kylebgorman

#!/usr/bin/env python
# 
# Copyright (c) 2012 Kyle Gorman
# 
# 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:

piscisaureus

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = git@github.com:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

stephenhardy

-- Remove the history from 
rm -rf .git

-- recreate the repos from the current content only
git init
git add .
git commit -m "Initial commit"

-- push to the github remote repos ensuring you overwrite history
git remote add origin git@github.com:<YOUR ACCOUNT>/<YOUR REPOS>.git

glamp

import numpy as np
import pylab as pl

x = np.random.uniform(1, 100, 1000)
y = np.log(x) + np.random.normal(0, .3, 1000)

pl.scatter(x, y, s=1, label="log(x) with noise")
pl.plot(np.arange(1, 100), np.log(np.arange(1, 100)), c="b", label="log(x) true function")
pl.xlabel("x")
pl.ylabel("f(x) = log(x)")

jnrbsn

sudo yum update
sudo yum install python27
curl https://bitbucket.org/pypa/setuptools/raw/bootstrap/ez_setup.py | sudo /usr/bin/python27
sudo easy_install pip
echo "alias python='python27'" >> ~/.bashrc
source ~/.bashrc

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

freeman-lab

package thunder.streaming

import org.apache.spark.{SparkConf, Logging}
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import org.apache.spark.streaming._
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.mllib.clustering.KMeansModel

import scala.util.Random.nextDouble

mreid

# Example Huffman coding implementation
# Distributions are represented as dictionaries of { 'symbol': probability }
# Codes are dictionaries too: { 'symbol': 'codeword' }

def huffman(p):
    '''Return a Huffman code for an ensemble with distribution p.'''
    assert(sum(p.values()) == 1.0) # Ensure probabilities sum to 1

    # Base case of only two symbols, assign 0 or 1 arbitrarily
    if(len(p) == 2):

jfarmer

Casual Raiding and Deliberate Learning

Note: I wrote this in 2015 when I was raiding with a casual guild in WoW.

de·lib·er·ate (adj.)

• done consciously and intentionally: a deliberate attempt to provoke conflict.

• fully considered; not impulsive: a deliberate decision.

  synonyms: intentional, calculated, conscious, intended, purposeful

gtallen1187

"A little bit of slope makes up for a lot of y-intercept"

01/13/2012. From a lecture by Professor John Ousterhout at Stanford, class CS140

Here's today's thought for the weekend. A little bit of slope makes up for a lot of Y-intercept.

[Laughter]