[julia-users] Re: How to define an array of floating point numbers?
Ok, the following definition works on Julia 0.4, but not with 0.3:
FloatArray = Union{Array{Float32, 1}, Array{Float64, 1}}
function sum(x::FloatArray).
Any idea?
Am Dienstag, 18. August 2015 11:07:16 UTC+2 schrieb Uwe Fechner:
Hello,
I want to write a function, that can operate on any one dimensional array
of floating point numbers.
The following works, but only for Float64:
function sum(x::Array{Float64,1})
The following does not work:
function sum(x::Array{AbstractFloat,1})
Any idea?
Uwe
[julia-users] Re: How to define an array of floating point numbers?
Ok, the following works for 0.3 and 0.4:
FloatArray = Union(Array{Float32, 1}, Array{Float64, 1})
Is this the best way to define an array of floating point numbers?
Am Dienstag, 18. August 2015 11:22:50 UTC+2 schrieb Uwe Fechner:
Ok, the following definition works on Julia 0.4, but not with 0.3:
FloatArray = Union{Array{Float32, 1}, Array{Float64, 1}}
function sum(x::FloatArray).
Any idea?
Am Dienstag, 18. August 2015 11:07:16 UTC+2 schrieb Uwe Fechner:
Hello,
I want to write a function, that can operate on any one dimensional array
of floating point numbers.
The following works, but only for Float64:
function sum(x::Array{Float64,1})
The following does not work:
function sum(x::Array{AbstractFloat,1})
Any idea?
Uwe
Re: [julia-users] Re: How to define an array of floating point numbers?
Hi,
I think this does what you want:
function sum{T:FloatingPoint}(x::Array{T,1})
println(hi)
end
Cheers,
Rene
Am 18.08.2015 um 11:22 schrieb Uwe Fechner uwe.fechner@gmail.com:
Ok, the following definition works on Julia 0.4, but not with 0.3:
FloatArray = Union{Array{Float32, 1}, Array{Float64, 1}}
function sum(x::FloatArray).
Any idea?
Am Dienstag, 18. August 2015 11:07:16 UTC+2 schrieb Uwe Fechner:
Hello,
I want to write a function, that can operate on any one dimensional array of
floating point numbers.
The following works, but only for Float64:
function sum(x::Array{Float64,1})
The following does not work:
function sum(x::Array{AbstractFloat,1})
Any idea?
Uwe
[julia-users] How to define an array of floating point numbers?
Hello,
I want to write a function, that can operate on any one dimensional array
of floating point numbers.
The following works, but only for Float64:
function sum(x::Array{Float64,1})
The following does not work:
function sum(x::Array{AbstractFloat,1})
Any idea?
Uwe
Re: [julia-users] Re: How to define an array of floating point numbers?
Is `function sum{T:FloatingPoint}(x::AbstractArray{T,1}) ` or
`function sum{T:FloatingPoint}(x::AbstractVector{T}) ` better?
On Tuesday, August 18, 2015 at 11:29:15 AM UTC+2, René Donner wrote:
Hi,
I think this does what you want:
function sum{T:FloatingPoint}(x::Array{T,1})
println(hi)
end
Cheers,
Rene
Am 18.08.2015 um 11:22 schrieb Uwe Fechner uwe.fec...@gmail.com
javascript::
Ok, the following definition works on Julia 0.4, but not with 0.3:
FloatArray = Union{Array{Float32, 1}, Array{Float64, 1}}
function sum(x::FloatArray).
Any idea?
Am Dienstag, 18. August 2015 11:07:16 UTC+2 schrieb Uwe Fechner:
Hello,
I want to write a function, that can operate on any one dimensional
array of floating point numbers.
The following works, but only for Float64:
function sum(x::Array{Float64,1})
The following does not work:
function sum(x::Array{AbstractFloat,1})
Any idea?
Uwe
[julia-users] Re: A cheat page for Julia
On Monday, August 17, 2015 at 10:14:25 PM UTC+2, Steven G. Johnson wrote:
Comments:
Your subtype declaration section is wrong: the abstract type does not
define a block (you should just have abstract Foo, not abstract Foo ...
end)
The String type is deprecated in favor of AbstractString in 0.4
[1:10] is deprecated in 0.4, in favor of [1:10;] to explicitly vcat.
You can just use +, not .+, for element-wise addition. (It doesn't hurt
to do .+, but it is unnecessary.)
help(func) is gone in 0.4
Pkg.build(Package) is useful to rebuild a package if the package build
failed or your system configuration changed.
char(n) is deprecated in favor of Char(n) in 0.4
Beware of multi-byte Unicode characters is a bit misleading; it is not
the Unicode character (technically you are referring to codepoints)
that is multi-byte, but rather the codepoint's *encoding* in UTF-8 (the
default in Julia). Maybe Beware of multi-byte Unicode encodings in UTF-8
(Of course, there are many other subtleties with Unicode: e.g. your
example string Ångström can also be written Ångström, which looks
identical but actually uses a different set of codepoints via combining
characters.)
UintN in Julia 0.4 is deprecated in favor of UIntN (note caps).
Note there is also Complex{T}; Complex128 is just a shorthand for
Complex{Float64}.
im is the imaginary unit, not the imaginary number.
eps() is an abbreviation for eps(Float64). eps(T) is also defined for
other types.
In 0.4, TypeName(val) automatically calls convert(TypeName, val) if no
other constructor is available. This also means that, when you are
defining your own type, you should extend Base.convert if you have new
conversions, rather than adding constructors.
eye(N) is an NxN identity; calling it N-dimensional may confuse people
into thinking it returns an N-dimensional array.
M' is a synonym for ctranspose(M) (conjugate transpose). M.' is
transpose(M) without conjugation. Of course, for real matrices they are
equivalent.
Note that we can also do for i in 1:10 ... this syntax is perhaps a bit
clearer in that it generalizes to iterating over other kinds of containers.
To exit a loop you do break, not exit (which is a function).
Functions with ! appended mutate at least one of their arguments. Not
necessarily the first one (e.g. see A_ldiv_B!), although this is the most
common.
If you need tail-call optimization, it is more idiomatic to simply write a
loop.
Instead of in(val,arr), you can simply do val in arr
The dictionary syntax has changed in 0.4: use Dict(a=b, ...)
{...} is deprecated in 0.4. Use Any[...].
it is more idiomatic to put do on the same line:
map(collection) do elem
...
end
You can override Base.show(io, ex) rather than Base.showerror.
import ModuleName does *not* import all names. It imports the module,
but the only symbol imported is ModuleName itself; everything else must be
accessed via ModuleName.something.
Or `importall MyModule` ?
You should think of macros as being evaluated at parse-time, not run-time.
Basically:
* a macro is a function evaluated at parse-time that takes an
expression in and returns an expression out, and the latter is inserted
into the parsed code.
* a generated function is a function evaluated at compile-time which
takes types in and returns an expression out, which is inserted into the
code and compiled
* an ordinary function is a function evaluated at run-time which takes
values in and returns values out.
@test_approx_eq does not check equality to machine precision. It checks
equality at much lower precision. In 0.4, you can also just use @test x ≈
y, where ≈ is \approxtab and is a synonym for the isapprox function.
