Re: [julia-users] Re: How to built an array with some keys of a dictionary ?

a = Vector{String} is also a good option. On Monday, November 7, 2016 at 5:27:49 PM UTC+2, Steven G. Johnson wrote: > > > > On Monday, November 7, 2016 at 9:45:37 AM UTC-5, Mauro wrote: >> >> On Mon, 2016-11-07 at 15:27, Fred wrote: >> > Hi Steven, >> > >> > >> > I

Re: [julia-users] Re: How to built an array with some keys of a dictionary ?

I meant, a = Vector{String}() Dan On Tuesday, November 8, 2016 at 9:39:53 AM UTC+2, Dan wrote: > > a = Vector{String} > > > > is also a good option. > > On Monday, November 7, 2016 at 5:27:49 PM UTC+2, Steven G. Johnson wrote: >> >> >> >> On Mon

[julia-users] Re: jump ahead in Mersenne Twister

not too hard as well. G'day, Dan Getz On Friday, October 21, 2016 at 3:43:05 AM UTC+3, Joaquim Masset Lacombe Dias Garcia wrote: > > Hi, > > Have anybody implemented an arbitrary jump ahead for Mersenne Twister in > julia? > > I saw that the randjump documentatio

Re: [julia-users] Please help me to convert a BitArray to a Integer array in Julia

If saving characters is a thing, then: julia> a = rand(Bool,3,2) 3×2 Array{Bool,2}: false false true false false true julia> 1a 3×2 Array{Int64,2}: 0 0 1 0 0 1 On Monday, October 17, 2016 at 2:08:44 PM UTC+3, Scott Jones wrote: > > Tim, do you know if there is any difference in

[julia-users] Re: julz: a command line utility for creating scalable Julia applications

This is what @dpsanders brought up in issue #22 . Summarizing my response there (repeated below), I wanted to get a prototype working to gauge the likelihood of adoption. " it probably could be, the truth is: + I wanted to bang

[julia-users] Re: julz: a command line utility for creating scalable Julia applications

Sorry this wasn't clear! - Julz is a framework for building Julia projects. - It is *not* designed for making webpages, I was just trying to highlight the analogy The reason for the weird structure of the project is that: - this is a CLI

[julia-users] Re: julz: a command line utility for creating scalable Julia applications

One more thing I wanted to say is, - I tried to do with julz what ember-cli did for ember.js - I really think ember-cli shaped their community into what it is today -- > *Code* > >- github repo:

[julia-users] Re: julz: a command line utility for creating scalable Julia applications

- One final note is I tried to do with julz what ember-cli did for Ember.js - I really think the addition of ember-cli into the ecosystem changed the community in a way they never thought possible

[julia-users] Re: julz: a command line utility for creating scalable Julia applications

ober 6, 2016 at 10:12:21 AM UTC-4, Dan Segal wrote: > > *Code* > >- github repo: https://github.com/djsegal/julz >- (see readme.rst as well as included dummy Julia project) > > *Summary* > > Julz is a command line utility for creating ambitious Julia appl

[julia-users] julz: a command line utility for creating scalable Julia applications

*Code* - github repo: https://github.com/djsegal/julz - (see readme.rst as well as included dummy Julia project) *Summary* Julz is a command line utility for creating ambitious Julia applications. Its goal is to remove many of the petty decisions involved in starting a project and

[julia-users] julz: a command line utility for creating scalable Julia applications

Summary Julz is a command line utility for creating ambitious Julia applications. Its goal is to remove many of the petty decisions involved in starting a project and establish a standard that can be adopted by the entire Julia ecosystem. In this way it channels Ruby on Rails’ mantra of

Re: [julia-users] Return structure-like elements from a function

That last line was probably meant to be paths = Vector{Matrix{Float64}}(4) On Tuesday, September 27, 2016 at 7:01:14 PM UTC-4, Yichao Yu wrote: > > On Tue, Sep 27, 2016 at 6:28 PM, Zhilong Liu > wrote: > > Hello All, > > > > I am trying to return values to elements

[julia-users] Re: Broadcast slices

best_response_dimension, _, 1:length(_) ) > zip(_...) > map(all, _) > end > > On Sunday, September 25, 2016 at 11:57:47 AM UTC-4, Dan wrote: >> >> Oops, that `cat` code was supposed to be: >> >> cat(1,map(x->reshape(x,1,size(x)...),array_of_array)...)

