optician

Tofu docs (community edition)

Memo

Dependency

It is in the core Tofu dependency - "ru.tinkoff" %% "tofu" % "0.7.0".

Functionality

• A cache of a single value on access.
• A cache of a values mapping on access.

optician

Theorem andb_true_id :
  forall a : bool, a = andb true a.
Proof.
  intros []; reflexivity.
Qed.

Theorem orb_false_id :
  forall a : bool, a = orb false a.
Proof.
  intros []; reflexivity.

optician

import cats.effect.concurrent.Ref
import cats.effect.{IO, Sync}
import cats._
import cats.implicits._
import fs2.Stream

def g(r: => IO[Ref[IO, Int]]): Stream[IO, Int] =
  Stream
    .eval(r)
    .flatMap(

optician

project.git = true
maxColumn = 120
lineEndings = unix
docstring = "ScalaDoc"
assumeStandardLibraryStripMargin = true

continuationIndent.callSite = 2
continuationIndent.defnSite = 4

style = defaultWithAlign

optician

scala> object A1 { val a = 5 }
defined object A1

scala> object A2 { final val a = 5 }
defined object A2

scala> :javap A1$
  Size 640 bytes
  MD5 checksum 0d6b181ccd3f32e7e81a7f452b2f5686
  Compiled from "<console>"

optician

class A{
	val x: PartialFunction[Any, Int] = {
      case Some(x: Int) => x
    }
}

optician

import scala.io._
import java.io._
import scala.collection.mutable.ArrayBuffer

object Main {
	def main(args: Array[String]): Unit = {
		var i = 0
		var v1: ArrayBuffer[String] = new ArrayBuffer(10000)
		var v2: ArrayBuffer[String] = new ArrayBuffer(10000)
		Source.stdin.getLines.foreach{

optician

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

optician

import pushka.json._
import pushka.annotation._

// First case.
// Simple as is.
@pushka
case class A(subModel: B, i: Int, str: String)

@pushka
case class B(field1: Int, field2: List[Int])

optician

import org.scalameter._

import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration.Duration
import scala.concurrent.{Await, ExecutionContext, Future}


object Main {
  
  /*