gavinwhyte

-- Create a new file, which we will call Main.hs

main :: IO ()
       main = do
         let d1 = [3,3,3,4,4,4,5,5,5]
         let d2 = [1,1,2,2,3,4,4,5,5]
         let r = pearson d1 d2
         print r
         
         pearson xs ys = (n * sumXY - sumX * sumY) /

gavinwhyte

$ mkdir vm-install
$ cd vm-install
$ vagrant init chef/centos-7.0

Edit the Vagrantfile to uncomment the line with 
$ config.vm.network "private_network", ip: "192.168.33.10" 
and add the line 
$ config.vm.hostname = "node1"
$ config.vm.provider "virtualbox" do |vb|
  #   # Display the VirtualBox GUI when booting the machine

gavinwhyte

import scalaz.\/
import scalaz.syntax.either._
 
object Example2 {
  // This example simulates error handling for a simple three tier web application
  //
  // The tiers are:
  // - the HTTP service
  // - a user authentication layer
  // - a database layer

gavinwhyte

import com.twitter.scalding.typed.{IterablePipe}
import com.twitter.scalding._

/**
 * 1) sbt
 * 2) project <project-name>
 * 3) run-main ExecutionAppSample --local
 * 4) See output in out.csv
 */
object ExecutionAppSample extends ExecutionApp {

gavinwhyte

import com.twitter.scalding._
import com.twitter.scalding.typed.IterablePipe

/**
 * Run this from the scalding Tool or other executor
 *
 * @param args
 */
class ExecutionJobSample(args: Args) extends ExecutionJob(args) {

gavinwhyte

-- Computing the moving average
main = do
  rawInput <- readFile "input.txt"
  let input = clean rawInput
  print input
  putStrLn $  "mean is " ++ (show.mean) input
  putStrLn $  "moving average is " ++ (show.avg) input
  
clean :: String -> [Double]
clean raw = map (\s -> read s :: Double) (lines raw)

gavinwhyte

import Statistics.LinearRegression
import qualified Data.Vector.Unboxed as U

-- Computing a linear regression
main = do
  let xs = U.fromList [1.0, 2.0, 3.0, 4.0, 5.0] :: U.Vector Double
  let ys = U.fromList [1.0, 2.0, 1.3, 3.75, 2.25] :: U.Vector Double
  let (b, m) = linearRegression xs ys
  print $ concat ["y = ", show m, " x + ", show b]

gavinwhyte

FROM java:8-jre

ENV KIBANA_VERSION 4.1.1-linux-x64

# Install ELK Required Dependancies
RUN set -x \
  && apt-get -qq update \
  && apt-get -qy install wget --no-install-recommends \
  && wget -qO - http://packages.elasticsearch.org/GPG-KEY-elasticsearch | apt-key add - \
  && echo "deb http://packages.elastic.co/elasticsearch/1.6/debian stable main" >> /etc/apt/sources.list \

gavinwhyte

zk1:
  image: jplock/zookeeper:3.4.6


mesos1:
  image: redjack/mesos-master:0.21.0
  ports:
    - "5050:5050"
  links:
    - "zk1:zookeeper1"

gavinwhyte

Multiple Linear Regression is the following model.

``` $$ 

h_{\theta}(x^{(i)}) = \theta^{T}x = \theta_{0} + \theta_{1}x 

``` $$