Re: [julia-users] Re: unknown option root
Just found that, this is exactly my bug: https://bugzilla.redhat.com/show_bug.cgi?id=1200381
Re: [julia-users] Re: unknown option root
I am not comfortable with Linux. Following the link below, do I have to remove something before doing *yum install xscreensaver* ? Le lundi 12 octobre 2015 11:56:02 UTC+2, Stéphane Laurent a écrit : > > Just found that, this is exactly my bug: > https://bugzilla.redhat.com/show_bug.cgi?id=1200381 > >
Re: [julia-users] Re: unknown option root
Thank you. I tried this: http://askubuntu.com/questions/111422/how-to-find-zombie-process But I got nothing. Le lundi 12 octobre 2015 03:40:44 UTC+2, Isaiah a écrit : > > I am not using Julia in the session. > > > Check `ps` for a zombie process? > > On Sun, Oct 11, 2015 at 3:40 PM, 'Stéphane Laurent' via julia-users < > julia...@googlegroups.com > wrote: > >> I get the beginning of the message ; >> >> >>> *Error unknown option: --rootin: process_options at ./client.jl:255* >> >> >> There is also a message about start.jl, but I didn't get the time to >> write it. >> > >
Re: [julia-users] Re: unknown option root
Ok, done. Thank you. Now I wait and see. Le lundi 12 octobre 2015 18:33:34 UTC+2, Pablo Zubieta a écrit : > > If you are running Ubuntu you should run > > sudo apt-get install xscreensaver-data-extra > > instead. >
[julia-users] Re: unknown option root
I get the beginning of the message ; > *Error unknown option: --rootin: process_options at ./client.jl:255* There is also a message about start.jl, but I didn't get the time to write it.
[julia-users] unknown option root
Hello, Sometimes when my screen switches to save mode (I'm using LXDE desktop on Ubuntu), I get a message like : unknown option --root > in client.jl This is not the exact message (next time I see it I will update this post). And I am not using Julia in the session.
Re: [julia-users] Re: julia unable to install on Ubuntu 13.10
Oh nice, I have installed cmake and that works. Wasn't cmake required before ? I'm surprised it was not installed. Le dimanche 18 janvier 2015 13:11:35 UTC+1, Stéphane Laurent a écrit : Hello, Currently my pc always freezes, so I use the console mode and I don't know how to copy-paste the output. I remember something like cmake not found, but I'm not sure. Isn't it better to install the latest 0.3 version rather than 0.4 ? Le dimanche 18 janvier 2015 12:56:16 UTC+1, Milan Bouchet-Valat a écrit : Le dimanche 18 janvier 2015 à 03:54 -0800, 'Stéphane Laurent' via julia-users a écrit : Hello, he Since Julia version 0.4 this way does not work anymore : git pull make clean make There are some errors when doing make. Can I simply delete my julia folder and reinstall ? Of course you can, but there are probably other ways too. Can you post the output you get somewhere? Regards Le mardi 20 mai 2014 12:46:41 UTC+2, Ivo Balbaert a écrit : Sorry, message was posted too quickly: Here is my report of the build: in /home/ivo/julia: git clone git://github.com/JuliaLang/julia.git cd julia in /home/ivo/julia/julia: make /bin/sh: 2: g++: not found make[2]: *** [/home/ivo/julia/julia/usr/lib/libgrisu.so] Error 127 make[1]: *** [julia-release] Error 2 make: *** [release] Error 2 -- sudo ap-get install build-essential make gfortran: command not found -- sudo apt-get install gfortran make checking for suitable m4... configure: error: No usable m4 in $PATH or /usr/5bin (see config.log for reasons). -- sudo apt-get install m4 make ./julia OK! To install latest version of Julia: git pull make clean make ./julia Cheers, Ivo Op zaterdag 25 januari 2014 04:30:18 UTC+1 schreef Rajn: After my several failed attempts to run PyPlot through Julia in Windows, I decided to give up and try Linux. Guess it was even worse. First I added to regular repository (not the nightlybuild) then added the dep-repository then updated and then installed julia Here's the latest: Unpacking librmath-dev (from .../librmath-dev_2.15.2-juliadeps2~raring_amd64.deb) ... dpkg: error processing /var/cache/apt/archives/librmath-dev_2.15.2-juliadeps2~raring_amd64.deb (--unpack): trying to overwrite '/usr/include/Rmath.h', which is also in package r-mathlib 3.0.1-3ubuntu1 No apport report written because MaxReports is reached already Selecting previously unselected package julia. Unpacking julia (from .../julia_0.2.0~saucyfinal1_amd64.deb) ... Processing triggers for man-db ... Errors were encountered while processing: /var/cache/apt/archives/librmath-dev_2.15.2-juliadeps2~raring_amd64.deb E: Sub-process /usr/bin/dpkg returned an error code (1) Have no clue absolutely how to proceed. The same issue occurs when I try nightly builds.
[julia-users] Re: julia unable to install on Ubuntu 13.10
Hello, Since Julia version 0.4 this way does not work anymore : *git pull* *make clean* *make* There are some errors when doing make. Can I simply delete my julia folder and reinstall ? Le mardi 20 mai 2014 12:46:41 UTC+2, Ivo Balbaert a écrit : Sorry, message was posted too quickly: Here is my report of the build: in /home/ivo/julia: git clone git://github.com/JuliaLang/julia.git cd julia in /home/ivo/julia/julia: make /bin/sh: 2: g++: not found make[2]: *** [/home/ivo/julia/julia/usr/lib/libgrisu.so] Error 127 make[1]: *** [julia-release] Error 2 make: *** [release] Error 2 -- sudo ap-get install build-essential make gfortran: command not found -- sudo apt-get install gfortran make checking for suitable m4... configure: error: No usable m4 in $PATH or /usr/5bin (see config.log for reasons). -- sudo apt-get install m4 make ./julia OK! To install latest version of Julia: git pull make clean make ./julia Cheers, Ivo Op zaterdag 25 januari 2014 04:30:18 UTC+1 schreef Rajn: After my several failed attempts to run PyPlot through Julia in Windows, I decided to give up and try Linux. Guess it was even worse. First I added to regular repository (not the nightlybuild) then added the dep-repository then updated and then installed julia Here's the latest: Unpacking librmath-dev (from .../librmath-dev_2.15.2-juliadeps2~raring_amd64.deb) ... dpkg: error processing /var/cache/apt/archives/librmath-dev_2.15.2-juliadeps2~raring_amd64.deb (--unpack): trying to overwrite '/usr/include/Rmath.h', which is also in package r-mathlib 3.0.1-3ubuntu1 No apport report written because MaxReports is reached already Selecting previously unselected package julia. Unpacking julia (from .../julia_0.2.0~saucyfinal1_amd64.deb) ... Processing triggers for man-db ... Errors were encountered while processing: /var/cache/apt/archives/librmath-dev_2.15.2-juliadeps2~raring_amd64.deb E: Sub-process /usr/bin/dpkg returned an error code (1) Have no clue absolutely how to proceed. The same issue occurs when I try nightly builds.
Re: [julia-users] Re: a good IDE for Julia ? (Julia Studio does not work with Julia v 0.3.0)
Also Liclipse : https://groups.google.com/forum/#!searchin/julia-users/liclipse/julia-users/cw0vLsHTUJk/Y5HO29VjpMQJ
Re: [julia-users] Support for Julia in Eclipse (LiClipse)
When I have two files in the same project, I source the first one by pressing F9, and if I source the second one then this is like a new session: everything sourced from the first file is forgotten. Is there a way to prevent that ?
Re: [julia-users] ApproxFun v0.0.1 with ODE solving
Hello Sheehan,
I get a failure with the following example, do you have an idea about the
why ?:
*# solves u = phi²*sinh(u)-2u'/(x+gamma) , u'(a)=-xi, u'(R)=0*
*a= 3.514457e-07*
*R= 7.60773e-07*
*x=Fun(identity, Interval(a,R))*
*d=x.domain*
*B=neumann(d)*
*D=diff(d)*
*# Solves Lu + g(u) == 0*
*phi=1.341211*
*gamma=0.8585931*
*L = D^2 + 2/(x.+gamma)*D*
*g = u - -phi^2*(exp(u)-exp(-u))/2; gp = u - -phi^2*(exp(u)+exp(-u))/2*
*u=0.x #initial guess *
*xi=9.403218*
*for k=1:5*
*u=u-[B,L+gp(u)]\[diff(u)[a]+xi,diff(u)[R],L*u+g(u)];*
*end*
*julia u*
*IFun{Float64,Interval{Float64}}([NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
…
NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN],Interval{Float64}(3.514457e-7,7.60773e-7))*
Re: [julia-users] Support for Julia in Eclipse (LiClipse)
Thank you. I have successfully modified the julia.liclipse file. It works now. S Le lundi 30 juin 2014 02:16:26 UTC+2, Fabio Zadrozny a écrit : On Sunday, June 29, 2014 12:14:59 PM UTC-3, Stéphane Laurent wrote: I have just installed Eclipse and LiClipse, but now I don't see what to do when I open a jl file. When typing F9 I get a message claiming julia is not found, whereas it is on the PATH variable of my bashrc file. Well, if your PATH env variable is set in your bashrc file, you may need to open a bash session and start LiClipse from the bash shell (as it'll inherit the configurations from the shell that started it). Alternatively, you may also update the run configuration to have the full path (i.e.: run run configurations select run configuration under 'liclipse launch' and use the full path for the executable). Or, for new launches, you may set the main path of the executable by opening the julia.liclipse file (see http://brainwy.github.io/liclipse/launch.html for details on the launch section and http://brainwy.github.io/liclipse/supported_languages.html for details on where the language files are located. Best Regards, Fabio
Re: [julia-users] Re: orthogonalize
A couple of years ago I used this function (maybe I copied the Matlab or Octave code, I don't remember): *## similar to the Matlab orth() function* *function orth(A)* * if (isempty (A))* *retval = []* * else* *(U, S, V) = svd(A) * *(rows, cols) = size(A) * *tol = max (size (A)) * S[1] * eps()* *r = sum (S . tol)* *if (r 0)* * retval = -U[:,1:r]* *else* * retval = zeros(rows, 0);* *end* * end* *return(retval)* *end*
Re: [julia-users] Re: orthogonalize
Ok sorry, I have deleted the code. BTW I have never really understand how some piece of code could be proprietary. Le lundi 30 juin 2014 16:09:37 UTC+2, John Myles White a écrit : Please don’t post code whose copyright status is not 100% certain. If you are even vaguely unsure, you are putting the community in needless danger. As a minimal example, it may be illegal for you to have written the e-mail you just wrote. — John On Jun 30, 2014, at 7:07 AM, 'Stéphane Laurent' via julia-users julia...@googlegroups.com javascript: wrote: A couple of years ago I used this function (maybe I copied the Matlab or Octave code, I don't remember): *## similar to the Matlab orth() function* *function orth(A)* * if (isempty (A))* *retval = []* * else* *(U, S, V) = svd(A) * *(rows, cols) = size(A) * *tol = max (size (A)) * S[1] * eps()* *r = sum (S . tol)* *if (r 0)* * retval = -U[:,1:r]* *else* * retval = zeros(rows, 0);* *end* * end* *return(retval)* *end*
[julia-users] Re: global assignement in functions
You don't like my slides ? :-( I'd like to know how to avoid the deepcopy at the beginning of updatePoly1(). Le dimanche 29 juin 2014 23:00:30 UTC+2, Stéphane Laurent a écrit : Thank you everyone for your reply. But finally, what should I do ? My real function is shown on slide 16 here http://stla.github.io/JULIAGFI01/#1. Basically the function takes an array and modifies two columns. I would also be glad to get your comments about all the code shown in these slides. My functions work, but I'm not sure I'm using the best practices.
