[julia-users] Re: a good IDE for Julia ? (Julia Studio does not work with Julia v 0.3.0)
No need to wait for Atom. I've been using Atom+Juno with Julia 0.4.2 for 2 weeks ow. It works very well. Easily the best IDE I have used with Julia.
[julia-users] Re: a good IDE for Julia ? (Julia Studio does not work with Julia v 0.3.0)
Just wanted to add that I use Atom+Julia client both on Windows and Mac. The firewall issue with Atom has been resolved. The regular instruction had some issues. I suggest following the dev install. https://github.com/JunoLab/atom-julia-client/blob/master/docs/README.md
[julia-users] Re: Juno stopped working - error message
Hi all. I upgraded to 0.4.0 and basically lighttables + Jewel stopped working. The errors are pretty mucb as above. - WARNING: LightTable.jl: cannot resize array with shared data in push! at /Applications/Julia-0.4.0.app/Contents/Resources/julia/lib/julia/sys.dylib in read_operator at /Users/Andre/.julia/v0.4/JuliaParser/src/lexer.jl:368 in next_token at /Users/Andre/.julia/v0.4/JuliaParser/src/lexer.jl:752 in qualifiedname at /Users/Andre/.julia/v0.4/Jewel/src/parse/scope.jl:59 in nexttoken at /Users/Andre/.julia/v0.4/Jewel/src/parse/scope.jl:78 in nextscope! at /Users/Andre/.julia/v0.4/Jewel/src/parse/scope.jl:116 in scopes at /Users/Andre/.julia/v0.4/Jewel/src/parse/scope.jl:149 [inlined code] from /Users/Andre/.julia/v0.4/Lazy/src/macros.jl:141 in codemodule at /Users/Andre/.julia/v0.4/Jewel/src/parse/parse.jl:8 in getmodule at /Users/Andre/.julia/v0.4/Jewel/src/eval.jl:42 in anonymous at /Users/Andre/.julia/v0.4/Jewel/src/LightTable/eval.jl:51 in handlecmd at /Users/Andre/.julia/v0.4/Jewel/src/LightTable/LightTable.jl:70 in handlenext at /Users/Andre/.julia/v0.4/Jewel/src/LightTable/LightTable.jl:86 in server at /Users/Andre/.julia/v0.4/Jewel/src/LightTable/LightTable.jl:27 in server at /Users/Andre/.julia/v0.4/Jewel/src/Jewel.jl:23 in include at /Applications/Julia-0.4.0.app/Contents/Resources/julia/lib/julia/sys.dylib in include_from_node1 at /Applications/Julia-0.4.0.app/Contents/Resources/julia/lib/julia/sys.dylib in process_options at /Applications/Julia-0.4.0.app/Contents/Resources/julia/lib/julia/sys.dylib in _start at /Applications/Julia-0.4.0.app/Contents/Resources/julia/lib/julia/sys.dylib - I can no longer use Lighttable because of this. Tried some of the fixes above but no seemed to work. Has this been solved some how? Julia Version 0.4.0 Commit 0ff703b* (2015-10-08 06:20 UTC) Platform Info: System: Darwin (x86_64-apple-darwin13.4.0) CPU: Intel(R) Core(TM) i7-4650U CPU @ 1.70GHz WORD_SIZE: 64 BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell) LAPACK: libopenblas64_ LIBM: libopenlibm LLVM: libLLVM-3.3 THANKS, Andre
[julia-users] Re: 4th Julia meetup in Japan: JuliaTokyo #4.
Some of the slides are already available here. More should be posted shortly. http://juliatokyo.connpass.com/event/16570/presentation/ I few of them are in English. I noticed that more and more participates are presenting using English slides despite the fact that the audience is near 100% native Japanese speakers. Which I think is pretty amazing! Also, the vibe at the Julia Tokyo is really great. Lots of people helping each out with some really fun n' interesting conversation in the after-party. Feel free to contact us if you are visiting Japan. We would love to have you! Andre On Monday, 13 July 2015 02:53:28 UTC+9, Viral Shah wrote: Please email juli...@googlegroups.com javascript: if you see such a timeout. Often it just means that a new machine is booting up, and things should work in a few minutes. Sounds like a really fun meetup. BTW, are any of these slides in English - and if so, are they available anywhere? -viral On Sunday, July 12, 2015 at 1:16:45 PM UTC+5:30, ther...