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View Storage.jl
module Storage
DB_HOST = ENV["POSTGRES_PORT_5432_TCP_ADDR"]
DB_PORT = ENV["POSTGRES_PORT_5432_TCP_PORT"]
DB_NAME = ENV["POSTGRES_DBNAME"]
DB_USER = ENV["POSTGRES_PGUSER"]
DB_PASS = ENV["POSTGRES_PGPASS"]
conn = connect(Postgres, DB_HOST, DB_USER, DB_PASS, DB_NAME, DB_PORT)
@stucchio
stucchio / Dockerfile
Created Oct 28, 2014
Dockerfile for julia web stack
View Dockerfile
FROM stucchio/juliabase:0.3.2
MAINTAINER Chris Stucchio <stucchio@gmail.com>
# Julia libs we want
ADD REQUIRE /.julia/v0.3/REQUIRE
RUN julia -e "Pkg.resolve()"
# C Libraries we need
RUN apt-get install -y unixodbc unixodbc-dev libsqliteodbc odbc-postgresql libhttp-parser-dev libicu-dev # Utility libs
@stucchio
stucchio / Dockerfile
Created Oct 28, 2014
Dockerfile for Julia
View Dockerfile
FROM ubuntu:14.04
MAINTAINER Chris Stucchio <stucchio@gmail.com>
# Necessary to add a ppa
RUN apt-get update && apt-get install -y python-software-properties software-properties-common
# Julia repository
RUN add-apt-repository ppa:staticfloat/juliareleases && apt-get update
# Yay julia
View StreamBenchmark.scala
package benchmark
import scala.concurrent._
import scala.concurrent.ExecutionContext
import scalaz._
import Scalaz._
import scalaz.stream._
import scalaz.stream.async._
import scalaz.concurrent.{Task, Strategy}
View ActorBenchmark.scala
package benchmark
import akka.actor._
object ActorBenchmark {
class IntermediateMapper(target: ActorRef) extends Actor {
var state: Wrapper = Wrapper(0,0)
def receive = {
case (w:Wrapper) => {
@stucchio
stucchio / monte_carlo_compare_theory_to_practice.py
Created Jun 17, 2014
Code to make other graph in equal weights post
View monte_carlo_compare_theory_to_practice.py
from pylab import *
from numpy.random import dirichlet, rand, binomial, uniform, normal
def _unit_weight(dim):
return ones(dim) / float(dim)
ONE_FRAC = 0.5
SQRT_TWO_INV = 1.0 / sqrt(2.0)
def _feature_vec(dim, method="bernoulli"):
if method == "bernoulli":
View asymptotic_comparison.jl
using PyPlot
function em_exact(a, b, c, d)
total = 0.0
for i = 0:(c-1)
total += exp(lbeta(a+i, d+b) - log(d+i) - lbeta(1+i, d) - lbeta(a, b))
end
return total
end
View equal_weights_monte_carlo.py
from pylab import *
from numpy.random import dirichlet, rand
def _unit_weight(dim):
return ones(dim) / float(dim)
def _feature_vec(dim, storage = None):
result = rand(dim)
result[where(result > 0.5)] = 1.0
result[where(result <= 0.5)] = 0.0
@stucchio
stucchio / mle_compute_z.jl
Last active Aug 29, 2015
Maximum likelihood computation of ad probability
View mle_compute_z.jl
#Pkg.add("Optim")
using Optim
function logLikelihood(z::Float64, clicks::Array{Float64,1}, shows::Array{Float64,1}, alpha::Array{Float64,1})
@assert size(clicks) == size(shows)
@assert size(shows) == size(alpha)
az = z * alpha
return sum(clicks .* log(az) .+ (shows .- clicks) .* log(1-az))
end
@stucchio
stucchio / bayesian_ab_test_empiritcs.py
Last active Jan 26, 2021
bayesian a/b test empirics
View bayesian_ab_test_empiritcs.py
from pylab import *
from scipy.stats import beta, norm, uniform
from random import random
from numpy import *
import numpy as np
import os
# Input data
prior_params = [ (1, 1), (1,1) ]