The typical convention in Julia is to avoid underscores unless they are
needed for readability. i.e. we have iseven, not is_even. This does not
differ for variables vs. functions.
I think the term CamelCase is more common than Pascal case; if you
write it as CamelCase it is clear that it starts with caps.
For performance, you should avoid abstract types in collections, but also
as fields of types.
I thinks using abstract types as fields doesn't hurt much, except that the
object will be packed into an Array.
[julia-users] Constructor or convert(), Upper case or lower case
We use `Int8(x)` to construct an `Int8`, but use `float(x)` to convert to a floating point. We defines `Symbol` as an object, but use `symbol()` to construct symbols. I am so confusing.
Re: [julia-users] type checking arrays
This is invariance striking. There should probably a FAQ entry for this
as it is often discussed:
https://groups.google.com/forum/#!searchin/julia-users/invariance
Docs:
http://docs.julialang.org/en/release-0.3/manual/types/#parametric-composite-types
On Tue, 2015-08-18 at 09:37, Kevin Kunzmann nl.vekm...@gmail.com wrote:
Hi guys,
So, I recently revisited Julia and fooled around a bit with Julia Box.
Amazing - this looks like the language I never actually dared dreaming of
;) Keep up the greedy work!
That being said, my question might be dumb, but I am struggling with the
type system of Julia. I want to guarantee that the arguments to a function
are numerical arrays of dimension 1 - how would I best do that in Julia?
The problem is that
Array{Number, 1}
exists as a type, but when I get the type system right it should not have
any subtypes except union. Especially
Array{Float64, 1} : Array{Number, 1}
evaluates to ' false'. I think I get why this is implemented the way it is,
however, would it not be most intuitive to have something like
function f(x::Array{Number, 1})
x
end
What would be the 'Julian' way of doing this?
function f{T:Number}(x::Array{T, 1})
x
end
Re: [julia-users] Re: How to define an array of floating point numbers?
Thank's a lot, that is cool.
Am Dienstag, 18. August 2015 11:29:44 UTC+2 schrieb Pontus Stenetorp:
On 18 August 2015 at 10:22, Uwe Fechner uwe.fec...@gmail.com
javascript: wrote:
Ok, the following definition works on Julia 0.4, but not with 0.3:
FloatArray = Union{Array{Float32, 1}, Array{Float64, 1}}
function sum(x::FloatArray).
Any idea?
I would simply go with:
function mysum{T:FloatingPoint}(x::Vector{T}) sum(x) end
[julia-users] type checking arrays
Hi guys,
So, I recently revisited Julia and fooled around a bit with Julia Box.
Amazing - this looks like the language I never actually dared dreaming of
;) Keep up the greedy work!
That being said, my question might be dumb, but I am struggling with the
type system of Julia. I want to guarantee that the arguments to a function
are numerical arrays of dimension 1 - how would I best do that in Julia?
The problem is that
Array{Number, 1}
exists as a type, but when I get the type system right it should not have
any subtypes except union. Especially
Array{Float64, 1} : Array{Number, 1}
evaluates to ' false'. I think I get why this is implemented the way it is,
however, would it not be most intuitive to have something like
function f(x::Array{Number, 1})
x
end
What would be the 'Julian' way of doing this?
Best,
Kevin
Re: [julia-users] type checking arrays
Great, thanks - that makes so much more sense x)
On Tuesday, 18 August 2015 10:10:40 UTC+2, Milan Bouchet-Valat wrote:
Le mardi 18 août 2015 à 00:37 -0700, Kevin Kunzmann a écrit :
Hi guys,
So, I recently revisited Julia and fooled around a bit with Julia
Box. Amazing - this looks like the language I never actually dared
dreaming of ;) Keep up the greedy work!
That being said, my question might be dumb, but I am struggling with
the type system of Julia. I want to guarantee that the arguments to a
function are numerical arrays of dimension 1 - how would I best do
that in Julia? The problem is that
Array{Number, 1}
exists as a type, but when I get the type system right it should not
have any subtypes except union. Especially
Array{Float64, 1} : Array{Number, 1}
evaluates to ' false'. I think I get why this is implemented the way
it is, however, would it not be most intuitive to have something like
function f(x::Array{Number, 1})
x
end
What would be the 'Julian' way of doing this?
f{T:Number}(x::Array{T, 1}) or
f{T:Number}(x::Vecotr{T})
You're hitting a subtle point which has to do with covariance vs.
invariance of types. This part of the manual should make it clearer,
but feel free to ask for more explanations:
http://docs.julialang.org/en/latest/manual/types/?highlight=covariance#
parametric-composite-types
http://docs.julialang.org/en/latest/manual/types/?highlight=covariance#parametric-composite-types
Regards
Re: [julia-users] Re: Segmentation fault on git-master `f3217a8d123e51f534`
On Tue, Aug 18, 2015 at 5:24 AM, Tony Kelman t...@kelman.net wrote: Have you tried make clean or make cleanall? Do you have any packages being loaded in your juliarc? Something might be corrupt, or using an older image file format, or not precompile-safe. Thanks, its fixed after I pulled dae8094198cc1b6c75 2015-08-17 22:20 and compiled it again - that took a few hours. You are right about the stale cache file for module Compat which may have been the problem and later it was Docile being called via juliarc. Fixed them all now. Thanks :) SVAKSHA ॥ http://about.me/svaksha ॥
Re: [julia-users] type checking arrays
Le mardi 18 août 2015 à 00:37 -0700, Kevin Kunzmann a écrit :
Hi guys,
So, I recently revisited Julia and fooled around a bit with Julia
Box. Amazing - this looks like the language I never actually dared
dreaming of ;) Keep up the greedy work!
That being said, my question might be dumb, but I am struggling with
the type system of Julia. I want to guarantee that the arguments to a
function are numerical arrays of dimension 1 - how would I best do
that in Julia? The problem is that
Array{Number, 1}
exists as a type, but when I get the type system right it should not
have any subtypes except union. Especially
Array{Float64, 1} : Array{Number, 1}
evaluates to ' false'. I think I get why this is implemented the way
it is, however, would it not be most intuitive to have something like
function f(x::Array{Number, 1})
x
end
What would be the 'Julian' way of doing this?
f{T:Number}(x::Array{T, 1}) or
f{T:Number}(x::Vecotr{T})
You're hitting a subtle point which has to do with covariance vs.
