name='sheet1')
\ No newline at end of file
# KNU 한국어 감성사전
# 작성자 : 온병원, 박상민, 나철원
# 소속 : 군산대학교 소프트웨어융합공학과 Data Intelligence Lab
# 홈페이지 : dilab.kunsan.ac.kr
# 작성일 : 2018.05.14
# 뜻풀이 데이터 출처 : https://github.com/mrchypark/stdkor
# 신조어 데이터 출처 : https://ko.wikipedia.org/wiki/%EB%8C%80%ED%95%9C%EB%AF%BC%EA%B5%AD%EC%9D%98_%EC%9D%B8%ED%84%B0%EB%84%B7_%EC%8B%A0%EC%A1%B0%EC%96%B4_%EB%AA%A9%EB%A1%9D
# 이모티콘 데이터 출처: https://ko.wikipedia.org/wiki/%EC%9D%B4%EB%AA%A8%ED%8B%B0%EC%BD%98
# SentiWordNet_3.0.0_20130122 데이터 출처 : http://sentiwordnet.isti.cnr.it/
# SenticNet-5.0 데이터 출처 : http://sentic.net/
# 감정단어사전0603 데이터 출처 : http://datascience.khu.ac.kr/board/bbs/board.php?bo_table=05_01&wr_id=91
# 김은영, “국어 감정동사 연구”, 2004.02, 학위논문(박사) - 전남대학교 국어국문학과 대학원
#-*-coding:utf-8-*-
import collections
import json
import warnings
warnings.simplefilter(("ignore"))
import openpyxl
import pandas as pd
import re
from datetime import datetime
############종목 감성 판단 ex)hmm뉴스키워드.xlsx 파일 넣는 과정
class KnuSL():
def data_list(wordname):
with open('KnuSentiLex/data/SentiWord_info.json', encoding='utf-8-sig', mode='r') as f:
data = json.load(f)
result = [0,0]
for i in range(0, len(data)):
if data[i]['word'] == wordname:
result.pop()
result.pop()
result.append(data[i]['word_root'])
result.append(int(data[i]['polarity']))
r_word = result[0] #어근
s_word = result[1] #극성
return s_word
if __name__ == "__main__":
ksl = KnuSL
print("\nKNU 한국어 감성사전입니다~ :)")
print("사전에 단어가 없는 경우 결과가 None으로 나타납니다!!!")
print("종료하시려면 #을 입력해주세요!!!")
print("-2:매우 부정, -1:부정, 0:중립 or Unkwon, 1:긍정, 2:매우 긍정")
print("\n")
#########
Newsfilefolder = input("종목폴더입력: ")
Newsfilename=input("파일이름입력:")
Newsfilepos = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스키워드/"+Newsfilefolder+"/" + Newsfilename + ".xlsx"
Newsfile = openpyxl.load_workbook(Newsfilepos)#파일이름입력
ws=Newsfile.active
data=[]
date=[]
i=0
for row in ws.rows:
data.append([])
date.append(row[1].value)
for cell in row:
if cell.value != None:
data[i].append(cell.value)
i += 1
del data[0] #첫번째 의미없는 열 삭제
del date[0]
for i in range(len(data)):
del data[i][0] #각 열의 첫번째 행 삭제
for i in range(len(data)):
del data[i][0] #각 열의 날짜 행 삭제
KNUdata=[]
Tdata=[]
for x in range(len(data)):
KNUdata.append([])
Tdata.append([])
for y in range(len(data[x])):
KNUdata[x].append(ksl.data_list(data[x][y]))
Tdata[x].append([data[x][y], KNUdata[x][y]])
result = { '날짜':date, '단어, 극성':Tdata }
df = pd.DataFrame(result)
#print(df)
list_df=df.values.tolist() #dataframe list로 변경
#print(list_df)
#print(list_df[0][0]) 날짜 2021.01.01.
new_date = [] # 날짜 중복 삭제
for v in date:
if v not in new_date:
new_date.append(v)
#print(new_date)
Setlist =[]# 날짜별 키워드 넣기
for v in range(len(new_date)):
Setlist.append([])
Setlist[v].append(new_date[v])
for i in range(len(list_df)):
for j in range(len(list_df[i][1])):
if new_date[v] == list_df[i][0]:
Setlist[v].append(list_df[i][1][j])
print(Setlist)
print(Setlist[0][0]) #2021.01.01
print(type(Setlist[0][0]))
print(Setlist[0][0].split('-'))
print(Setlist[0][1][1]) #극성 0
print(type(Setlist[0][1][1])) #극성 모든 타입 int
#print(list_df[0][1][0]) 키워드와 극성 ['HMM…"체질개선해', 'X']
#print(list_df[0][1][0][1]) 극성 x
#print(list_df[0][0].split('.')[:3]) ['2021', '01', '01']
#df.to_excel(Newsfilename+' KNU.xlsx',sheet_name='sheet1')
Stockfilefolder = input("종목시세폴더입력: ")
Stockfilename=input("시세파일이름입력:")
fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/종목별시세/"+Stockfilefolder+"/" + Stockfilename + ".xlsx"
Stockfile = openpyxl.load_workbook(fileStock)#파일이름입력
stock_ws=Stockfile.active
Stock_data=[] #list 타입
i=0
for row in stock_ws.rows:
Stock_data.append([])
for cell in row:
if cell.value != None:
Stock_data[i].append(cell.value)
i += 1
del Stock_data[0]
for i in range(len(Stock_data)):
del Stock_data[i][2] # 대비 삭제
for i in range(len(Stock_data)):
del Stock_data[i][7] #거래대금 삭제
for i in range(len(Stock_data)):
del Stock_data[i][7] #시가 총액 삭제
for i in range(len(Stock_data)):
del Stock_data[i][7] #상장주식 수 삭제 / 결과:'일자', '종가', '등락률', '시가', '고가', '저가', '거래량'
#print(Stock_data)
def Calpercentage(a,b): #시초가 대비 고점/저점 비율
return abs(a-b)/a*100
####아래로 수정 필요 (미완성)####
i=0
for k in range(len(Setlist)):
