In Gadfly website, it also said the building was failed in Julia 0.4.0
http://pkg.julialang.org/?pkg=Gadfly&ver=nightly
I finially fixed this problem by brutally copy the @grisu_ccall macro from
Julia 0.3 to the Gadfly file format.jl.
On Wednesday, September 24, 2014 9:58:43 AM UTC+2, xiong...@
Hi,
Is there a reference to the algorithm used for solution of least-squares
problems like A\b, with A \in R^{m \times n} and b \in R^m ? Documentation says
it uses a decomposition to bidiagonal form, but it would be nice to have a more
precise reference to that.
Thanks,
Davide
On Wednesday, September 24, 2014 5:44:42 PM UTC+2, Isaiah wrote:
>
> Since we're off the ranch anyway, the brochure is a neat read:
>
> http://www.isro.gov.in/pslv-c25/pdf/pslv-c25-brochure.pdf
>
> (the MAR1750 processor is a 16-bit ISA from 1980, implemented in a
> radiation-hardened package an
I do this by defining JULIA_PKGDIR to be the central repository, and then
define
an alias "myjulia" that starts up as
JULIA_PKGDIR=/my/path julia
--Tim
On Wednesday, September 24, 2014 08:41:33 PM Giulio Valentino Dalla Riva
wrote:
> The issue is concerned with Julia's packages install
I don't see the statement you're referring to, or there's been a
misunderstanding. Regardless: you're probably using "julianightlies," but you
want "juliareleases."
Here's how I think about it:
"juliareleases" = for users
"julianightlies" = for testing the current development version
Both are u
Very good. Thumbs up!
Dňa streda, 24. septembra 2014 16:43:36 UTC+2 Stefan Karpinski napísal(-a):
>
> Yes, having a debugger is important and it's being actively worked on.
>
> On Tue, Sep 23, 2014 at 4:03 AM, Ján Dolinský > wrote:
>
>> Hello,
>>
>> Thanks for another tip. I read a recent post of
Hello,
How do I make Julia to use threaded version of OpenBLAS ? Do I have to
compile using some special option or there is a config file ?
What is the best setup for fast linear algebra operations ?
Best Regards,
Jan
Dňa nedeľa, 21. septembra 2014 9:50:52 UTC+2 Stephan Buchert napísal(-a):
>
>
Yes, I was told that in 0.4, [] is planned to return a view. At the moment
in 0.3 it returns a copy which is obviously undesirable for larger arrays.
Thumbs up for 0.4 :).
Dňa streda, 24. septembra 2014 15:29:36 UTC+2 Mauro napísal(-a):
>
> > OK, thanks for this info. I wonder whether e.g. view(
I was surprised like Hans (Ubuntu 14.04):
dpkg -l julia
i julia 0.3.1~trusty amd64high-performance programming
langua
/etc/apt/sources.list
deb http://ppa.launchpad.net/staticfloat/juliareleases/ubuntu trusty main
deb-src http://ppa.launchpad.net/staticfloat/juliareleases/ub
Sounds like a bug. @staticfloat?
--Tim
On Thursday, September 25, 2014 03:01:58 AM Kaj Wiik wrote:
> I was surprised like Hans (Ubuntu 14.04):
>
> dpkg -l julia
> i julia 0.3.1~trusty amd64high-performance programming
> langua
>
>
> /etc/apt/sources.list
> deb http://ppa.lau
Also Liclipse :
https://groups.google.com/forum/#!searchin/julia-users/liclipse/julia-users/cw0vLsHTUJk/Y5HO29VjpMQJ
I downloaded the package from releases PPA, extracted it and executed julia
binary:
/tmp/foo/usr/bin$ ./julia
_
_ _ _(_)_ | A fresh approach to technical computing
(_) | (_) (_)| Documentation: http://docs.julialang.org
_ _ _| |_ __ _ | Type "help
It looks like reinstalling julia gives 0.3.1 back:
$ sudo apt-get --purge remove julia
$ sudo apt-get install julia
$ julia
_
_ _ _(_)_ | A fresh approach to technical computing
(_) | (_) (_)| Documentation: http://docs.julialang.org
_ _ _| |_ __ _
Instead of changing the text in the manual, wouldn't it be reasonable
to make the the line (emphasizing *one*)
"Some operators without dots operate elementwise anyway when one argument
is a scalar. These operators are *, /, \, and the bitwise operators."
