matsu-chara

package OptionValueAccessor
 
import scala.language.experimental.macros
import scala.reflect.macros.whitebox.Context
 
object OptionValueAccessor {
 
  implicit class OptValue[A, B](val self: Option[A])(implicit ev: A <:< {def value: B}) {
    def ? : Option[B] = macro Impl.optValImpl[A, B]
  }

txels

Change the base branch for your current branch

Some cases:

• you started a bugfix out of develop, but it should be applied to a release branch
• you started on a branch and want to rebase it on some colleague's changes

Command

wdullaer

[Background]
Color=40,40,40

[BackgroundIntense]
Color=40,40,40

[Color0]
Color=73,72,62

[Color0Intense]

wenkokke

#!/usr/bin/env runhaskell

-- cabal depedencies:
--
--   * containers
--   * pandoc-types
--   * uu-parsinglib
--

{-# LANGUAGE RankNTypes #-}

milessabin

scala> trait Assoc[K] { type V ; val value: V }
defined trait Assoc

scala> def mkAssoc[V0](k: String, v: V0): Assoc[k.type] { type V = V0 } =
     |   new Assoc[k.type] { type V = V0 ; val value = v }
mkAssoc: [V0](k: String, v: V0)Assoc[k.type]{type V = V0}

scala> implicit def nameAssoc = mkAssoc("Name", "Mary")
nameAssoc: Assoc[String("Name")]{type V = String}

vrischmann

realm=Sonatype Nexus Repository Manager
host=nexus.company.com
user=admin
password=admin123

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

paulp

trait Collections {
  type CC[+X]         // the overarching container type (in scala: any covariant collection, e.g. List, Vector)
  type Min[+X]        // the minimum type constructor which can be reconstituted to CC[X] (in scala: GenTraversableOnce)
  type Opt[+X]        // the container type for optional results (in scala: Option)
  type CCPair[+X]     // some representation of a divided CC[A] (at simplest, (CC[A], CC[A]))
  type ~>[-V1, +V2]   // some means of composing operations (at simplest, Function1)

  type Iso[A]            = CC[A] ~> CC[A]       // e.g. filter, take, drop, reverse, etc.
  type Map[-A, +B]       = CC[A] ~> CC[B]       // e.g. map, collect
  type FlatMap[-A, +B]   = CC[A] ~> Min[B]      // e.g. flatMap

viktorklang

/*
Copyright 2018 Viktor Klang
 
   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at
 
       http://www.apache.org/licenses/LICENSE-2.0
 
   Unless required by applicable law or agreed to in writing, software

travisbrown

sealed trait Piece
case class Place(p: String) extends Piece
case class Name(n: String) extends Piece

case class LocatedPieces(located: Seq[(String, Piece, (Int, Int))])

object S2 extends ReflectionUtils {
  import scala.language.experimental.macros
  import scala.language.reflectiveCalls
  import scala.reflect.macros.Context