[julia-users] Re: Broadcast slices

Oops, that `cat` code was supposed to be: cat(1,map(x->reshape(x,1,size(x)...),array_of_array)...) Mew! On Sunday, September 25, 2016 at 11:54:43 AM UTC-4, Dan wrote: > > OK. So, to get the array to have the first dim as the player selector, you > can go: > > cat(1,map(x

[julia-users] Re: Broadcast slices

1 array just seems so much more neat. Which is why I > was looking for a neat implementation of broadcast_slices to match. > > On Sunday, September 25, 2016 at 10:53:57 AM UTC-4, Dan wrote: >> >> Have you found the right implementation? >> >> Fiddling a bit, I ten

[julia-users] Re: Broadcast slices

Have you found the right implementation? Fiddling a bit, I tend to agree with Steven G. Johnson `for` loops would be the most efficient and probably the most understandable implementation. Also, would it not be easier to have the first index in the `payoff_matrix` determine which player's

[julia-users] Re: dependent types in julia?

Maybe the following is the form you are looking for: julia> decomplexify{T}(::Type{Complex{T}}) = T decomplexify (generic function with 1 method) julia> type bar{S,T} sum::S sumsqr::T function bar(s,ss) if typeof(ss) != decomplexify(typeof(s))

Re: [julia-users] Julia Zmq Server creates Memory Leaks

This did the trick. Thanks!

[julia-users] Julia Zmq Server creates Memory Leaks

Hi, I am trying to create a Julia server instance by using ZMQ with the Req/Rep pattern. My Julia code for the Rep-server looks like this: module JServer # import zmq using ZMQ import Base.gc # create context ctx=Context(); # create socket socket=Socket(ctx, REP); # msg msg = 0; #

[julia-users] Re: Unexpected slowdown

Try keeping the loop over all primes and breaking when `a > maxp` where maxp is calculated once before the loop as `maxp = floor(Int,sqrt(n))`. On Monday, August 22, 2016 at 4:58:30 PM UTC-4, Achu wrote: > > I have a simple piece of code that finds me 10001 prime numbers. > > function a() >

[julia-users] Re: Unexpected Performance Behaviour

The following standalone version recovers the speed of `test2` using the @fastmath macro. Integer exponents have trade-offs in implementation using repeated squaring and multiplication and intrinsic power instructions. Not really sure how to control the implementation used in every instance.

[julia-users] Re: Linear-Equation-System Solver("\") does not work reliably for the "Rational" Type

be A*x == v is true. Hope this helps. -- Dan On Friday, July 15, 2016 at 2:50:01 PM UTC-4, Kurolong wrote: > > Hey Guys and Gals ^^ > I'm having trouble with Julia. Maybe one of you can help me out > The program i am writing requires the > linear-equation-system-solver(command &qu

[julia-users] Re: Want: "Deprecation" badge for Github

+1 There was also a suggestion at JuliaCon the badge could count the number of warnings displayed - perhaps, this would give some incentive for progressive cleanup/levelingup of code. On Tuesday, July 12, 2016 at 1:15:24 PM UTC-4, Erik Schnetter wrote: > > Our Julia projects live on Github,

[julia-users] Re: caching the pivot ordering for a sparse cholesky factorization?

solution it would be interesting to hear about it. On Sunday, July 10, 2016 at 9:38:44 PM UTC-4, Gabriel Goh wrote: > > I'd love more details Dan! > > On Saturday, July 9, 2016 at 8:53:16 PM UTC-7, Dan wrote: >> >> Hi Gabe, >> >> SuiteSparse which comes togeth

[julia-users] Re: caching the pivot ordering for a sparse cholesky factorization?

