krishnanraman

How many ways can you invest $1000 in facebook, microsoft, hp ?

https://github.com/krishnanraman/scalding/blob/develop/src/main/scala/com/twitter/scalding/mathematics/Combinatorics.scala
Using the Combinatorics package in a Scalding job, its absolutely trivial.

------ACTUAL CODE --------
val cash = 1000
val error = 5 // max error $5, so its ok if we cannot invest the last $5 or less
val (FB, MSFT, HP) = (23,27,51) // share prices
val stocks = IndexedSeq( FB,MSFT,HP )

krishnanraman

// Compute a cayley table for a finite abelian group given its zero

def zero(x:Int, group:Vector[Int]) = {
    val n = group.size
    val pos = group.indexOf(x)
    val base = group.slice(pos, n) ++ group.slice(0, pos)
    Vector.tabulate(n,n)((x,y)=> {
       val row = base.slice(x,n)++base.slice(0,x)
       row(y)
    })

krishnanraman

def ip(host:String)=java.net.InetAddress.getAllByName(host)(0).getHostAddress
var map = Map[String,String]()
val hosts = List("google.com","twitter.com","facebook.com")
hosts.par.map(host => (host,ip(host))).foreach(hostip => map+=(hostip._1->hostip._2))


scala> map
res1: scala.collection.immutable.Map[String,String] = Map(google.com -> 74.125.224.71, twitter.com -> 199.59.150.7, facebook.com -> 69.171.229.16)

krishnanraman

import com.twitter.scalding._

import cern.colt.matrix.{DoubleFactory2D, DoubleFactory1D }
import cern.colt.matrix.linalg.Algebra
import java.util.StringTokenizer

class Portfolios(args : Args) extends Job(args) {

val cash = 1000.0 // money at hand
val error = 1 // its ok if we cannot invest the last dollar

krishnanraman

package com.twitter.scalding.mathematics

import org.specs._
import com.twitter.scalding._

class CombinatoricsJob(args : Args) extends Job(args) {
  val C = Combinatorics
  C.permutations( 5,2 ).write(Tsv("perms.txt"))

  C.combinations( 5,2 ).write(Tsv("combs.txt"))

krishnanraman

import scala.actors._
import scala.actors.Actor._
import scala.math.{pow,abs}
import collection.mutable.HashMap

 class CoordinatesActor extends Actor {


    def act = {
      react {

krishnanraman

val data = List((1,2),(2,3),(3,4),(4,5),(5,7))
def line(x:Int) = x+1  // guess based on data
 
def mean(xy:List[(Int,Int)]) = xy.map(a=>a._2).sum/(0.0+xy.size)  // average the y's
def sstot(xy:List[(Int,Int)]) = { val mu = mean(data); xy.map(a=>(a._2-mu)*(a._2-mu)).sum } // total sum of squares  
def sserr(xy:List[(Int,Int)]) = { xy.map(a=>(a._2-line(a._1))*(a._2-line(a._1))).sum }  // sum of squares of residuals
def rsq(xy:List[(Int,Int)]) = 1.0 - sserr(xy)/sstot(xy) 
 
scala> rsq(data)
res5: Double = 0.9324324324324325  // 93% fit, not bad for a guess.

krishnanraman

case class Employee(age:Int, tenure:Int, salary:Int, name:String)

def rnd(min:Int,max:Int) = math.round(min+math.random*(max-min)).toInt 

val empDB = for(employees<-1 to 10000) yield Employee( rnd(25,35),rnd(1,6),rnd(85000,150000),"emp"+employees)

case class Monoid(e:Set[Employee]) {     
  def plus(a:Employee,b:Employee)= {
  if(a.age > b.age) a else 
  if(b.age > a.age) b else 

krishnanraman

case class Employee(age:Int, tenure:Int, salary:Int, name:String)
 
def rnd(min:Int,max:Int) = math.round(min+math.random*(max-min)).toInt 
 
val empDB = for(employees<-1 to 10000) yield Employee( rnd(25,35),rnd(1,6),rnd(85000,150000),"emp"+employees)

krishnanraman

scala> empDB.sortBy(e=>(e.tenure,e.age,-e.salary)).reverse.head
res0: Employee = Employee(35,6,85079,emp7435)