Julia 0.5 Highlights • Hacker News Board

Re: Julia 0.5 Highlights

#61

Post by pathsjs » Wed, Oct 12, 2016, 6:15 AM UTC

pklausler wrote: post #31

Is there anything like Julia, but with strong typing and pure FP? addendum: read 'static' for 'strong', please

Nim is statically typed, very similar to python syntax-wise, fast and with trivial c interop. This makes it a good target for scientific programming, although unfortunately there are not many libraries yet http://rnduja.github.io/2015/10/21/scientific-nim/

As for being functional, strictly speaking it is not, but still has some features like closures and higher order functions, immutable variables, the usual suspects (map, filter, ...) can be inlined with zero overhead, side effect tracking and so on (in addition to a very good macro system)

Re: Julia 0.5 Highlights

#62

Post by baldfat » Wed, Oct 12, 2016, 12:27 PM UTC

papaf wrote: post #60

baldfat wrote: post #8

5 years ago I thought for sure I would be using Julia today. (I mostly use R) I have found that news tools have come into play that I haven't really felt the need for a faster language then R for my work. I'm interested in people's everyday use of Julia and how it has impacted your workflow. I don't work with "Big Data" most of my data sets are bellow 100k in size. Anyone using Julia for medium and small data sets?

Anyone using Julia for medium and small data sets? I have used Julia with a 50GB dataset for feature extraction while I was waiting for R to process the same dataset (10 minutes to an hour depending on the function). I actually learned some Julia while waiting for R. For what I was doing, Julia felt roughly 100 times faster (most delays under a minute). However, I did not know about dplyr at the time and I will certa…

dplyr is faster, BUT not that much faster. dplyr advantage is the way it works for programming. Really a lot easier and more consistent way to think about working with your data.

data.table is fast for your project I would imagine.

Re: Julia 0.5 Highlights

#63

Post by baldfat » Wed, Oct 12, 2016, 12:31 PM UTC

hasch wrote: post #23

baldfat wrote: post #7

Earlier quoted context omitted.

1-based for statistics is a STRONG reason to use it. 0 based is ridiculous for a statistics program and the reason why I left Python. I understand for looping and the arras to be internally 0 but when doing statistics I should have to use two different index.

can you give me an example? I did all sorts of numerical indexing programming, from fortran, matlab, python/numpy and R, and I am definitely more comfortable with 0 based indexing.

My quick philosophy/logic background can you make a strong arguement for why Julia/R (statistical programming) should use 0 based?

I am totally fine with zero base in Python and Lisp and such. BUT when I am doing statistics and the math is 1 based I think the ability to make a mistake is to big. Also for subset in R df[1, 1] would be the 2nd row and 2nd column would just throw most users of R.

Re: Julia 0.5 Highlights

#64

Post by kkylin » Wed, Oct 12, 2016, 6:28 PM UTC

baldfat wrote: post #40

kkylin wrote: post #27

Earlier quoted context omitted.

I've used Julia for prototyping Monte Carlo and data assimilation algorithms, and also solving simple (1d) nonlinear PDEs (Fourier-spectral code with exponential RK timestepping -- not very complicated as these things go). It's also more or less completely replaced Matlab for me in terms of plotting and visualization (most of what I need is very simple). So far it has been very pleasant, so long as one remembers that…

I taught myself Racket and that was the single biggest changer in terms of my programming in R and enjoyment of the language. After digging around I learned that scheme was a major influence in R's creation. Your comment now makes me excited to give Julia a spin.

I should add that one of the things I really like about Julia is having closures and higher-order functions, and also lambda. Unlike other Schemers switching to Julia, I don't miss not having tail recursion (if I was writing the kind of code that's more naturally recursive, I likely would have used Scheme / Lisp to start with). About the only thing I have a hard time wrapping my head around is writing macros in Julia -- CL-style macros are somehow a lot easier to think about. But this is likely to change with more experience. Also, I haven't needed Julia macros very oten.