Hi Gabe, SuiteSparse which comes together with Julia includes a library KLU which does sparse LU factorization. It has the option to return the fill-in reducing order it manages to find for a given sparsity pattern. With this returned structure it can subsequently factorize many matrices (and

[julia-users] Re: changing record separator to read text files

`readuntil` is your friend. `readline` uses `readuntil`. In fact, it is defined as: readuntil(s,'\n') On Saturday, July 9, 2016 at 7:40:37 AM UTC-4, Fred wrote: > > Hi ! > > It is often very useful to read a text files by blocks of lines, using > another line separator than '\n'. > >

[julia-users] Re: Another nested function scope issue

Can you explain why it modified the one from the second block? On Friday, July 1, 2016 at 9:58:26 AM UTC-4, FANG Colin wrote: > > Never mind, I get it. > > On the third run, the `state` variable that `counter` modified is the one > from the second let block. >

[julia-users] Re: readdlm() pound sign at beginning of string is not read

Yes, this is the intended functionality because # is used a character which starts a comment in the file which is not parsed. The simplest way around this is to disble comments using the named parameter `comments` like this: readdlm(file,'\t',comments=false) You can also change the

Re: [julia-users] How to make a Matrix of Matrix's?

[[M] [M]] works. And is the same as Matrix{Float64}[[M] [M]] But concur it is unintuitive. On Thursday, June 23, 2016 at 10:28:39 PM UTC-4, Sheehan Olver wrote: > > > [M,M] will do a Vector of Matrices in 0.5. But [M M] still does > concatenation. So the question is how to do

[julia-users] Re: Value written to file different from value printed

The print to screen statement is rather unequivocal. By elimination, the -1 in the file is the culprit. My guess is the -1 in the file is the value of a different variable. When writing to the file, add some text to identify which line is which var. Notice all vars are initialized to -1. P.S.

Re: [julia-users] Abstract version of rng=1:end ?

A reason such 'extended' ranges might be good, is the ability to dispatch on them for efficiency. For example, `deleteat!(vec,5:end)` could be implemented faster than just any `deleteat!` of any range. This would be applicable to other such structures with edge effects. Indeed, we can learn

[julia-users] Re: local package, howto?

Not sure, but I may have had a similar problem and doing (in the shell): git config --global url."https://".insteadOf git:// solved it. On Sunday, June 12, 2016 at 1:42:13 PM UTC-4, Andreas Lobinger wrote: > > Hello colleagues, > > like with similar problems, i don't know how i ended up

[julia-users] Re: Keeping heads and tails

Great. This is exactly what I needed. On Thursday, June 9, 2016 at 5:11:05 PM UTC-4, Kristoffer Carlsson wrote: > > Use resize! maybe.

[julia-users] Keeping heads and tails

Hello All, Given a vector (v) of length n, if I want to truncate it to its first k elements. How does one do this? My code currently uses `deleteat!(v,(k+1):length(v))`. But this is somewhat cumbersome and might be inefficient since `deleteat!` has to take care of all other ranges of elements

Re: [julia-users] confusion with Julia -p 2

The most funny part - I managed to reduce the running time to 3 seconds, rofl. In my code I use a lot of computations of the dot(A*B*f,f) for matrices A,B and vectors f filled with complex{float64}. But this is the same as dot(B*f,A'*f) !! So, multiplication of matrices was the most

Re: [julia-users] confusion with Julia -p 2

I must add that making code parallel was worth it! In my particular case I've got the following numbers: 21s - parallel code, blas_set_num_threads doesn't affect this much 32s - single-threaded code with blas_set_num_threads(2) 94s - single-threaded code with blas_set_num_threads(1) Also

Re: [julia-users] confusion with Julia -p 2

g > line: blas_set_num_threads(CPU_CORES) > > I've done this myself and had parallel julia processes each using more > than one core. Make use that line of code is used in each parallel > instance being called. > > On Saturday, April 23, 2016 at 4:29:40 AM UTC-6, Dan Y wrote: &

Re: [julia-users] confusion with Julia -p 2

Maybe it is because I use matrix multiplication in my code. I guess that base library which is responsible for linear algebra uses multiple cores automatically. But with "-p 2" it is forced to work only on 1 core for some reason. It's only a guess, though.

[julia-users] Re: Should Iterators be nonmutating?

ere it was before. > So it looks like we are not mutating the underlying object. > (cos we undo our changes) > This would not be threadsafe though. > > > On Saturday, 23 April 2016 05:22:11 UTC+8, Dan wrote: >> >> Looks like it might be appropriate to make the `stream` in `Each

[julia-users] Re: Should Iterators be nonmutating?