come true, that is 1/x operates a
Just copying @grisu_ccall is a good temporary fix, but as Julia is no
longer building and distributing the double-conversion library, it will not
work everywhere. It will work lots of places tough, and working lots of
places is better than not working anywhere.
Ivar
kl. 09:41:29 UTC+2 torsdag
I think there was a problem with a 0.4-prerelease version that got pushed
out to juliareleases. Seems like it has been fixed now, but we are probably
going to get more reports on the topic when time passes, and people give up
trying to find out why their packages is missing and Gadfly is broken.
>
> come true, that is 1/x operates as element wise division.
No! If you want element wise operations and broadcasting then you should
use the dot version. It is much clearer and it is only one key stroke more.
Division and multiplication with a scalar is not ambiguous, but division
with a vect
OpenBLAS uses threads by default, but Milan reported that Fedora's
maintainer had them disabled. Hence, unless you are using Fedora, you
should have threaded OpenBLAS.
What is the best setup for fast linear algebra operations ?
That question doesn't have a single answer. Often when people want t
Hi Davide
Unfortunately the documentation is not correct. A\b for least squares
problems uses a pivoted qr factorization algorithm identical to the
algorithm you can in LAPACK's xgelsy. I'll update the documentation.
Med venlig hilsen
Andreas Noack
2014-09-25 4:05 GMT-04:00 Davide Lasagna :
>
Le jeudi 25 septembre 2014 à 08:47 -0400, Andreas Noack a écrit :
> OpenBLAS uses threads by default, but Milan reported that Fedora's
> maintainer had them disabled. Hence, unless you are using Fedora, you
> should have threaded OpenBLAS.
>
No, actually as I said above it was my mistake, and it's
Thank you Andreas.
Sooner or later one needs to have a precise idea of what is going on behing the
scenes. Having a reference to the relevant lapack function is fine.
Davide
Just FYI: you can easily find out the answer for yourself like this,
julia> A = rand(5,4)
5x4 Array{Float64,2}:
0.248302 0.330028 0.893083 0.390297
0.0306052 0.298042 0.343798 0.569406
0.935467 0.384105 0.972919 0.716717
0.455494 0.351314 0.443435 0.848758
0.752286 0.827971
That is right but it can be slightly difficult to find the actual
algorithm/LAPACK function due to the initial promotion and allocation steps.
Med venlig hilsen
Andreas Noack
2014-09-25 9:35 GMT-04:00 Tim Holy :
> Just FYI: you can easily find out the answer for yourself like this,
>
> julia> A
The parallelism is flat. You can certainly launch one julia per node, and
then use a multi-threaded solver on each node and such. If you want n
julias per node, just repeat that node n times.
-viral
On Thursday, September 25, 2014 11:52:23 AM UTC+5:30, Clemens Heitzinger
wrote:
>
> Hi all:
>
>
Hello,
Yes, Andreas point makes sense. Sometimes you may not want threaded linear
algebra routines.
My current installation reports this:
versioninfo()
Julia Version 0.3.0
Commit 7681878 (2014-08-20 20:43 UTC)
Platform Info:
System: Linux (x86_64-linux-gnu)
CPU: Intel(R) Core(TM) i5-4300U C
It appears that you are not using a fast BLAS. The BLAS and LAPACK entries
in versioninfo() should say libopenblas instead of libblas and liblapack.
You should use
https://launchpad.net/~staticfloat/+archive/ubuntu/juliareleases
as your repo for julia. That should give you Julia with fast linear
I would very much like to contribute, but i am not sure if i had time, some
projects are keeping me busy. would to look at it when i have spare time.
