mkolod

import mesosphere.mesos.util.FrameworkInfo
import org.apache.mesos.MesosSchedulerDriver


/**
 * @author Tobi Knaup
 */

object Main extends App {

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/** Hive/Pig/Cascading/Scalding-style inner join which will perform a map-side/replicated/broadcast
  * join if the "small" relation has fewer than maxNumRows, and a reduce-side join otherwise. 
  * @param big the large relation
  * @param small the small relation
  * @maxNumRows the maximum number of rows that the small relation can have to be a
  *             candidate for a map-side/replicated/broadcast join
  * @return a joined RDD with a common key and a tuple of values from the two
  *         relations (the big relation value first, followed by the small one)
  */
private def optimizedInnerJoin[A : ClassTag, B : ClassTag, C : ClassTag]

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// Demo of automatic memory management in C++ using smart pointers.

#include <iostream>
#include <sstream>

using namespace std;

#define SpFoo shared_ptr<Foo>

class Foo {

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# import forecast library
library(tseries)
library(forecast)

########## Data loading and exploratory analysis

# NYC birth data
births <- scan("http://robjhyndman.com/tsdldata/data/nybirths.dat")

# convert to time series object - monthly data, starts in January 1946

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# sine wave in time domain

x <- seq(0, 20*pi, 0.01)
y <- sin(x)
plot(x, y, type='l')

# inverted time domain after 2 FFT applications

invertedY <- fft(fft(y)) / length(y)
plot(x, invertedY, type='l')

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object DiffieHellmanMerkle {

  import java.math.BigInteger
  import scala.language.implicitConversions
  import scala.util.Random

  def main(args: Array[String]): Unit =
    println(
      diffieHellmanMerkle(generator = 3, modulus = 17, alicePrivateKey = 54, bobPrivateKey = 24)
    )

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