invariance of types. This part of the manual should make it clearer,
but feel free to ask for more explanations:
http://docs.julialang.org/en/latest/manual/types/?highlight=covariance#
parametric-composite-types
Regards
Re: [julia-users] Re: A cheat page for Julia
Well, even a Bactrian camel’s humps are in the middle, not right on top of his bum! The camel case conventions IIRC were really popularized first by Smalltalk, then by Wirth with Pascal and Modula, and also (later) by Microsoft. Another reference: https://msdn.microsoft.com/en-us/library/x2dbyw72(v=vs.71).aspx On Tuesday, August 18, 2015 at 7:13:57 AM UTC-4, Milan Bouchet-Valat wrote: Le lundi 17 août 2015 à 22:01 -0700, Scott Jones a écrit : On Monday, August 17, 2015 at 4:14:25 PM UTC-4, Steven G. Johnson wrote: I think the term CamelCase is more common than Pascal case; if you write it as CamelCase it is clear that it starts with caps. I've heard Pascal case to indicate starting out with caps, but Camel case more often refers to lower case first letter. (i.e. there is a hump in the *middle* of the name, like a camel). Well, camel is ambiguous. Lower case first letters certainly match the dromedary camel... :-) https://en.wikipedia.org/wiki/Camel Regards Here's the definition from Wikipedia: Although the first letter of a camel case compound word may or may not be capitalized, the term camel case generally implies lowercase first letter.[2][3] For clarity, this article calls the two alternatives upper camel case and lower camel case. Some people and organizations use the term camel case only for lower camel case. Other synonyms include:
Re: [julia-users] range and NaN
Le mardi 18 août 2015 à 06:04 -0700, Michele Zaffalon a écrit :
Are `collect(a:b)` and `collect(range(a, b))` supposed to give the
same results? Does the following make sense?
julia typeof(NaN:5)
FloatRange{Float64}
julia collect(NaN:5)
0-element Array{Float64,1}
julia typeof(range(NaN, 5))
FloatRange{Float64}
julia collect(range(NaN, 5))
5-element Array{Float64,1}:
NaN
NaN
NaN
NaN
NaN
This is on 0.4.0, commit 6becc96 on Windows 7 64 bit.
michele
The difference isn't in collect(), it's in the creation of the range.
range() expects a length as its second argument, while the colon syntax
takes the end value instead. So you're not creating the same range at
all.
BTW, you can use dump() to see the fields of the created range, which
allows you to check what's actually created.
Regards
Re: [julia-users] Metaprogramming: the use of `$`
On Tue, Aug 18, 2015 at 6:49 AM, Sisyphuss zhengwend...@gmail.com wrote: I'm reading the documentation Metaprogramming chapter. To me, the `$` operator is different from the one in the string interpolation. In the string, `$` is a runtime interpolation. In the expression, `$` is a compile-time interpolation. No. Both of them happens at whenever the expression is evaluated. It happens for `@eval` at compile time because that's when the macro expansion happens. In one example provided, `ex = :(a in $:((1,2,3)) )`, I find that it's equal to `ex = :(a in (1,2,3) )`. So I do not see the intention of `$`. The examples are meant to show you the basic rules not necessarily the best/only way to do it. In the next example, there is `:a + :b` in the expression, but the sum of `Symbols` is not defined. Is it wrong? It just construct an expression and to show you that the result is quoted symbol rather than the symbol themselves.
Re: [julia-users] Constructor or convert(), Upper case or lower case
On Tue, Aug 18, 2015 at 6:04 AM, Sisyphuss zhengwend...@gmail.com wrote: We use `Int8(x)` to construct an `Int8`, but use `float(x)` to convert to a floating point. Float is not a type. FWIW, `AbstractFloat(1)` works. We defines `Symbol` as an object, but use `symbol()` to construct symbols. `Symbol(a)` works, just not for arbitrary types as `symbol`. However, `Int*` don't allow converting from arbitrary types either so it is consistent. From what I understand, `int*` are deprecated because you should be explicit about what you want to do. For symbols this is less of an issue. I am so confusing. Hopefully you don't literally mean this.
[julia-users] Re: Problems with docile on travis / conditional require?
From what I can see in that travis log this is not a problem on 0.3, hence not really a Docile problem. It looks like the Julia 0.4 version being used on travis is older than the fix https://github.com/JuliaLang/julia/pull/11932 for the LoadError: error in method definition: function Base.sum must be explicitly imported to be extended error that you’re seeing there. You'll need to wait until the version is updated I guess. Also note that you only need to import Docile in Julia 0.3 if you are using @doc. Bare docstrings can be used without importing the package. — Mike On Tuesday, 18 August 2015 14:13:26 UTC+2, Uwe Fechner wrote: Hello, I solved my problems with Docile on my local machine (see: https://groups.google.com/forum/#!topic/julia-users/ukvwAI52u0Y ), but now it appears on travis: My pull request passes on Julia 0.3, but fails on Julia 0.4: https://github.com/mlubin/NaNMath.jl/pull/4 Here you can see the error message: https://travis-ci.org/mlubin/NaNMath.jl/jobs/76089910 In short: ERROR: LoadError: LoadError: error in method definition: function Base.sum must be explicitly imported to be extended Perhaps this would not happen, if I could say that Docile is only required for Julia 0.3 and not for Julia 0.4, but I don't know how to specify this in the REQUIRE file. Any idea? Uwe
Re: [julia-users] range and NaN
Apologies and thank you.
http://docs.julialang.org/en/latest/stdlib/math/?highlight=range#Base.range
On Tue, Aug 18, 2015 at 3:11 PM, Milan Bouchet-Valat nalimi...@club.fr
wrote:
Le mardi 18 août 2015 à 06:04 -0700, Michele Zaffalon a écrit :
Are `collect(a:b)` and `collect(range(a, b))` supposed to give the
same results? Does the following make sense?
julia typeof(NaN:5)
FloatRange{Float64}
julia collect(NaN:5)
0-element Array{Float64,1}
julia typeof(range(NaN, 5))
FloatRange{Float64}
julia collect(range(NaN, 5))
5-element Array{Float64,1}:
NaN
NaN
NaN
NaN
NaN
This is on 0.4.0, commit 6becc96 on Windows 7 64 bit.
michele
The difference isn't in collect(), it's in the creation of the range.
range() expects a length as its second argument, while the colon syntax
takes the end value instead. So you're not creating the same range at
all.
BTW, you can use dump() to see the fields of the created range, which
allows you to check what's actually created.
Regards
Re: [julia-users] Re: Array Index Limits
This sounds like the right approach. I'll try it soon and report what I did/didn't succeed at. Thanks. On Mon, Aug 17, 2015 at 6:10 PM, Matt Bauman mbau...@gmail.com wrote: One way to make OffsetArrays safe would be to make them index normally with integer indices, but introduce a *new* Integer type that gives it the special offset behavior. You could name it something short, like F (for Fortran or oFFset) to reduce typing: `A[F(-10), F(0)]` ... and you could do even better by making it construct by multiplication with the typename: `A[-10F, 0F]` Or even better, you should be able to write a macro that automatically converts all indices to this special type: `@offset A[-10,0]` # This also could disallow the pesky end keyword! I was playing with RaggedArrays over the weekend, and there I had to solve a similar problem because there are two possible meanings for linear indexing. So I just created a LinearIndex type that behaves differently: https://github.com/mbauman/RaggedArrays.jl/blob/d2b6aeb854855c7912c104cb5312c13e989f2cf4/src/core.jl#L229-L247 Matt On Monday, August 17, 2015 at 4:36:27 PM UTC-4, lawrence dworsky wrote: Thanks for putting your time into this. Right now I'm still using 0.3.11, waiting for 0.4 to be the standard release. Then I'll dig into this and see if I get it to do what I want without undue aggravation. I had the same indexing issue with MatLab. Sometimes I miss the brute straightforwardness of Fortran. Larry On Mon, Aug 17, 2015 at 1:16 PM, Matt Bauman mba...@gmail.com wrote: On Monday, August 17, 2015 at 1:03:17 PM UTC-4, Sisyphuss wrote: I read the interfaces http://docs.julialang.org/en/latest/manual/interfaces/ chapter of the documentation today. I learned that, if you define an iterable as a subtype of AbstractArray, with only defining three methods (including `size()`, excluding `start()`), you can iterate on it just like iterate on an normal Array. Iteration should work just fine in 0.4 if OffsetArray defines its own `eachindex` method. Although more and more for loops are written generically using `eachindex`, there are still a lot of methods that use the old linear indexing standby: for i=1:length(A) @inbounds A[i] = … end This is where things get really hairy for OffsetArrays. That `@inbounds` propagates through to the inner array assignment, which will lead to silent data corruption and/or segfaults. That's really why it shouldn't be an AbstractArray.