Re: [julia-users] Re: global assignement in functions
Ahh ok, thank you. I don't know why I was searching somrthing more complicated. Le lundi 30 juin 2014 17:01:16 UTC+2, Stefan Karpinski a écrit : Linking directly to the relevant slide is helpful: http://stla.github.io/JULIAGFI01/#16 You don't need a deepcopy, just a copy; there's no way to avoid it if you don't want to modify the original matrix. In R there's an implicit copy that happens as soon as you modify the matrix so the only difference is that the copy in Julia is explicit. On Mon, Jun 30, 2014 at 10:43 AM, 'Stéphane Laurent' via julia-users julia...@googlegroups.com javascript: wrote: You don't like my slides ? :-( I'd like to know how to avoid the deepcopy at the beginning of updatePoly1(). Le dimanche 29 juin 2014 23:00:30 UTC+2, Stéphane Laurent a écrit : Thank you everyone for your reply. But finally, what should I do ? My real function is shown on slide 16 here http://stla.github.io/JULIAGFI01/#1. Basically the function takes an array and modifies two columns. I would also be glad to get your comments about all the code shown in these slides. My functions work, but I'm not sure I'm using the best practices.
[julia-users] Re: Support for Julia in Eclipse (LiClipse)
Cool ! *Note that to be installed, LiClipse requires Eclipse 4.3 (Kepler) onwards. * Do you know whether Eclipse 4.4 (Luna) is fine too ? Le dimanche 29 juin 2014 04:13:20 UTC+2, Fabio Zadrozny a écrit : Hi Julia-users, Just wanted to let you know that LiClipse (which is mostly Eclipse plus some customizations... see: http://brainwy.github.io/liclipse/) now has support for Julia: http://liclipse.blogspot.com.br/2014/06/liclipse-10-released-julia-now-supported.html Hope Julia users like it. Note that the integration is 'lightweight' in Eclipse terms, so, it's syntax highlighting, basic outline, (really) basic code-completion, templates (LiClipse style: http://brainwy.github.io/liclipse/templates.html), launching, hyperlink in console, multi-edition (LiClipse style: http://brainwy.github.io/liclipse/multi_edition_video.html) -- but on top of that, you can use the Eclipse plugins you'd like (EGit, Mylyn, etc). Best Regards, Fabio
Re: [julia-users] Re: Capture the output of Julia's console
These days I have experienced Yihui's runr package and this is indeed not
very satisfactory yet.
Do you know whether we can straightforwardly get in Julia, the console
output as a ready-to-print character string, for example here:
*julia [1,2,3]*
*3-element Array{Int64,1}:*
* 1*
* 2*
* 3*
I'd like to get:
*3-element Array{Int64,1}:\n 1\n 2 \n 3*
[julia-users] global assignement in functions
Hello,
As a R user I am a little puzzled by this behaviour:
*julia ftest = function(x) *
*x[2] = 0 *
*return x*
* end*
*(anonymous function)*
*julia y = [1,2]*
*2-element Array{Int64,1}:*
* 1*
* 2*
*julia ftest(y)*
*2-element Array{Int64,1}:*
* 1*
* 0*
*julia y*
*2-element Array{Int64,1}:*
* 1*
* 0*
In R, this function doesn't modify the variable passed in the argument:
* f - function(x){ x[2] - 0; return(x)}*
* y=1:2*
* f(y)*
*[1] 1 0*
* y*
*[1] 1 2*
Please could you tell me whether this is a good solution :
*ftest = function(x) *
* x = deepcopy(x)*
* x[2] = 0 *
* return x*
*end*
[julia-users] Re: global assignement in functions
And please, could you explain why this behaviour occurs for the previous function but not for the following one: *ftest2 = function(x) * *x = x[[2,1]] * *return x* *end*
Re: [julia-users] Support for Julia in Eclipse (LiClipse)
I have just installed Eclipse and LiClipse, but now I don't see what to do when I open a jl file. When typing F9 I get a message claiming julia is not found, whereas it is on the PATH variable of my bashrc file.
[julia-users] Re: global assignement in functions
Thank you everyone for your reply. But finally, what should I do ? My real function is shown on slide 16 here http://stla.github.io/JULIAGFI01/#1. Basically the function takes an array and modifies two columns. I would also be glad to get your comments about all the code shown in these slides. My functions work, but I'm not sure I'm using the best practices.
[julia-users] Re: Capture the output of Julia's console
I really don't know how it works, but this is what Yihui Xie's R package runr https://github.com/yihui/runr does for R.
Re: [julia-users] Re: Julia T-shirt and Sticker
My ex-girl friend suspected me to use Julia because of an emotional attachment with a girl named Julia. Ok this was insanely jealous but I'll never wear such a T-shirt.
Re: [julia-users] ApproxFun v0.0.1 with ODE solving
Thank you for the explanations. Reading your papers is on my LOTTD. Some pub for you here http://stats.stackexchange.com/a/104290/8402 ;)
Re: [julia-users] ApproxFun v0.0.1 with ODE solving
Hello, I'd like to solve this equation with Neumann boundary conditions. My code below does not work. Am I doing something bad or is it a failure of the Newton algorithm ? *# solves u = (exp(u)-exp(-u))-2u'/(x+1) , u'(0)=-1, u(1)=0* *x=Fun(identity, Interval(0.,1.))* *d=x.domain* *B=neumann(d)* *D=diff(d)* *# Solves Lu + g(u)-1==0* *L = D^2 + 2/(x.+1)*D* *g = u - -(exp(u)-exp(-u)); gp = u - -(exp(u)+exp(-u))* *u=-0.3*x+0.5** #initial guess * *for k=1:5 # this crashes if Ii increase the number of iterations* *u=u-[B,L+gp(u)]\[-1.,0.,L*u+g(u)];* *end* The solution should look like that : https://lh5.googleusercontent.com/-BCYNzh8ckCA/U6VIJcT2-tI/AGI/r08hknI2EIg/s1600/Screenshot+from+2014-06-21+10%3A37%3A46.png
Re: [julia-users] ApproxFun v0.0.1 with ODE solving
Ok, I understand. It works and it is really awesome. Thank you ! Le samedi 21 juin 2014 12:17:22 UTC+2, Sheehan Olver a écrit : Hi, You didn’t quite get the Newton iteration right: if you want to solve B u + [1,0] = 0 L u + g(u) = 0 then Newton iteration becomes u = u - [B, L + g’(u)]\[B u + [1,0], L u + g(u)] i.e., your bc right hand side is not right. Below is the corrected code. Cheers, Sheehan x=Fun(identity, Interval(0.,1.)) d=x.domain B=neumann(d) D=diff(d) # Solves Lu + g(u)-1==0 L = D^2 + 2/(x.+1)*D g = u - -(exp(u)-exp(-u)); gp = u - -(exp(u)+exp(-u)) u=-0.3*x+0.5 #initial guess for k=1:5 # this crashes if Ii increase the number of iterations u=u-[B,L+gp(u)]\[diff(u)[0.]+1.,diff(u)[1.],L*u+g(u)]; end plot(u) On 21 Jun 2014, at 6:54 pm, 'Stéphane Laurent' via julia-users julia...@googlegroups.com javascript: wrote: Hello, I'd like to solve this equation with Neumann boundary conditions. My code below does not work. Am I doing something bad or is it a failure of the Newton algorithm ? *# solves u = (exp(u)-exp(-u))-2u'/(x+1) , u'(0)=-1, u(1)=0* *x=Fun(identity, Interval(0.,1.))* *d=x.domain* *B=neumann(d)* *D=diff(d)* *# Solves Lu + g(u)-1==0* *L = D^2 + 2/(x.+1)*D* *g = u - -(exp(u)-exp(-u)); gp = u - -(exp(u)+exp(-u))* *u=-0.3*x+0.5** #initial guess * *for k=1:5 # this crashes if Ii increase the number of iterations* *u=u-[B,L+gp(u)]\[-1.,0.,L*u+g(u)];* *end* The solution should look like that : https://lh5.googleusercontent.com/-BCYNzh8ckCA/U6VIJcT2-tI/AGI/r08hknI2EIg/s1600/Screenshot+from+2014-06-21+10%3A37%3A46.png
Re: [julia-users] ApproxFun v0.0.1 with ODE solving
Sorry but I really don't understand you first sentence. About the code, I will possibly experiment it during the next days or weeks, and I'll send you some feedback. Thank you again for this library and your help. - Stéphane Le samedi 21 juin 2014 14:24:03 UTC+2, Sheehan Olver a écrit : Thanks! I'm interested to know if the ultraspherical spectral approach has any clear advantages over spectral collocation for nonlinear odes. (For linear odes the advantages are clear.) So if you find the code better than expected please let me know. It's a bit buggy and unoptimized, so chances are you won't. Sent from my iPad On 21 Jun 2014, at 10:15 pm, 'Stéphane Laurent' via julia-users julia...@googlegroups.com javascript: wrote: Ok, I understand. It works and it is really awesome. Thank you ! Le samedi 21 juin 2014 12:17:22 UTC+2, Sheehan Olver a écrit : Hi, You didn’t quite get the Newton iteration right: if you want to solve B u + [1,0] = 0 L u + g(u) = 0 then Newton iteration becomes u = u - [B, L + g’(u)]\[B u + [1,0], L u + g(u)] i.e., your bc right hand side is not right. Below is the corrected code. Cheers, Sheehan x=Fun(identity, Interval(0.,1.)) d=x.domain B=neumann(d) D=diff(d) # Solves Lu + g(u)-1==0 L = D^2 + 2/(x.+1)*D g = u - -(exp(u)-exp(-u)); gp = u - -(exp(u)+exp(-u)) u=-0.3*x+0.5 #initial guess for k=1:5 # this crashes if Ii increase the number of iterations u=u-[B,L+gp(u)]\[diff(u)[0.]+1.,diff(u)[1.],L*u+g(u)]; end plot(u) On 21 Jun 2014, at 6:54 pm, 'Stéphane Laurent' via julia-users julia...@googlegroups.com wrote: Hello, I'd like to solve this equation with Neumann boundary conditions. My code below does not work. Am I doing something bad or is it a failure of the Newton algorithm ? *# solves u = (exp(u)-exp(-u))-2u'/(x+1) , u'(0)=-1, u(1)=0* *x=Fun(identity, Interval(0.,1.))* *d=x.domain* *B=neumann(d)* *D=diff(d)* *# Solves Lu + g(u)-1==0* *L = D^2 + 2/(x.+1)*D* *g = u - -(exp(u)-exp(-u)); gp = u - -(exp(u)+exp(-u))* *u=-0.3*x+0.5** #initial guess * *for k=1:5 # this crashes if Ii increase the number of iterations* *u=u-[B,L+gp(u)]\[-1.,0.,L*u+g(u)];* *end* The solution should look like that : https://lh5.googleusercontent.com/-BCYNzh8ckCA/U6VIJcT2-tI/AGI/r08hknI2EIg/s1600/Screenshot+from+2014-06-21+10%3A37%3A46.png
