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Jon Zelner jzelner

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jzelner / starterpackages.R
Created April 19, 2016 16:59
# ipak function: install and load multiple R packages.
# check to see if packages are installed. Install them if they are not, then load them into the R session.
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE)
sapply(pkg, require, character.only = TRUE)
}
@jzelner
jzelner / index.html
Created January 28, 2015 14:37
<!DOCTYPE html>
<meta charset="utf-8">
<head>
<style>
.enter {
fill: green;
}
.update {
@jzelner
jzelner / render_post.sh
Last active August 29, 2015 14:03
#!/bin/bash
FILE="${1}"
if [[ $# != 1 ]]; then
echo "Usage: ./render_post.sh <file>"
exit
fi
if [[ ! -f "${FILE}" ]]; then
@jzelner
jzelner / render_post.R
Last active August 29, 2015 14:03
#!/usr/bin/env Rscript
input <- commandArgs(trailingOnly = TRUE)
KnitPost <- function(input, base.url = "/") {
require(knitr)
opts_knit$set(base.url = base.url)
fig.path <- paste0("../figs/", sub(".Rmd$", "", basename(input)), "/")
opts_chunk$set(fig.path = fig.path)
opts_chunk$set(fig.cap = "center")
render_jekyll()
@jzelner
jzelner / Multinomial_Logit_HMM.py
Last active August 30, 2016 20:33
Example of time-varying HMM transition matrix
import numpy as np
#A simple observation series
obs = np.array([0, 0, 1, 0, 0])
#initial state occupation probabilities
#(flat prior over state occupancies when t = 0)
initProbs = np.ones(4)/4
#These are the time-constant transition *rates* (with zeros
@jzelner
jzelner / hmm.py
Created December 12, 2012 12:25
Example of the Forward-Backward algorithm implemented using Theano
#Forward-backward algorithm implementation in Theano,
#with example taken from Wikipedia:
#http://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm
import theano
import theano.tensor as T
import numpy as np
#Set up vectors, etc to represent
#observations, indexes into the observation
@jzelner
jzelner / simpleSIR.py
Created July 29, 2010 21:07
import random
import scipy
import pylab as pl
pl.figure()
for i in range(30):
S = 99999
I = 1
R = 0
@jzelner
jzelner / simpleNetworkSIRWithClasses.py
Created November 13, 2009 20:24
#a simple network-based SIR model written in Python
#Jon Zelner
#University of Michigan
#October 20, 2009
#import this file (networkSIRWithClasses) to use the model components
#or run as 'python networkSIRWithClasses.py' to do sample runs
import igraph
import random
@jzelner
jzelner / sirWithClasses.py
Created October 20, 2009 18:24
#a simple object-based SIR model written in Python
#Jon Zelner
#University of Michigan
#October 20, 2009
#import 'sirWithClasses' to use the components of this model as you wish
#or just run this script as 'python sirWithClasses.py' to do a sample model run
import random
import heapq
@jzelner
jzelner / simpleSIR.py
Created October 8, 2009 18:08
#A simple SIR model written in Python
#Jon Zelner
#University of Michigan
#October 8, 2009
#Ok, so first we're going to import our random number and plotting libraries
# this is Python's standard random number library.
#For heavy-duty applications you'll want to use something else
#(like Scipy) but for this example, this will be more than adequate.