Re: [julia-users] Re: A cheat page for Julia
Le lundi 17 août 2015 à 22:01 -0700, Scott Jones a écrit : On Monday, August 17, 2015 at 4:14:25 PM UTC-4, Steven G. Johnson wrote: I think the term CamelCase is more common than Pascal case; if you write it as CamelCase it is clear that it starts with caps. I've heard Pascal case to indicate starting out with caps, but Camel case more often refers to lower case first letter. (i.e. there is a hump in the *middle* of the name, like a camel). Well, camel is ambiguous. Lower case first letters certainly match the dromedary camel... :-) https://en.wikipedia.org/wiki/Camel Regards Here's the definition from Wikipedia: Although the first letter of a camel case compound word may or may not be capitalized, the term camel case generally implies lowercase first letter.[2][3] For clarity, this article calls the two alternatives upper camel case and lower camel case. Some people and organizations use the term camel case only for lower camel case. Other synonyms include:
[julia-users] Problems with docile on travis / conditional require?
Hello, I solved my problems with Docile on my local machine (see: https://groups.google.com/forum/#!topic/julia-users/ukvwAI52u0Y ), but now it appears on travis: My pull request passes on Julia 0.3, but fails on Julia 0.4: https://github.com/mlubin/NaNMath.jl/pull/4 Here you can see the error message: https://travis-ci.org/mlubin/NaNMath.jl/jobs/76089910 In short: ERROR: LoadError: LoadError: error in method definition: function Base.sum must be explicitly imported to be extended Perhaps this would not happen, if I could say that Docile is only required for Julia 0.3 and not for Julia 0.4, but I don't know how to specify this in the REQUIRE file. Any idea? Uwe
Re: [julia-users] JuliaCon 2015 videos
Same for Shashi's session. On Monday, August 17, 2015 at 11:32:26 PM UTC+1, Keno Fischer wrote: Whoever owns the youtube channel should put an annotation that allows users o click on it to skip that part. On Mon, Aug 17, 2015 at 8:08 PM, Sisyphuss zhengw...@gmail.com javascript: wrote: In the Keno's video, he spent more than 5 minutes to go through the technical difficulty. I think this can be cut to save time. On Monday, August 17, 2015 at 3:30:48 PM UTC+2, Viral Shah wrote: One more batch: 1. Iain Dunning: JuliaOpt - Optimization related projects in Julia: http://youtu.be/ZibQdqR6gqc?a 2. Yee Sian Ng: JuliaGeo - Working with Geospatial data in Julia: http://youtu.be/MLGFzPS4FTg?a 3. Isaiah Norton: Automatic ccall wrapper generation with Clang.jl: http://youtu.be/Jt5sv-8iRGc?a 4. Simon Kornblith: L1 regularized regression: http://youtu.be/z4Zcud2vE0s?a 5. Tony Fong: Lint.jl - Building a Lint tool for Julia: http://youtu.be/9T3R14R3X7k?a 6. Douglas Bates: Adventures with Statistical Models and Sparse Matrices: http://youtu.be/lSr0oRfyd1Y?a -viral On 17-Aug-2015, at 2:25 am, Viral Shah vi...@mayin.org wrote: A few more: 1. Blake Johnson: Quickly building simulations of quantum systems: http://youtu.be/kogTKuytg1g?a 2. Katharine Hyatt: Quantum Statistical Simulations with Julia: http://youtu.be/QThApV3HIxc?a 3. Making GUIs with Escher jl: http://youtu.be/Sq7DEnV4dfI?a 4. John Myles White: What needs to be done to move JuliaStats forward: http://youtu.be/lf1_FhMR7xA?a 5. Jacob Quinn: Managing Data in Julia Old Tricks, New Tricks: http://youtu.be/QLWhsZ3yzBk?a -viral On 16-Aug-2015, at 11:15 am, Viral Shah vi...@mayin.org wrote: Next set: 1. Avik Sengupta: A practical guide to exposing Julia APIs on the web: http://youtu.be/o40OpLe2k7Q?a 2. Hongbo Rong: Accelerating sparse matrix kernels in Julia: http://youtu.be/k40K2zJVv0A?a 3. Luis Benet: Taylor series expansions in Julia: http://youtu.be/6zrQuq5mODQ?a 4. Westley Hennigh: Who optimizes the optimizers: http://youtu.be/Qito5AGSv4s?a 5. Mauro Werder: Interfaces for Julia -Traits.jl: youtu.be/j9w8oHfG1Ic?a 6. David Gold: Nullable arrays - http://youtu.be/2v5k28F80BQ?a -viral On 16-Aug-2015, at 12:01 am, Viral Shah vi...@mayin.org wrote: Next set: 1. Sebastien Martin: Taxi fleet simulation and visualization: http://youtu.be/MjERK9Xajrg?a 2. Bob Carpenter: Stan.jl - Statistical Modeling and Inference Made Easy: http://youtu.be/YdgMJ37CDws?a 3. Pontus Stenetorp: Suitably Naming a Child with Multiple Nationalities: http://youtu.be/MJzRf3Exlqc?a 4. Chiyuan Zhang: Mocha.jl - Deep Learning for Julia: http://youtu.be/ljySoebYylE?a 5. Kiran Pamnany and Ranjan Anantharaman: Multi-threading Julia: http://youtu.be/GvLhseZ4D8M?a 6. St. Louis University - Text Mining Research Group: TextMining.jl: http://youtu.be/dgfIIZ5yA4E?a 7. Zachary Yedidia: SFML.jl - Julia bindings for the Simple Fast: http://youtu.be/UKfM7EopMe0?a -viral On 15-Aug-2015, at 10:07 am, Viral Shah vi...@mayin.org wrote: One more batch posted: 1. Jacob Quinn: Composable streams for data transfer and processing - http://t.co/DPuN42F3Qu 2. Tanmay Mohapatra: Interfacing Julia with Complex systems using Protocol Buffers - http://t.co/Ddxj60KL7g 3. Eric Davies: Towards A Consistent Database Interface - http://t.co/vdQwFRp7aD 4. Viral Shah: The present and future of sparse matrices in Julia - http://t.co/WVQY89GsiT 5. Jake Bolewski: Staged programming in Julia - http://t.co/XlYl2XGB9O 6. Robert Moss: Julia as a Specification Language for the next-generation Airborne Collision Avoidance System - http://t.co/QJN2ZfP1ZI -viral On Friday, August 14, 2015 at 7:33:04 PM UTC+5:30, Viral Shah wrote: Here are the next batch of videos. There is a JuliaCon 2015 channel now: https://www.youtube.com/playlist?list=PLP8iPy9hna6Sdx4soiGrSefrmOPdUWixM This batch includes: David Sanders: Validated numerics in Julia - http://t.co/WRZcYGjhfl Patrick Sanan: Using Julia on a Cray Supercomputer - https://youtu.be/NwyKz2KLdtY Keno Fischer: Shaving the Yak - http://t.co/cEJqCqAdRC Spencer Lyon: Methods, Models, and Moments - Julian Economics with QuantEcon.jl - http://t.co/l0f5CH6k3u Randy Zwitch: Everyday Analytics and Visualization - http://t.co/7pbL7yd010 -viral On 13-Aug-2015, at 10:51 pm, Viral Shah vi...@mayin.org wrote: Folks, I am happy to announce that the videos are almost all ready, and I will start posting them in batches. I am starting with Jeff’s talk on our Youtube channel: https://www.youtube.com/user/JuliaLanguage Direct link to the video: https://www.youtube.com/watch?v=xUP3cSKb8sI -viral
[julia-users] range and NaN
Are `collect(a:b)` and `collect(range(a, b))` supposed to give the same
results? Does the following make sense?