Re: [julia-users] ApproxFun v0.0.1 with ODE solving
Thank you, it works !! NB: I'm Stéphane and not Stéphanie :-) Le vendredi 20 juin 2014 00:13:08 UTC+2, Sheehan Olver a écrit : Hi Stephanie, Are you on the latest GitHub version? You can get on it with Pkg.checkout(ApproxFun) Sent from my iPad On 20 Jun 2014, at 3:19 am, 'Stéphane Laurent' via julia-users julia...@googlegroups.com javascript: wrote: Hello Sheehan, I get this error when I run your code: julia for k=1:5 u=u-[B,L+gp(u)]\[0.,0.,L*u+g(u)-1.]; end ERROR: Reducing over an empty array is not allowed. in _mapreduce at reduce.jl:151 in mapreduce at reduce.jl:173 in old_addentries! at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/OperatorAlgebra.jl:106 in addentries! at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/OperatorAlgebra.jl:141 in ShiftArray at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/ShiftArray.jl:16 in BandedArray at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/ShiftArray.jl:110 in getindex at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/Operator.jl:46 in getindex at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/AlmostBandedOperator.jl:133 in backsubstitution! at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/adaptiveqr.jl:78 in ultraiconversion at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/Operator.jl:71 in * at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/Operator.jl:97 in anonymous at no file:2 Le mercredi 11 juin 2014 23:27:52 UTC+2, Sheehan Olver a écrit : Hi Stèphane, Nonlinear is not built in, but it’s easy enough to do by hand with Newton iteration in function space. Let me know if there is any confusion with the code below. I suppose I could just add a “nonlinsolve” routine that bundles this up. (I am on the latest branch so this may or may not work on the 0.0.1 tag.) Cheers, Sheehan x=Fun(identity,[-1.,1.]) d=x.domain B=dirichlet(d) D=diff(d) # Sets up L and g for equation in the form Lu + g(u)-1==0 L=D^2 + 2(1-x.^2)*D g=u-u.^2;gp=u-2u u=0.x # initial guess for the solution is zero for k=1:5 u=u-[B,L+gp(u)]\[0.,0.,L*u+g(u)-1.]; end norm(diff(u,2) + 2(1-x.^2).*diff(u) + g(u) -1) # This equals 0.0 On 12 Jun 2014, at 1:02 am, 'Stéphane Laurent' via julia-users julia...@googlegroups.com wrote: Hello Sheehan, I have unsuccessfully tried to understand how works the differential equation solver (I do not understand the Airy example). It would be nice to have an example of code for a simple BVP such as : u + 2(1-x^2)u + u^2 = 1 , u(-1) = u(1) = 0 Regards, Stéphane Le lundi 24 mars 2014 02:04:25 UTC+1, Sheehan Olver a écrit : I tagged a new release for ApproxFun ( https://github.com/dlfivefifty/ApproxFun) with major new features that might interest people. Below are ODE solving and random number sampling examples, find more in ApproxFun/examples. The code is meant as alpha quality, so don't expect too much beyond the examples. There is rudimentary support for PDE solving (e.g. Helmholtz in a square), but it's reliability is limited without a better Lyapanov solver ( https://github.com/JuliaLang/julia/issues/5814). Cheers, Sheehan Pkg.add(ApproxFun) using ApproxFun *ODE Solving: solve the Airy equation on [-1000,10]* x=Fun(identity,[-2000.,10.]) d=x.domain D=diff(d) ai=[dirichlet(d),D^2 - x]\[airyai(-2000.),0.] plot(ai) *Random number sampling: Sample a 2D Cauchy distribution on (-∞,∞)^2* f = Fun2D((x,y)-1./(2π.*(x.^2 .+ y.^2 .+ 1).^(3/2)),Line(),Line()) r = sample(f,100)
Re: Re: [julia-users] ApproxFun v0.0.1 with ODE solving
Hello Sheehan, I get this error when I run your code: julia for k=1:5 u=u-[B,L+gp(u)]\[0.,0.,L*u+g(u)-1.]; end ERROR: Reducing over an empty array is not allowed. in _mapreduce at reduce.jl:151 in mapreduce at reduce.jl:173 in old_addentries! at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/OperatorAlgebra.jl:106 in addentries! at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/OperatorAlgebra.jl:141 in ShiftArray at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/ShiftArray.jl:16 in BandedArray at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/ShiftArray.jl:110 in getindex at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/Operator.jl:46 in getindex at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/AlmostBandedOperator.jl:133 in backsubstitution! at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/adaptiveqr.jl:78 in ultraiconversion at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/Operator.jl:71 in * at /home/sdl/.julia/v0.3/ApproxFun/src/Operators/Operator.jl:97 in anonymous at no file:2 Le mercredi 11 juin 2014 23:27:52 UTC+2, Sheehan Olver a écrit : Hi Stèphane, Nonlinear is not built in, but it’s easy enough to do by hand with Newton iteration in function space. Let me know if there is any confusion with the code below. I suppose I could just add a “nonlinsolve” routine that bundles this up. (I am on the latest branch so this may or may not work on the 0.0.1 tag.) Cheers, Sheehan x=Fun(identity,[-1.,1.]) d=x.domain B=dirichlet(d) D=diff(d) # Sets up L and g for equation in the form Lu + g(u)-1==0 L=D^2 + 2(1-x.^2)*D g=u-u.^2;gp=u-2u u=0.x # initial guess for the solution is zero for k=1:5 u=u-[B,L+gp(u)]\[0.,0.,L*u+g(u)-1.]; end norm(diff(u,2) + 2(1-x.^2).*diff(u) + g(u) -1) # This equals 0.0 On 12 Jun 2014, at 1:02 am, 'Stéphane Laurent' via julia-users julia...@googlegroups.com javascript: wrote: Hello Sheehan, I have unsuccessfully tried to understand how works the differential equation solver (I do not understand the Airy example). It would be nice to have an example of code for a simple BVP such as : u + 2(1-x^2)u + u^2 = 1 , u(-1) = u(1) = 0 Regards, Stéphane Le lundi 24 mars 2014 02:04:25 UTC+1, Sheehan Olver a écrit : I tagged a new release for ApproxFun ( https://github.com/dlfivefifty/ApproxFun) with major new features that might interest people. Below are ODE solving and random number sampling examples, find more in ApproxFun/examples. The code is meant as alpha quality, so don't expect too much beyond the examples. There is rudimentary support for PDE solving (e.g. Helmholtz in a square), but it's reliability is limited without a better Lyapanov solver ( https://github.com/JuliaLang/julia/issues/5814). Cheers, Sheehan Pkg.add(ApproxFun) using ApproxFun *ODE Solving: solve the Airy equation on [-1000,10]* x=Fun(identity,[-2000.,10.]) d=x.domain D=diff(d) ai=[dirichlet(d),D^2 - x]\[airyai(-2000.),0.] plot(ai) *Random number sampling: Sample a 2D Cauchy distribution on (-∞,∞)^2* f = Fun2D((x,y)-1./(2π.*(x.^2 .+ y.^2 .+ 1).^(3/2)),Line(),Line()) r = sample(f,100)
Re: [julia-users] problem with Multinomial Distribution
Le jeudi 19 juin 2014 19:09:45 UTC+2, John Myles White a écrit : Maybe we should start removing all the default distributions. I agree a default binomial distribution is a strange concept. But the default Gaussian distribution is nice.
[julia-users] Re: ApproxFun v0.0.1 with ODE solving
Thank you ! I will not try soon, but in the near future, I hope. Stéphane
[julia-users] Re: ApproxFun v0.0.1 with ODE solving
Hello Sheehan, I have unsuccessfully tried to understand how works the differential equation solver (I do not understand the Airy example). It would be nice to have an example of code for a simple BVP such as : u + 2(1-x^2)u + u^2 = 1 , u(-1) = u(1) = 0 Regards, Stéphane Le lundi 24 mars 2014 02:04:25 UTC+1, Sheehan Olver a écrit : I tagged a new release for ApproxFun ( https://github.com/dlfivefifty/ApproxFun) with major new features that might interest people. Below are ODE solving and random number sampling examples, find more in ApproxFun/examples. The code is meant as alpha quality, so don't expect too much beyond the examples. There is rudimentary support for PDE solving (e.g. Helmholtz in a square), but it's reliability is limited without a better Lyapanov solver ( https://github.com/JuliaLang/julia/issues/5814). Cheers, Sheehan Pkg.add(ApproxFun) using ApproxFun *ODE Solving: solve the Airy equation on [-1000,10]* x=Fun(identity,[-2000.,10.]) d=x.domain D=diff(d) ai=[dirichlet(d),D^2 - x]\[airyai(-2000.),0.] plot(ai) *Random number sampling: Sample a 2D Cauchy distribution on (-∞,∞)^2* f = Fun2D((x,y)-1./(2π.*(x.^2 .+ y.^2 .+ 1).^(3/2)),Line(),Line()) r = sample(f,100)
Re: [julia-users] Re: ApproxFun v0.0.1 with ODE solving
Hmmm there' are more details here https://github.com/dlfivefifty/ApproxFun/blob/f0b1cd8e9bb1dacc99c198ea2b895d656749613f/examples/Airy%20equation.jl about the Airy example. I think I understand now.
[julia-users] Re: Gadfly: adding plots to an existing plot
Hello is there something in Gadfly like the geom_abline() function in the ggplot2 R package to add a single line to an existing plot ?
