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Revisiting Tagless Final Interpreters

Tageless Final interpreters are an alternative to the traditional Algebraic Data Type (and generalized ADT) based implementation of the interpreter pattern. This document presents the Tageless Final approach with Scala, and shows how Dotty with it's recently added implicits functions makes the approach even more appealing. All examples are direct translations of their Haskell version presented in the Typed Tagless Final Interpreters: Lecture Notes (section 2).

The interpreter pattern has recently received a lot of attention in the Scala community. A lot of efforts have been invested in trying to address the biggest shortcomings of ADT/GADT based solutions: extensibility. One can first look at cats' Inject typeclass for an implementation of [Data Type à la Carte](http://www.cs.ru.nl/~W.Swierstra/Publications/DataTypesA

Eta Expansion vs Eta Reduction

Eta expansion is going from point-free to pointy: id => \x -> id x.
Eta reduction is going from pointy to point-free: \x -> id x => id.

In general, check the crt/host_config.h file to find out which versions are supported. Sometimes it is possible to hack the requirements there to get some newer versions working, too :)

Thrust version can be found in $CUDA_ROOT/include/thrust/version.h.

Download Archives: https://developer.nvidia.com/cuda-toolkit-archive

Release notes for CUDA Toolkit (CTK):

latest: https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
archive: https://docs.nvidia.com/cuda/archive/

This configuration is no longer updated

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=rep1&t

	(menhir
	(modules parser))

	(ocamllex lexer)

	(executable
	(name ex))

	" :[range]SortGroup[!] [n\|f\|o\|b\|x] /{pattern}/
	" e.g. :SortGroup /^header/
	" e.g. :SortGroup n /^header/
	" See :h :sort for details

	function! s:sort_by_header(bang, pat) range
	let pat = a:pat
	let opts = ""
	if pat =~ '^\s*[nfxbo]\s'
	let opts = matchstr(pat, '^\s*\zs[nfxbo]')

	#!/usr/bin/env bash
	# Created @ 13.01.2015 by Christian Mayer <http://fox21.at>

	brew install dbus
	cp /usr/local/Cellar/d-bus/1.8.8/org.freedesktop.dbus-session.plist ~/Library/LaunchAgents

	# Load DBus
	launchctl load -w ~/Library/LaunchAgents/org.freedesktop.dbus-session.plist

	from __future__ import print_function

	import sys
	import argparse
	import os.path
	import textwrap
	import re
	from pprint import pprint
	import xml.etree.ElementTree as ET

	module AlgorithmW

	// HINDLEY-MILNER TYPE INFERENCE
	// Based on http://catamorph.de/documents/AlgorithmW.pdf
	// (Now at http://web.archive.org/web/20170704013532/http://catamorph.de/documents/AlgorithmW.pdf)

	type Lit =
	\| LInt of int
	\| LBool of bool