julia typeof(NaN:5)
FloatRange{Float64}
julia collect(NaN:5)
0-element Array{Float64,1}
julia typeof(range(NaN, 5))
FloatRange{Float64}
julia collect(range(NaN, 5))
5-element Array{Float64,1}:
NaN
NaN
NaN
NaN
NaN
This is on 0.4.0, commit 6becc96 on Windows 7 64 bit.
michele
Re: [julia-users] Re: JuliaCon 2015 videos
Shashi how are you doing your presentations? They are awesome! Where can I find the code for that one? El martes, 18 de agosto de 2015, 11:55:01 (UTC-5), Shashi Gowda escribió: If you have Escher installed and ready to go (See instructions on escher-jl.org) you can safely skip the first 20 mins of my Escher.jl video. A better talk on Escher is this one I gave recently at a conference in Bangalore: https://youtu.be/UeVUsOX5rAo?t=150 it definitely packs more content in lesser time. On Tue, Aug 18, 2015 at 9:42 PM, Frank Kampas fka...@gmail.com javascript: wrote: The optimization video is private On Thursday, August 13, 2015 at 1:22:09 PM UTC-4, Viral Shah wrote: Folks, I am happy to announce that the videos are almost all ready, and I will start posting them in batches. I am starting with Jeff’s talk on our Youtube channel: https://www.youtube.com/user/JuliaLanguage Direct link to the video: https://www.youtube.com/watch?v=xUP3cSKb8sI -viral
[julia-users] package compatibility for julia v0.3 and v0.4
I understand that if I release a new version of a package foo and put `julia 0.4` in `REQUIRE`, the package won't be updated in julia v0.3 installations. If, after the updated package is released, somebody tries to install package foo from julia v0.3 what happens? Will they still be able to install the older version of the package? I'm asking because I'd like to release an updated version of a package to make it work with julia v0.4. The changes, however, are incompatible with julia v0.3. I tried using @compat to work around the issues but couldn't figure out how to fix this deprecation warning without breaking v0.3 compatibility: `uint8(x::Integer) is deprecated, use x % UInt8 instead` Cheers, Sam
[julia-users] Re: Julia nightlies
Well, removing the link from the website does not help with the travis failures: https://travis-ci.org/mlubin/NaNMath.jl/jobs/76114115 Regards: Uwe Am Dienstag, 18. August 2015 18:46:31 UTC+2 schrieb Viral Shah: Elliot has been busy lately, and this has not been maintained for a long time. We should probably remove that link from our website. -viral On Tuesday, August 18, 2015 at 9:52:17 PM UTC+5:30, Uwe Fechner wrote: Hello, the nightly Julia builds from: https://launchpad.net/~staticfloat/+archive/ubuntu/julianightlies are currently quiet outdated. As far as I understand they should be auto-generated every night from the head branch of the Julia repository. This seams to be currently broken. Any idea, why? The outdated version is causing problems with failing checks of Travis. Best regards: Uwe
[julia-users] ANN: SimJulia 0.3.4
Hi SimJulia, a (combined continuous time / ) discrete event process oriented simulation framework, has reached another milestone: - v0.3.4 synchronizes the API with SimPy v3 but using some specific Julia semantics - It is a complete rewrite allowing a more powerful and unified discrete event approach - 10 min tutorial is included to give users a taste of discrete event simulation with (Sim)Julia - A detailed topical guide is also written For the moment the continuous time part is not operational. A *quantized state system* (QSS) solver is being developed for continuous system simulation using the discrete event framework. Once this is done v0.4 will be tagged. New ideas or interesting examples are always welcome and can be submitted as an issue or a pull request on GitHub. Enjoy! Ben
[julia-users] help with try/catch?
I wasn't sure how to search for this in issues: julia try using LightGraphs catch end julia Graph ERROR: UndefVarError: Graph not defined but julia try using LightGraphs true catch false end true julia Graph LightGraphs.Graph Why doesn't the first one work?
[julia-users] Re: help with try/catch?
following up, try using LightGraphs nothing catch end works. On Tuesday, August 18, 2015 at 2:18:43 PM UTC-7, Seth wrote: I wasn't sure how to search for this in issues: julia try using LightGraphs catch end julia Graph ERROR: UndefVarError: Graph not defined but julia try using LightGraphs true catch false end true julia Graph LightGraphs.Graph Why doesn't the first one work?
[julia-users] Re: help with try/catch?
Another followup: julia try using LightGraphs catch y println(y = $y) end y = ErrorException(unsupported or misplaced expression using) On Tuesday, August 18, 2015 at 2:18:43 PM UTC-7, Seth wrote: I wasn't sure how to search for this in issues: julia try using LightGraphs catch end julia Graph ERROR: UndefVarError: Graph not defined but julia try using LightGraphs true catch false end true julia Graph LightGraphs.Graph Why doesn't the first one work?
Re: [julia-users] package compatibility for julia v0.3 and v0.4
On Tuesday, August 18, 2015 at 7:22:49 PM UTC-4, Stefan Karpinski wrote: Did you try `@compat x % UInt8`? You shouldn't need the @compat macro. The Compat package should just add methods to % (rem).
