samklr

##MixedTweets

We will see how to mix 2 twitter searches and push the results to the browser in real time.

First, checkout this mini project

To try it, you need to install Play 2.0

###Controller

samklr

import util.matching.Regex

object ExtremeStartup {

  def answer(q: String): Any = {

    if (q.contains("what is your name"))
      return "Mikkel"
    if (q.contains("which city is the Eiffel tower in"))
      return "Paris"

samklr

import spark.SparkContext
import SparkContext._

/**
 * A port of [[http://blog.echen.me/2012/02/09/movie-recommendations-and-more-via-mapreduce-and-scalding/]]
 * to Spark.
 * Uses movie ratings data from MovieLens 100k dataset found at [[http://www.grouplens.org/node/73]]
 */
object MovieSimilarities {

samklr

package com.mansur.scalding

import com.twitter.scalding._
import org.apache.lucene.search.spell._
import org.apache.mahout.common.distance.TanimotoDistanceMeasure
import org.apache.mahout.math.DenseVector
import org.apache.commons.math.util.MathUtils


/**

samklr

samklr

    public synchronized int read() throws IOException {
      int result = in.read();
      if (result != -1) {
        pos++;
      }
      return result;
    }

samklr

bash-3.2$ elastic-mapreduce --create --name "Sample Recommender" \
>   --jar s3n://temp.cascading.org/sample/recommender.jar \
>     --arg s3n://temp.cascading.org/sample/en.stop \
>     --arg s3n://temp.cascading.org/sample/tweets/ \
>     --arg s3n://temp.cascading.org/sample/out/token \
>     --arg s3n://temp.cascading.org/sample/out/similarity
Created job flow j-2HA2BVCBJGMVX
bash-3.2$ elastic-mapreduce --list
j-2HA2BVCBJGMVX     STARTING                                                         Sample Recommender
   PENDING        Example Jar Step              

samklr

// This F# dojo is directly inspired by the Digit Recognizer
// competition from Kaggle.com: 
// http://www.kaggle.com/c/digit-recognizer
// The datasets below are simply shorter versions of
// the training dataset from Kaggle.

// 0. Load data files from the following location:

// http://brandewinder.blob.core.windows.net/public/digitssample.csv
// http://brandewinder.blob.core.windows.net/public/digitscheck.csv

samklr

This is how we create an inclusive/exclusive range of Ints:

  val ie = 0 until 500

//1. Fill in the missing item to create a range of Ints from 1 to 100 inclusive

  val ints = 1 ??? 100

//2. Find the sum of the integers in this range

samklr

object TradingDays extends App {

  import scalaz._
  import Scalaz._

  case class Trade(sym: String, trader: String, qty: Int)

  case class TradingDay(symbols: Map[String, SymDay] = Map.empty)

  object TradingDay {