A lot of smart guys have recently blogged about Julia, see e.g. Julia, I love You by John Myles White. Even Doug Bates started to play with it, and in particular bring us with an easy way to call R’s statistical distributions with Julia.
The installation went fine for me. I’m using XCode 4.3.2 on OS X 10.7.3, with command-line tools installed and 64-bit gfortran (first time I installed a different fortran from the one released on R Development Tools and Libraries). Of course, I forgot to ask for a parallel build, so it took far longer than expected. To get a working web REPL, I also needed to fetch lighttpd, which is basically as simple as typing
make -C external install-lighttpd in
julia/ root directory. Although the web REPL looks great, it has very limited plotting facilities at the moment, so I will stick with the console REPL. It is worth noting that during the first install all external dependencies will be downloaded and everything goes into a
root folder. In the end, it amounts to about 1.3 Go on your hard drive, so you can safely delete everything but the
external/root folder (and
lighttpd.conf if you want to use the webserver).
Let’s start with some very basic stuff. Note that I am using the REPL and for plotting purpose I choose the recently released Gaston interface to Gnuplot.1 There’s an embedded plotting library,
Winston, but I got a lot of
Cairo-related errors when trying it. To use R’s probability distributions and RNGs, we need to have a working
libRmath somewhere. I happened to compile it following instructions in the R Installation and Administration manual, as pointed out by Doug Bates. (We really just need to type
make in the
src/nmath/standalone subdirectory of R source tree.)
_jl_libRmath = dlopen("julia/libRmath") load("julia/extras/Rmath.jl") x = -3:.1:3 y = dnorm(x, 0, 1) load("julia/extensions/gaston-0.1/gaston.jl") figure(1) a = Axes_conf() a.title = "The gaussian density from R" addcoords(x, y) addconf(a) plot()
Here is what I got:
Nothing fabulous actually. But wait, we can do other funny things. First, let’s implement a simplified t-test. There’s already a bunch of handy functions in
base/statistics.jl. Basically, we just need to compute a difference of means and a pooled estimate for the variance. Note that we assume equal variance and a two-sided test. I also added a switch-statement for the case of paired samples. This has been tested with the famous Student’s sleep data set ( student1908.csv).
function t_test(x, y, paired) nx = length(x) ny = length(y) dobs = mean(y) - mean(x) if paired == true && nx == ny df = nx - 1 se = std(x-y) / sqrt(nx) else df = nx + ny - 2 se = sqrt(((nx-1) * std(x)^2 + (ny-1) * std(y)^2) / df) end if paired == false se = se * sqrt(1/nx + 1/ny) end tobs = dobs / se pval = 2*(1 - pt(tobs, df)) res = HashTable() res["stats"] = tobs; res["df"]=df; res["p_value"]=pval res end dat = dlmread("student1908.csv") t_test(dat[:,1], dat[:,2], false) # p = 0.079 t_test(dat[:,1], dat[:,2], true) # p = 0.003
Nothing really difficult there. Let’s go for a rough PCA, on Fisher-Anderson iris.txt dataset. Julia already has some basic linear algebra functions in
base/linalg.jl (plus additional LAPACK bindings in
linalg_lapack.jl, including SVD computation).
function scale(x) (x - mean(x)) /std(x) end function pca(a, standardize) if standardize == true for i = 1:size(a, 2) a[:,i] = scale(a[:,i]) end end res = svd(a) sd = res / sqrt(size(a, 1) - 1) rot = transpose(res) sd, rot end iris = dlmread("iris.txt") pca(iris[:,1:4], true)
We can also compare results from an SVD and an eigenvalue decomposition:
n = 100 x1 = randn(n) x2 = randn(n) x3 = .6 * x1 + randn(n) X = [x1 x2 x3] for i = 1:size(X, 2) X[:,i] = scale(X[:,i]) end amap(mean, X, 2) r = 1/(n-1) * X' * X res1 = pca(X, false) res2 = sortr(real(sqrt(eig(r))))
The above code (it probably looks really ugly because these are my very first lines of Julia code!) reminds me of my 4-5 years of Matlab, but overall the documentatin points toward interesting features that I just nead to learn more.
As a conclusion of this 2-hour session, it is evident that I am missing a lot of things about Julia. There’s a
cor method that is supposed to work with one or two vectors, but also with a matrix, but it doesn’t seem to work when I call it like
cor(X). I also had some trouble with
csv header which when present gave me an
Any Array that I know nothing about. Well, in my defense I haven’t finished reading the documentation set, and just started from scratch with a console REPL early in the morning.
A reply by Harlan Harris to a recent question on Cross Validated, Does Julia have any hope of sticking in the statistical community?, summarizes a lot of missing features as compared to R. But it still looks like a great project, and it’s always interesting to follow the development of a new programming language from the start.