Re: [julia-users] negative power throws error
A Float is a decimal number, hence it also is a rational number : *2.243423592592582385923 = 2243423592592582385923 / 10* Hence, depending on the limitations about the sizes of the integers, the set Rational could be bigger than the set Float. In this case, the better approximation of an irrational is achieved by a Rational. Le lundi 9 juin 2014 02:16:24 UTC+2, Miguel Bazdresch a écrit : Thanks -- I wasn't aware that a^b is irrational in general in this case. Now I wonder if a Float is a better approximation to an irrational number than a Rational. Of course, one could say that sqrt(a) is complex in general for real a, but Julia returns a real. As Stefan says, some of these cases have no good solutions, only less worse ones. -- mb On Sun, Jun 8, 2014 at 7:09 PM, 'Stéphane Laurent' via julia-users julia...@googlegroups.com javascript: wrote: If b is rational then a^b is irrational in general, even for a integer, so this output is quite expected, as well as julia (10//1)^(2//1) 100.0 Le lundi 9 juin 2014 00:54:37 UTC+2, Miguel Bazdresch a écrit : I just tried this (on 0.2.1): julia (10//1)^(-2//1) 0.01 Is this expected? -- mb On Sun, Jun 8, 2014 at 6:36 PM, 'Stéphane Laurent' via julia-users julia...@googlegroups.com wrote: julia (10//1)^(-2) 1//100 Would it be problematic to return a rational for (a::Integer)^(b::Integer) ? Le dimanche 8 juin 2014 21:53:45 UTC+2, Stefan Karpinski a écrit : There are three obvious options for (a::Integer)^(b::Integer): 1. Always return an integer ⟹ fails for negative b. 2. Always return a float ⟹ a^2 is not the same as a*a. 3. Return float for negative b, integer otherwise ⟹ not type-stable. As you can see, all of these choices are problematic. The first one, which is what we currently do, seems to be the least problematic. One somewhat crazier option that has been proposed is making ^- as in a^-b parse as a different operator and have a^b return an integer for integer arguments but a^-b return a float for integer arguments. This would have the unfortunate effect of making a^-b different from a^(-b). On Sat, Jun 7, 2014 at 12:41 PM, Daniel Jones daniel...@gmail.com wrote: I'd definitely be in favor of '^' converting to float, like '/', having fallen for than recently https://github.com/JuliaLang/Color.jl/commit/c3d05dd2b94f0d38b64ef86022accdfec886a673 . On Sat, Jun 7, 2014, at 12:53 AM, Ivar Nesje wrote: There has also been discussion on whether ^(a::Integer,b::Integer) should return a Float64 by default, and defer to pow() like /(a::Integer, b::Integer) defers to div(). The problem is that many people like the 10^45 vs 1e45 notation for large integers vs float constants, and we can make it a clean error instead of a silent bug.
Re: [julia-users] negative power throws error
That's why we need BigRational https://github.com/andrioni/BigRationals.jl :) Le lundi 9 juin 2014 12:52:48 UTC+2, Ivar Nesje a écrit : A float is a binary fraction, and can not represent most decimal fractions exactly. 0.1 is a famous example of a decimal fraction that can not be represented exactly in binary, just like 1/3 is impossible to represent in decimal (0.3...) Rational can express many fractions exactly, but as soon as you start working with irrational numbers there is no clear way to do rounding. Rational also have a limited range compared to floating point, so you get overflow (or underflow) much easier. kl. 11:13:06 UTC+2 mandag 9. juni 2014 skrev Stéphane Laurent følgende: A Float is a decimal number, hence it also is a rational number : *2.243423592592582385923 = 2243423592592582385923 / 10* Hence, depending on the limitations about the sizes of the integers, the set Rational could be bigger than the set Float. In this case, the better approximation of an irrational is achieved by a Rational. Le lundi 9 juin 2014 02:16:24 UTC+2, Miguel Bazdresch a écrit : Thanks -- I wasn't aware that a^b is irrational in general in this case. Now I wonder if a Float is a better approximation to an irrational number than a Rational. Of course, one could say that sqrt(a) is complex in general for real a, but Julia returns a real. As Stefan says, some of these cases have no good solutions, only less worse ones. -- mb On Sun, Jun 8, 2014 at 7:09 PM, 'Stéphane Laurent' via julia-users julia...@googlegroups.com wrote: If b is rational then a^b is irrational in general, even for a integer, so this output is quite expected, as well as julia (10//1)^(2//1) 100.0 Le lundi 9 juin 2014 00:54:37 UTC+2, Miguel Bazdresch a écrit : I just tried this (on 0.2.1): julia (10//1)^(-2//1) 0.01 Is this expected? -- mb On Sun, Jun 8, 2014 at 6:36 PM, 'Stéphane Laurent' via julia-users julia...@googlegroups.com wrote: julia (10//1)^(-2) 1//100 Would it be problematic to return a rational for (a::Integer)^(b::Integer) ? Le dimanche 8 juin 2014 21:53:45 UTC+2, Stefan Karpinski a écrit : There are three obvious options for (a::Integer)^(b::Integer): 1. Always return an integer ⟹ fails for negative b. 2. Always return a float ⟹ a^2 is not the same as a*a. 3. Return float for negative b, integer otherwise ⟹ not type-stable. As you can see, all of these choices are problematic. The first one, which is what we currently do, seems to be the least problematic. One somewhat crazier option that has been proposed is making ^- as in a^-b parse as a different operator and have a^b return an integer for integer arguments but a^-b return a float for integer arguments. This would have the unfortunate effect of making a^-b different from a^(-b). On Sat, Jun 7, 2014 at 12:41 PM, Daniel Jones daniel...@gmail.com wrote: I'd definitely be in favor of '^' converting to float, like '/', having fallen for than recently https://github.com/JuliaLang/Color.jl/commit/c3d05dd2b94f0d38b64ef86022accdfec886a673 . On Sat, Jun 7, 2014, at 12:53 AM, Ivar Nesje wrote: There has also been discussion on whether ^(a::Integer,b::Integer) should return a Float64 by default, and defer to pow() like /(a::Integer, b::Integer) defers to div(). The problem is that many people like the 10^45 vs 1e45 notation for large integers vs float constants, and we can make it a clean error instead of a silent bug.
Re: [julia-users] negative power throws error
More seriously, are there really no case in Julia like that : *Return float for negative b, integer otherwise ⟹ not type-stable.* ? Otherwise, a better solution is to return Rational for negative, and Int for positive. Always returning Rational is not cool because we use positive numbers more often than negative ones. In this way, *a^b would always return an exact result of the operation*. These operations belong to arithmetic on integer numbers, and rounding has nothing to do here ! If i had a code performing some calculations on integer numbers, I would be sad to discover a Float in the output in the case when I forgot to handle the negative integers. In this spirit, *10^-2.0* is not the good reflex to have : it should be *(10//1)^-2*
[julia-users] duplicating the value of a variable
Hi, In a program I have some *n*1* arrays, say *A* *B* *C* *D* and in order to make a certain calculation, for convenience, I construct the array *A B* *B C* *C D* *D A* There will be millions of such arrays generated by my final program, with *n* greater than 4 and the components A, B, C, D, ... are *BigFloat*. Then I'm wondering how to optimize my copy from the first array to the second array. Is there something to gain performance when we duplicate some variables ?
[julia-users] Re: duplicating the value of a variable
Thank you, I will think about your proposal and take a look at ArrayViews in the near future. - Stéphane Le lundi 9 juin 2014 19:37:38 UTC+2, Stéphane Laurent a écrit : Hi, In a program I have some *n*1* arrays, say *A* *B* *C* *D* and in order to make a certain calculation, for convenience, I construct the array *A B* *B C* *C D* *D A* There will be millions of such arrays generated by my final program, with *n* greater than 4 and the components A, B, C, D, ... are *BigFloat*. Then I'm wondering how to optimize my copy from the first array to the second array. Is there something to gain performance when we duplicate some variables ?
Re: [julia-users] Re: numerical solving of ODE with boundary values constraints
Thank you. I have tried to understand, but really unsuccessful (moreover the ApproxFun example given on github doesn't work). Le mardi 20 mai 2014 11:55:35 UTC+2, Mauro a écrit : I can't help you but maybe it could help to look at the extensive documentation for chebfun, the matlab original: http://www2.maths.ox.ac.uk/chebfun/guide/ On Tue, 2014-05-20 at 10:07, julia...@googlegroups.com javascript: wrote: Maybe, but I really don't understand this code :(
[julia-users] Re: julia unable to install on Ubuntu 13.10
Excellent, I already had all the requited tools (gfortran, curl), and the installation has been successful. Thank you ! Le mardi 20 mai 2014 12:46:41 UTC+2, Ivo Balbaert a écrit : Sorry, message was posted too quickly: Here is my report of the build: in /home/ivo/julia: git clone git://github.com/JuliaLang/julia.git cd julia in /home/ivo/julia/julia: make /bin/sh: 2: g++: not found make[2]: *** [/home/ivo/julia/julia/usr/lib/libgrisu.so] Error 127 make[1]: *** [julia-release] Error 2 make: *** [release] Error 2 -- sudo ap-get install build-essential make gfortran: command not found -- sudo apt-get install gfortran make checking for suitable m4... configure: error: No usable m4 in $PATH or /usr/5bin (see config.log for reasons). -- sudo apt-get install m4 make ./julia OK! To install latest version of Julia: git pull make clean make ./julia Cheers, Ivo Op zaterdag 25 januari 2014 04:30:18 UTC+1 schreef Rajn: After my several failed attempts to run PyPlot through Julia in Windows, I decided to give up and try Linux. Guess it was even worse. First I added to regular repository (not the nightlybuild) then added the dep-repository then updated and then installed julia Here's the latest: Unpacking librmath-dev (from .../librmath-dev_2.15.2-juliadeps2~raring_amd64.deb) ... dpkg: error processing /var/cache/apt/archives/librmath-dev_2.15.2-juliadeps2~raring_amd64.deb (--unpack): trying to overwrite '/usr/include/Rmath.h', which is also in package r-mathlib 3.0.1-3ubuntu1 No apport report written because MaxReports is reached already Selecting previously unselected package julia. Unpacking julia (from .../julia_0.2.0~saucyfinal1_amd64.deb) ... Processing triggers for man-db ... Errors were encountered while processing: /var/cache/apt/archives/librmath-dev_2.15.2-juliadeps2~raring_amd64.deb E: Sub-process /usr/bin/dpkg returned an error code (1) Have no clue absolutely how to proceed. The same issue occurs when I try nightly builds.
