Well I made it into a minimal test-case, but I still don't understand
what's happening:
Run_Boot_code.jl:
parm = rand(10,10)
M = { parm[i,:] for i in 1:2} # size(parm,1)}
require(Boot_code.jl)
pmap(myfun,M)
Boot_code.jl:
using Devectorize
function myfun(pam)
println(Entering myfun)
foo =
No worries! In thinking about it more, I don't think a 'right' solution is
as simple as I first hoped (a design tradeoff is hiding just beneath the
surface), but the pain points can hopefully be ameliorated.
On Friday, January 2, 2015 5:29:27 PM UTC-6, Stefan Karpinski wrote:
Jason, I've been
For Venn diagrams there is https://github.com/HarlanH/VennEuler.jl
And I started a project with a simpler API
at https://github.com/binarybana/VennDiagrams.jl (currently a skeleton)
which is related to the above
by https://github.com/HarlanH/VennEuler.jl/issues/14
On Saturday, November 22,
Donald,
I would enjoy seeing your code once you release it. I'm doing research with
Bayesian bioinformatics models using MCMC.
You can place all your MCMC samples in a DataFrame and then call describe
on it to get a very similar output to what you described.
You can also write your own
I usually go straight to the source when looking for things in DataFrames
as the documentation is missing quite a bit of functionality (push!, hcat,
vcat, melt etc..) but in this case as Kevin mentioned: no dice.
But you can always hack it:
using DataFrames
fname = datat.csv
data =
Happy reading: https://github.com/JuliaLang/julia/issues/3988 :)
On Sunday, August 24, 2014 5:03:04 PM UTC-5, Job van der Zwan wrote:
Any plans? Discussions on Github worth reading through?
I personally am really charmed by the godoc
http://blog.golang.org/godoc-documenting-go-code approach
I just ran across the very cool https://github.com/Russell91/pythonpy
If someone has a free afternoon, then a Julia port would be cool (hint,
hint, ;). As I've found myself in the past doing things like:
cat numbers.csv | julia -E 'sum(map(int,readlines(STDIN)))'
And a few more flags for
I'm currently working with Optimal Bayesian Classifiers with my
not-really-ready-for-public-consumption package OBC.jl
https://github.com/binarybana/obc.jl. It's currently quite specialized
for bioinformatics (RNA-Seq) data, so probably not what you want, but I
thought I'd throw it out there.
So in short, I coded a model-fitting Markov chain to fit some parameters
of a neural network to existing data.
With few amounts of Markov chain iterations, the code works fine, but when
I run for longer, the code dies before it completes my function of finding
the parameters matching
What version/platform are you running this on? I'm on Linux using Julia
master and everything prints normally for me.
On Saturday, February 22, 2014 8:21:18 AM UTC-6, Barry Andersen wrote:
I am beginning with julia. Here is a fibonacci series in julia.
code:
a, b = 1, 1
for i in [1:6]
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