@gmail.com javascript: wrote: Hi, On July 11th we had our 4th Julia meetup in Japan, JuliaTokyo #4. This time we had 30+ perticipants. --- JuliaTokyo #4 Presentation List in English # Hands-on Session by Michiaki Ariga https://github.com/chezou/JuliaTokyoTutorial (We tired to use JuliaBox, but failed with Maximum number of JuliaBox instances active. Please try after sometime. ...) # Main Talks 1. JuliaCon2015 Report - Sorami Hisamoto 2. Julia Summer of Code: An Interim Report - Kenta Sato 3. High-performance Streaming Analytics using Julia - Andre Pemmelaar 4. Why don't you create Spark.jl? - @sfchaos 5. Introducing QuantEcon.jl - Daisuke Oyama # Lightning Talks 1. Material for Julia Introduction Materials - @yomichi_137 2. Characteristic Color Extraction from Images - @mrkn 3. Julia and I, sometimes Mocha - @vanquish 4. It's Time for 3D Priting with Julia - uk24s 5. Mecha-Joshi Shogi (AI Japanese Chess) - @kimrin 6. Gitter and Slack - Michiaki Ariga --- We also had a survey on what kind of languages and softwares people use on a daily basis. 56 people (multiple choices allowed); language, #people Python, 37 R, 21 C / Julia, 14 Java, 13 C++ / Ruby, 12 Excel, 7 Perl, 5 SAS / Scala, 4 Go / JavaScript, 3 Matlab / Visual Basic / Haskell / PHP / Objective C / D, 2 Clojure / F# / C# / .Net / SQL / Apex / ECMAScript / Elixir / Swift / Erlang / CUDA, 1 - sorami
[julia-users] Matlab minfunc - Optim.jl where the function evaluates the main cost function and its gradient simultaneously
I'm porting some Matlab code to Julia. The optimization objective function evaluates the main cost function and its gradient simultaneously. Some of the interim calculations from the cost function are plugged into to gradient calculation to avoid making the same calculation twice. Here is the actual function. function SparseFilteringObj (W, X, N) # Reshape W into matrix form W = reshape(W, (N, size(X,1))) # Feed Forward F = W * X # Linear Activation Fs = sqrt(F.^2 + 1e-8) # Soft-Absolute Activation NFs, L2Fs = l2row(Fs) # Normalize by Rows Fhat, L2Fn = l2row(NFs') # Normalize by Columns # Compute Objective Function Obj = sum(sum(Fhat, 2), 1) # Backprop through each feedforward step DeltaW = l2grad(NFs', Fhat, L2Fn, ones(size(Fhat))) DeltaW = l2grad(Fs, NFs, L2Fs, DeltaW') DeltaW = (DeltaW .* (F ./ Fs)) * X' DeltaW = DeltaW[:] return Obj, DeltaW end This is my first time using Optim.jl. It seems the interface requires that the objective function be separated into a cost and a gradient function, but it also says that I can get better performance by providing a third function, DifferentiableFunction(f,g!), that calculates both of these simultaneously. So, as I understand it, I have to split them up, and then re-combine them using DifferentiableFunction(f,g!) to get better performance. Is this correct? Any suggestions on how to split this in a way that avoids duplicating calculation? Do all the calculation that are shared as inline calcs perhaps? It feels like I missing some easy solution. Any advice would be appreciated. Gist of the as yet incomplete port: https://gist.github.com/Andy-P/5c88e524d46a3749ba5f Original matlab code http://cs.stanford.edu/~jngiam/papers/NgiamKohChenBhaskarNg2011_Supplementary.pdf
[julia-users] Succinct syntax for removing a column from a DataFrame using the column name
I have a data frame with a large number of columns and I would like to remove certain unnecessary columns as part of the initial data wrangling. I can do this succinctly using the numeric index... using DataFrames A = DataFrame(A=1:3, B=4:6, C=7:9, D=10:12, E=13:15) B = A[:, setdiff(1:end,[3,4])] # returns DFs without columns C D Is there a way to do this using the column names rather than their numeric indices? Andre
[julia-users] Re: Succinct syntax for removing a column from a DataFrame using the column name
I figured out one way to do this... B = A[:, setdiff(names(A),[symbol(D), symbol(E)])] # removes columns C D using column names A less verbose way? On Wednesday, July 2, 2014 7:01:56 AM UTC+9, Andre P. wrote: I have a data frame with a large number of columns and I would like to remove certain unnecessary columns as part of the initial data wrangling. I can do this succinctly using the numeric index... using DataFrames A = DataFrame(A=1:3, B=4:6, C=7:9, D=10:12, E=13:15) B = A[:, setdiff(1:end,[3,4])] # returns DFs without columns C D Is there a way to do this using the column names rather than their numeric indices? Andre
Re: [julia-users] Re: orthogonalize
I have some Matlab code I'm porting to Julia
BiasHN = rand(HN(i+1),1)*2 -1;
BiasHN = orth(BiasHN);
Was the equivalent of the orth() from Matlab added to Julia? Can't seem to
find it.