[julia-users] Re: numerical solving of ODE with boundary values constraints
Maybe, but I really don't understand this code :(
Re: [julia-users] julia unable to install on Ubuntu 13.10
Is it supposed to work with Ubuntu 14 too ? Le samedi 25 janvier 2014 20:37:03 UTC+1, Stefan Karpinski a écrit : Just do this: git clone https://github.com/JuliaLang/julia.git cd julia make It will take a while, but it will download all the things you need under the deps directory, configure them, compile them, and then compile julia using those versions. Later, if you don't want it anymore, just delete the julia directory and everything it built will be gone too. On Sat, Jan 25, 2014 at 12:25 AM, Rajn rjngr...@gmail.com javascript:wrote: I use R quite often. Would it be affected if I uninstall Rmath? I kind of understand what you are suggesting in your second step but never worked with Github so don't know about cloning and not sure what do you mean by switching to 0.2.0- switching from what? And how do I do that? Should I just copy the files from Github and extract it and then do makefile? Are there somewhere good instructions on how to do that? Thanks Joao On Saturday, January 25, 2014 12:13:23 AM UTC-5, João Felipe Santos wrote: There is a conflict between Ubuntu's r-mathlib package and Julia's RMath package: they both want to install Rmath.h to the same path. If you can live without r-libmath, you can uninstall it and then install Julia. Having said that, I usually compile my own distribution and install it to my home directory. You can clone the repository from Github and then switch to 0.2.0. It takes a while but if your PC is not that old you'll be done in less than an hour. -- João Felipe Santos On Fri, Jan 24, 2014 at 10:30 PM, Rajn rjngr...@gmail.com wrote: After my several failed attempts to run PyPlot through Julia in Windows, I decided to give up and try Linux. Guess it was even worse. First I added to regular repository (not the nightlybuild) then added the dep-repository then updated and then installed julia Here's the latest: Unpacking librmath-dev (from .../librmath-dev_2.15.2-juliadeps2~raring_amd64.deb) ... dpkg: error processing /var/cache/apt/archives/librmath-dev_2.15.2-juliadeps2~raring_amd64.deb (--unpack): trying to overwrite '/usr/include/Rmath.h', which is also in package r-mathlib 3.0.1-3ubuntu1 No apport report written because MaxReports is reached already Selecting previously unselected package julia. Unpacking julia (from .../julia_0.2.0~saucyfinal1_amd64.deb) ... Processing triggers for man-db ... Errors were encountered while processing: /var/cache/apt/archives/librmath-dev_2.15.2- juliadeps2~raring_amd64.deb E: Sub-process /usr/bin/dpkg returned an error code (1) Have no clue absolutely how to proceed. The same issue occurs when I try nightly builds.
[julia-users] loop over a single element
Hello, I have a strange behaviour. This code works fine: for file = (newLine, intersect, findRange, getLine, orderPart, plotPart) include(*(pwd(), \\function_, file, .jl)) end But when there's only one string to loop over I get this error: julia for file = newLine include(*(pwd(), \\function_, file, .jl)) end MethodError(*,(D:\\Github\\JuliaGFI01\\assets\\Julia\\function_,'n')) Also strange when using string(), ony the first letter is taken : julia for file = newLine include(*(pwd(), \\function_, string(file), .jl)) end ErrorException(could not open file D:\\Github\\JuliaGFI01\\assets\\Julia\\function_n.jl)
[julia-users] numerical solving of ODE with boundary values constraints
Hello, Is there a Julia library allowing to solve ordinary differential equations with boundary values constraints, similarly to the bvpSolve package for Rhttp://cran.r-project.org/web/packages/bvpSolve/vignettes/bvpSolve.pdf ?
Re: [julia-users] array with different column types
Hello everybody,
Below I define two new types : Line and Poly. The Poly type is intended
for stacking some lines.
type Line
a::Float64 # intercept
b::BigFloat # slope
x1::BigFloat # x-coordinate of first vertex
y1::BigFloat # y-coordinate of first vertex
x2::BigFloat # x-coordinate of second vertex
y2::BigFloat # y-coordinate of second vertex
typ::Bool# type of the line (true:upper, false:lower)
end
type Poly
a::Vector{Float64}
b::Vector{BigFloat}
x1::Vector{BigFloat}
y1::Vector{BigFloat}
x2::Vector{BigFloat}
y2::Vector{BigFloat}
typ::Vector{Bool}
end
I also define the empty Poly:
emptyPoly = Poly(Array(Float64,0), Array(BigFloat,0), Array(BigFloat,0),
Array(BigFloat,0), Array(BigFloat,0), Array(BigFloat,0), Array(Bool,0));
And a function to generate a new Line:
function newLine(a::Float64, b::BigFloat, typ::Bool)
return Line(a, b, BigFloat(Inf), BigFloat(Inf), BigFloat(Inf),
BigFloat(Inf), typ)
end
Now I define some macros but it seems that I'm doing something bad :
macro addLine(poly, line)
for op = (:a, :b, :x1, :y1, :x2, :y2, :typ)
@eval $poly.$op = [$poly.$op, $line.$op]
end
end
macro removeLine(poly, index)
for op = (:a, :b, :x1, :y1, :x2, :y2, :typ)
@eval splice!($poly.$op, $index)
end
end
There's something bad when I'm doing this:
D = newLine(0.4, BigFloat(1.5), false)
poly = emptyPoly;
@addLine poly D
*Question 1:* I don't understand why the previous code changes the value of
emptyPoly, as if poly and emptyPoly were linked:
julia emptyPoly
Poly([0.4],[1.5e+00 with 256 bits of precision],[inf with 256 bits of
precision],[inf with 256 bits of precision],[inf with 256 bits of
precision],[inf with 256 bits of precision],[false])
*Question 2:* There's another problem when I'm using the removeLine macro
inside a loop :
@addLine poly D
@addLine poly D
for index = [1 2]
@removeLine(poly, index)
end
This code doesn't work and generates the error message index not found.
Re: [julia-users] array with different column types
Thank you Johan and Jameson.
Johan, I don't know how to make a loop on the fields with a function. For
example this doesn't work:
function removeLine(poly::Poly, index::Int)
for op = (:a, :b, :x1, :y1, :x2, :y2, :typ)
splice!(poly.$op, index)
end
end
How to do, please ?
Le jeudi 8 mai 2014 15:59:06 UTC+2, Jameson a écrit :
Replace your macro with a function and delete the uses of eval. You code
will be faster, and easier to understand. Most of the difficulty people
seem to have with macros comes from thinking they are a type of function
call -- the @ character is supposed to remind you that this is not true.
On Thursday, May 8, 2014, Johan Sigfrids johan.s...@gmail.comjavascript:
wrote:
I myself have been hitting my head against the wall that is
meta-programming in Julia. I think I can answer your first question at
least.
Q1: This is because the line poly = emptyPoly doesn't create a new copy
of a ploygon but a reference to the empty one so that both poly and
emptyPoly
refer to the same data. You need to do poly = deepcopy(emptyPoly) .
On Thursday, May 8, 2014 9:56:27 AM UTC+3, Stéphane Laurent wrote:
Hello everybody,
Below I define two new types : Line and Poly. The Poly type is intended
for stacking some lines.
type Line
a::Float64 # intercept
b::BigFloat # slope
x1::BigFloat # x-coordinate of first vertex
y1::BigFloat # y-coordinate of first vertex
x2::BigFloat # x-coordinate of second vertex
y2::BigFloat # y-coordinate of second vertex
typ::Bool# type of the line (true:upper, false:lower)
end
type Poly
a::Vector{Float64}
b::Vector{BigFloat}
x1::Vector{BigFloat}
y1::Vector{BigFloat}
x2::Vector{BigFloat}
y2::Vector{BigFloat}
typ::Vector{Bool}
end
I also define the empty Poly:
emptyPoly = Poly(Array(Float64,0), Array
[julia-users] delete some entries of a n*1 array
Hello,
Assume I want to delete the first and the third entries of this array:
julia x = [3,5,9,7]
4-element Array{Int64,1}:
3
5
9
7
How to do ? Using !splice I can only delete one entry or a range.
[julia-users] Re: delete some entries of a n*1 array
julia deleteat!(x, 1)
ErrorException(deleteat! not defined)
Le jeudi 8 mai 2014 17:00:01 UTC+2, Tobias Knopp a écrit :
deleteat!(x,(1,3))
But am I missing something or are splice! and deleteat! a little redundant?
Am Donnerstag, 8. Mai 2014 16:49:58 UTC+2 schrieb Stéphane Laurent:
Hello,
Assume I want to delete the first and the third entries of this array:
julia x = [3,5,9,7]
4-element Array{Int64,1}:
3
5
9
7
How to do ? Using !splice I can only delete one entry or a range.
Re: [julia-users] array with different column types
No that generates an error:
ErrorException(error compiling removeLine: syntax: prefix \$ in non-quoted
expression)
Le jeudi 8 mai 2014 17:09:24 UTC+2, Johan Sigfrids a écrit :
You can still use meta-programming to generate the code
function removeLine(poly, index)
for op = (:a, :b, :x1, :y1, :x2, :y2, :typ)
@eval splice!($poly.$op, $index)
end
end
On Thursday, May 8, 2014 5:40:35 PM UTC+3, Stéphane Laurent wrote:
Thank you Johan and Jameson.
Johan, I don't know how to make a loop on the fields with a function. For
example this doesn't work:
function removeLine(poly::Poly, index::Int)
for op = (:a, :b, :x1, :y1, :x2, :y2, :typ)
splice!(poly.$op, index)
end
end
How to do, please ?
Le jeudi 8 mai 2014 15:59:06 UTC+2, Jameson a écrit :
Replace your macro with a function and delete the uses of eval. You code
will be faster, and easier to understand. Most of the difficulty people
seem to have with macros comes from thinking they are a type of function
call -- the @ character is supposed to remind you that this is not true.
On Thursday, May 8, 2014, Johan Sigfrids johan.s...@gmail.com wrote:
I myself have been hitting my head against the wall that is
meta-programming in Julia. I think I can answer your first question at
least.
Q1: This is because the line poly = emptyPoly doesn't create a new
copy of a ploygon but a reference to the empty one so that both polyand
emptyPoly
refer to the same data. You need to do poly = deepcopy(emptyPoly) .
On Thursday, May 8, 2014 9:56:27 AM UTC+3, Stéphane Laurent wrote:
Hello everybody,
Below I define two new types : Line and Poly. The Poly type is
intended for stacking some lines.
type Line
a::Float64 # intercept
b::BigFloat # slope
x1::BigFloat # x-coordinate of first vertex
y1::BigFloat # y-coordinate of first vertex
x2::BigFloat # x-coordinate of second vertex
y2::BigFloat # y-coordinate of second vertex
typ::Bool# type of the line (true:upper, false:lower)
end
type Poly
a::Vector{Float64}
b::Vector{BigFloat}
x1::Vector{BigFloat}
y1::Vector{BigFloat}
x2::Vector{BigFloat}
y2::Vector{BigFloat}
typ::Vector{Bool}
end
I also define the empty Poly:
emptyPoly = Poly(Array(Float64,0), Array
Re: [julia-users] array with different column types
Sorry, actually it works. Maybe there was a problem previously because of JuliaStudio. Sometimes when I copy-paste some code in the JuliaStudio console it is not executed. Thanks for your help.
