mblondel

# Mathieu Blondel, October 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg

def linear_kernel(x1, x2):
    return np.dot(x1, x2)

def polynomial_kernel(x, y, p=3):

jseabold

"""
Context manager or function to send text messages to your phone when a
process is done.

Edit the global variables. You might be able to find your phone e-mail 
address here: http://tinywords.com/about-old/mobile/

Usage:
with SendText("long running process"):
    do_something()

hrldcpr

One-line Tree in Python

Using Python's built-in defaultdict we can easily define a tree data structure:

def tree(): return defaultdict(tree)

That's it!

fonnesbeck

andrequeiroz

#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:

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

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

johnmyleswhite

Julia and Statistical Computing

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.

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

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.

---

wrobstory

#!/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 &&