LukaJCB

trait KVStore[F[_]] {
  def get(key: String): F[Option[String]]
  def put(key: String, a: String): F[Unit]
}

class TestInterpreter() extends KVStore[IO] {
  def get(key: String): IO[Option[String]] = IO {
    println("Hit network!")

    Option(key + "!")

debasishg

// Finally tagless lambda calculus
object Final {
  def varZ[A,B](env1: A, env2: B): A = env1
  def varS[A,B,T](vp: B => T)(env1: A, env2: B): T = vp(env2)
  def b[E](bv: Boolean)(env: E): Boolean = bv
  def lam[A,E,T](e: (A, E) => T)(env: E): A => T = x => e(x, env)
  def app[A,E,T](e1: E => A => T, e2: E => A)(env: E): T = e1(env)(e2(env))

  def testf1[E,A](env: E): Boolean =
    app(lam[Boolean,E,Boolean](varZ) _, b(true))(env)

jdegoes

Applied Functional Programming with Scala - Notes

Copyright © 2016-2018 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

sebmarkbage

/** @jsx React.DOM */

var Graphic = React.createClass({ 

  componentDidMount: function() {
    var context = this.getDOMNode().getContext('2d');
    this.paint(context);
  },

  componentDidUpdate: function() {

runarorama

// Finally tagless lambda calculus
object Final {
  def varZ[A,B](env1: A, env2: B): A = env1
  def varS[A,B,T](vp: B => T)(env1: A, env2: B): T = vp(env2)
  def b[E](bv: Boolean)(env: E): Boolean = bv
  def lam[A,E,T](e: (A, E) => T)(env: E): A => T = x => e(x, env)
  def app[A,E,T](e1: E => A => T, e2: E => A)(env: E): T = e1(env)(e2(env))

  def testf1[E,A](env: E): Boolean =
    app(lam[Boolean,E,Boolean](varZ) _, b(true))(env)

igorw

<?php
$data = <<<'DATA'
$program = <<<PROGRAM
<?php
\$data = <<<'DATA'\n$data\nDATA;
$data

PROGRAM;
$n = 0;
if ($n >= 5) {

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

igorw

<?php

const StateManager = '';
const State = '';
const HARMFUL = 'harmful';

function getConsiderationSetter($subject, $state) {
    global $consideration;
    $consideration = "$subject considered $state";
}

bwoebi

<?php

spl_autoload_register(function ($name) {
	// $exp will be the name of the class without the \with\traitname part => array_splice 
	$exp = explode("\\", $name);
	// serach from end
	$index = array_search("with", array_reverse($exp, true));
	
	if (!$index || $index - 1 == count($exp)) // also fail when value is 0
		return false;

igorw

<?php
$data = <<<'DATA'
$program = <<<PROGRAM
<?php
\$data = <<<'DATA'\n$data\nDATA;
$data

PROGRAM;
echo $program;
DATA;