Re: [julia-users] array with different column types
Cool, thank you Jameson. So what is the best choice between these two possibilities : function removeLine(poly::Poly, index::Int) for op = (:a, :b, :x1, :y1, :x2, :y2, :typ) @eval splice!($poly.$op, $index) end end function removeLine2(poly::Poly, index::Int) for op = (:a, :b, :x1, :y1, :x2, :y2, :typ) splice!(getfield(poly, op), index) end end
Re: [julia-users] array with different column types
Actually I'm rather using the followig function allowing to remove several
lines.
function removeLines(poly::Poly, indices::BitArray{1})
for op = (:a, :b, :x1, :y1, :x2, :y2, :typ)
@eval $poly.$op = ($poly.$op)[!$indices]
end
end
function removeLines2(poly::Poly, indices::BitArray{1})
for op = (:a, :b, :x1, :y1, :x2, :y2, :typ)
poly.(op) = (poly.(op))[!indices]
end
end
Le jeudi 8 mai 2014 21:04:52 UTC+2, Stéphane Laurent a écrit :
Cool, thank you Jameson.
So what is the best choice between these two possibilities :
function removeLine(poly::Poly, index::Int)
for op = (:a, :b, :x1, :y1, :x2, :y2, :typ)
@eval splice!($poly.$op, $index)
end
end
function removeLine2(poly::Poly, index::Int)
for op = (:a, :b, :x1, :y1, :x2, :y2, :typ)
splice!(getfield(poly, op), index)
end
end
Re: [julia-users] Re: float approximation of bigfloat
Right, it works. Thanks.
Le dimanche 4 mai 2014 20:13:25 UTC+2, Keno Fischer a écrit :
Or just use the `float64` function.
On Sun, May 4, 2014 at 2:11 PM, 'Stéphane Laurent' via julia-users
julia...@googlegroups.com javascript: wrote:
Cool like this;
julia [convert(Float64,a) for a in [x x]]
2-element Array{Any,1}:
2.13
2.13
[julia-users] float approximation of bigfloat
Hello, Assume I have a BigFloat number, how to get its Float64 approximation ?
[julia-users] Re: float approximation of bigfloat
convert works fine on a single BigFloat but it doesn't apply to an array ?
julia x = BigFloat(2.13)
2.130003e+00
with 256 bits of precision
julia convert(Float64,x)
2.13
julia convert(Float64, [x x])
MethodError(convert,(Float64,
1x2 Array{BigFloat,2}:
2.130003e+00
…
2.130003e+00))
[julia-users] Re: float approximation of bigfloat
Cool like this;
julia [convert(Float64,a) for a in [x x]]
2-element Array{Any,1}:
2.13
2.13
Re: [julia-users] Re: How to use GLPK.exact ?
Thank you everybody, almost every point discussed here is now written on my blog http://stla.github.io/stlapblog/posts/KantorovichWithJulia.html.
Re: [julia-users] Re: How to use GLPK.exact ?
Right, it works. Thank you.
If I don't call GLPKMathProgInterface, does JuMP use an internal solver ?
Le mardi 22 avril 2014 23:25:07 UTC+2, Carlo Baldassi a écrit :
Note that you can still use GLPK.exact with JuMP, you just need to add
change the m=Model() line to this:
using GLPKMathProgInterface
m = Model(solver=GLPKSolverLP(method=:Exact))
while all the rest stays the same.
As an aside, it's really kind of annoying that GLPK.exact uses (basically)
Rational{BigInt} internally, but the interface does not allow to access
this. Seems a waste.
On Tuesday, April 22, 2014 8:28:01 PM UTC+2, Stéphane Laurent wrote:
Miles, I have successfully installed JuMP and GLPKMathProgInterface on
Windows 32-bit.
Your code works very well, this is really awesome !! However the result
is not as precise as the one given by *GLPK.exact*.
using JuMP
mu = [1/7, 2/7, 4/7]
nu = [1/4, 1/4, 1/2]
n = length(mu)
m = Model()
@defVar(m, p[1:n,1:n] = 0)
@setObjective(m, Min, sum{p[i,j], i in 1:n, j in 1:n; i != j})
for k in 1:n
@addConstraint(m, sum(p[k,:]) == mu[k])
@addConstraint(m, sum(p[:,k]) == nu[k])
end
solve(m)
julia println(Optimal objective value is:, getObjectiveValue(m))
Optimal objective value is:0.10714285714285715
julia 3/28
0.10714285714285714
Le jeudi 10 avril 2014 01:28:41 UTC+2, Miles Lubin a écrit :
When we have a simplex solver (either in Julia or external) that
supports rational inputs, we could consider making this work with JuMP, but
for now JuMP stores all data as floating-point as well.
Stephane, nice work. LP definitely needs more exposure in the
probability community. Please please write your LPs algebraically, there's
really no excuse not to do this in Julia when your original model is in
this form.
Compare this:
using JuMP
m = Model()
@defVar(m, p[1:n,1:n] = 0)
@setObjective(m, Max, sum{p[i,j], i in 1:n; i != j})
for k in 1:n
@addConstraint(m, sum(p[k,:]) == μ[k])
@addConstraint(m, sum(p[:,k]) == ν[k])
end
solve(m)
println(Optimal objective value is:, getObjectiveValue(m))
with the matrix gymnastics that you had to do to use the low-level GLPK
interface. Writing down a linear programming problem shouldn't be that
hard! (Note: I haven't tested that JuMP code).
Miles
On Wednesday, April 9, 2014 11:18:26 PM UTC+1, Carlo Baldassi wrote:
About GLPK.exact it is not possible to get the rational number 3/28
instead of a decimal approximation ?
No, unfortunately. Also, for that to happen/make sense, you'd also need
to be able to pass all the *inputs* as exact rational values, i.e. as
1//7 instead of 1/7. This would be possible if we had a native generic
Julia linear programming solver, but it's not possible with GLPK, which
can
only use exact arithmetic internally.
[julia-users] Re: problem installing JuMP
Thank you Tony, but I don't know what to do with this file. I have been able to install JuMP and the solver GLPKMathProgInterface (but not the other solvers) on Windows 32-bit. Le dimanche 13 avril 2014 09:52:14 UTC+2, Tony Kelman a écrit : That's fixable. I spent a bunch of time last year making it much easier to build dll's of the COIN-OR solvers. There are 64-bit binaries here https://projects.coin-or.org/CoinMP/browser/releases/1.7.6/CoinMP/CoinMP.zipand an issue open here https://github.com/JuliaOpt/Cbc.jl/issues/5 On Saturday, April 12, 2014 2:57:27 PM UTC-7, Stéphane Laurent wrote: Yes. Moreover the solver packages (Clp, ...) are not correctly built, but this is expected on windows 64bit with the 64bit version of Julia: https://jump.readthedocs.org/en/release-0.4/jump.html Thus I should try Julia 32bit anyway. Le samedi 12 avril 2014 22:07:20 UTC+2, Iain Dunning a écrit : Does the same thing happen if you do `using JuMP`? On Saturday, April 12, 2014 10:05:12 AM UTC-4, Stéphane Laurent wrote: Hello, I have just installed JuMP but impossible to use it. If I type julia import JuMP then Julia is freezed, even after 5 minutes nothing happens. MathProgBase has been installed too and I have no problem to load it. *julia versioninfo()* *Julia Version 0.2.0* *Commit 05c6461 (2013-11-16 23:44 UTC)* *Platform Info:* * System: Windows (x86_64-w64-mingw32)* * WORD_SIZE: 64* * BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY)* * LAPACK: libopenblas* * LIBM: libopenlibm* *julia Pkg.status()* *Required packages:* * - DataFrames0.4.2* * - Distributions 0.3.0* * - GLM 0.2.4* * - GLPK 0.2.10* * - GSL 0.1.1* * - GnuTLS0.0.0* * - JuMP 0.4.1* * - MathProgBase 0.1.6* * - MixedModels 0.2.3* * - NLopt 0.0.3* * - NumericExtensions 0.3.6* * - RDatasets 0.1.1* * - Stats 0.1.0* * - Winston 0.10.2* *Additional packages:* * - BinDeps 0.2.12* * - Blocks0.0.2* * - Cairo 0.2.12* * - Color 0.2.9* * - DataArrays0.0.2* * - GZip 0.2.12* * - HTTPClient0.1.1* * - IniFile 0.2.2* *julia * * - LibCURL 0.1.1* * - LibExpat 0.0.4* * - Nettle0.1.3* * - SortingAlgorithms 0.0.1* * - StatsBase 0.2.10* * - Tk0.2.11* * - URIParser 0.0.1* * - URLParse 0.0.0* * - WinRPM0.0.13* * - Zlib 0.1.5 *
Re: [julia-users] Re: How to use GLPK.exact ?
Miles, I have successfully installed JuMP and GLPKMathProgInterface on
Windows 32-bit.
Your code works very well, this is really awesome !! However the result is
not as precise as the one given by *GLPK.exact*.
using JuMP
mu = [1/7, 2/7, 4/7]
nu = [1/4, 1/4, 1/2]
n = length(mu)
m = Model()
@defVar(m, p[1:n,1:n] = 0)
@setObjective(m, Min, sum{p[i,j], i in 1:n, j in 1:n; i != j})
for k in 1:n
@addConstraint(m, sum(p[k,:]) == mu[k])
@addConstraint(m, sum(p[:,k]) == nu[k])
end
solve(m)
julia println(Optimal objective value is:, getObjectiveValue(m))
Optimal objective value is:0.10714285714285715
julia 3/28
0.10714285714285714
Le jeudi 10 avril 2014 01:28:41 UTC+2, Miles Lubin a écrit :
When we have a simplex solver (either in Julia or external) that supports
rational inputs, we could consider making this work with JuMP, but for now
JuMP stores all data as floating-point as well.
Stephane, nice work. LP definitely needs more exposure in the probability
community. Please please write your LPs algebraically, there's really no
excuse not to do this in Julia when your original model is in this form.
Compare this:
using JuMP
m = Model()
@defVar(m, p[1:n,1:n] = 0)
@setObjective(m, Max, sum{p[i,j], i in 1:n; i != j})
for k in 1:n
@addConstraint(m, sum(p[k,:]) == μ[k])
@addConstraint(m, sum(p[:,k]) == ν[k])
end
solve(m)
println(Optimal objective value is:, getObjectiveValue(m))
with the matrix gymnastics that you had to do to use the low-level GLPK
interface. Writing down a linear programming problem shouldn't be that
hard! (Note: I haven't tested that JuMP code).
Miles
On Wednesday, April 9, 2014 11:18:26 PM UTC+1, Carlo Baldassi wrote:
About GLPK.exact it is not possible to get the rational number 3/28
instead of a decimal approximation ?
