austinjp austinjp

Initialize

gitflow	git
`git flow init`	`git init`
	`git commit --allow-empty -m "Initial commit"`
	`git checkout -b develop master`

Connect to the remote repository

Introduction

Heroku differs from traditional build & deploy by having the concept of "buildpacks", which are a generic method to identify, compile and package builds. The traditional method here would be to have hardcoded build commands, in a Makefile for instance. Buildpacks generally make custom build+deploy steps uneccessary, although one can specify custom buildpacks if necessary.

When notifying Heroku that a deploy is needed (usually by a Github webook), Heroku will run through all supported buildpacks until one can identify your app, which is then compiled and packaged by the buildpack. The output of this process is referred to as a "slug", which contains your app and all dependencies. For instance, a Python app would have the virtualenv packaged inside it. Heroku automatically deploys slugs that are built successfully.

This document describes a way to use LXC and existing third-party tools to reproduce this setup.

You need to have a server running that accepts webhooks and can take act

sepal_length	sepal_width	petal_length	petal_width	species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa
4.6	3.4	1.4	0.3	setosa
5.0	3.4	1.5	0.2	setosa
4.4	2.9	1.4	0.2	setosa

	"""
	A perl Data.Dumper clone for Python
	Author: [email protected]
	2011-07-08

	Copyright 2011 Jinyu LIU

	Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

	The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

	<?xml version="1.0" encoding="UTF-8"?>
	<!DOCTYPE en-export SYSTEM "http://xml.evernote.com/pub/evernote-export2.dtd">
	<en-export export-date="20120727T073610Z" application="Evernote" version="Evernote Mac 3.0.5 (209942)">
	<note><title>Vim Tips</title><content><![CDATA[<?xml version="1.0" encoding="UTF-8" standalone="no"?>
	<!DOCTYPE en-note SYSTEM "http://xml.evernote.com/pub/enml2.dtd">
	<en-note style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;">
	yank for copy, delete for cut, put for parse
	<div><br/></div>
	<div>Move in context, not position</div>
	<div>/ search forward</div>

	var svg = document.querySelector( "svg" );
	var svgData = new XMLSerializer().serializeToString( svg );

	var canvas = document.createElement( "canvas" );
	var ctx = canvas.getContext( "2d" );

	var img = document.createElement( "img" );
	img.setAttribute( "src", "data:image/svg+xml;base64," + btoa( svgData ) );

	img.onload = function() {

	<html>
	<head>
	<script type="text/javascript">
	function sign(input, password) {
	// salt should be Uint8Array or ArrayBuffer
	var saltBuffer = str2ab('e85c53e7f119d41fd7895cdc9d7bb9dd');

	// don't use naive approaches for converting text, otherwise international
	// characters won't have the correct byte sequences. Use TextEncoder when
	// available or otherwise use relevant polyfills

	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="utf-8">
	</head>
	<body>

	<svg height=500 width=500>
	<circle cx=250 cy=250 r=200 stroke="black" stroke-width=3 fill="red" />
	Sorry, your browser does not support inline SVG.

	BEGIN MESSAGE.
	pNDvwhystUSfREk 6CZlgP5pDbEthDx kgJ3uLpwjCHE6L5 ZkhZXiR3iEMwvdk
	Knc3lRi7AIRtdkn f7U6NZcbzpbTCKq 6Xr2MZHgg6ZqWjy Abevu3bUnOUBioP
	IiYKZkK9uN6KORI H0iHRG4SavIZXan 1C7Sab8tHSIjkDI X8YTHeED989atrc
	SxK4zyOwyNZfQdJ 8PF4ggKvsPkonSH XC4Ht0Qz5Z.
	END MESSAGE.

	import nltk
	import re
	import string
	from gensim.models import Word2Vec
	from nltk.tokenize import sent_tokenize as nlkt_sent_tokenize
	from nltk.tokenize import word_tokenize as nlkt_word_tokenize
	from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
	from nltk.corpus import stopwords
	import numpy as np
	from scipy.spatial.distance import cosine