cangoal

// Suppose you are at a party with n people (labeled from 0 to n - 1) and among them, there may exist one celebrity. The definition of a celebrity is that all the other n - 1 people know him/her but he/she does not know any of them.

// Now you want to find out who the celebrity is or verify that there is not one. The only thing you are allowed to do is to ask questions like: "Hi, A. Do you know B?" to get information of whether A knows B. You need to find out the celebrity (or verify there is not one) by asking as few questions as possible (in the asymptotic sense).

// You are given a helper function bool knows(a, b) which tells you whether A knows B. Implement a function int findCelebrity(n), your function should minimize the number of calls to knows.

// Note: There will be exactly one celebrity if he/she is in the party. Return the celebrity's label if there is a celebrity in the party. If there is no celebrity, return -1.

public int findCelebrity(int n) {
      if(n <= 1) return n - 1;

tzachz

import java.text.SimpleDateFormat
import java.util.Date
import org.json4s._
import org.json4s.jackson.JsonMethods.parse
import scala.io.Source.fromURL

object SparkAppStats {

  val url = "http://<host>:4040/api/v1/applications/<app-name>/jobs"

RobColeman

package com.preact.platform.math.models

import java.lang.System._
import java.nio.ByteBuffer

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.commons.math3.distribution.ExponentialDistribution
import org.apache.commons.math3.distribution.NormalDistribution
import com.tdunning.math.stats.TreeDigest

Jaza

*

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

patriknw

  case object Run
  case object Msg

  class Destination extends Actor {
    def receive = {
      case Msg ⇒ sender ! Msg
    }
  }

  class Client(