artjomb

import selenium
import time
from selenium import webdriver

browser = webdriver.PhantomJS("phantomjs")
browser.get("https://twitter.com/StackStatus")
print browser.title

pause = 3

averagehuman

#!/usr/bin/env python
"""
The twitter-text-python library (https://pypi.python.org/pypi/twitter-text-python) can be used
to urlify text containing @<username>s and #<hashtag>s. It is a bit trickier if you want to do
the same with HTML, but BeautifulSoup makes it straightforward.
"""
from bs4 import BeautifulSoup, NavigableString
from ttp import ttp

parse_text = ttp.Parser().parse

zachguo

from sklearn.metrics import confusion_matrix

def print_cm(cm, labels, hide_zeroes=False, hide_diagonal=False, hide_threshold=None):
    """pretty print for confusion matrixes"""
    columnwidth = max([len(x) for x in labels]+[5]) # 5 is value length
    empty_cell = " " * columnwidth
    # Print header
    print "    " + empty_cell,
    for label in labels: 
        print "%{0}s".format(columnwidth) % label,

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

ivant

-- Build a sorted word frequency list from a file, trimmed to a given quantile.
--
-- Usage: WordStats <book.txt> <quantile>
-- 
-- `quantile` is a number between 0 and 1.
--
-- Example:
-- ./WordStats "Don Quijote.txt" 0.85 > "Don Quijote.words.85"

import Control.Applicative

dylanjf

########3 rep 10 fold CV to determine feature sparsity percentage via RFE#########

#X = concatenated text features for training set (title, body, url) transformed via TfIdfVectorizer
#y = training set classification (0, 1)

import numpy as np
import pandas as pd
import sklearn.linear_model as lm
from sklearn.cross_validation import KFold
from sklearn import metrics

elijahmanor

var doctors = [
    { number: 1,  actor: "William Hartnell",      begin: 1963, end: 1966 },
    { number: 2,  actor: "Patrick Troughton",     begin: 1966, end: 1969 },
    { number: 3,  actor: "Jon Pertwee",           begin: 1970, end: 1974 },
    { number: 4,  actor: "Tom Baker",             begin: 1974, end: 1981 },
    { number: 5,  actor: "Peter Davison",         begin: 1982, end: 1984 },
    { number: 6,  actor: "Colin Baker",           begin: 1984, end: 1986 },
    { number: 7,  actor: "Sylvester McCoy",       begin: 1987, end: 1989 },
    { number: 8,  actor: "Paul McGann",           begin: 1996, end: 1996 },
    { number: 9,  actor: "Christopher Eccleston", begin: 2005, end: 2005 },

paulirish

I wrote this in early January 2012, but never finished it. The research and thinking in this area led to a lot of the design of Yeoman and talks like "Javascript Development Workflow of 2013", "Web Application Development Workflow" and "App development stack for JS developers" (surpisingly little overlap in those talks, btw).

Now it's June 2013 and the state of web app tooling has matured quite a bit. But here's a snapshot of the story from 18 months ago, even if a little ugly and incomplete. :p

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In the beginning…

• Intro to tooling

dypsilon

Backend Development

• node.js
  • Installation paths: use one of these techniques to install node and npm without having to sudo.
  • Node.js HOWTO: Install Node+NPM as user (not root) under Unix OSes
  • Felix's Node.js Guide
  • Creating a REST API using Node.js, Express, and MongoDB
  • Node Cellar Sample Application with Backbone.js, Twitter Bootstrap, Node.js, Express, and MongoDB
  • JavaScript Event Loop
• Node.js for PHP programmers

dypsilon

Frontend Development

Attention: the list was moved to

https://github.com/dypsilon/frontend-dev-bookmarks

This page is not maintained anymore, please update your bookmarks.

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