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

branneman

Better local require() paths for Node.js

Problem

When the directory structure of your Node.js application (not library!) has some depth, you end up with a lot of annoying relative paths in your require calls like:

const Article = require('../../../../app/models/article');

Those suck for maintenance and they're ugly.

Possible solutions

slickplaid

sudo apt-get update && sudo apt-get upgrade -y;
sudo apt-get build-dep -y libqt4-gui libqt4-network libqt4-webkit;
sudo apt-get install -y openssl build-essential xorg git git-core libssl-dev libxrender-dev t1-xfree86-nonfree xfonts-scalable ttf-ubuntu-font-family ttf-mscorefonts-installer poppler-utils libqt4-dev qt4-dev-tools;
cd ~;
git clone git://gitorious.org/~antialize/qt/antializes-qt.git wkhtmltopdf-qt;
git clone git://github.com/antialize/wkhtmltopdf.git wkhtmltopdf;
cd wkhtmltopdf;
qmake-qt4;
cd ../wkhtmltopdf-qt;
git checkout 4.8.4;

juanplopes

import re, sys, json

TOKENS = {
    'NUMBER': '[0-9]+', 
    'IDENTIFIER': '[a-zA-Z][a-zA-Z0-9]*', 
    'ADD': '\+', 
    'SUB': '-', 
    'POW': '\^',
    'MUL': '\*', 
    'DIV': '/',

jasonrudolph

About

This gist is a collection of my rough notes from Strange Loop 2012.

@jasonrudolph on Twitter | @jasonrudolph on GitHub

Did I Mention That They're Rough?

rgreenjr

-- show running queries (pre 9.2)
SELECT procpid, age(clock_timestamp(), query_start), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(clock_timestamp(), query_start), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' 

ckirkendall

(use '[clojure.core.match :only [match]])

(defn evaluate [env [sym x y]]
  (match [sym]
    ['Number]   x
    ['Add]      (+ (evaluate env x) (evaluate env y))
    ['Multiply] (* (evaluate env x) (evaluate env y))
    ['Variable] (env x)))
    
(def environment {"a" 3, "b" 4, "c" 5})

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

swannodette

(ns datomic-play.core
  (:use [datomic.api :only [db q] :as d])
  (:require [clojure.core.logic :as l]
            [clojure.pprint :as pp]))

(def uri "datomic:dev://localhost:4334/hello")

(defprotocol IUnifyWithDatum
  (unify-with-datum [u v s]))

jkeirstead

# Demo of Gaussian process regression with R
# James Keirstead
# 5 April 2012

# Chapter 2 of Rasmussen and Williams's book `Gaussian Processes
# for Machine Learning' provides a detailed explanation of the
# math for Gaussian process regression.  It doesn't provide
# much in the way of code though.  This Gist is a brief demo
# of the basic elements of Gaussian process regression, as
# described on pages 13 to 16.