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package mapreduce | |
/** | |
* This is an attempt to find a minimal set of type classes that describe the map-reduce programming model | |
* (the underlying model of Google map/reduce, Hadoop, Spark and others) | |
* The idea is to have: | |
* 1) lawful types that fully constrain correctness | |
* 2) a minimal set of laws (i.e. we can't remove any laws, | |
* 3) able to fully express existing map/reduce in terms of these types | |
* |
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# Author: Randal S. Olson (randalolson.com / @randal_olson) | |
# Uses Tableau's Tableau20 color scheme | |
figure.figsize: 12, 7 | |
figure.edgecolor: white | |
figure.facecolor: white | |
lines.linewidth: 2.5 | |
lines.markeredgewidth: 0 | |
lines.markersize: 10 |
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Robs-MacBook-Pro:testenv robstory$ sh PIPALLTHETHINGS.sh | |
New python executable in .env/bin/python2.7 | |
Also creating executable in .env/bin/python | |
Installing setuptools, pip...done. | |
Warning: libevent-2.0.21 already installed | |
Warning: libyaml-0.1.6 already installed | |
Downloading/unpacking cython | |
Downloading Cython-0.20.2-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.whl (3.4MB): 3.4MB downloaded | |
Installing collected packages: cython | |
Successfully installed cython |
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#!/bin/bash | |
virtualenv .env && | |
source .env/bin/activate && | |
brew install libevent && | |
brew install libyaml && | |
pip install cython && | |
pip install numpy && | |
pip install scipy && | |
pip install pandas && | |
pip install matplotlib && |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
Julia is a new language for technical computing. The language is designed to solve the "two language problem", in which scientists prototype code in a higher-level language like R and then rewrite parts (or all) of their code in a lower-level language like C. Julia strives to expose a set of basic abstractions that allow programmers to transition easily between quick-and-dirty prototype code and production-quality code.
- 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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#The MIT License (MIT) | |
# | |
#Copyright (c) 2015 Andre Queiroz | |
# | |
#Permission is hereby granted, free of charge, to any person obtaining a copy | |
#of this software and associated documentation files (the "Software"), to deal | |
#in the Software without restriction, including without limitation the rights | |
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
#copies of the Software, and to permit persons to whom the Software is | |
#furnished to do so, subject to the following conditions: |