Looks like it might be appropriate to make the `stream` in `Eachline` be the state of the iterator (instead of the `nada` returned by `next`). Opening an issue (perhaps with a PR of a fix) could be the next step. Unless someone finds some other logic for keeping `Eachline` the way it is. On

[julia-users] confusion with Julia -p 2

I use julia-0.4.5 on windows10 with pentium B970 (2 cores, 2 threads) I have a code with no parallel instructions. When I run it in a standard session with no options my CPU utilization is about 80-90%. But when I run it in a session with "Julia -p 2" the CPU utilization is only about 20-25% .

[julia-users] Re: Modifying large vectors of repeated data

Similar `rle` and `inverse_rle` functions exists in StatsBase.jl https://github.com/JuliaStats/StatsBase.jl/blob/master/src/misc.jl#L15 . These function may be enough for the application involved. On Thursday, April 14, 2016 at 6:48:40 PM UTC+3, Kenta Sato wrote: > > I guess what you want is

[julia-users] Re: Fast coroutines

variable (and not const) which is a source of trouble for Julia type-inference based optimization (it is mentioned in the Performance Tips). So try to wrap up things in a function or make them const (but make sure they are not global which cannot be relied to keep their types). Dan On Saturday

[julia-users] Re: How do I, as efficiently as possible, iterate over the first and last elements along a given dimension of an array?

as a matrix reshape(map(x->A3[x[1]]+A3[x[2]],mapedges(A3,3)),dropdim(A3,3)) On Thursday, March 24, 2016 at 3:21:02 AM UTC+2, Dan wrote: > > # something along these lines: > > topleft(A,d) = tuple(ones(Int,ndims(A))...) > bottomleft(A,d) = tuple([i==d ? 1 : e for (i,e)

[julia-users] Re: How do I, as efficiently as possible, iterate over the first and last elements along a given dimension of an array?

# something along these lines: topleft(A,d) = tuple(ones(Int,ndims(A))...) bottomleft(A,d) = tuple([i==d ? 1 : e for (i,e) in enumerate(size(A))]...) topright(A,d) = tuple([i==d ? e : 1 for (i,e) in enumerate(size(A))]...) bottomright(A,d) = size(A) mapedges(A,d) = zip(

Re: [julia-users] Re: Mmap allocation question

g the code from the REPL, may that be a problem? (As I read in > the REPL everything is global). In the file nothing is global. > Also, the counters are UInt16s, but that shouldnt matter I guess. > > Thanks for the help so far! > > On Saturday, 12 March 2016 14:22:38 UTC+1, Dan wrot

Re: [julia-users] Re: Mmap allocation question

It's better to have code which actually runs in the post. In any case, the allocations at the `for` lines is suspicious - the for should basically only allocate a counter. Are there any global variables? Is `counter1` or `counter2` or `dims` global? Globals are always a good source of confusion

[julia-users] Re: what name would you give to the functional 'complement' of trunc()?

trunc_to_inf and add an alias trunc_to_zero for regular trunc ?? On Saturday, March 12, 2016 at 1:49:41 PM UTC+2, Jeffrey Sarnoff wrote: > > x = trunc(fp) yields fp rounded towards zero to the closest integer valued > float. > What name would you give to the complementary function, rounding away

[julia-users] Re: What wrong in (D'*D)/k.-(mean(D,1)'*mean(D,1)) ?

The matrix `D` is based on type `Int8` which overflows and wraps (without error) very quickly. The `cov` function automatically converts to a `Float64` but the manual calculation does not. To make the calculations match, try: D = convert(Matrix{Int},D) On Friday, March 4, 2016 at 10:42:02 AM

Re: [julia-users] Adding element to array and filtering multi-dimensional arrays.

