Get flash to fully experience Pearltrees
So it turns out getting set up to write Clojure code can be a little tricky. There are a lot of disconnected tidbits about how folks have figured out how to configure things, but it can be a bit tricky to tell the difference between, "hey, this is how I finally got it to work" and "this is how you really should be doing it". I figure I know about as much about using Clojure with Emacs as anybody, so here's a run-through of how I've done my setup. There are a lot of moving parts, but bear with me; most of the installation is automated. Spoiler Alert : Using SLIME with Clojure is now much easier than the instructions detailed below indicate.
Salut à tous, J'ai récemment commencé à jouer avec Leiningen, et il semble que c'est va aider à éliminer une grande partie de la complexité accidentelle à obtenir projets ont démarré avec clojure! Cependant j'ai eu ce que certains de mal à ça va se faire avec Emacs / clojure-mode / slime ...
If you've been following, you know that I'm trying to build the Web 2.0iest site out there. In fact, this is so Web 2.0, I'm tempted to call it Web 2.1. I'm using only the hottest language ( Clojure ) and the coolest social networking APIs ( twitter ).
Overview PCollections serves as a persistent and immutable analogue of the Java Collections Framework . This includes efficient , thread-safe , generic , immutable , and persistent stacks, maps, vectors, sets, and bags, compatible with their Java Collections counterparts. Persistent and immutable datatypes are increasingly appreciated as a simple , design-friendly , concurrency-friendly , and sometimes more time- and space-efficient alternative to mutable datatypes. Persistent versus Unmodifiable
> Je suis encore à essayer d'obtenir ma tête autour des nouvelles fonctionnalités. Voir plus Des exemples de code> va certainement aider.
Using libraries is easy but getting all the right ones onto your system can be a pain. Many languages solve the problem with repositories that host the libraries + metadata and tools that make it easy to pull the right versions of the libraries. In the Ruby space the solution is Ruby Gems , both a tool and packaging format; a default repository hosted at Rubyforge ( Gems hosting will soon move to the GemCutter system , which offers more flexibility) makes it єasy to publish and consume Gems. In the Java space, Maven is a popular solution. Clojars is a new repository that aims to make it easy to share and consume Clojure libraries . A Clojure specific build tool called Leiningen , created by Phil Hagelberg , makes it easy for producers to bundle and push Clojure libraries to Clojars, and for consumers to pull them and make sure they're in the right default place for Clojure programs.
I originally wrote this in a Google Groups thread , but I figured it’s worth repeating here. Somebody posted a Java and Clojure snippet to the Clojure Google group and mentioned that the Java code was vastly faster than the Clojure code and he wondered if Clojure could get within reach of Java’s speed. In my own clj-starcraft project, I faced — and actually, still face — performance problems vis-à-vis Java. Specifically, at the time of this writing, my Clojure code is roughly 6 times slower than Java (Clojure takes around 70 seconds to parse 1,050 files, Java takes 12.)
I’ve been playing with Tokyo Cabinet and Clojure for a bit, and while I will go on about both of them in another blog post (or not), I have to mention that Clojure is such a well designed language that it’s a pleasure to play with. It has much of the same intrinsic power as Haskell, but in a fashion that might be more approachable for people coming from Python or Ruby. At any rate, I made a small, thin layer around the Tokyo Cabinet API, and put it on Github .
by R. Mark Volkmann , Partner Object Computing, Inc. (OCI) last updated on 9/29/12
Online Learning is a relatively old branch of machine learning that has recently regained favour for two reasons. Firstly, online learning algorithms such as Stochastic Gradient Descent work extremely well on very large data sets which have become increasingly prevalent (and increasingly large!). Secondly, there has been a lot of important theoretical steps made recently in understand the convergence behaviour of these algorithms and their relationship to traditional Empirical Risk Minimisation (ERM) algorithms such as Support Vector Machines (SVMs).
This will hopefully be the first of a series of posts based on a book that has substantially influenced me over the last several years, The Elements of Statistical Learning (EoSL) by Hastie, Tibshirani, and Friedman (I went and got a degree in statistics essentially for the purposes of better understanding this book). Best of all, the pdf version of EoSL is now available free of charge at the book’s website , along with data, code, errata, and more. This post will demonstrate the use of Linear Discriminant Analysis and Quadratric Discriminant Analysis for classification, as described in chapter 4, “Linear Methods for Classification”, of EoSL. I will implement the classifiers in Clojure and Incanter, and use the same data set as EoSL to train and test them.