if( Stock_data[i][0].split('/') == Setlist[k][0].split('-')): # 날짜 비교 날짜가 같다면
if Calpercentage(Stock_data[i][3],Stock_data[i][4]) > 2 : #당일 시가 대비 고가가 2퍼 높을때
for j in range(1,len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[0][j][1] += 1
elif Calpercentage(Stock_data[i][3],Stock_data[i][5]) < -2 : #당일 시가 대비 저가가 2퍼 낮을 때
for j in range(1,len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[0][j][1] -= 1
else:
if Stock_data[i+1][2] > 0: # 다음날 주가 등락률이 양수면
for j in range(1,len(Setlist[k])): #어제뉴스는 호재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Stock_data[i+1][2] < 0:
for j in range(1,len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
i+=1
else:
if Calpercentage(Stock_data[i][3], Stock_data[i][4]) > 2: # 당일 시가 대비 고가가 2퍼 높을때
for j in range(1, len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Calpercentage(Stock_data[i][3], Stock_data[i][5]) < -2: # 당일 시가 대비 저가가 2퍼 낮을 때
for j in range(1, len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
else:
if Stock_data[i + 1][2] > 0: # 다음날 주가 등락률이 양수면
for j in range(1, len(Setlist[k])): # 어제뉴스는 호재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Stock_data[i + 1][2] < 0:
for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
i+=1 #<이거 삭제서 hmm한번 더 돌려보기
print(Setlist)
#df_Setlist = pd.DataFrame(Setlist)
#df_Setlist.to_excel(Stockfilename+' KNU_New.xlsx',sheet_name='sheet1')
Setlist_w = []
for i in range(len(Setlist)):
Setlist_w.append([])
for j in range(1, len(Setlist[i])):
Setlist_w[i].append(Setlist[i][j][0]) # 극성 제외 단어만 추출
counter = {}
for i in range(len(Setlist_w)):
counter[i] = collections.Counter(Setlist_w[i]) # 누적치
for i in range(len(Setlist_w)):
Setlist_w[i] = list(zip(counter[i].keys(), counter[i].values())) # 튜플 리스트화 [(값, 값)]
Plist = []
for i in range(len(Setlist_w)):
Plist.append([])
for j in range(len(Setlist_w[i])):
Plist[i].append(list(Setlist_w[i][j])) # 튜플 -> 리스트화 [[값, 값]]
for i in range(len(Plist)):
for j in range(len(Plist[i])):
Plist[i][j][1] = 0 # 극성 0으로 초기화
for i in range(len(Setlist)):
for j in range(1, len(Setlist[i])):
for h in range(len(Plist[i])):
if Setlist[i][j][0] == Plist[i][h][0]:
Plist[i][h][1] += Setlist[i][j][1] #누적치
vert_p=[] #수직 중복 삭제
for i in range(len(Plist)):
for j in range(len(Plist[i])):
vert_p.append(Plist[i][j]) #단어만 넣기
#print(vert_p)
vert_p.sort(key=lambda x:x[0]) #단어 기준으로 정렬
for i in range(len(vert_p)-2): #단어 비교해서 같으면 누적 다르면 값 바꾸기
for j in range(i+1,len(vert_p)):
if vert_p[i][0] == vert_p[j][0]:
vert_p[i][1]+=vert_p[j][1]
vert_p[j]=['0',0]
print(vert_p)
vert_p=[i for i in vert_p if not '0' in i] #'0'들어간 열 제거
df_ver= pd.DataFrame(vert_p)
df_ver.to_excel(Stockfilename+' KNU_New_Vdic2.xlsx',sheet_name='sheet1')
####키워드파일 월별로 돌려서 그 나온 결과 파일들을 합쳐서 Merge_dictionay.py에 넣어서 사전 만들기 ####
# KNU 한국어 감성사전
# 작성자 : 온병원, 박상민, 나철원
# 소속 : 군산대학교 소프트웨어융합공학과 Data Intelligence Lab
# 홈페이지 : dilab.kunsan.ac.kr
# 작성일 : 2018.05.14
# 뜻풀이 데이터 출처 : https://github.com/mrchypark/stdkor
# 신조어 데이터 출처 : https://ko.wikipedia.org/wiki/%EB%8C%80%ED%95%9C%EB%AF%BC%EA%B5%AD%EC%9D%98_%EC%9D%B8%ED%84%B0%EB%84%B7_%EC%8B%A0%EC%A1%B0%EC%96%B4_%EB%AA%A9%EB%A1%9D
# 이모티콘 데이터 출처: https://ko.wikipedia.org/wiki/%EC%9D%B4%EB%AA%A8%ED%8B%B0%EC%BD%98
# SentiWordNet_3.0.0_20130122 데이터 출처 : http://sentiwordnet.isti.cnr.it/
# SenticNet-5.0 데이터 출처 : http://sentic.net/
# 감정단어사전0603 데이터 출처 : http://datascience.khu.ac.kr/board/bbs/board.php?bo_table=05_01&wr_id=91
# 김은영, “국어 감정동사 연구”, 2004.02, 학위논문(박사) - 전남대학교 국어국문학과 대학원
#-*-coding:utf-8-*-
import collections
import json
import warnings
warnings.simplefilter(("ignore"))
import openpyxl
import pandas as pd
import re
from datetime import datetime
class KnuSL():
def data_list(wordname):
with open('KnuSentiLex/data/SentiWord_info.json', encoding='utf-8-sig', mode='r') as f:
data = json.load(f)
result = [0,0]
for i in range(0, len(data)):
if data[i]['word'] == wordname:
result.pop()
result.pop()
result.append(data[i]['word_root'])
result.append(int(data[i]['polarity']))
r_word = result[0] #어근
s_word = result[1] #극성
return s_word
if __name__ == "__main__":
ksl = KnuSL
print("\nKNU 한국어 감성사전입니다~ :)")
print("사전에 단어가 없는 경우 결과가 None으로 나타납니다!!!")