On Monday, February 22, 2016 at 12:18:02 PM UTC+2, Tamas Papp wrote: > > > On Mon, Feb 22 2016, Aleksandr Mikheev wrote: > > > Hi everyone. I have two simple questions, answer to which I cannot find > by > > myself. > > > > 1. Is there any way to add an element to array? For example, I have

Re: [julia-users] Adding a mtrix to the third dimension of a multi-dimensional array

>From the help for `cat`: cat(dims, A...) Concatenate the input arrays along the specified dimensions in the iterable dims And indeed, if size(M1)=(3,3,3) and size(M2)=(3,3) Then, size(cat(3,M1,M2)) = (3,3,4) This method may not be efficient (though in terms of memory

Re: [julia-users] randperm run time is slow

A good description of random permutation generation is given in Wikipedia. It's the Fisher-Yates algorithm and its variants. On Saturday, January 23, 2016 at 4:43:36 PM UTC+2, Kevin Squire wrote: > > Hi Dan, > > It would also be good if you deleted that post (or edited it

Re: [julia-users] randperm run time is slow

at 4:43:36 PM UTC+2, Kevin Squire wrote: > > Hi Dan, > > It would also be good if you deleted that post (or edited it and removed > the code), for the same reason Viral mentioned: if someone reads the post > and then creates a pull request for changing the Julia implement

Re: [julia-users] randperm run time is slow

Another way to go about it, is to look at R's implementation of a random permutation and recreate it in Julia, hoping for similar performance. Having done so, the resulting Julia code is: function nrandperm(r::AbstractRNG, n::Integer) res = Array(typeof(n),n) if n == 0

[julia-users] Re: What's the difference between unique(a) and union(a)

Essentially, not much. `union` accepts multiple arguments with multiple types, but `unique` accepts a single collection. For example: union(1,2,3,3) == unique([1,2,3,3]) This would probably make `union` slightly slower. On Wednesday, January 13, 2016 at 11:58:18 PM UTC+2, Sheehan Olver wrote: >

Re: [julia-users] Iterating over leafs of a tree: use Cartesian?

For the special problem of getting all the betas in the post, they can be calculated with combinatorics: betas(levels,parts) = map(x->([x;levels+parts].-[0;x]-1)/parts, combinations(1:(levels+parts-1 ),(levels-1))) The idea is to choose which parts of (0.0,0.2), (0.2,0.4)... (0.8,1.0) go

Re: [julia-users] Re: Optimize sum of elements in an Array according to some conditions

at 11:54:27 PM UTC+2, Charles Santana wrote: > > Thanks, Dan! Indeed my "x" vector is sorted and your suggestion is really > fast! > > Best, > > Charles > > On 30 December 2015 at 12:08, Dan <get...@gmail.com > wrote: > >> Thanks Kristoffer, tu

[julia-users] Re: Optimize sum of elements in an Array according to some conditions

Thanks Kristoffer, turns out there is always interesting stuff in the bag of optimization tricks. Regarding the original function, a cheat could make it faster: The `x` vector is sorted, which means: function calcSum4(x::Array{Float64,1}, y::Array{Float64,1}, Ei::Float64, Ef::Float64,

[julia-users] Re: python a[range(x), y] equivalent in Julia

Is the following red expression a good Julia equivalent: julia> a = [ -1 2; 3 -4 ] ⋮ julia> y = [ 1, 2 ] ⋮ julia> [a[i,y[i]] for i=1:2] 2-element Array{Any,1}: -1 -4 (it's also fast) On Sunday, December 20, 2015 at 2:44:20 AM UTC+2, Lex wrote: > > I am expecting [-1 -4] in the Julia

[julia-users] Re: Using callable types or FastAnonymous with Sundials

The parameter for the `cvode` function is `f` and so is the function you want to use. These get confused, and it tries to use the "function" `J` instead. Changing the parameter name to something other than `{f}` should work On Wednesday, December 16, 2015 at 4:26:41 PM UTC+2, Simon Frost

[julia-users] Re: Compute selected eigenvectors with eigvecs

Linear algebra tells us Symmetric matrices have real eigenvalues and general matrices have complex eigenvalues in general. `eigvecs` can be called with an upper and lower bound on the eigenvalues of the eigenvectors to be found, but upper and lower bounds are applicable only to Real numbers,

[julia-users] Re: DataFrame melt question

the following re-reads the header and generates a dictionary which assigns the original column name to the converted one, in a one-liner-ish: df = readtable("/the/file.csv") h = Dict(zip(keys(df.colindex.lookup),split(open("/tmp/file.csv") do f chomp(readline(f)) ;

[julia-users] A non-breaking Julia control-flow change to separate the One language from mortal languages, doomed to die

