thexa4

// Paste in console

    var clickBestThing = function(){
       var things = [...document.querySelectorAll("#manufacture__container > div")].map(function(e){
        var res = {e};
        [...e.getElementsByTagName("span")].map(function(s){
          res[s.id] = +s.innerText.replace(/[^0-9]/g, '')
        })
        if(e.id === "item__spudGun" || e.id === "item__potatoLauncher") res.powerGain *= 1000;
        res.score = res.cost * (powerGain + res.powerGain)/res.powerGain;

ktuite

import boto.mturk.connection

sandbox_host = 'mechanicalturk.sandbox.amazonaws.com'
real_host = 'mechanicalturk.amazonaws.com'

mturk = boto.mturk.connection.MTurkConnection(
    aws_access_key_id = 'XXX',
    aws_secret_access_key = 'XXX',
    host = sandbox_host,
    debug = 1 # debug = 2 prints out all requests. but we'll just keep it at 1

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

michiexile

# gap.py
# (c) 2013 Mikael Vejdemo-Johansson
# BSD License
#
# SciPy function to compute the gap statistic for evaluating k-means clustering.
# Gap statistic defined in
# Tibshirani, Walther, Hastie:
#  Estimating the number of clusters in a data set via the gap statistic
#  J. R. Statist. Soc. B (2001) 63, Part 2, pp 411-423