print("종료하시려면 #을 입력해주세요!!!")
print("-2:매우 부정, -1:부정, 0:중립 or Unkwon, 1:긍정, 2:매우 긍정")
print("\n")
#########
Newsfilefolder = input("종목폴더입력: ")
Newsfilename=input("파일이름입력:")
Newsfilepos = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스키워드/"+Newsfilefolder+"/" + Newsfilename + ".xlsx"
Newsfile = openpyxl.load_workbook(Newsfilepos)#파일이름입력
ws=Newsfile.active
data=[]
date=[]
i=0
for row in ws.rows:
data.append([])
date.append(row[1].value)
for cell in row:
if cell.value != None:
data[i].append(cell.value)
i += 1
del data[0] #첫번째 의미없는 열 삭제
del date[0]
for i in range(len(data)):
del data[i][0] #각 열의 첫번째 행 삭제
for i in range(len(data)):
del data[i][0] #각 열의 날짜 행 삭제
KNUdata=[]
Tdata=[]
for x in range(len(data)):
KNUdata.append([])
Tdata.append([])
for y in range(len(data[x])):
KNUdata[x].append(ksl.data_list(data[x][y]))
Tdata[x].append([data[x][y], KNUdata[x][y]])
result = { '날짜':date, '단어, 극성':Tdata }
df = pd.DataFrame(result)
#print(df)
list_df=df.values.tolist() #dataframe list로 변경
#print(list_df)
#print(list_df[0][0]) 날짜 2021.01.01.
new_date = [] # 날짜 중복 삭제
for v in date:
if v not in new_date:
new_date.append(v)
#print(new_date)
Setlist =[]# 날짜별 키워드 넣기
for v in range(len(new_date)):
Setlist.append([])
Setlist[v].append(new_date[v])
for i in range(len(list_df)):
for j in range(len(list_df[i][1])):
if new_date[v] == list_df[i][0]:
Setlist[v].append(list_df[i][1][j])
#print(Setlist)
#print(Setlist[0][0]) #2021.01.01
#print(Setlist[0][1][1]) #극성 0
#print(type(Setlist[0][1][1])) #극성 모든 타입 int
#print(list_df[0][1][0]) 키워드와 극성 ['HMM…"체질개선해', 'X']
#print(list_df[0][1][0][1]) 극성 x
#print(list_df[0][0].split('.')[:3]) ['2021', '01', '01']
#df.to_excel(Newsfilename+' KNU.xlsx',sheet_name='sheet1')
Stockfilefolder = input("종목시세폴더입력: ")
Stockfilename=input("시세파일이름입력:")
fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/종목별시세/"+Stockfilefolder+"/" + Stockfilename + ".xlsx"
Stockfile = openpyxl.load_workbook(fileStock)#파일이름입력
stock_ws=Stockfile.active
Stock_data=[] #list 타입
i=0
for row in stock_ws.rows:
Stock_data.append([])
for cell in row:
if cell.value != None:
Stock_data[i].append(cell.value)
i += 1
del Stock_data[0]
for i in range(len(Stock_data)):
del Stock_data[i][2] # 대비 삭제
for i in range(len(Stock_data)):
del Stock_data[i][7] #거래대금 삭제
for i in range(len(Stock_data)):
del Stock_data[i][7] #시가 총액 삭제
for i in range(len(Stock_data)):
del Stock_data[i][7] #상장주식 수 삭제 / 결과:'일자', '종가', '등락률', '시가', '고가', '저가', '거래량'
#print(Stock_data)
def Calpercentage(a,b): #시초가 대비 고점/저점 비율
return abs(a-b)/a*100
####아래로 수정 필요 (미완성)####
i=0
for k in range(len(Setlist)):
if( Stock_data[i][0].split('/') == Setlist[k][0].split('.')[:3]): # 날짜 비교 날짜가 같다면
if Calpercentage(Stock_data[i][3],Stock_data[i][4]) > 2 : #당일 시가 대비 고가가 2퍼 높을때
for j in range(1,len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[0][j][1] += 1
elif Calpercentage(Stock_data[i][3],Stock_data[i][5]) < -2 : #당일 시가 대비 저가가 2퍼 낮을 때
for j in range(1,len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[0][j][1] -= 1
else:
if Stock_data[i+1][2] > 0: # 다음날 주가 등락률이 양수면
for j in range(1,len(Setlist[k])): #어제뉴스는 호재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Stock_data[i+1][2] < 0:
for j in range(1,len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
i+=1
else:
if Calpercentage(Stock_data[i][3], Stock_data[i][4]) > 2: # 당일 시가 대비 고가가 2퍼 높을때
for j in range(1, len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Calpercentage(Stock_data[i][3], Stock_data[i][5]) < -2: # 당일 시가 대비 저가가 2퍼 낮을 때
for j in range(1, len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
else:
if Stock_data[i + 1][2] > 0: # 다음날 주가 등락률이 양수면
for j in range(1, len(Setlist[k])): # 어제뉴스는 호재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Stock_data[i + 1][2] < 0:
for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
i+=1
print(Setlist)
'''
Stock_dic=[]
for i in range(len(Setlist)):
Stock_dic.append([])
j = 2
for k in range(1,len(Setlist[i])-1):
if Setlist[i][k][0]== Setlist[i][j][0]:
Setlist[i][k][1]+=Setlist[i][j][1]
Stock_dic.append(Setlist[i][k])
j+=1
else:
Stock_dic.append(Setlist[i][k])
j+=1
print(Stock_dic)
'''
#df_Setlist = pd.DataFrame(Setlist)
#df_Setlist.to_excel(Stockfilename+' KNU_New.xlsx',sheet_name='sheet1')
Setlist_w = []
for i in range(len(Setlist)):
Setlist_w.append([])
for j in range(1, len(Setlist[i])):
Setlist_w[i].append(Setlist[i][j][0]) # 극성 제외 단어만 추출
counter = {}
for i in range(len(Setlist_w)):
counter[i] = collections.Counter(Setlist_w[i]) # 누적치
for i in range(len(Setlist_w)):
Setlist_w[i] = list(zip(counter[i].keys(), counter[i].values())) # 튜플 리스트화 [(값, 값)]
Plist = []
for i in range(len(Setlist_w)):
Plist.append([])
for j in range(len(Setlist_w[i])):
Plist[i].append(list(Setlist_w[i][j])) # 튜플 -> 리스트화 [[값, 값]]
for i in range(len(Plist)):
for j in range(len(Plist[i])):
Plist[i][j][1] = 0 # 극성 0으로 초기화
for i in range(len(Setlist)):
for j in range(1, len(Setlist[i])):
for h in range(len(Plist[i])):
if Setlist[i][j][0] == Plist[i][h][0]:
Plist[i][h][1] += Setlist[i][j][1] #누적치
vert_p=[] #수직 중복 삭제
for i in range(len(Plist)):
for j in range(len(Plist[i])):
vert_p.append(Plist[i][j]) #단어만 넣기
#print(vert_p)
vert_p.sort(key=lambda x:x[0]) #단어 기준으로 정렬
for i in range(len(vert_p)-2): #단어 비교해서 같으면 누적 다르면 값 바꾸기
for j in range(i+1,len(vert_p)):
if vert_p[i][0] == vert_p[j][0]:
vert_p[i][1]+=vert_p[j][1]
vert_p[j]=['0',0]
print(vert_p)
vert_p=[i for i in vert_p if not '0' in i] #'0'들어간 열 제거
df_ver= pd.DataFrame(vert_p)
df_ver.to_excel(Stockfilename+' KNU_New_vdic2.xlsx',sheet_name='sheet1')
#p_result = {'날짜': new_date, '단어, 극성': Plist}
#print(p_result)
#df_p_result = pd.DataFrame(p_result)
#df_p_result.to_excel(Stockfilename+' KNU_New_dic.xlsx',sheet_name='sheet1')
'''
for i in range(len(Stock_data)-1):
for k in range(len(Setlist)):
if (Stock_data[i][0].split('/') == Setlist[k][0].split('.')[:3]): # 날짜 비교
if Calpercentage(Stock_data[i][3], Stock_data[i][4]) > 2 : #당일 시가 대비 고가가 2퍼 높을때
for j in range(1, len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Calpercentage(Stock_data[i][3], Stock_data[i][5]) < -2 : #당일 시가 대비 저가가 2퍼 낮을 때
for j in range(1, len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
else:
if Stock_data[i+1][2] > 0: # 다음날 주가 등락률이 양수면
for j in range(1, len(Setlist[k])): #어제뉴스는 호재 취급
if Calpercentage(Stock_data[i+1][3], Stock_data[i+1][5]) < -2 :
for j in range(1, len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
else:
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Stock_data[i+1][2] < 0:
if Calpercentage(Stock_data[i+1][3], Stock_data[i+1][4]) > 2 :
for j in range(1, len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
else:
for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= -1
else:
print(Setlist)
'''
# -*-coding:utf-8-*-
import collections
import json
import warnings
warnings.simplefilter(("ignore"))
import openpyxl
import pandas as pd
import re
from datetime import datetime
########코스피 감성 판단
class KnuSL():
def data_list(wordname):
with open('KnuSentiLex/data/SentiWord_info.json', encoding='utf-8-sig', mode='r') as f:
data = json.load(f)
result = [0, 0]
for i in range(0, len(data)):
if data[i]['word'] == wordname:
result.pop()
result.pop()
result.append(data[i]['word_root'])
result.append(int(data[i]['polarity']))
r_word = result[0] # 어근
s_word = result[1] # 극성
return s_word
if __name__ == "__main__":
ksl = KnuSL
print("\nKNU 한국어 감성사전입니다~ :)")
print("사전에 단어가 없는 경우 결과가 None으로 나타납니다!!!")
print("종료하시려면 #을 입력해주세요!!!")