*A non-breaking Julia control-flow change to separate the One language from mortal languages, doomed to die * >From a design patterns and DRY perspective it seems `for` loops together with >the `break` statement are serving as a useful `try`/`catch` mechanism for non-error situations. A common

[julia-users] A non-breaking Julia control-flow change to separate the One language from mortal languages, doomed to die

*A non-breaking Julia control-flow change to separate the One language from * *mortal languages, doomed to die * >From a design patterns and DRY perspective it seems `for` loops together with >the `break` statement are serving as a useful `try`/`catch` mechanism for non-error situations. A

[julia-users] A non-breaking Julia control-flow change to separate the One language from mortal languages, doomed to die

*A non-breaking Julia control-flow change to separate the One language from * *mortal languages, doomed to die * >From a design patterns and DRY perspective it seems `for` loops together with >the `break` statement are serving as a useful `try`/`catch` mechanism for non-error situations. A

Re: [julia-users] Re: A non-breaking Julia control-flow change to separate the One language from mortal languages, doomed to die

yntax needs cleaning up. > > -erik > > > > On Wed, Dec 9, 2015 at 10:47 AM, Seth <catc...@bromberger.com > > wrote: > >> Seems to me that this is just a more complex repeat...until or do...while >> bottom-conditional loop, which has already been discusse

Re: [julia-users] Re: `findmin` with arbitrary comparison operator?

suggestion: call the named argument `lt` to match the argument of `sort`. On Tuesday, December 1, 2015 at 6:23:07 PM UTC+2, Júlio Hoffimann wrote: > > Hi Erik, > > What I meant was a derived type from Julia's Tuple type. Though I agree > with you that this may be too much of an overhead for such

Re: [julia-users] Re: slow wordcount

and replacing println("$(t.first)\t$(t.second)") with @printf("%s\t%d\n",t.first,t.second) also halves the print time (which might or might not make a big difference but definitely > 1 second) On Monday, November 30, 2015 at 6:20:54 PM UTC+2, Dan wrote:

Re: [julia-users] Re: slow wordcount

ditacs/wordcount#leaderboard for details) > > Dan: > I can use external packages, it's not a big issue. However, FastAnonymous > didn't give results comparable to python. > The baseline python code I compare to is here: > https://github.com/juditacs/wordcount/blob/m

Re: [julia-users] Re: slow wordcount

: for t in sort(collect(wc), lt=customlt) println(t.first, "\t", t.second) end end On Monday, November 30, 2015 at 5:08:56 PM UTC+2, Dan wrote: > > Can you provide the comparable python code? Perhaps even the data used for > testing? > > Since you are eval

Re: [julia-users] Re: slow wordcount

Can you provide the comparable python code? Perhaps even the data used for testing? Since you are evaluating Julia, there are two important points to remember: 1) In Julia because the language is fast enough to implement basic functionality in Julia, then the distinction between Base Julia and

Re: [julia-users] Re: slow wordcount

Some points: 1) If you are timing the program by running from shell (i.e. including the whole Julia load time) - it's not a good idea for a bunch of reasons. 2) The printouts are also a little dubious to be used in a test. 3) Too short an input makes the test noisy. On Monday, November 30, 2015

Re: [julia-users] Re: Arrays of arrays.

`. all this is unnecessary, but might help for readability purposes. On Sunday, November 29, 2015 at 3:49:19 PM UTC+2, Milan Bouchet-Valat wrote: > > Le dimanche 29 novembre 2015 à 04:21 -0800, Dan a écrit : > > Since this situation comes up once in a while, it might be nice to > &g

[julia-users] Re: is it possible for collect() to return a collection of a specific type?

have you tried running collect within a function? sometimes the Global scope is bad for type inference. On Saturday, November 28, 2015 at 2:45:26 AM UTC+2, ele...@gmail.com wrote: > > Collect only requires that the collection is iterable, for which providing > an eltype() function is optional.

[julia-users] Re: is it possible for collect() to return a collection of a specific type?