No, unfortunately. Also, for that to happen/make sense, you'd also need
to be able to pass all the *inputs* as exact rational values, i.e. as
1//7 instead of 1/7. This would be possible if we had a native generic
Julia linear programming solver, but it's not possible with GLPK, which can
only use exact arithmetic internally.
Re: [julia-users] Re: How to use GLPK.exact ?
My blog post is updated.
Iain, I have tried your code with the example of my blog. I see the good
result in the output (*3//28*), but I don't understand how to know it is
the good one.
using RationalSimplex
using Base.Test
b = [1//7, 2//7, 4//7, 1//4, 1//4, 1//2]
c = [0//1, 1//1, 1//1, 1//1, 0//1, 1//1, 1//1, 1//1, 0//1]
c = [c, repmat([0//1], size(b)[1])] # surely clumsy
M = [1//1 1//1 1//1 0//1 0//1 0//1 0//1 0//1 0//1;
0//1 0//1 0//1 1//1 1//1 1//1 0//1 0//1 0//1;
0//1 0//1 0//1 0//1 0//1 0//1 1//1 1//1 1//1;
1//1 0//1 0//1 1//1 0//1 0//1 1//1 0//1 0//1;
0//1 1//1 0//1 0//1 1//1 0//1 0//1 1//1 0//1;
0//1 0//1 1//1 0//1 0//1 1//1 0//1 0//1 1//1]
Id = zeros(Rational{Int64}, size(M)[1], size(M)[1])
for i in 1:size(M)[1]
Id[i,i] = 1//1
end
M = hcat(M, Id)
julia simplex(c, :Min, M, b, ['=','=','=','=','=','='])
(:Optimal,[1//7,0//1,0//1,0//1,1//4,0//1,0//1,0//1,1//2,0//1,1//28,1//14,*3//28*,0//1,0//1])
Le jeudi 17 avril 2014 05:07:29 UTC+2, Iain Dunning a écrit :
I implemented a version of simplex method for rational numbers - so you
solve it exactly in pure Julia.
https://github.com/IainNZ/RationalSimplex.jl
Not for serious work - just for fun!
On Saturday, April 12, 2014 11:50:26 AM UTC-4, Stéphane Laurent wrote:
Thank you everybody. I have updated my blog
posthttp://stla.github.io/stlapblog/posts/KantorovichWithJulia.html,
especially to include Carlo's comments.
Unfortunately I have some problems to use JuMP (I have opened another
discussion about it). And installing pycddlib on Windows 64bit is a real
nightmare.
Re: [julia-users] array with different column types
Is it a good idea to write a function *asLine* which would take a one-row
DataFrame as argument and return it as a Line object ?
Le vendredi 18 avril 2014 00:18:32 UTC+2, Stéphane Laurent a écrit :
Thank you. I need to extract the lines too. A line looks like
type Line{T}
a::T
pair:Int
end
This doesn't work, do you have something to propose :
D = DataFrame(A = [1.,2.], B = [1,2])
D[1,:]::Line{Float64}
?
Le jeudi 17 avril 2014 23:48:29 UTC+2, Simon Kornblith a écrit :
The most performant approach would be to store the columns as vectors in
a tuple or immutable. DataFrames can be nearly as performant if you:
- Extract columns (df[:mycol]) and index into them whenever possible
instead of indexing individual elements (df[1, :mycol])
- Add typeasserts when you perform indexing operations
(df[:mycol]::Vector{Int}), or pass the columns to another function
Otherwise you will incur a slowdown because the compiler doesn't know the
types.
Simon
On Thursday, April 17, 2014 5:34:24 PM UTC-4, John Myles White wrote:
It's actually possible to place pure Julia vectors in a DataFrame, which
might be convenient in this case. But you could always just store the
columns in a Vector{Any}, which is what the DataFrame does behind the
scenes anyway.
-- John
On Apr 17, 2014, at 2:27 PM, Stefan Karpinski ste...@karpinski.org
wrote:
A DataFrame does seem like a good option, but those have NA support that
you may not need. Can you elaborate a little more on the use case? Is it a
fixed set of column names and types? Or will you need to support different
schemas?
On Thu, Apr 17, 2014 at 5:16 PM, Stéphane Laurent lauren...@yahoo.frwrote:
Hello,
I need to deal with some objects represented as arrays whose some
columns are BigFloat, some columns are Int, some columns are logical. Is
it
a good idea to use a DataFrame ? Is there a better solution ?This is for a
computationally intensive program.
Re: [julia-users] julia installation on Ubuntu
Before trying 'aptitude', should I remove the previous installation, and how ? Le mercredi 16 avril 2014 04:05:17 UTC+2, Jameson a écrit : you can also try using `aptitude` instead of `apt-get`, which has more intelligent conflict and dependency resolution sudo aptitude update sudo aptitude install julia On Tue, Apr 15, 2014 at 7:44 PM, Stefan Karpinski stefan.k...@gmail.com javascript: wrote: Alternately, if you have preinstalled software that conflicts with what Julia needs, of you compile from source the Julia makefiles will download and configure exactly what they need in a subdirectory, avoiding such conflicts. On Apr 15, 2014, at 7:15 PM, Elliot Saba stati...@gmail.comjavascript: wrote: sudo authorizes a program to have administrator access to your computer. It gives what are called root permissions to a process so that it can make changes to your system. (Linux's root is Windows' Administrator) That apt-get upgrade command is complaining about an inability to upgrade a program you already have installed on your computer, looks like it has something to do with R. I'm afraid I can't help you with that. The apt-get install julia command is complaining because there's a conflict between BLAS libraries, which are the linear algebra libraries fundamental to most technical computing platforms. This usually happens when you have some other technical computing package which has installed a linear algebra library that Julia doesn't know how to use, but installing Julia's linear algebra libraries would conflict with the already installed program's. The way to move forward is to figure out which libraries are conflicting and resolve them somehow. Can you post the output of dpkg --get-selections | grep hold? -E On Tue, Apr 15, 2014 at 3:17 PM, Stéphane Laurent lauren...@yahoo.frjavascript: wrote: Hi, I have just tried to install Julia but typing julia as a command line does not run anything (command not found). I have followed the following steps: sudo add-apt-repository ppa:staticfloat/juliareleases sudo apt-get update sudo apt-get install julia I got a problem at the second step : $ sudo apt-get update ... Hit http://www.openprinting.org lsb3.2/contrib Translation-en Fetched 4,461 kB in 28s (155 kB/s) Reading package lists... Done W: GPG error: http://cran.rstudio.com precise/ Release: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 51716619E084DAB9 and at the third step : $ sudo apt-get install julia Reading package lists... Done Building dependency tree Reading state information... Done Some packages could not be installed. This may mean that you have requested an impossible situation or if you are using the unstable distribution that some required packages have not yet been created or been moved out of Incoming. The following information may help to resolve the situation: The following packages have unmet dependencies: julia : Depends: libopenblas-base but it is not going to be installed or libblas3 but it is not installable or libatlas3-base but it is not installable Depends: liblapack3 but it is not going to be installed or libatlas3-base but it is not installable Depends: libcholmod1.7.1 but it is not going to be installed Depends: libumfpack5.4.0 but it is not going to be installed Depends: libarpack2 but it is not going to be installed E: Unable to correct problems, you have held broken packages. What should I do ? Please consider I'm a newbie in Linux (I don't even know what sudo means, I'm only copying-pasting some instructions).
Re: [julia-users] Re: How to use GLPK.exact ?
Thank you Iain I will try your solver soon I hope. And thank you again Carlo, I will update my post.
[julia-users] array with different column types
Hello, I need to deal with some objects represented as arrays whose some columns are BigFloat, some columns are Int, some columns are logical. Is it a good idea to use a DataFrame ? Is there a better solution ?This is for a computationally intensive program.
Re: [julia-users] array with different column types
Yes, there is a fixed number of columns with fixed types. There's no slowness with DataFrame or the Any type (as for dataframe and list in R) ?
Re: [julia-users] array with different column types
Yes, I need BigFloat.
For example, a DataFrame with only Int columns, is as performant as an
Array{Int} ?
Le jeudi 17 avril 2014 23:39:28 UTC+2, John Myles White a écrit :
The thing that matters for speed is making sure that the columns has
appropriate types. For example, do you really need BigFloat? That's a big
performance loss -- much worse than fetching out of a Vector{Any}.
-- John
On Apr 17, 2014, at 2:38 PM, Stéphane Laurent
lauren...@yahoo.frjavascript:
wrote:
Yes, there is a fixed number of columns with fixed types. There's no
slowness with DataFrame or the Any type (as for dataframe and list in R) ?
Re: [julia-users] array with different column types
Ah ok I see now. But it's not cool because I can't extract a column. For
example
df = DataFrame(A = Line[Line(1.0, 1), Line(2.0, 2)])
I'd like to extract the column [1.0, 2.0].
I don't know what is immutable, I will see if I find. Thank you for your
help.
Le vendredi 18 avril 2014 00:34:57 UTC+2, John Myles White a écrit :
df[:A] gives you a column, which is vector of Line objects.
df[:A][1] gives you the first entry of that vector.
df[:A][1].a gives you the a element of the first entry of that vector.
I think you're better off just using a raw vector of immutables. Instead
of defining type Line, define immutable Line.
-- John
On Apr 17, 2014, at 3:33 PM, Stéphane Laurent
lauren...@yahoo.frjavascript:
wrote:
How do I extract a line and a column with this method ?
Le vendredi 18 avril 2014 00:21:26 UTC+2, John Myles White a écrit :
Each row of a DataFrame is itself a DataFrame.
Why not just store things in a vector of Line objects?
type Line{T}
a::T
pair::Int
end
df = DataFrame(A = Line[Line(1.0, 1), Line(2.0, 2)])
I've changed things from your code because there's a convention of using
uppercase letters to start the names of types.
-- John
On Apr 17, 2014, at 3:18 PM, Stéphane Laurent lauren...@yahoo.fr wrote:
Thank you. I need to extract the lines too. A line looks like
type line{T}
a::T
pair:Int
end
This doesn't work, do you have something to propose :
D = DataFrame(A = [1.,2.], B = [1,2])
D[1,:]::line{Float64}
?
Le jeudi 17 avril 2014 23:48:29 UTC+2, Simon Kornblith a écrit :
The most performant approach would be to store the columns as vectors in
a tuple or immutable. DataFrames can be nearly as performant if you:
- Extract columns (df[:mycol]) and index into them whenever possible
instead of indexing individual elements (df[1, :mycol])
- Add typeasserts when you perform indexing operations
(df[:mycol]::Vector{Int}), or pass the columns to another function
Otherwise you will incur a slowdown because the compiler doesn't know
the types.
Simon
On Thursday, April 17, 2014 5:34:24 PM UTC-4, John Myles White wrote:
It's actually possible to place pure Julia vectors in a DataFrame,
which might be convenient in this case. But you could always just store
the
columns in a Vector{Any}, which is what the DataFrame does behind the
scenes anyway.
