Vishal Goklani vgoklani

The Gibbs sampler discussed on Darren Wilkinson's blog and also on Dirk Eddelbuettel's blog has been implemented in several languages, the first of which was R.

The task is to create a Gibbs sampler for the unscaled density

 f(x,y) = x x^2 \exp(-xy^2 - y^2 + 2y - 4x)

using the conditional distributions

 x|y \sim Gamma(3, y^2 +4)

y|x \sim Normal(\frac{1}{1+x}, \frac{1}{2(1+x)})

Who am I?

Philip Thrasher

Web hacker at a private cyber security company.

	<html>
	<head>
	<title>Line Graph with Dual-scaled Axes using SVG and d3.js</title>
	<script src="http://mbostock.github.com/d3/d3.v2.js"></script>
	<style>
	/* tell the SVG path to be a thin blue line without any area fill */
	path {
	stroke-width: 1;
	fill: none;
	}

	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="utf-8">
	<title>Tornado WebSocket Sample</title>
	</head>
	<body>
	<input type="text" id="username" value="myname" />
	<button id="connect">connect</button>
	<div id="result"></div>

	// set-up a connection between the client and the server
	var socket = io.connect();

	// let's assume that the client page, once rendered, knows what room it wants to join
	var room = "abc123";

	socket.on('connect', function() {
	// Connected, let's sign-up for to receive messages for this room
	socket.emit('room', room);
	});

	#!/usr/bin/env python
	import sys

	Dict = []
	with open('aclImdb/imdb.vocab','r') as f:
	for line in f:
	Dict.append(line.strip())

	with open('audit.log','r') as f:
	f.readline()

	#!/bin/bash

	# Requires vw (https://github.com/JohnLangford/vowpal_wabbit/wiki/),
	# the IMDB dataset (http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz),
	# and the perf utility from http://osmot.cs.cornell.edu/kddcup/software.html.

	cat aclImdb/train/labeledBow.feat \| \
	sed -n 's/^\([7-9]\\|10\)\s/&/p' \| \
	sed -e "s/^\([7-9]\\|10\)\s//" \| \
	awk '{ print "1 '"'"'pos_" (NR-1) " \|features " $0}' > train.vw

	Original tweet size in JSON 2624 Msgpack = 1817 Gzip + Json = 1058 Gzip + Msgpack = 1116 LZ4 + Json = 1628 LZ4 + Msgpack = 1361
	Original tweet size in JSON 1863 Msgpack = 1443 Gzip + Json = 0783 Gzip + Msgpack = 0835 LZ4 + Json = 1153 LZ4 + Msgpack = 1040
	Original tweet size in JSON 2074 Msgpack = 1670 Gzip + Json = 0842 Gzip + Msgpack = 0894 LZ4 + Json = 1229 LZ4 + Msgpack = 1139
	Original tweet size in JSON 2025 Msgpack = 1617 Gzip + Json = 0845 Gzip + Msgpack = 0895 LZ4 + Json = 1238 LZ4 + Msgpack = 1143
	Original tweet size in JSON 2069 Msgpack = 1663 Gzip + Json = 0846 Gzip + Msgpack = 0901 LZ4 + Json = 1243 LZ4 + Msgpack = 1164
	Original tweet size in JSON 2035 Msgpack = 1634 Gzip + Json = 0852 Gzip + Msgpack = 0907 LZ4 + Json = 1247 LZ4 + Msgpack = 1167
	Original tweet size in JSON 1988 Msgpack = 1464 Gzip + Json = 0804 Gzip + Msgpack = 0862 LZ4 + Json = 1220 LZ4 + Msgpack = 1061
	Original tweet size in JSON 1910 Msgpack = 1502 Gzip + Json = 0775 Gzip + Msgpack = 0832 LZ4 + Json = 1154 LZ4 + Msgpack = 1060

	System
	======

	Windows 7
	Python 2.7.2 (default, Jun 12 2011, 15:08:59) [MSC v.1500 32 bit (Intel)]


	Small data
	==========

	from sklearn.linear_model import SGDClassifier
	from sklearn import cross_validation
	from sklearn import metrics
	from sklearn.grid_search import GridSearchCV

	from sklearn.datasets import load_iris

	data = load_iris()
	sample_vector = data.data
	targets = data.target