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How to use Tim Pope’s Pathogen

I’ll assume you are on Linux or Mac OSX. For Windows, replace ~/.vim/ with $HOME\vimfiles\ and forward slashes with backward slashes.

The idea

Vim plugins can be single scripts or collections of specialized scripts that you are supposed to put in “standard” locations under your ~/.vim/ directory. Syntax scripts go into ~/.vim/syntax/, plugin scripts go into ~/.vim/plugin, documentation goes into ~/.vim/doc/ and so on. That design can lead to a messy config where it quickly becomes hard to manage your plugins.

This is not the place to explain the technicalities behind Pathogen but the basic concept is quite straightforward: each plugin lives in its own directory under ~/.vim/bundle/, where each directory simulates the standard structure of your ~/.vim/ directory.

Comparison of Julia, Python and Octave

Overview

Julia is a new languange for technical computing. It is a mix of R, Matlab, Python and other similar languages. Its main advantage is its speed: it is just in time (JIT) compiled and almost as fast as C. Another advantage is its type inference, i.e. you do not have to specify types (although you can), but all variables are statically typed. It is a high level language that is fast as well. Here I compare the behavior to similar

Movies Recommendation:

MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

	stratified <- function(df, group, size, select = NULL,
	replace = FALSE, bothSets = FALSE) {
	if (is.null(select)) {
	df <- df
	} else {
	if (is.null(names(select))) stop("'select' must be a named list")
	if (!all(names(select) %in% names(df)))
	stop("Please verify your 'select' argument")
	temp <- sapply(names(select),
	function(x) df[[x]] %in% select[[x]])