- 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.
- Models and Issues in Data Stream Systems
- 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
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
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# value_iteration.r | |
# George Lesica | |
# CSCI 555 - FA 2012 | |
# Homework 5 | |
# Solution to problem 3 | |
INTENDED <- 0.8 | |
LEFT <- 0.1 | |
RIGHT <- 0.1 |
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/* Default (OS X).sublime-keymap */ | |
/* Key Bindings - User */ | |
[ | |
{ "keys": ["ctrl+shift+."], "command": "erb", "context": | |
[ | |
{ | |
"key": "selector", | |
"operator": "equal", | |
"operand": "text.html.ruby, text.haml, source.yaml, source.css, source.scss, source.js, source.coffee" | |
} |
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#OS junk files | |
[Tt]humbs.db | |
*.DS_Store | |
#Visual Studio files | |
*.[Oo]bj | |
*.user | |
*.aps | |
*.pch | |
*.vspscc |
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require 'rubygems' | |
require 'rack' | |
class Object | |
def webapp | |
class << self | |
define_method :call do |env| | |
func, *attrs = env['PATH_INFO'].split('/').reject(&:empty?) | |
[200, {}, send(func, *attrs)] | |
end |
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# Answering this question: | |
# http://stackoverflow.com/questions/3679306/ | |
>>> import json | |
>>> class Foo(object): | |
... def __init__(self, a, b): | |
... self.a = a | |
... self.b = b | |
... | |
>>> foo = Foo('a string', ['a', 'list', 'of', 'strings']) |