[julia-users] Re: Julia nightlies
OK, I'm gonna git pull make then. That's the only way, right? On Wednesday, August 19, 2015 at 4:54:05 AM UTC+10, Uwe Fechner wrote: Well, removing the link from the website does not help with the travis failures: https://travis-ci.org/mlubin/NaNMath.jl/jobs/76114115 Regards: Uwe Am Dienstag, 18. August 2015 18:46:31 UTC+2 schrieb Viral Shah: Elliot has been busy lately, and this has not been maintained for a long time. We should probably remove that link from our website. -viral On Tuesday, August 18, 2015 at 9:52:17 PM UTC+5:30, Uwe Fechner wrote: Hello, the nightly Julia builds from: https://launchpad.net/~staticfloat/+archive/ubuntu/julianightlies are currently quiet outdated. As far as I understand they should be auto-generated every night from the head branch of the Julia repository. This seams to be currently broken. Any idea, why? The outdated version is causing problems with failing checks of Travis. Best regards: Uwe
[julia-users] Re: Removing the 'DIVIDES' (U+2223) from the inside of a character string
On Wednesday, August 19, 2015 at 1:59:13 PM UTC+10, Lauren Kersey wrote: Hi all. I have a text tool that removes punctuation from the ends of words (split). This seemed to work well enough for cleaning my corpus, but I'm now working with a dataset of texts published from 1600-1700. Back then, printers split a lot of lines in the middle of a word. When the printer came to the end of the page, he simply split the word with a hyphen. My digital copies mark this symbol as a \u2223 and I need to remove this from the word to perform computational analysis. Is there a way to remove this character from the inside of a character string? Just replace it with an empty string http://docs.julialang.org/en/latest/stdlib/strings/?highlight=replace#Base.replace Cheers Lex
[julia-users] Syntax for slicing with an array of ranges?
Is there an easy way to slice an array using an array of ranges? I'm
looking for something like the following:
foo = [2,6,2,8,4,9]
some_ranges = UnitRange{Int64}[2:3, 5:6]
foo[some_ranges] # gives error; desired output is [6,2,4,9]
[julia-users] Removing the 'DIVIDES' (U+2223) from the inside of a character string
Hi all. I have a text tool that removes punctuation from the ends of words (split). This seemed to work well enough for cleaning my corpus, but I'm now working with a dataset of texts published from 1600-1700. Back then, printers split a lot of lines in the middle of a word. When the printer came to the end of the page, he simply split the word with a hyphen. My digital copies mark this symbol as a \u2223 and I need to remove this from the word to perform computational analysis. Is there a way to remove this character from the inside of a character string?
Re: [julia-users] ANN: SimJulia 0.3.4
Tom The event scheduling in SimJulia is based on the standard PriorityQueue* implementation of Julia and the overhead of SimJulia is really limited. If you throw away the process oriented way of defining a simulation model, you can do theoretically better, i.e. pure event simulation. This is certainly true for small models but for bigger ones you (I for sure) will end up developing a massive spaghetti code. I have used the previous version of SimJulia in some large simulation models and compared to SimPy or commercial simulation software (ARENA) the speedup I measured was often more than two orders of magnitude. We are right now porting two large projects (HR planning and medical battlefield management) from ARENA to SimJulia. The benefits of Julia are enormous. No more mixed code (SIMAN and Visual Basic) and being able to troubleshoot the internals of the simulation are for me the most important advantages. * The standard PriorityQueue is based on a binary heap and in my PhD I have shown that if you have no prior knowledge on the timing of the events, no event calendar (tree, heap, sorting, ...) can do better in a real implementation if you consider the extreme low overhead of its implementation (Vector based, no pointers and limited number of comparisons). I know that some other heap mechanisms (Binomial, Fibonacci, ...) have better time complexities but I have never found one that performed as well on real use cases as as binary heap. Most people are afraid of this process oriented programming. Programmers prefer the idea of sequential execution and the main philosophy of processes is asynchronous tasking. It takes my master students some weeks to get used to it but once they passed this difficulty, they are ready to tackle real world simulation problems. Often (almost always) we are doing Montecarlo simulations of 1000 or more repetitions and use the SimJulia framework in the inner loop. We are also working on simulation based optimization and the SimJulia framework is also used in the inner loop. To estimate the loss of speed compared to a more direct approach is very difficult to predict. One thing I am quite sure of is the ease of development when using process based simulations. I hope that I somehow answered your question. Please feel free to contact me if you need some assistance with your SimJulia coding. Ben On Tuesday, August 18, 2015 at 9:52:21 PM UTC+2, Tom Breloff wrote: Thanks for the effort Ben. I'm curious... do you have any performance comparisons of SimJulia vs other (simpler) code designs? How much speed would I be giving up if I was using events in a tight inner loop? On Tue, Aug 18, 2015 at 1:25 PM, Ben Lauwens ben.l...@gmail.com javascript: wrote: Hi SimJulia, a (combined continuous time / ) discrete event process oriented simulation framework, has reached another milestone: - v0.3.4 synchronizes the API with SimPy v3 but using some specific Julia semantics - It is a complete rewrite allowing a more powerful and unified discrete event approach - 10 min tutorial is included to give users a taste of discrete event simulation with (Sim)Julia - A detailed topical guide is also written For the moment the continuous time part is not operational. A *quantized state system* (QSS) solver is being developed for continuous system simulation using the discrete event framework. Once this is done v0.4 will be tagged. New ideas or interesting examples are always welcome and can be submitted as an issue or a pull request on GitHub. Enjoy! Ben
Re: [julia-users] Re: help with try/catch?
On Aug 18, 2015 5:50 PM, Seth catch...@bromberger.com wrote: Another followup: julia try using LightGraphs catch y println(y = $y) end y = ErrorException(unsupported or misplaced expression using) You cannot use import in local scope. Use eval or @eval On Tuesday, August 18, 2015 at 2:18:43 PM UTC-7, Seth wrote: I wasn't sure how to search for this in issues: julia try using LightGraphs catch end julia Graph ERROR: UndefVarError: Graph not defined but julia try using LightGraphs true catch false end true julia Graph LightGraphs.Graph Why doesn't the first one work?
Re: [julia-users] Re: help with try/catch?
On Tuesday, August 18, 2015 at 3:16:48 PM UTC-7, Yichao Yu wrote: You cannot use import in local scope. Use eval or @eval But why do the other ones (including when the try returns nothing) work?
Re: [julia-users] Re: How to define an array of floating point numbers?
On 18 August 2015 at 10:22, Uwe Fechner uwe.fechner@gmail.com wrote:
Ok, the following definition works on Julia 0.4, but not with 0.3:
FloatArray = Union{Array{Float32, 1}, Array{Float64, 1}}
function sum(x::FloatArray).
Any idea?
I would simply go with:
function mysum{T:FloatingPoint}(x::Vector{T}) sum(x) end
[julia-users] Metaprogramming: the use of `$`
I'm reading the documentation Metaprogramming http://docs.julialang.org/en/latest/manual/metaprogramming/ chapter. To me, the `$` operator is different from the one in the string interpolation. In the string, `$` is a runtime interpolation. In the expression, `$` is a compile-time interpolation. In one example provided, `ex = :(a in $:((1,2,3)) )`, I find that it's equal to `ex = :(a in (1,2,3) )`. So I do not see the intention of `$`. In the next example, there is `:a + :b` in the expression, but the sum of `Symbols` is not defined. Is it wrong?
Re: [julia-users] Re: help with try/catch?
On Aug 18, 2015 6:27 PM, Seth catch...@bromberger.com wrote: On Tuesday, August 18, 2015 at 3:16:48 PM UTC-7, Yichao Yu wrote: You cannot use import in local scope. Use eval or @eval But why do the other ones (including when the try returns nothing) work? Didn't notice that I guess its an parser bug then
Re: [julia-users] package compatibility for julia v0.3 and v0.4
Did you try `@compat x % UInt8`?