Looking at the collect code, it seems you really should define Base.eltype for your iterator and things will work out. On Saturday, November 28, 2015 at 3:15:18 AM UTC+2, Seth wrote: > > I guess that makes sense, though I struggle to see why one would create an > iterator that produces multiple

[julia-users] Re: Precompilation and functions with keyword arguments

Aha! tested the same sequence in a non-REPL run and the 1st and 2nd runtimes after a `precompile` are the same. On Wednesday, November 25, 2015 at 10:23:04 PM UTC+2, Dan wrote: > > I've managed to reproduce the timing peculiarities you described, with a > different function. In fa

[julia-users] Re: Precompilation and functions with keyword arguments

if (sf->linfo->functionObject != NULL) { // found in the system image: force a recompile Function *llvmf = (Function*)sf->linfo->functionObject; if (llvmf->isDeclaration()) { sf->linfo->specFunctionObject = NULL; sf->linfo->func

[julia-users] Re: Precompilation and functions with keyword arguments

I've managed to reproduce the timing peculiarities you described, with a different function. In fact, I get the same first run slower behavior even with regular functions with -no- keywords. It is almost as if `precompile` is not doing the entire job. Have you tried, just testing the basic

Re: [julia-users] Concatenation without splatting

Using `append!` instead of `push!` and letting efficient dynamic reallocation of vector do the resizing: function vcat_nosplat2a(y) result = Array(eltype(y[1]), 0) for a in y append!(result, a) end result end (@benchmark shows way less allocations and 2-3x time) BTW the

Re: [julia-users] Re: split a utf-8 string

`split` already has hooks for using other splitters. To achieve the UTF8 space splitting functionality, one can leverage the `isspace` function and add some decorations. For example: type FuncSplitter pred::Function end function Base.search(s::AbstractString, splt::FuncSplitter,

[julia-users] Re: What is the best way to get two by two tables in Julia?

Hi Arin, It would be helpful to have more details about the input (a dataframe?) and output (a two-by-two table or a table indexed by categories?). Some code to give context to the question would be even more help (possibly in another language, such as R). Having said this, here is a starting

Re: [julia-users] Re: Better alternative to find all permutations?

If we are already at this, it would be cool to have versions with Gray ordering. Something like: for (i1,i2) in permutations(vector,indices=true,Gray=true) theperm[[i1,i2]] = theperm[[i2,i1]] @show theperm end would go over all permutations. The syntax would be different and

[julia-users] Re: Coveralls/Codecov report certain lines are not covered, but test for the lines do appear to exist in code

Regarding the last printing point only print(string(eltype(8)) * ", " * string(eltype(8))) is probably what you meant. AbstractString(Int64), tries to create an AbstractString from an Int64, while string(Int64) asks for a string representation. The latter semantics is more accurate, and works.

[julia-users] Re: Better alternative to find all permutations?

Well, the previous listing worked only for Int vectors. Following is a more properly typed version. Various optimizations are possible: @inbounds, reducing allocations, etc. function uniquepermutations(base) zpos = Vector{Vector{Vector{Int}}}() zval = Vector{eltype(base)}() left =

[julia-users] Re: Better alternative to find all permutations?

How about this: using StatsBase using Iterators function uniquepermutations(base) zpos = Vector{Vector{Vector{Int}}}() zval = Vector{Int}() left = length(base) for (v,c) in countmap(base) push!(zpos,collect(subsets(collect(1:left),c))) push!(zval,v) left

[julia-users] Re: Converting ASCIIString to and from Array{UInt8,1}

`convert` is the go-to equivalent in Julia of casting. In this case: uint_array = convert(Vector{UInt8},str) but there are many ways of getting similar results! BTW `convert(ASCIIString,uint_array)` gets back. On Wednesday, November 18, 2015 at 1:49:36 AM UTC+2, James Gilbert wrote: > >

[julia-users] Re: Looking for a few good algorithms

Well, its not as simple as that because of everything being sparse (adding columns is not just a loop over sum). Actually, it would be interesting to see a clever solution. On Wednesday, November 18, 2015 at 2:14:39 AM UTC+2, Dan wrote: > > the columns of A'A when A is sparse are just

[julia-users] Re: Looking for a few good algorithms

]*sp.nzval[j] end end res end On Wednesday, November 18, 2015 at 3:54:56 AM UTC+2, Dan wrote: > > Oops... a bug (which showed itself when matrix was sparser than my > previous tests): > [when columns where completely empty, the first `if` should have been a > `while

