acominotto

What is Scala?

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Créé par Martin en 2003 -- Son nom vient de l'anglais Scalable language Scala intègre les paradigmes de programmation orientée objet et de programmation fonctionnelle, avec un typage statique

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1. Fonctionnel basique

a) Immutability

andypetrella

case class Banque(nom:String) {banque =>

  def accepte(m:Montant) =
    CompteBuilder(banque, m)
    
}

implicit def stringToBanque(s:String) = Banque(s)

implicit def intWithDevise(i:Int) = new {

Thell

```{r setup, echo=FALSE, results='hide'}
chunkref <- local({
  function(chunklabel) {
    sprintf('[%s](#%s)', chunklabel, chunklabel )
  }  
})

secref <- local({
  function(seclabel) {
    sprintf('[%s](#%s)', seclabel, seclabel )

helena

import scala.collection.immutable
import scala.collection.immutable.Queue
import akka.actor._
 
/**
 * Rough, initial cut of a trait to mixin when an Actor requires
 * initalization, where the initialization is long and arduous (for
 * example, data initialization related). This strategy allows the 
 * implementing actor to delegate the work to another Actor, on a
 * separate, dedicated Dispatcher, and not block any other related

etrepum

module Bob (responseFor) where
import Data.Char (isSpace, isLower, isAlpha)
import Control.Applicative ((<$>), (<*>), (<|>))
data Prompt = Silence | Yell | Question | Other
            deriving (Show, Eq)

data P a = P { pCanParse :: Char -> Bool
             , pParse :: Char -> Either (P a) (Maybe a)
             , pParseEOF :: Maybe a
             }

cjmatta

##Building and using Spark Notebook for MapR

The spark-notebook is a useful browser-based REPL that can be used to explore data and build visualizations. This guide will illustrate the MapR-specific requirements for building, and using the spark notebook on MapR clusters.

###Building Checkout the source:

$ git clone https://github.com/andypetrella/spark-notebook.git

pchiusano

import scalaz.Applicative
import scalaz.concurrent.Task

/** 
 * This `Applicative[Task]` runs tasks in parallel, by defining
 * `ap` and `apply2` in terms of `mapBoth`. This differs from the
 *  default `Applicative[Task]`, where effects are sequenced 
 *  deterministically, in left to right order.
 */
val T = new Applicative[Task] {

ayosec

package foo.bar

import spray.routing._
import spray.http._
import spray.http.StatusCodes.Forbidden


// See https://developer.mozilla.org/en-US/docs/HTTP/Access_control_CORS

case class Origin(origin: String) extends HttpHeader {

wilhelmberg

sudo add-apt-repository ppa:webupd8team/sublime-text-3
sudo apt-get update
sudo apt-get install sublime-text-installer

run /opt/sublime_text/sublime_text or sudo ln -s /opt/sublime_text/sublime_text /usr/local/bin/subl

with ubuntu bash on Windows 10 and Xming:

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t