On Tue, Aug 18, 2015 at 7:18 PM, Samuele Carcagno sam.carca...@gmail.com
wrote:
On 18/08/15 21:46, Yichao Yu wrote:
On Aug 18, 2015 2:54 PM, Samuele Carcagno sam.carca...@gmail.com
mailto:sam.carca...@gmail.com wrote:
I understand that if I release a new version of a package foo and
put `julia 0.4` in `REQUIRE`, the package won't be updated in julia v0.3
installations. If, after the updated package is released, somebody tries
to install package foo from julia v0.3 what happens? Will they still
be able to install the older version of the package?
I'm asking because I'd like to release an updated version of a
package to make it work with julia v0.4. The changes, however, are
incompatible with julia v0.3. I tried using @compat to work around the
issues but couldn't figure out how to fix this deprecation warning
without breaking v0.3 compatibility:
`uint8(x::Integer) is deprecated, use x % UInt8 instead`
@compat UInt8(x)
thanks for the suggestion but that doesn't do what I need. The problem is
that I need to write 32-bit integers into a 24-bit file format. This
requires some bit fiddling. In julia v0.3 this worked without deprecation
warnings:
write(fid, uint8(thisSample));
write(fid, uint8(thisSample 8));
write(fid, uint8(thisSample 16));
`thisSample` is a 32 bit integer. In julia v0.4 `UInt8(x)` fails when `x`
is greater tha 2^8-1, instead the following works:
write(fid, thisSample % UInt8);
write(fid, (thisSample 8) % UInt8);
write(fid, (thisSample 16) % UInt8);
the problem is it doesn't work in julia v0.3 `x % @compat UInt8` gives:
`ERROR: `rem` has no method matching rem(::Int32, ::Type{Uint8})`
Sam
Cheers,
Sam
Re: [julia-users] package compatibility for julia v0.3 and v0.4
On Tue, Aug 18, 2015 at 8:20 PM, Samuele Carcagno
sam.carca...@gmail.com wrote:
On 19/08/15 00:22, Stefan Karpinski wrote:
Did you try `@compat x % UInt8`?
yes, it gives the same error `ERROR: `rem` has no method matching
rem(::Int32, ::Type{Uint8})`
Sorry, didn't realize you are converting to a smaller type. Have you
tried just `trunc(Uint8, x)`? (or `@compat trunc(UInt8, x)` if you
want to use the new type name)
On Tue, Aug 18, 2015 at 7:18 PM, Samuele Carcagno
sam.carca...@gmail.com mailto:sam.carca...@gmail.com wrote:
On 18/08/15 21:46, Yichao Yu wrote:
On Aug 18, 2015 2:54 PM, Samuele Carcagno
sam.carca...@gmail.com mailto:sam.carca...@gmail.com
mailto:sam.carca...@gmail.com mailto:sam.carca...@gmail.com
wrote:
I understand that if I release a new version of a package
foo and
put `julia 0.4` in `REQUIRE`, the package won't be updated in
julia v0.3
installations. If, after the updated package is released,
somebody tries
to install package foo from julia v0.3 what happens? Will they
still
be able to install the older version of the package?
I'm asking because I'd like to release an updated version of a
package to make it work with julia v0.4. The changes, however, are
incompatible with julia v0.3. I tried using @compat to work
around the
issues but couldn't figure out how to fix this deprecation warning
without breaking v0.3 compatibility:
`uint8(x::Integer) is deprecated, use x % UInt8 instead`
@compat UInt8(x)
thanks for the suggestion but that doesn't do what I need. The
problem is that I need to write 32-bit integers into a 24-bit file
format. This requires some bit fiddling. In julia v0.3 this worked
without deprecation warnings:
write(fid, uint8(thisSample));
write(fid, uint8(thisSample 8));
write(fid, uint8(thisSample 16));
`thisSample` is a 32 bit integer. In julia v0.4 `UInt8(x)` fails
when `x` is greater tha 2^8-1, instead the following works:
write(fid, thisSample % UInt8);
write(fid, (thisSample 8) % UInt8);
write(fid, (thisSample 16) % UInt8);
the problem is it doesn't work in julia v0.3 `x % @compat UInt8`
gives:
`ERROR: `rem` has no method matching rem(::Int32, ::Type{Uint8})`
Sam
Cheers,
Sam
Re: [julia-users] package compatibility for julia v0.3 and v0.4
On 18/08/15 21:46, Yichao Yu wrote:
On Aug 18, 2015 2:54 PM, Samuele Carcagno sam.carca...@gmail.com
mailto:sam.carca...@gmail.com wrote:
I understand that if I release a new version of a package foo and
put `julia 0.4` in `REQUIRE`, the package won't be updated in julia v0.3
installations. If, after the updated package is released, somebody tries
to install package foo from julia v0.3 what happens? Will they still
be able to install the older version of the package?
I'm asking because I'd like to release an updated version of a
package to make it work with julia v0.4. The changes, however, are
incompatible with julia v0.3. I tried using @compat to work around the
issues but couldn't figure out how to fix this deprecation warning
without breaking v0.3 compatibility:
`uint8(x::Integer) is deprecated, use x % UInt8 instead`
@compat UInt8(x)
thanks for the suggestion but that doesn't do what I need. The problem
is that I need to write 32-bit integers into a 24-bit file format. This
requires some bit fiddling. In julia v0.3 this worked without
deprecation warnings:
write(fid, uint8(thisSample));
write(fid, uint8(thisSample 8));
write(fid, uint8(thisSample 16));
`thisSample` is a 32 bit integer. In julia v0.4 `UInt8(x)` fails when
`x` is greater tha 2^8-1, instead the following works:
write(fid, thisSample % UInt8);
write(fid, (thisSample 8) % UInt8);
write(fid, (thisSample 16) % UInt8);
the problem is it doesn't work in julia v0.3 `x % @compat UInt8` gives:
`ERROR: `rem` has no method matching rem(::Int32, ::Type{Uint8})`
Sam
Cheers,
Sam
[julia-users] Re: A cheat page for Julia
Thanks for the detailed comments, Steven. I really appreciate it. For now I have changed what was wrong/unclear and valid for 0.3 (locally, not yet online). I’ll prepare a 0.4 version and put that online as soon as 0.4 is officially released. I know that the naming PascalCase vs camelCase vs init-cap CamelCase is almost up to each dev. Microsoft seems to be most pedantic about PascalCase vs camelCase: https://msdn.microsoft.com/en-us/library/x2dbyw72(v=vs.71).aspx. I don’t mind changing it. The official documentation calls it camel case, so I’ll go with that to make it more consistent. What’s the reason you say “You can override Base.show(io, ex) rather than Base.showerror”? The doc seems to go with the latter and I also think it matches the intent (exception - showerror): http://julia.readthedocs.org/en/latest/manual/control-flow/. Is there an advantage to overriding Base.show instead of Base.showerror?