[julia-users] Re: Looking for a few good algorithms

) while sp.colptr[col+1]=sp.colptr[col2+1] col2+= 1 end @show col,col2,sp.nzval[i],sp.nzval[j] res[col,col2] += sp.nzval[i]*sp.nzval[j] end end res end On Wednesday, November 18, 2015 at 3:35:30 AM UTC+2, Dan wrote

[julia-users] Re: Looking for a few good algorithms

It works, but it is slow (compared to `full(A'*A)`) On Wednesday, November 18, 2015 at 3:56:31 AM UTC+2, Dan wrote: > > And finally, getting rid of debug print. > > function AtrA(sp::SparseMatrixCSC) > res = zeros(eltype(sp),sp.n,sp.n) > col = 1 > @inbounds fo

[julia-users] Re: Looking for a few good algorithms

end On Wednesday, November 18, 2015 at 2:30:14 AM UTC+2, Dan wrote: > > Well, its not as simple as that because of everything being sparse (adding > columns is not just a loop over sum). Actually, it would be interesting to > see a clever solution. > > On Wednesday, November 18, 201

[julia-users] Re: Looking for a few good algorithms

Embarrassingly, there is yet another bug. while sp.colptr[col+1]=sp.colptr[col2+1] col2+= 1 end res[col,col2] += sp.nzval[i]*sp.nzval[j] end end res end On Wednesday, November 18, 2015 at 4:18:37 AM UTC+2, Dan wrote: > > It

[julia-users] Re: Looking for a few good algorithms

problem. The trick is doing the calculation in the right direction for the sparse storage, and it also uses the fact A multiplied by its own transpose. The exact speedup depends on the density and size. But I got 4x on `sprand(200,400,0.1)` On Wednesday, November 18, 2015 at 4:41:36 AM UTC+2, Dan

[julia-users] Re: push! performance

AFAIK the implementation of `push!` in Julia only occasionally allocates a bigger buffer for the new vector. This results in a very low average time for adding multiple elements (O(1) amortized time in complexity jargon). Notice, `a = [a new_element ]` would in Julia do something else than

[julia-users] Find

`x .> 3` is a bit-array. The function form, which is probably what fits, is `x->x.>3` (the back-ticks are for quotation, not part of the code).

[julia-users] Re: Median filter

As a quick stopgap, you could use the following: medianfilter1(v,ws) = [median(v[i:(i+ws-1)]) for i=1:(length(v)-ws+1)] medianfilter(v) = medianfilter1(vcat(0,v,0),3) `medianfilter` does a 3 window median filter. It is not the fastest implementation, but it works (hopefully). On Tuesday,

Re: [julia-users] Arrays as streams / consuming data with take et al

XXX in your questions = chain. Or more clearly: julia> stream = chain([1,2,3,4,5]) Iterators.Chain(Any[[1,2,3,4,5]]) julia> collect(take(stream, 3)) 3-element Array{Any,1}: 1 2 3 On Monday, November 9, 2015 at 7:47:51 PM UTC+2, andrew cooke wrote: > > > hmmm. maybe i'm doing it wrong as

Re: [julia-users] Arrays as streams / consuming data with take et al

ators.jl i guess. > > it doesn't support read though (which i didn't realise i needed when i > first asked). > > i'll add a warning to StatefulIterators pointing people to this. > > thanks, > andrew > > On Monday, 9 November 2015 17:07:52 UTC-3, Dan wrote: >> &g

Re: [julia-users] Arrays as streams / consuming data with take et al

lia> collect(rest(1:3,start(1:3))) 3-element Array{Any,1}: 1 2 3 Shouldn't the type of both arrays be the same? (the latter defined in non-global context still yield Any Array). On Monday, November 9, 2015 at 10:45:12 PM UTC+2, Dan wrote: > > the example with `pull` before, traverses th

Re: [julia-users] Arrays as streams / consuming data with take et al

bered somewhere. On Monday, November 9, 2015 at 10:07:52 PM UTC+2, Dan wrote: > > XXX in your questions = chain. > Or more clearly: > julia> stream = chain([1,2,3,4,5]) > Iterators.Chain(Any[[1,2,3,4,5]]) > > julia> collect(take(stream, 3)) > 3-element Array{Any,1}: > 1 >

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