-- John
On Apr 17, 2014, at 2:27 PM, Stefan Karpinski ste...@karpinski.org
wrote:
A DataFrame does seem like a good option, but those have NA support
that you may not need. Can you elaborate a little more on the use case? Is
it a fixed set of column names and types? Or will you need to support
different schemas?
On Thu, Apr 17, 2014 at 5:16 PM, Stéphane Laurent
lauren...@yahoo.frwrote:
Hello,
I need to deal with some objects represented as arrays whose some
columns are BigFloat, some columns are Int, some columns are logical. Is
it
a good idea to use a DataFrame ? Is there a better solution ?This is for
a
computationally intensive program.
[julia-users] julia installation on Ubuntu
Hi, I have just tried to install Julia but typing julia as a command line does not run anything (command not found). I have followed the following steps: * sudo add-apt-repository ppa:staticfloat/juliareleases* * sudo apt-get update* * sudo apt-get install julia* I got a problem at the second step : *$ sudo apt-get update* *...* *Hit http://www.openprinting.org lsb3.2/contrib Translation-en * *Fetched 4,461 kB in 28s (155 kB/s) * *Reading package lists... Done* *W: GPG error: http://cran.rstudio.com precise/ Release: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 51716619E084DAB9* and at the third step : *$ sudo apt-get install julia* *Reading package lists... Done* *Building dependency tree * *Reading state information... Done* *Some packages could not be installed. This may mean that you have* *requested an impossible situation or if you are using the unstable* *distribution that some required packages have not yet been created* *or been moved out of Incoming.* *The following information may help to resolve the situation:* *The following packages have unmet dependencies:* * julia : Depends: libopenblas-base but it is not going to be installed or* * libblas3 but it is not installable or* * libatlas3-base but it is not installable* * Depends: liblapack3 but it is not going to be installed or* * libatlas3-base but it is not installable* * Depends: libcholmod1.7.1 but it is not going to be installed* * Depends: libumfpack5.4.0 but it is not going to be installed* * Depends: libarpack2 but it is not going to be installed* *E: Unable to correct problems, you have held broken packages.* What should I do ? Please consider I'm a newbie in Linux (I don't even know what sudo means, I'm only copying-pasting some instructions).
[julia-users] problem installing JuMP
Hello, I have just installed JuMP but impossible to use it. If I type julia import JuMP then Julia is freezed, even after 5 minutes nothing happens. MathProgBase has been installed too and I have no problem to load it. *julia versioninfo()* *Julia Version 0.2.0* *Commit 05c6461 (2013-11-16 23:44 UTC)* *Platform Info:* * System: Windows (x86_64-w64-mingw32)* * WORD_SIZE: 64* * BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY)* * LAPACK: libopenblas* * LIBM: libopenlibm* *julia Pkg.status()* *Required packages:* * - DataFrames0.4.2* * - Distributions 0.3.0* * - GLM 0.2.4* * - GLPK 0.2.10* * - GSL 0.1.1* * - GnuTLS0.0.0* * - JuMP 0.4.1* * - MathProgBase 0.1.6* * - MixedModels 0.2.3* * - NLopt 0.0.3* * - NumericExtensions 0.3.6* * - RDatasets 0.1.1* * - Stats 0.1.0* * - Winston 0.10.2* *Additional packages:* * - BinDeps 0.2.12* * - Blocks0.0.2* * - Cairo 0.2.12* * - Color 0.2.9* * - DataArrays0.0.2* * - GZip 0.2.12* * - HTTPClient0.1.1* * - IniFile 0.2.2* *julia * * - LibCURL 0.1.1* * - LibExpat 0.0.4* * - Nettle0.1.3* * - SortingAlgorithms 0.0.1* * - StatsBase 0.2.10* * - Tk0.2.11* * - URIParser 0.0.1* * - URLParse 0.0.0* * - WinRPM0.0.13* * - Zlib 0.1.5 *
Re: [julia-users] Re: How to use GLPK.exact ?
Thank you everybody. I have updated my blog posthttp://stla.github.io/stlapblog/posts/KantorovichWithJulia.html, especially to include Carlo's comments. Unfortunately I have some problems to use JuMP (I have opened another discussion about it). And installing pycddlib on Windows 64bit is a real nightmare.
[julia-users] Re: problem installing JuMP
Yes. Moreover the solver packages (Clp, ...) are not correctly built, but this is expected on windows 64bit with the 64bit version of Julia: https://jump.readthedocs.org/en/release-0.4/jump.html Thus I should try Julia 32bit anyway. Le samedi 12 avril 2014 22:07:20 UTC+2, Iain Dunning a écrit : Does the same thing happen if you do `using JuMP`? On Saturday, April 12, 2014 10:05:12 AM UTC-4, Stéphane Laurent wrote: Hello, I have just installed JuMP but impossible to use it. If I type julia import JuMP then Julia is freezed, even after 5 minutes nothing happens. MathProgBase has been installed too and I have no problem to load it. *julia versioninfo()* *Julia Version 0.2.0* *Commit 05c6461 (2013-11-16 23:44 UTC)* *Platform Info:* * System: Windows (x86_64-w64-mingw32)* * WORD_SIZE: 64* * BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY)* * LAPACK: libopenblas* * LIBM: libopenlibm* *julia Pkg.status()* *Required packages:* * - DataFrames0.4.2* * - Distributions 0.3.0* * - GLM 0.2.4* * - GLPK 0.2.10* * - GSL 0.1.1* * - GnuTLS0.0.0* * - JuMP 0.4.1* * - MathProgBase 0.1.6* * - MixedModels 0.2.3* * - NLopt 0.0.3* * - NumericExtensions 0.3.6* * - RDatasets 0.1.1* * - Stats 0.1.0* * - Winston 0.10.2* *Additional packages:* * - BinDeps 0.2.12* * - Blocks0.0.2* * - Cairo 0.2.12* * - Color 0.2.9* * - DataArrays0.0.2* * - GZip 0.2.12* * - HTTPClient0.1.1* * - IniFile 0.2.2* *julia * * - LibCURL 0.1.1* * - LibExpat 0.0.4* * - Nettle0.1.3* * - SortingAlgorithms 0.0.1* * - StatsBase 0.2.10* * - Tk0.2.11* * - URIParser 0.0.1* * - URLParse 0.0.0* * - WinRPM0.0.13* * - Zlib 0.1.5 *
Re: [julia-users] Re: How to use GLPK.exact ?
Thank you for these precious informations. The JuMP package looks very awesome, I hope to give it a try soon. There was a Julia age during which BigInt(3)/BigInt(28) was equal to the BigRational 3/28, why this feature has been removed ? It would be too long to explain what my R appli here http://glimmer.rstudio.com/stla/ShinyIntrinsicMetric/ does but it returns some Kantorovich distances in exact rational numbers, it would be sad if we can't do this with Julia. By the way for another problem I need to get the vertices of the polyhedron defined by the linear constraints, as with the cddlib library, do you know how I could get that ?
Re: [julia-users] Re: How to use GLPK.exact ?
Again, thank you for all these answers. Sorry Carlo, I missed the double slash in your previous answer. It would be a good opportunity for me to call Python in order to train my skills in Python in addition to Julia. But what do you suggest me to call pycddlib with PyCall rather than calling cddlib with ccall ?
Re: [julia-users] Re: How to use GLPK.exact ?
Again, thank you for all these answers. Sorry Carlo, I missed the double slash in your previous answer. It would be a good opportunity for me to call Python in order to train my skills in Python in addition to Julia. But why do you suggest me to call pycddlib with PyCall rather than calling cddlib with ccall ?
[julia-users] Re: How to use GLPK.exact ?
Hello guys, I hope you'll enjoy this article on my bloghttp://stla.github.io/stlapblog/posts/KantorovichWithJulia.html . If you're able to use GNU MP on your machine, would you be able to find *3/28* ? Any other comment is welcomed !
[julia-users] Re: How to use GLPK.exact ?
Hello Iain, I don't understand what you mean : *julia versioninfo()* *Julia Version 0.2.0* *Commit 05c6461 (2013-11-16 23:44 UTC)* *Platform Info:* * System: Windows (x86_64-w64-mingw32)* * WORD_SIZE: * *julia * *64* *julia * * BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY)* * LAPACK: libopenblas* * LIBM: libopenlibm* *julia Pkg.status()* *Required packages:* * - DataFrames0.4.2* * - Distributions 0.3.0* * - GLM 0.2.4* * - GLPK 0.2.10* * - GSL 0.1.1* * - GnuTLS0.0.0* * - MixedModels 0.2.3* * - NLopt 0.0.3* * - NumericExtensions 0.3.6* * - RDatasets 0.1.1* * - Stats 0.1.0* * - Winston 0.9.0* *Additional packages:* * - BinDeps 0.2.12* * - Blocks0.0.2* * - Cairo 0.2.12* * - Color 0.2.9* * - DataArrays0.0.2* * - GZip 0.2.12* * - HTTPClient0.1.1* * - IniFile 0.2* *julia * *.2* * - LibCURL 0.1.1* * - LibExpat 0.0.4* * - Nettle0.1.3* * - SortingAlgorithms 0.0.1* * - StatsBase 0.2.10* * - Tk0.2.11* * - URIParser 0.0.1* * - URLParse 0.0.0* * - WinRPM0.0.13* * - Zlib 0.1.5*
[julia-users] Re: How to use GLPK.exact ?
Thank you for your answers. Unfortunately, (pre)-compiled binaries, dll, etc, is like Chinese for me. Moreover when you talk about GLP I don't know if you talk about the C library or the julia Package. Currently I was just trying GLPK for fun so this is not important. Thank you again.
[julia-users] Re: Any ANOVA or ANCOVA examples for Julia?
Hello, Please, does someone have an example of a dataframe with a factor variable ?
[julia-users] Re: Installing Julia Studio properly on Windows 7 - Step by Step Instructions
Ok after doing: julia Pkg.init() Le vendredi 4 avril 2014 00:15:47 UTC+2, Stéphane Laurent a écrit : That does not work for me: julia Pkg.status() ErrorException(Unable to read directory METADATA.)
[julia-users] Re: Any ANOVA or ANCOVA examples for Julia?
Ok I get one:
julia df = DataFrame(A = 1:4, B = [M, F, F, M])
4x2 DataFrame:
A B
[1,]1 M
[2,]2 F
[3,]3 F
[4,]4 M
julia pool!(df)
julia ModelMatrix(ModelFrame(Formula(:A,:B), df))
ModelMatrix{Float64}(4x2 Array{Float64,2}:
1.0 1.0
1.0 0.0
1.0 0.0
1.0 1.0,[0,1])
Le jeudi 3 avril 2014 23:53:46 UTC+2, Stéphane Laurent a écrit :
Hello,
Please, does someone have an example of a dataframe with a factor
variable ?