Andre
On Thursday, April 3, 2014 10:32:31 PM UTC+9, Alan Edelman wrote:
Maybe something like this
function orth(A,thresh=eps(A[1]))
(U,S)=svd(A)
U[:, S.S[1]*thresh]
end
orth(float(A))
20x4 Array{Float64,2}:
-0.223607 -0.2136510.1837530.265684
-0.223607 -0.2136510.1837530.265684
-0.223607 -0.2136510.1837530.265684
-0.223607 -0.2136510.1837530.265684
-0.223607 -0.2136510.1837530.265684
-0.223607 0.0371419 -0.3725030.0993058
-0.223607 0.0371419 -0.3725030.0993058
-0.223607 0.0371419 -0.3725030.0993058
-0.223607 0.0371419 -0.3725030.0993058
-0.223607 0.0371419 -0.3725030.0993058
-0.223607 0.3503580.163883 -0.0197973
-0.223607 0.3503580.163883 -0.0197973
-0.223607 0.3503580.163883 -0.0197973
-0.223607 0.3503580.163883 -0.0197973
-0.223607 0.3503580.163883 -0.0197973
-0.223607 -0.1738490.0248676 -0.345193
-0.223607 -0.1738490.0248676 -0.345193
-0.223607 -0.1738490.0248676 -0.345193
-0.223607 -0.1738490.0248676 -0.345193
-0.223607 -0.1738490.0248676 -0.345193
On Thursday, April 3, 2014 9:23:14 AM UTC-4, Alan Edelman wrote:
I would use the svd with a threshold based on the norm
and optionally adjustable
[julia-users] deepcopy() immutable types working as intended?
Hi,
When using deepcopy() to copy an immutable type it does not copy everything
recursively.
There is a discussion here
https://groups.google.com/d/msg/julia-users/kug8rdy5JYo/0d9014DugOgJ in
which Stefan Karpinski requests a bug be filed resulting in #3301.
But the behavior seems to be the same. So, is deepcopy() supposed to copy
inner arrays or not?
Here is an example:
First using an non-immutable type
type foo
myArr::Array{Int64,1}
end
f1 = foo([1,1])
f2 = f1
f3 = deepcopy(f1)
f4 = foo(copy(f1.myArr))
println(f1: $(f1) \nf2: $(f2) \nf3: $(f3) \nf4: $(f4)\n)
# prints...
# f1: foo([1,1])
# f2: foo([1,1])
# f3: foo([1,1])
# f4: foo([1,1])
f2.myArr[1]= 2
f3.myArr[1]= 3
f4.myArr[1]= 4
println(f1: $(f1) \nf2: $(f2) \nf3: $(f3) \nf4: $(f4)\n)
#prints:
# f1: foo([2,1])
# f2: foo([2,1])
# f3: foo([3,1])
# f4: foo([4,1])
The above non-immutable type functions exactly as expected.
Now, the same using immutable types
immutable Immfoo
myArr::Array{Int64,1}
end
Imm1 = Immfoo([1,1])
Imm2 = Imm1
Imm3 = deepcopy(Imm1)
Imm4 = Immfoo(copy(Imm1.myArr))
println(Imm1: $(Imm1) \nImm2: $(Imm2) \nImm3: $(Imm3) \nImm4: $(Imm4)\n)
# prints...
# Imm1: Immfoo([1,1])
# Imm2: Immfoo([1,1])
# Imm3: Immfoo([1,1])
# Imm4: Immfoo([1,1])
Imm2.myArr[1]= 2
Imm3.myArr[1]= 3
Imm4.myArr[1]= 4
println(Imm1: $(Imm1) \nImm2: $(Imm2) \nImm3: $(Imm3) \nImm4: $(Imm4)\n)
# prints...
# Imm1: Immfoo([3,1])
# Imm2: Immfoo([3,1])
# Imm3: Immfoo([3,1])
# Imm4: Immfoo([4,1])
Here, Imm3 is pointing at the same array as Imm1 and Imm2.
Is this as intended?
Andre
[julia-users] Re: Julia meetup in Japan: JuliaTokyo
I thought you all might find it interesting to know that this was over subscribed in a matter of hours. It was at 43 people for 40 person space within 2-3 hours and it now at 53/40. Some will not show up, but this is a very good sign. Andre On Thursday, June 12, 2014 1:20:09 PM UTC+9, Viral Shah wrote: This is really great. I have added it to the communities section of the Julia homepage. -viral On Wednesday, June 11, 2014 7:55:28 PM UTC+5:30, ther...@gmail.com wrote: Hi all, We will start a Julia study group in Tokyo, Japan named JuliaTokyo. http://juliatokyo.connpass.com/event/6891/ http://www.google.com/url?q=http%3A%2F%2Fjuliatokyo.connpass.com%2Fevent%2F6891%2Fsa=Dsntz=1usg=AFQjCNHDFSoVasK9J5HrBHoJUg4uDteW2A The first event will be on 5th July 2014; we've got more than 50 participants in less than a day! - Sorami
[julia-users] Re: deepcopy() immutable types working as intended?
After thinking about this a bit more deeply, it occurred to me that maybe this *is* the right behavior. After all, this is an immutable type, so I really shouldn't be modifying the internal values of array fields anyway. If this immutable type array field happened to have a massive array attached to it, it might make sense to have both immutable objects point at that same array provided that array could not be mutated. The problem is that one *can* mutate the underlying array and undisciplined sods like me go and doing things like the above.
[julia-users] Importing Datetime data with microsecond fidelity
I have just started using Julia. I have a csv data files that use microsecond fidelity time stamps ex: 2013-10-20T23:00:00.036928Z I would like to import these into Julia, but I haven't been able to find any documentation on using Datetime types with anything beyond millisecond fidelity. Is there a way to do this? Andre
