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function [u_sig,u_paren] = pretty_errors(u,u_err)
% Convert a number and its error to a more readable version:
% Ex:
% u = 1.234567, u_err = 0.012345 -> u_sig = '1.23', u_paren = '1'
% (for use with notation u = 1.23(1))

n = -1;
u_paren = 0;

while ~u_paren

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function comma_N = commatic(N)
% COMMATIC Insert commas into integers, e.g. 1234567 -> '1,234,567'.
% (c) Jack Peterson, 4/29/2013

N = num2str(N);
num_digits = length(N);
comma_N = '';

for j = 1:num_digits
    comma_N = strcat(N(num_digits-j+1),comma_N);

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Perceptron neural network.  Can learn any linearly separable logic.

(c) Jack Peterson, 6/12/2008
"""
from random import random

def hardlimit(row, col, bias):

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#!/usr/bin/perl
foreach(<>){
    chomp;
}
exit;

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/**
 * SockJS wrapper with some semantic sugar to make it look more like
 * socket.io. Supports socket.emit and socket.on.  Based on:
 * http://simplapi.wordpress.com/2013/09/22/sockjs-on-steroids/
 *
 * @author [email protected] (Jack Peterson)
 */
var SOCKJSIO = (function (my, $) {    
    // Module exports
    var _exports = my._exports = my._exports || {};

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CC = mpic++
CXXFLAGS = -g -W -Wall -Werror
LIBS = -lboost_mpi -lboost_serialization -lboost_system -lboost_filesystem -lboost_graph_parallel -lboost_iostreams

all: Boost

Boost: Boost.o
        $(CC) $(CXXFLAGS) -o Boost Boost.o $(LIBS)

Boost.o: Boost.cpp

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# connect julia to mssql using the magic of pycall
# @author Jack Peterson ([email protected])

using PyCall

@pyimport pymssql

# Connect to MSSQL Server
conn = pymssql.connect(server="SERVER:PORT",
                       user="USER",

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
calculate a weighted median
@author Jack Peterson ([email protected])
"""
from __future__ import division
import numpy as np

def weighted_median(data, weights):

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using Base.Test

function weighted_median{T<:Real,W<:Real}(data::Array{T,1}, weights::Array{W,1};
                                          checknan::Bool=true)
    isempty(data) && error("median of an empty array is undefined")
    if length(data) != length(weights)
        error("data and weight vectors must be the same size")
    end
    if checknan
        for (i, x) in enumerate(data)

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# "Find a full-rank matrix with smallest singular value less than X."
# Question: is this a good way to do proof-of-work?

using Winston
using Distributions

function svd_work(N, target)
    tic()
    while true
        A = rand(N, N)