print("-2:매우 부정, -1:부정, 0:중립 or Unkwon, 1:긍정, 2:매우 긍정")
print("\n")
#########
Newsfilefolder = input("종목폴더입력: ")
Newsfilename = input("파일이름입력:")
Newsfilepos = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스키워드/" + Newsfilefolder + "/" + Newsfilename + ".xlsx"
Newsfile = openpyxl.load_workbook(Newsfilepos) # 파일이름입력
ws = Newsfile.active
data = []
date = []
i = 0
for row in ws.rows:
data.append([])
date.append(row[1].value)
for cell in row:
if cell.value != None:
data[i].append(cell.value)
i += 1
del data[0] # 첫번째 의미없는 열 삭제
del date[0]
for i in range(len(data)):
del data[i][0] # 각 열의 첫번째 행 삭제
for i in range(len(data)):
del data[i][0] # 각 열의 날짜 행 삭제
KNUdata = []
Tdata = []
for x in range(len(data)):
KNUdata.append([])
Tdata.append([])
for y in range(len(data[x])):
KNUdata[x].append(ksl.data_list(data[x][y]))
Tdata[x].append([data[x][y], KNUdata[x][y]])
result = {'날짜': date, '단어, 극성': Tdata}
df = pd.DataFrame(result)
list_df = df.values.tolist() # dataframe list로 변경
new_date = [] # 날짜 중복 삭제
for v in date:
if v not in new_date:
new_date.append(v)
# print(new_date)
Setlist = [] # 날짜별 키워드 넣기
for v in range(len(new_date)):
Setlist.append([])
Setlist[v].append(new_date[v])
for i in range(len(list_df)):
for j in range(len(list_df[i][1])):
if new_date[v] == list_df[i][0]:
Setlist[v].append(list_df[i][1][j])
Stockfilefolder = input("종목시세폴더입력: ")
Stockfilename = input("시세파일이름입력:")
fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/종목별시세/" + Stockfilefolder + "/" + Stockfilename + ".xlsx"
Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
stock_ws = Stockfile.active
Stock_data = [] # list 타입
i = 0
for row in stock_ws.rows:
Stock_data.append([])
for cell in row:
if cell.value != None:
Stock_data[i].append(cell.value)
i += 1
del Stock_data[0]
for i in range(len(Stock_data)):
del Stock_data[i][2] # 대비 삭제
for i in range(len(Stock_data)):
del Stock_data[i][7] # 거래대금 삭제
for i in range(len(Stock_data)):
del Stock_data[i][7] # 시가 총액 삭제
i = 0
for k in range(len(Setlist)):
if (Stock_data[i][0].split('/') == Setlist[k][0].split('.')[:3]): # 날짜 비교 날짜가 같다면
if Stock_data[i][2] > 0: # 코스피 등락이 양수
for j in range(1, len(Setlist[k])):
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Stock_data[i][2] < 0:
for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
i += 1
else:
if Stock_data[i+1][2] > 0: # 다음날 주가 등락률이 양수면
for j in range(1, len(Setlist[k])): # 어제뉴스는 호재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = 1
else:
Setlist[k][j][1] += 1
elif Stock_data[i+1][2] < 0:
for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
if Setlist[k][j][1] == 0:
Setlist[k][j][1] = -1
else:
Setlist[k][j][1] -= 1
Setlist_w = []
for i in range(len(Setlist)):
Setlist_w.append([])
for j in range(1, len(Setlist[i])):
Setlist_w[i].append(Setlist[i][j][0]) # 극성 제외 단어만 추출
counter = {}
for i in range(len(Setlist_w)):
counter[i] = collections.Counter(Setlist_w[i]) # 누적치
for i in range(len(Setlist_w)):
Setlist_w[i] = list(zip(counter[i].keys(), counter[i].values())) # 튜플 리스트화 [(값, 값)]
Plist = []
for i in range(len(Setlist_w)):
Plist.append([])
for j in range(len(Setlist_w[i])):
Plist[i].append(list(Setlist_w[i][j])) # 튜플 -> 리스트화 [[값, 값]]
for i in range(len(Plist)):
for j in range(len(Plist[i])):
Plist[i][j][1] = 0 # 극성 0으로 초기화
for i in range(len(Setlist)):
for j in range(1, len(Setlist[i])):
for h in range(len(Plist[i])):
if Setlist[i][j][0] == Plist[i][h][0]:
Plist[i][h][1] += Setlist[i][j][1] # 누적치
vert_p = [] # 수직 중복 삭제
for i in range(len(Plist)):
for j in range(len(Plist[i])):
vert_p.append(Plist[i][j]) # 단어만 넣기
# print(vert_p)
vert_p.sort(key=lambda x: x[0]) # 단어 기준으로 정렬
for i in range(len(vert_p) - 2): # 단어 비교해서 같으면 누적 다르면 값 바꾸기
for j in range(i + 1, len(vert_p)):
if vert_p[i][0] == vert_p[j][0]:
vert_p[i][1] += vert_p[j][1]
vert_p[j] = ['0', 0]
vert_p = [i for i in vert_p if not '0' in i] # '0'들어간 열 제거
df_ver = pd.DataFrame(vert_p)
df_ver.to_excel(Stockfilename + ' KNU_New_vdic2.xlsx', sheet_name='sheet1')
# This file was automatically generated by SWIG (http://www.swig.org).
# Version 3.0.0
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info
if version_info >= (2,6,0):
def swig_import_helper():
from os.path import dirname
import imp
fp = None
try:
fp, pathname, description = imp.find_module('_MeCab', [dirname(__file__)])
except ImportError:
import _MeCab
return _MeCab
if fp is not None:
try:
_mod = imp.load_module('_MeCab', fp, pathname, description)
finally:
fp.close()
return _mod
_MeCab = swig_import_helper()
del swig_import_helper
else:
import _MeCab
del version_info
try:
_swig_property = property
except NameError:
pass # Python < 2.2 doesn't have 'property'.
