daien

""" Module to compute projections on the positive simplex or the L1-ball

A positive simplex is a set X = { \mathbf{x} | \sum_i x_i = s, x_i \geq 0 }

The (unit) L1-ball is the set X = { \mathbf{x} | || x ||_1 \leq 1 }

Adrien Gaidon - INRIA - 2011
"""

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

ryanlecompte

import scala.collection.mutable

/**
 * Bounded priority queue trait that is intended to be mixed into instances of
 * scala.collection.mutable.PriorityQueue. By default PriorityQueue instances in
 * Scala are unbounded. This trait modifies the original PriorityQueue's
 * enqueue methods such that we only retain the top K elements.
 * The top K elements are defined by an implicit Ordering[A].
 * @author Ryan LeCompte ([email protected])
 */

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

computermacgyver

#!/usr/bin/R

# Functions for R and ggplot2 to create log-scale density plots

# Main function: density.log(...)
# Produce a dataframe used to produce a density plot
# Input:
#  data: dataframe, 
#  var: variable to plot on y-axis
#  split: factor to split on

soulslicer

# This script installs everything you need on an EC2 GPU Instance
# Create an Ubuntu 12.04 GPU Instance with at least 16GB of free space

# Base packages
sudo apt-get update
sudo apt-get install build-essential
sudo apt-get install gcc git cmake

# CUDA/Nvidia installations
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb

jarutis

# install general deps
sudo yum groupinstall "Development Tools"
sudo yum install wget unzip

# install java
cd /opt/
sudo wget --no-cookies --no-check-certificate --header "Cookie: gpw_e24=http%3A%2F%2Fwww.oracle.com%2F; oraclelicense=accept-securebackup-cookie" "http://download.oracle.com/otn-pub/java/jdk/8u60-b27/jdk-8u60-linux-x64.tar.gz"
sudo tar xzf jdk-8u60-linux-x64.tar.gz

cd /opt/jdk1.8.0_60/

dgrtwo

## helper and setup functions

wrap_party_df <- function(func) {
    function(x, object, ...) {
        n <- col_name(substitute(object))
        
        # have to create an expression since we cannot rely on local
        # variables
        args <- list(...)
        expr <- substitute(do.call(func, c(list(.[[colname]][[1]]), args)),

exit99

find . -exec ls -dl \{\} \; | grep ".*\.py" | awk '{print $9}' | xargs 2to3 -w