def ensure_data(data_name, data_url):
root_path = test_data_path()
dest_path = os.path.join(root_path, data_name)
if os.path.exists(dest_path):
return dest_path
if data_url.endswith('.zip'):
dest_path += '.zip'
download(data_url, dest_path)
if data_url.endswith('.zip'):
with zipfile.ZipFile(dest_path, "r") as archive:
archive.extractall(root_path)
remove_file(dest_path)
dest_path = dest_path[:-len('.zip')]
return dest_path
def ensure_data(data_name, data_url): #保证数据，存在本地文件则返回，否则进行下载解压缩
root_path = test_data_path()
dest_path = os.path.join(root_path, data_name)
print("111dest_path:" + dest_path)
if os.path.exists(dest_path):
return dest_path
if data_url.endswith('.zip'):
dest_path += '.zip'
download(data_url, dest_path)
if data_url.endswith('.zip'):
with zipfile.ZipFile(dest_path, "r") as archive:
archive.extractall(root_path)
remove_file(dest_path)
dest_path = dest_path[:-len('.zip')]
print("dest_path:" + dest_path)
return dest_path
def install_jar(name, filepath, url):
dst = os.path.join(filepath, name)
if os.path.isfile(dst):
return dst
download(url, dst)
return dst
def install_jar(name, url):
dst = os.path.join(STATIC_ROOT, name)
if os.path.isfile(dst):
return dst
download(url, dst)
return dst
# -*- coding:utf-8 -*-
# Author：hankcs
# Date: 2018-04-28 10:07
import zipfile
from pyhanlp import *
from pyhanlp.static import download
if not os.path.isdir('tests/data/'):
print('请在项目根目录下运行本脚本')
exit(1)
if not os.path.isdir('tests/data/hanlp-wiki-vec-zh'):
model_path = 'tests/data/hanlp-wiki-vec-zh.zip'
download('http://hanlp.linrunsoft.com/release/model/hanlp-wiki-vec-zh.zip',
model_path)
with zipfile.ZipFile(model_path, "r") as archive:
archive.extractall('tests/data/')
WordVectorModel = JClass('com.hankcs.hanlp.mining.word2vec.WordVectorModel')
DocVectorModel = JClass('com.hankcs.hanlp.mining.word2vec.DocVectorModel')
word2vec = WordVectorModel(
'tests/data/hanlp-wiki-vec-zh/hanlp-wiki-vec-zh.txt')
doc2vec = DocVectorModel(word2vec)
docs = ["山东苹果丰收", "农民在江苏种水稻", "奥运会女排夺冠", "世界锦标赛胜出", "中国足球失败"]
for idx, doc in enumerate(docs):
doc2vec.addDocument(idx, doc)
print(word2vec.nearest('语言'))
for res in doc2vec.nearest('我要看比赛'):
