beaucronin

x = initial()
prob = target_dist(x)
for i in range(steps):
  x_star = propose(x)
  prob_star = target_dist(x)
  if prob_star > prob or random() < prob_star / prob:
    x = x_star
    prob = prob_star

beaucronin

pr = analysis.predict({'petal_length': 1.5, 'petal_width': None})
interval = pr.credible_values('petal_width')
# => (0.06619570898596525, 0.45519138428493605)
interval[1] - interval[0]
# => 0.38899567529897083

pr = analysis.predict({'petal_length': 5.0, 'petal_width': None})
interval = pr.credible_values('petal_width')
# => (1.3341578189754613, 2.4761532421771784)
interval[1] - interval[0]

beaucronin

# generate some noisy-XOR data

from random import random

N = 1000
noise = 0.1

data = []
for _ in range(N):
    x1 = random() < 0.5

beaucronin

import csv
import json

rd = csv.reader(open('scotch.csv'))
header = rd.next()
colors = header[1:15]
data = []
schema = {
    'color': { 'type': 'categorical' },
    'AGE': { 'type': 'count' },

beaucronin

import veritable
import csv
import matplotlib.pyplot as plt

# Load the csv and read into a Veritable dataset using inches and pounds
print 'Reading data from file'
data_inches_pounds = []
with open('heights_weights_genders.csv') as fd:
    rd = csv.reader(fd)
    rd.next() # skip the header

beaucronin

Keybase proof

I hereby claim:

• I am beaucronin on github.
• I am beaucronin (https://keybase.io/beaucronin) on keybase.
• I have a public key ASA8QFopPai5WRhYagivUf6FMWlePH57Q7CPv5R3NK3Irgo

To claim this, I am signing this object:

beaucronin

Usage:

$ ec2-login-amazon <tag>

• Aside from the AWS CLI tools, you'll need jq: https://stedolan.github.io/jq/
• If multiple instances have the tag you specify, the first one will be chosen
• There are separate commands for Amazon Linux and Ubuntu because, aside from looking at the AMI ID, there's no way to know which user name to use

beaucronin

#From http://en.wikipedia.org/wiki/File:Correlation_examples2.svg

#Title: An example of the correlation of x and y for various distributions of (x,y) pairs
#Tags: Mathematics; Statistics; Correlation
#Author: Denis Boigelot
#Packets needed : mvtnorm (rmvnorm), RSVGTipsDevice (devSVGTips)
#How to use: output()
#
#This is an translated version in R of an Matematica 6 code by Imagecreator.

beaucronin

Verifying my Blockstack ID is secured with the address 18UUDfVMEwcVHGtjM8jqxzHvLSX95i6fJ9 https://explorer.blockstack.org/address/18UUDfVMEwcVHGtjM8jqxzHvLSX95i6fJ9

beaucronin