jack dempsey jackdempsey

I like Learn You a Haskell as a reference and cheat-sheet but I found it a little slow for learning Haskell.

Here's my recommended order for just learning Haskell:

http://yannesposito.com/Scratch/en/blog/Haskell-the-Hard-Way/ 80% completion here is fine if you feel your attention waning, the next thing will address hammering in things like functors and monads via typeclasses.

https://github.com/NICTA/course/ this will hammer in the lessons in a very direct form by forcing you to confront the challenges and lessons learned by the creators and community of Haskell itself. Doing the exercises here is critical for being fluent.

Real World Haskell is available online. (Thanks bos!)

The Anatomy of an Onyx Program

In this tutorial, we'll take an in-depth view of what's happening when you execute a simple Onyx program. All of the code can be found in the Onyx Starter repository if you'd like to follow along. The code uses the development environment with HornetQ and ZooKeeper running in memory, so you don't need additional dependencies to run the example for yourself on your machine.

The Workflow

At the core of the program is the workflow - the flow of data that we ingest, apply transformations to, and send to an output for storage. In this program, we're going to ingest some sentences from an input source, split the sentence into individual words, play with capitalization, and add a suffix. Finally, we'll send the transformed data to an output source.

Let's examine the workflow pictorially:

	#!/usr/bin/env ruby

	# Written by Kieran P
	# http://github.com/KieranP
	# http://twitter.com/k776
	# http://k776.tumblr.com
	#
	# Feel free to fork and modify.
	# If you do, send me a message on
	# Github details changes and I'll

	class Component < ActiveRecord::Base
	belongs_to :cabinet
	belongs_to :component_model
	has_many :port, :as => :device, :extend => CreatePortFromModel
	has_many :sub_components

	def elevation
	orientation + elevation_index.to_s
	end

	class Synapse
	constructor: (@sourceNeuron, @destNeuron) ->
	@prevWeight = @weight = (Math.random() * 2.0) - 1.0

	class Neuron
	learningRate: 1.0
	momentum: 0.3

	constructor: ->
	@synapsesIn = []