jfarmer

true and false vs. "truthy" and "falsey" (or "falsy") in Ruby, Python, and JavaScript

Many programming languages, including Ruby, have native boolean (true and false) data types. In Ruby they're called true and false. In Python, for example, they're written as True and False. But oftentimes we want to use a non-boolean value (integers, strings, arrays, etc.) in a boolean context (if statement, &&, ||, etc.).

This outlines how this works in Ruby, with some basic examples from Python and JavaScript, too. The idea is much more general than any of these specific languages, though. It's really a question of how the people designing a programming language wants booleans and conditionals to work.

If you want to use or share this material, please see the license file, below.

Update

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

devdazed

"""
Simple Linear Probabilistic Counters

Credit for idea goes to:
http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html
http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/

Installation:
    pip install smhasher
    pip install bitarray

badboy

Original link: http://www.concentric.net/~Ttwang/tech/inthash.htm
Taken from: http://web.archive.org/web/20071223173210/http://www.concentric.net/~Ttwang/tech/inthash.htm
Reformatted using pandoc

Integer Hash Function

Thomas Wang, Jan 1997

last update Mar 2007

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

gerasiov

#!/bin/sh
DEVICE=/dev/video0
RESOLUTION="width=1024:height=768"
FRAMES_SKIP=3
ROTATE=1
REMOTE_HOST=lvk.cs.msu.su
REMOTE_DIR=public_html/webcam
INTERVAL=300

WORK_DIR=$(mktemp -d)

CMCDragonkai

Understanding the Memory Layout of Linux Executables

Required tools for playing around with memory:

• hexdump
• objdump
• readelf
• xxd
• gcore