By the way, john, great work for julia statistics:)
On Tuesday, September 23, 2014 9:11:20 PM UTC-4, Li Zhang wrote:
>
>
>
> a=@data([NA,3,5,7,NA
Thanks, but these days I'd say Simon Kornblith deserves the credit for what's
good about DataArrays.
-- John
On Sep 25, 2014, at 8:11 AM, Li Zhang wrote:
> I would very much like to contribute, but i am not sure if i had time, some
> projects are keeping me busy. would to look at it when i h
I have quite a number of difficulties with IJulia on Windows 7.
It complained about WinRPM, LibCurl, Nettle. I managed to resolve them all
by following advice on various tickets.
But the following issue still eludes me. I'm loading Nemo in the usual way:
using Nemo.Rings
inside IJulia. It compl
Hi--I'm new to parallel julia. I ran a process with -p 31 and it normally
generates the requisite number of workers. I've noticed sometimes the
worker processes are terminated for no obvious reason and no indication as
to why. I don't know enough to tell why. Can a worker undergo its own SEGV,
Hi.
I am doing some matrix factorizations like LU, LDL'. I find that the
julia's lufact() is much faster than my code. I wonder why my code is slow
and why lufact is fast(is this one question or two?). I use
vectorization(is that vectorization?).
I have to mention that I use *Crout* method wh
I think there are two major reasons. First of all the slicing makes copies
of the arrays. Hence you end up with a lot of temporary arrays. Try @time
lufact!(B1) vs @time crout(B1) and see if the allocation difference.
The second reason is that lufact calls LAPACK which uses a blocked
algorithm tha
The short answer is the Lapack benchmark. For years computers, especially
supercomputers, have been benchmarked on how fast they perform this particular
calculation. As a result, the code actually used in various accelerated BLAS
implementations is highly tuned.
I've sorted this out. The Windows filesystem is case insensitive, and there
was a file called C:\users\User\nemo. Apparently the IJulia notebook was
using C:\users\User as the starting directory and apparently Julia looks in
the pwd for any files named Nemo before trying to load the module Nemo
Hi,
I am running a performance test on MORSEL using HttpServer.
on load getting below message.
ERROR: connect: address not available (EADDRNOTAVAIL)
in connect! at socket.jl:582
in connect at socket.jl:603
in open_stream at /root/.julia/v0.3/Requests/src/Requests.jl:210
in open_stream at /ro
And Staro Pickle writes:
> I am doing some matrix factorizations like LU, LDL'. I find that the
> julia's lufact() is much faster than my code. I wonder why my code is
> slow and why lufact is fast(is this one question or two?). I use
> vectorization(is that vectorization?).
Others have given som
I am trying to use a sklearn model in Julia. The first part works ok and I
get back the model object but when I try to fit the model, I get an error
@pyimport sklearn.hmm as hmm
hmmmodel = hmm.GaussianHMM(3, "full")
PyObject GaussianHMM(algorithm='viterbi', covariance_type='full',
covars_prio
Julia doesn't allow overloading field access for types, so you have to use
this workaround in pycall -> hmmmodel[:fit](df[:abc])
On Thursday, September 25, 2014 3:22:16 PM UTC-4, Arshak Navruzyan wrote:
>
> I am trying to use a sklearn model in Julia. The first part works ok and
> I get back th
I have one central manager and other 3 nodes. I installed HTCondor and
Julia on all these machines and when I check condor_status, it list down
all the nodes. Now, I am trying to use julia with htcondor, So I ran below
commands:
export HOSTNAME=`hostname`
julia> Pkg.add("ClusterManagers")
jul
https://github.com/JuliaWeb/HttpServer.jl/blob/master/test/runtests.jl
contains an example that may be helpful. See lines 27-37.
Hi all,
I wrote a bit of code to do something of the same sort. I wrote it to be
able to get the wage of a given individual in a panel in the same quarter
in the year before.
