megafauna

from __future__ import print_function
from random import shuffle, seed
from itertools import product
from collections import Counter

#seed(100)
ranks = list('23456789TJQKA')
alt_ranks = list('A23456789TJQK')
suits = list('CSHD')
new_deck = list(product(ranks, suits))

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

esperlu

#!/bin/sh

# Converts a mysqldump file into a Sqlite 3 compatible file. It also extracts the MySQL `KEY xxxxx` from the
# CREATE block and create them in separate commands _after_ all the INSERTs.

# Awk is choosen because it's fast and portable. You can use gawk, original awk or even the lightning fast mawk.
# The mysqldump file is traversed only once.

# Usage: $ ./mysql2sqlite mysqldump-opts db-name | sqlite3 database.sqlite
# Example: $ ./mysql2sqlite --no-data -u root -pMySecretPassWord myDbase | sqlite3 database.sqlite