def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
if (name == "thisown"): return self.this.own(value)
if (name == "this"):
if type(value).__name__ == 'SwigPyObject':
self.__dict__[name] = value
return
method = class_type.__swig_setmethods__.get(name,None)
if method: return method(self,value)
if (not static):
self.__dict__[name] = value
else:
raise AttributeError("You cannot add attributes to %s" % self)
def _swig_setattr(self,class_type,name,value):
return _swig_setattr_nondynamic(self,class_type,name,value,0)
def _swig_getattr(self,class_type,name):
if (name == "thisown"): return self.this.own()
method = class_type.__swig_getmethods__.get(name,None)
if method: return method(self)
raise AttributeError(name)
def _swig_repr(self):
try: strthis = "proxy of " + self.this.__repr__()
except: strthis = ""
return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
try:
_object = object
_newclass = 1
except AttributeError:
class _object : pass
_newclass = 0
class DictionaryInfo(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, DictionaryInfo, name, value)
__swig_getmethods__ = {}
__getattr__ = lambda self, name: _swig_getattr(self, DictionaryInfo, name)
__repr__ = _swig_repr
__swig_getmethods__["filename"] = _MeCab.DictionaryInfo_filename_get
if _newclass:filename = _swig_property(_MeCab.DictionaryInfo_filename_get)
__swig_getmethods__["charset"] = _MeCab.DictionaryInfo_charset_get
if _newclass:charset = _swig_property(_MeCab.DictionaryInfo_charset_get)
__swig_getmethods__["size"] = _MeCab.DictionaryInfo_size_get
if _newclass:size = _swig_property(_MeCab.DictionaryInfo_size_get)
__swig_getmethods__["type"] = _MeCab.DictionaryInfo_type_get
if _newclass:type = _swig_property(_MeCab.DictionaryInfo_type_get)
__swig_getmethods__["lsize"] = _MeCab.DictionaryInfo_lsize_get
if _newclass:lsize = _swig_property(_MeCab.DictionaryInfo_lsize_get)
__swig_getmethods__["rsize"] = _MeCab.DictionaryInfo_rsize_get
if _newclass:rsize = _swig_property(_MeCab.DictionaryInfo_rsize_get)
__swig_getmethods__["version"] = _MeCab.DictionaryInfo_version_get
if _newclass:version = _swig_property(_MeCab.DictionaryInfo_version_get)
__swig_getmethods__["next"] = _MeCab.DictionaryInfo_next_get
if _newclass:next = _swig_property(_MeCab.DictionaryInfo_next_get)
def __init__(self):
this = _MeCab.new_DictionaryInfo()
try: self.this.append(this)
except: self.this = this
__swig_destroy__ = _MeCab.delete_DictionaryInfo
__del__ = lambda self : None;
DictionaryInfo_swigregister = _MeCab.DictionaryInfo_swigregister
DictionaryInfo_swigregister(DictionaryInfo)
class Path(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, Path, name, value)
__swig_getmethods__ = {}
__getattr__ = lambda self, name: _swig_getattr(self, Path, name)
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
__repr__ = _swig_repr
__swig_getmethods__["rnode"] = _MeCab.Path_rnode_get
if _newclass:rnode = _swig_property(_MeCab.Path_rnode_get)
__swig_getmethods__["rnext"] = _MeCab.Path_rnext_get
if _newclass:rnext = _swig_property(_MeCab.Path_rnext_get)
__swig_getmethods__["lnode"] = _MeCab.Path_lnode_get
if _newclass:lnode = _swig_property(_MeCab.Path_lnode_get)
__swig_getmethods__["lnext"] = _MeCab.Path_lnext_get
if _newclass:lnext = _swig_property(_MeCab.Path_lnext_get)
__swig_getmethods__["cost"] = _MeCab.Path_cost_get
if _newclass:cost = _swig_property(_MeCab.Path_cost_get)
__swig_setmethods__["prob"] = _MeCab.Path_prob_set
__swig_getmethods__["prob"] = _MeCab.Path_prob_get
if _newclass:prob = _swig_property(_MeCab.Path_prob_get, _MeCab.Path_prob_set)
Path_swigregister = _MeCab.Path_swigregister
Path_swigregister(Path)
class Node(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, Node, name, value)
__swig_getmethods__ = {}
__getattr__ = lambda self, name: _swig_getattr(self, Node, name)
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
__repr__ = _swig_repr
__swig_getmethods__["prev"] = _MeCab.Node_prev_get
if _newclass:prev = _swig_property(_MeCab.Node_prev_get)
__swig_getmethods__["next"] = _MeCab.Node_next_get
if _newclass:next = _swig_property(_MeCab.Node_next_get)
__swig_getmethods__["enext"] = _MeCab.Node_enext_get
if _newclass:enext = _swig_property(_MeCab.Node_enext_get)
__swig_getmethods__["bnext"] = _MeCab.Node_bnext_get
if _newclass:bnext = _swig_property(_MeCab.Node_bnext_get)
__swig_getmethods__["rpath"] = _MeCab.Node_rpath_get
if _newclass:rpath = _swig_property(_MeCab.Node_rpath_get)
__swig_getmethods__["lpath"] = _MeCab.Node_lpath_get
if _newclass:lpath = _swig_property(_MeCab.Node_lpath_get)
