filippovitale

filippovitale

filippovitale

Keybase proof

I hereby claim:

• I am filippovitale on github.
• I am filippovitale (https://keybase.io/filippovitale) on keybase.
• I have a public key whose fingerprint is D500 122C 2E12 6B3F E9B7 1577 051C 7654 7F1C 75E1

To claim this, I am signing this object:

filippovitale

Verifying that +filippovitale is my Bitcoin username. You can send me #bitcoin here: https://onename.io/filippovitale

filippovitale

/**
 * reference: http://www.cs.ox.ac.uk/ralf.hinze/publications/GGTries/Encode.lhs
 */
object GenTrie {

  sealed trait Bit
  case object △ extends Bit
  case object ▲ extends Bit

  type Bits = List[Bit]

filippovitale

import org.scalameter.api._

import scala.collection.mutable

/**
 * Source code related to the presentation: "Will it blend?" 
 * ScalaSyd Episode 33 - February 2015
 */
class BlendBenchmark extends PerformanceTest.OfflineReport {

filippovitale

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>Disabled</key>
	<true/>
	<key>EnvironmentVariables</key>
	<dict>
		<key>SERVER_JVMFLAGS</key>
		<string>-Dapple.awt.UIElement=true</string>

filippovitale

import org.specs2._
import scalaz.syntax.id._

/**
 * https://github.com/swlaschin/PropertyBasedTesting/blob/master/part2.fsx
 */
class Examples extends Specification with ScalaCheck {
  def is = commutative ^ inverse ^ easyToVerify ^ invariant ^ structuralInduction ^ idempotent ^ oracle

  /**

filippovitale

1. General Background and Overview

• 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.
• Models and Issues in Data Stream Systems
• 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
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

filippovitale

import scalaz.effect.IO
import scalaz.std.function._
import scalaz.{Coyoneda, Free, Monad, State, ~>}

object NonFunctor extends App {
  // my simple algebra
  sealed trait Console[A]
  case class PrintLine(msg: String) extends Console[Unit]
  case object ReadLine extends Console[String]