stucchio

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

FROM stucchio/juliabase:0.3.2
MAINTAINER Chris Stucchio <[email protected]>

# 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

FROM ubuntu:14.04
MAINTAINER Chris Stucchio <[email protected]>

# 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

stucchio

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}

stucchio

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

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":

stucchio

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

stucchio

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

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#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

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) ]