__swig_getmethods__["feature"] = _MeCab.Node_feature_get
if _newclass:feature = _swig_property(_MeCab.Node_feature_get)
__swig_getmethods__["id"] = _MeCab.Node_id_get
if _newclass:id = _swig_property(_MeCab.Node_id_get)
__swig_getmethods__["length"] = _MeCab.Node_length_get
if _newclass:length = _swig_property(_MeCab.Node_length_get)
__swig_getmethods__["rlength"] = _MeCab.Node_rlength_get
if _newclass:rlength = _swig_property(_MeCab.Node_rlength_get)
__swig_getmethods__["rcAttr"] = _MeCab.Node_rcAttr_get
if _newclass:rcAttr = _swig_property(_MeCab.Node_rcAttr_get)
__swig_getmethods__["lcAttr"] = _MeCab.Node_lcAttr_get
if _newclass:lcAttr = _swig_property(_MeCab.Node_lcAttr_get)
__swig_getmethods__["posid"] = _MeCab.Node_posid_get
if _newclass:posid = _swig_property(_MeCab.Node_posid_get)
__swig_getmethods__["char_type"] = _MeCab.Node_char_type_get
if _newclass:char_type = _swig_property(_MeCab.Node_char_type_get)
__swig_getmethods__["stat"] = _MeCab.Node_stat_get
if _newclass:stat = _swig_property(_MeCab.Node_stat_get)
__swig_getmethods__["isbest"] = _MeCab.Node_isbest_get
if _newclass:isbest = _swig_property(_MeCab.Node_isbest_get)
__swig_getmethods__["alpha"] = _MeCab.Node_alpha_get
if _newclass:alpha = _swig_property(_MeCab.Node_alpha_get)
__swig_getmethods__["beta"] = _MeCab.Node_beta_get
if _newclass:beta = _swig_property(_MeCab.Node_beta_get)
__swig_setmethods__["prob"] = _MeCab.Node_prob_set
__swig_getmethods__["prob"] = _MeCab.Node_prob_get
if _newclass:prob = _swig_property(_MeCab.Node_prob_get, _MeCab.Node_prob_set)
__swig_getmethods__["wcost"] = _MeCab.Node_wcost_get
if _newclass:wcost = _swig_property(_MeCab.Node_wcost_get)
__swig_getmethods__["cost"] = _MeCab.Node_cost_get
if _newclass:cost = _swig_property(_MeCab.Node_cost_get)
__swig_getmethods__["surface"] = _MeCab.Node_surface_get
if _newclass:surface = _swig_property(_MeCab.Node_surface_get)
Node_swigregister = _MeCab.Node_swigregister
Node_swigregister(Node)
MECAB_NOR_NODE = _MeCab.MECAB_NOR_NODE
MECAB_UNK_NODE = _MeCab.MECAB_UNK_NODE
MECAB_BOS_NODE = _MeCab.MECAB_BOS_NODE
MECAB_EOS_NODE = _MeCab.MECAB_EOS_NODE
MECAB_EON_NODE = _MeCab.MECAB_EON_NODE
MECAB_SYS_DIC = _MeCab.MECAB_SYS_DIC
MECAB_USR_DIC = _MeCab.MECAB_USR_DIC
MECAB_UNK_DIC = _MeCab.MECAB_UNK_DIC
MECAB_ONE_BEST = _MeCab.MECAB_ONE_BEST
MECAB_NBEST = _MeCab.MECAB_NBEST
MECAB_PARTIAL = _MeCab.MECAB_PARTIAL
MECAB_MARGINAL_PROB = _MeCab.MECAB_MARGINAL_PROB
MECAB_ALTERNATIVE = _MeCab.MECAB_ALTERNATIVE
MECAB_ALL_MORPHS = _MeCab.MECAB_ALL_MORPHS
MECAB_ALLOCATE_SENTENCE = _MeCab.MECAB_ALLOCATE_SENTENCE
MECAB_ANY_BOUNDARY = _MeCab.MECAB_ANY_BOUNDARY
MECAB_TOKEN_BOUNDARY = _MeCab.MECAB_TOKEN_BOUNDARY
MECAB_INSIDE_TOKEN = _MeCab.MECAB_INSIDE_TOKEN
class Lattice(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, Lattice, name, value)
__swig_getmethods__ = {}
__getattr__ = lambda self, name: _swig_getattr(self, Lattice, name)
__repr__ = _swig_repr
def clear(self): return _MeCab.Lattice_clear(self)
def is_available(self): return _MeCab.Lattice_is_available(self)
def bos_node(self): return _MeCab.Lattice_bos_node(self)
def eos_node(self): return _MeCab.Lattice_eos_node(self)
def end_nodes(self, *args): return _MeCab.Lattice_end_nodes(self, *args)
def begin_nodes(self, *args): return _MeCab.Lattice_begin_nodes(self, *args)
def sentence(self): return _MeCab.Lattice_sentence(self)
def size(self): return _MeCab.Lattice_size(self)
def set_Z(self, *args): return _MeCab.Lattice_set_Z(self, *args)
def Z(self): return _MeCab.Lattice_Z(self)
def set_theta(self, *args): return _MeCab.Lattice_set_theta(self, *args)
def theta(self): return _MeCab.Lattice_theta(self)
def next(self): return _MeCab.Lattice_next(self)
def request_type(self): return _MeCab.Lattice_request_type(self)
def has_request_type(self, *args): return _MeCab.Lattice_has_request_type(self, *args)
def set_request_type(self, *args): return _MeCab.Lattice_set_request_type(self, *args)
def add_request_type(self, *args): return _MeCab.Lattice_add_request_type(self, *args)
def remove_request_type(self, *args): return _MeCab.Lattice_remove_request_type(self, *args)
def newNode(self): return _MeCab.Lattice_newNode(self)
def toString(self, *args): return _MeCab.Lattice_toString(self, *args)
def enumNBestAsString(self, *args): return _MeCab.Lattice_enumNBestAsString(self, *args)
def has_constraint(self): return _MeCab.Lattice_has_constraint(self)
def boundary_constraint(self, *args): return _MeCab.Lattice_boundary_constraint(self, *args)
def feature_constraint(self, *args): return _MeCab.Lattice_feature_constraint(self, *args)
def set_boundary_constraint(self, *args): return _MeCab.Lattice_set_boundary_constraint(self, *args)