With data.table I would setkey(data, individual, year, quarter) and then
use the amazing J(individual, year-1, quarter
Thanks, Viral. I guess I'll go with the second option for now.
Cheers,
Clemens
On Thursday, September 25, 2014 7:26:37 AM UTC-7, Viral Shah wrote:
>
> The parallelism is flat. You can certainly launch one julia per node, and
> then use a multi-threaded solver on each node and such. If you want
Hey,
in the context of linear regression/OLS, using Julia 0.4 on Mac OSX 10.9,
x\y
returns
ERROR: argument matrix must be square
in lufact at linalg/umfpack.jl:110
in factorize at linalg/cholmod.jl:1047
Indeed, x is a sparse, rectangular matrix, approx. 10x1000. y is a
dense matrix of 1000
I just wanted to suggest that almost everyone on this mailing list should be
using Julia 0.3, not Julia 0.4. Julia 0.4 changes dramatically from day to day
and is probably not safe for most use cases.
I'd suggest the following criterion: "are you reading the comment threads for
the majority of
Hi, Jona.
I had a very similar question recently, and got some excellent advice on
this news group.
See https://groups.google.com/d/msg/julia-users/--RaT-2QDSI/sOpsPEiQ4F4J
--Peter
On Thursday, September 25, 2014 4:38:02 PM UTC-7, Jona Sassenhagen wrote:
>
> Hey,
> in the context of linear re
El jueves, 25 de septiembre de 2014 19:59:41 UTC-5, John Myles White
escribió:
>
> I just wanted to suggest that almost everyone on this mailing list should
> be using Julia 0.3, not Julia 0.4. Julia 0.4 changes dramatically from day
> to day and is probably not safe for most use cases.
>
> I
Jake,
Thanks for the suggestion. When I do that, I get back (anonymous
function). What I would like to get back is the actual model (with the new
parameters) to be able to do things like this
hmmmodel.means_
hmmmodel.predict([[1 2 3]])
Thanks,
Arshak
On Thu, Sep 25, 2014 at 12:37 PM, Jake Bol
I think it's more correct to check out tags since there seems to be work being
done progressively on that branch to keep up with backports.
Not totally sure, though.
-- John
On Sep 25, 2014, at 7:58 PM, David P. Sanders wrote:
>
>
> El jueves, 25 de septiembre de 2014 19:59:41 UTC-5, John
On Thursday, September 25, 2014 11:10:22 PM UTC-4, Arshak Navruzyan wrote:
>
> Jake,
>
> Thanks for the suggestion. When I do that, I get back (anonymous
> function). What I would like to get back is the actual model (with the new
> parameters) to be able to do things like this
>
If you want
Checking out the release branch is fine; the 0.3.1 tag is on that branch.
On Thu, Sep 25, 2014 at 11:12 PM, John Myles White wrote:
> I think it's more correct to check out tags since there seems to be work
> being done progressively on that branch to keep up with backports.
>
> Not totally sure
Didn't seem to work either
PyError (PyObject_Call)
AttributeError("'float' object has no attribute 'shape'",)
File "/Users/arshakn/anaconda/lib/python2.7/site-packages/sklearn/hmm.py",
line 419, in fit
self._init(obs, self.init_params)
File "/Users/arshakn/anaconda/lib/python2.7/site-pack
Case and point:
https://github.com/JuliaLang/julia/commit/2ef8d31b6b05ed0a8934c7a13f6490939a30b24b
:)
On Thu, Sep 25, 2014 at 11:46 PM, Isaiah Norton
wrote:
> Checking out the release branch is fine; the 0.3.1 tag is on that branch.
>
> On Thu, Sep 25, 2014 at 11:12 PM, John Myles White <
> joh
I had upgraded to 0.4 and then decided to revert to 0.3, with:
git checkout release-0.3
make cleanall
make
I then did a Pkg.update(), but the Images update failed.
Pkg.build("Images") also.
Here's the info...
julia> BinDeps.debug("Images")
INFO: Reading build script...
ERROR: __init__ not defined
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