I hereby claim:
- I am thinrhino on github.
- I am thinrhino (https://keybase.io/thinrhino) on keybase.
- I have a public key whose fingerprint is 76EB 936C 5D76 6E82 E9A9 68FF 96C8 359B CC53 9C5F
To claim this, I am signing this object:
| from __future__ import division | |
| from bs4 import BeautifulSoup as bs | |
| import requests | |
| import re | |
| import time | |
| from pymongo import MongoClient | |
| from time import mktime | |
| from datetime import datetime | |
| import plotly.plotly as py | |
| import plotly.graph_objs as go |
| function FindProxyForURL(url, host) | |
| { | |
| if (dnsDomainIs(host, ".pandora.com")) | |
| return "PROXY 199.189.84.217:3128" | |
| if (dnsDomainIs(host, ".spotify.com")) | |
| return "PROXY 54.246.92.203:80" | |
| return "DIRECT" | |
| } |
I hereby claim:
To claim this, I am signing this object:
| # First download the twitter archive | |
| # Get API_KEY and API_SECRET from developer.twitter.com | |
| import os | |
| import json | |
| import glob | |
| import base64 | |
| import requests | |
| from requests_oauthlib import OAuth1Session |
| ;; Enable mouse support | |
| (unless window-system | |
| (require 'mouse) | |
| (xterm-mouse-mode t) | |
| (global-set-key [mouse-4] '(lambda () | |
| (interactive) | |
| (scroll-down 1))) | |
| (global-set-key [mouse-5] '(lambda () | |
| (interactive) | |
| (scroll-up 1))) |
| #!/usr/bin/env python -i | |
| """ | |
| A local interactive IPython shell for Google App Engine on Mac OSX. | |
| Usage: | |
| cd /to/project/folder/with/app.yaml | |
| python gae_shell.py | |
| Notes: |
| $(document).ready(function() { | |
| $(chart_id).highcharts({ | |
| chart: chart, | |
| title: title, | |
| xAxis: xAxis, | |
| yAxis: yAxis, | |
| series: series | |
| }); | |
| }); |
| #!/bin/sh | |
| # Change these settings to match what you are wanting to do | |
| FILE=/File/To/Copy | |
| SERVER=localhost | |
| PATH=/Where/To/Put/File | |
| OPTIONS=`vagrant ssh-config | awk -v ORS=' ' '{print "-o " $1 "=" $2}'` | |
| scp ${OPTIONS} $FILE vagrant@$SERVER:$PATH |
| # ref: http://www.tfidf.com/ | |
| # Example: | |
| # Consider a document containing 100 words wherein the word cat appears 3 times. | |
| # The term frequency (i.e., tf) for cat is then (3 / 100) = 0.03. Now, assume we | |
| # have 10 million documents and the word cat appears in one thousand of these. | |
| # Then, the inverse document frequency (i.e., idf) is calculated as log(10,000,000 / 1,000) = 4. | |
| # Thus, the Tf-idf weight is the product of these quantities: 0.03 * 4 = 0.12. | |
| # | |
| # Hence: | |
| # 1. Calculate term frequency |
| from collections import defaultdict | |
| import matplotlib.pyplot as plt | |
| data = open('<data_file>', 'r') | |
| r_data = [] | |
| # reading relevant data | |
| while True: | |
| l = data.readline() | |
| if l == '': |