def set_feature_constraint(self, *args): return _MeCab.Lattice_set_feature_constraint(self, *args)
def set_result(self, *args): return _MeCab.Lattice_set_result(self, *args)
def what(self): return _MeCab.Lattice_what(self)
def set_what(self, *args): return _MeCab.Lattice_set_what(self, *args)
__swig_destroy__ = _MeCab.delete_Lattice
__del__ = lambda self : None;
def __init__(self):
this = _MeCab.new_Lattice()
try: self.this.append(this)
except: self.this = this
def set_sentence(self, *args): return _MeCab.Lattice_set_sentence(self, *args)
Lattice_swigregister = _MeCab.Lattice_swigregister
Lattice_swigregister(Lattice)
class Model(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, Model, name, value)
__swig_getmethods__ = {}
__getattr__ = lambda self, name: _swig_getattr(self, Model, name)
__repr__ = _swig_repr
def dictionary_info(self): return _MeCab.Model_dictionary_info(self)
def transition_cost(self, *args): return _MeCab.Model_transition_cost(self, *args)
def lookup(self, *args): return _MeCab.Model_lookup(self, *args)
def createTagger(self): return _MeCab.Model_createTagger(self)
def createLattice(self): return _MeCab.Model_createLattice(self)
def swap(self, *args): return _MeCab.Model_swap(self, *args)
__swig_getmethods__["version"] = lambda x: _MeCab.Model_version
if _newclass:version = staticmethod(_MeCab.Model_version)
__swig_destroy__ = _MeCab.delete_Model
__del__ = lambda self : None;
__swig_getmethods__["create"] = lambda x: _MeCab.Model_create
if _newclass:create = staticmethod(_MeCab.Model_create)
def __init__(self, *args):
this = _MeCab.new_Model(*args)
try: self.this.append(this)
except: self.this = this
Model_swigregister = _MeCab.Model_swigregister
Model_swigregister(Model)
def Model_version():
return _MeCab.Model_version()
Model_version = _MeCab.Model_version
def Model_create(*args):
return _MeCab.Model_create(*args)
Model_create = _MeCab.Model_create
class Tagger(_object):
__swig_setmethods__ = {}
__setattr__ = lambda self, name, value: _swig_setattr(self, Tagger, name, value)
__swig_getmethods__ = {}
__getattr__ = lambda self, name: _swig_getattr(self, Tagger, name)
__repr__ = _swig_repr
def parse(self, *args): return _MeCab.Tagger_parse(self, *args)
def parseToNode(self, *args): return _MeCab.Tagger_parseToNode(self, *args)
def parseNBest(self, *args): return _MeCab.Tagger_parseNBest(self, *args)
def parseNBestInit(self, *args): return _MeCab.Tagger_parseNBestInit(self, *args)
def nextNode(self): return _MeCab.Tagger_nextNode(self)
def next(self): return _MeCab.Tagger_next(self)
def formatNode(self, *args): return _MeCab.Tagger_formatNode(self, *args)
def set_request_type(self, *args): return _MeCab.Tagger_set_request_type(self, *args)
def request_type(self): return _MeCab.Tagger_request_type(self)
def partial(self): return _MeCab.Tagger_partial(self)
def set_partial(self, *args): return _MeCab.Tagger_set_partial(self, *args)
def lattice_level(self): return _MeCab.Tagger_lattice_level(self)
def set_lattice_level(self, *args): return _MeCab.Tagger_set_lattice_level(self, *args)
def all_morphs(self): return _MeCab.Tagger_all_morphs(self)
def set_all_morphs(self, *args): return _MeCab.Tagger_set_all_morphs(self, *args)
def set_theta(self, *args): return _MeCab.Tagger_set_theta(self, *args)
def theta(self): return _MeCab.Tagger_theta(self)
def dictionary_info(self): return _MeCab.Tagger_dictionary_info(self)
def what(self): return _MeCab.Tagger_what(self)
__swig_destroy__ = _MeCab.delete_Tagger
__del__ = lambda self : None;
__swig_getmethods__["create"] = lambda x: _MeCab.Tagger_create
if _newclass:create = staticmethod(_MeCab.Tagger_create)
__swig_getmethods__["version"] = lambda x: _MeCab.Tagger_version
if _newclass:version = staticmethod(_MeCab.Tagger_version)
def __init__(self, *args):
this = _MeCab.new_Tagger(*args)
try: self.this.append(this)
except: self.this = this
def parseToString(self, *args): return _MeCab.Tagger_parseToString(self, *args)
Tagger_swigregister = _MeCab.Tagger_swigregister
Tagger_swigregister(Tagger)
def Tagger_create(*args):
return _MeCab.Tagger_create(*args)
Tagger_create = _MeCab.Tagger_create
def Tagger_version():
return _MeCab.Tagger_version()
Tagger_version = _MeCab.Tagger_version
VERSION = _MeCab.VERSION
# This file is compatible with both classic and new-style classes.
No preview for this file type
Metadata-Version: 2.1
Name: mecab-python
Version: 0.996-ko-0.9.2-msvc
Summary: UNKNOWN
Home-page: UNKNOWN
Author: UNKNOWN
Author-email: UNKNOWN
License: UNKNOWN
Platform: UNKNOWN
UNKNOWN
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