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# -*- coding: utf-8 -*-
Extract features from htmls
import sys, os, random, datetime
from bs4 import BeautifulSoup
# 檔案在這裡
def getTopic(self, ai, imgtopic, Dict):
# JW: features 是準備要被預測 topic 的變數, 應該要挪到下方 prediction 開始前, 程式比較連貫
#extrat the features of the element
features = str(re.sub(' +', ' ', ' '.join(self.extract_features(ai, imgtopic, Dict, 1))))
#print (features)
# JW: 從這裡開始可以獨立切一個 function, 只呼叫一次, 把 train 好的 model 存起來
#open training data file
current_dir = os.path.dirname(_file_)
corpus_dir = os.path.join(current_dir, 'corpus', 'all-corpus')
View cs295-first-look.ipynb
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with open('hahow_courses.json', 'r', encoding='utf-8') as f:
courses = json.load(f)
# 取出程式類課程的募資價/上線價/學生數,並顯示統計資料
pre_order_prices = list()
prices = list()
tickets = list()
lengths = list()
for c in courses:
if '55de81ac9d1fa51000f94770' in c['categories']:
View blog_hahow_crawler_2.json
"_id": "58744feda8aae907000d06c0",
"categories": [
"coverImage": {
"_id": "588421e46ecf3a0700b7a31d",
"url": ""
def crawl():
# 初始 API:
# 接續 API:
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
'AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/59.0.3071.115 Safari/537.36'}
url = ''
courses = list()
resp_courses = requests.get(url + '?limit=30&status=PUBLISHED', headers=headers).json()
while resp_courses: # 有回傳資料則繼續下一輪擷取
View hahow_courses.json
"_id": "58d5c70c27ea7d070060160e",
"categories": [
"coverImage": {
"_id": "58f318cc4909c907004ac575",
import requests
import json
import time
import numpy as np
import os
category = {
'55de818a9d1fa51000f94767': '生活',
'55de818d9d1fa51000f94768': '藝術',
'55de819a9d1fa51000f9476b': '運動',
View example.json
"href": "/bbs/Beauty/M.1482072854.A.DDC.html",
"num_image": 3,
"push_count": 18,
"title": "[神人] 長榮空姐"
"href": "/bbs/Beauty/M.1482075654.A.C1D.html",
"num_image": 7,
import json
import math
from collections import Counter
from matplotlib import pyplot as plt
def mean(x):
return sum(x) / len(x)