Re: [julia-users] Re: A cheat page for Julia
For installing packages, 'Pkg.status(pkgName)' should be 'Pkg.add(pkgName)'.
Re: [julia-users] Re: A cheat page for Julia
It says installed not install, so I believe it's correct. I may write list installed... if that helps.
Re: [julia-users] JuliaCon 2015 videos
I added an annotation for my video. If you tell me a timestamp for Shashi's I can do the same there. On Tue, Aug 18, 2015 at 2:31 PM, Waldir Pimenta waldir.pime...@gmail.com wrote: Same for Shashi's session. On Monday, August 17, 2015 at 11:32:26 PM UTC+1, Keno Fischer wrote: Whoever owns the youtube channel should put an annotation that allows users o click on it to skip that part. On Mon, Aug 17, 2015 at 8:08 PM, Sisyphuss zhengw...@gmail.com wrote: In the Keno's video, he spent more than 5 minutes to go through the technical difficulty. I think this can be cut to save time. On Monday, August 17, 2015 at 3:30:48 PM UTC+2, Viral Shah wrote: One more batch: 1. Iain Dunning: JuliaOpt - Optimization related projects in Julia: http://youtu.be/ZibQdqR6gqc?a 2. Yee Sian Ng: JuliaGeo - Working with Geospatial data in Julia: http://youtu.be/MLGFzPS4FTg?a 3. Isaiah Norton: Automatic ccall wrapper generation with Clang.jl: http://youtu.be/Jt5sv-8iRGc?a 4. Simon Kornblith: L1 regularized regression: http://youtu.be/z4Zcud2vE0s?a 5. Tony Fong: Lint.jl - Building a Lint tool for Julia: http://youtu.be/9T3R14R3X7k?a 6. Douglas Bates: Adventures with Statistical Models and Sparse Matrices: http://youtu.be/lSr0oRfyd1Y?a -viral On 17-Aug-2015, at 2:25 am, Viral Shah vi...@mayin.org wrote: A few more: 1. Blake Johnson: Quickly building simulations of quantum systems: http://youtu.be/kogTKuytg1g?a 2. Katharine Hyatt: Quantum Statistical Simulations with Julia: http://youtu.be/QThApV3HIxc?a 3. Making GUIs with Escher jl: http://youtu.be/Sq7DEnV4dfI?a 4. John Myles White: What needs to be done to move JuliaStats forward: http://youtu.be/lf1_FhMR7xA?a 5. Jacob Quinn: Managing Data in Julia Old Tricks, New Tricks: http://youtu.be/QLWhsZ3yzBk?a -viral On 16-Aug-2015, at 11:15 am, Viral Shah vi...@mayin.org wrote: Next set: 1. Avik Sengupta: A practical guide to exposing Julia APIs on the web: http://youtu.be/o40OpLe2k7Q?a 2. Hongbo Rong: Accelerating sparse matrix kernels in Julia: http://youtu.be/k40K2zJVv0A?a 3. Luis Benet: Taylor series expansions in Julia: http://youtu.be/6zrQuq5mODQ?a 4. Westley Hennigh: Who optimizes the optimizers: http://youtu.be/Qito5AGSv4s?a 5. Mauro Werder: Interfaces for Julia -Traits.jl: youtu.be/j9w8oHfG1Ic?a 6. David Gold: Nullable arrays - http://youtu.be/2v5k28F80BQ?a -viral On 16-Aug-2015, at 12:01 am, Viral Shah vi...@mayin.org wrote: Next set: 1. Sebastien Martin: Taxi fleet simulation and visualization: http://youtu.be/MjERK9Xajrg?a 2. Bob Carpenter: Stan.jl - Statistical Modeling and Inference Made Easy: http://youtu.be/YdgMJ37CDws?a 3. Pontus Stenetorp: Suitably Naming a Child with Multiple Nationalities: http://youtu.be/MJzRf3Exlqc?a 4. Chiyuan Zhang: Mocha.jl - Deep Learning for Julia: http://youtu.be/ljySoebYylE?a 5. Kiran Pamnany and Ranjan Anantharaman: Multi-threading Julia: http://youtu.be/GvLhseZ4D8M?a 6. St. Louis University - Text Mining Research Group: TextMining.jl: http://youtu.be/dgfIIZ5yA4E?a 7. Zachary Yedidia: SFML.jl - Julia bindings for the Simple Fast: http://youtu.be/UKfM7EopMe0?a -viral On 15-Aug-2015, at 10:07 am, Viral Shah vi...@mayin.org wrote: One more batch posted: 1. Jacob Quinn: Composable streams for data transfer and processing - http://t.co/DPuN42F3Qu 2. Tanmay Mohapatra: Interfacing Julia with Complex systems using Protocol Buffers - http://t.co/Ddxj60KL7g 3. Eric Davies: Towards A Consistent Database Interface - http://t.co/vdQwFRp7aD 4. Viral Shah: The present and future of sparse matrices in Julia - http://t.co/WVQY89GsiT 5. Jake Bolewski: Staged programming in Julia - http://t.co/XlYl2XGB9O 6. Robert Moss: Julia as a Specification Language for the next-generation Airborne Collision Avoidance System - http://t.co/QJN2ZfP1ZI -viral On Friday, August 14, 2015 at 7:33:04 PM UTC+5:30, Viral Shah wrote: Here are the next batch of videos. There is a JuliaCon 2015 channel now: https://www.youtube.com/playlist?list=PLP8iPy9hna6Sdx4soiGrSefrmOPdUWixM This batch includes: David Sanders: Validated numerics in Julia - http://t.co/WRZcYGjhfl Patrick Sanan: Using Julia on a Cray Supercomputer - https://youtu.be/NwyKz2KLdtY Keno Fischer: Shaving the Yak - http://t.co/cEJqCqAdRC Spencer Lyon: Methods, Models, and Moments - Julian Economics with QuantEcon.jl - http://t.co/l0f5CH6k3u Randy Zwitch: Everyday Analytics and Visualization - http://t.co/7pbL7yd010 -viral On 13-Aug-2015, at 10:51 pm, Viral Shah vi...@mayin.org wrote: Folks, I am happy to announce that the videos are almost all ready, and I will start posting them in batches. I am starting with Jeff’s talk on our Youtube channel: https://www.youtube.com/user/JuliaLanguage Direct link to the video: https://www.youtube.com/watch?v=xUP3cSKb8sI -viral
Re: [julia-users] Re: How to define an array of floating point numbers?
They are the same. The vector is just a type alias for the array of size 1.
Personally I like vector because to me it is clearer to read.
On Tuesday, August 18, 2015 at 12:09:23 PM UTC+2, Sisyphuss wrote:
Is `function sum{T:FloatingPoint}(x::AbstractArray{T,1}) ` or
`function sum{T:FloatingPoint}(x::AbstractVector{T}) ` better?
On Tuesday, August 18, 2015 at 11:29:15 AM UTC+2, René Donner wrote:
Hi,
I think this does what you want:
function sum{T:FloatingPoint}(x::Array{T,1})
println(hi)
end
Cheers,
Rene
Am 18.08.2015 um 11:22 schrieb Uwe Fechner uwe.fec...@gmail.com:
Ok, the following definition works on Julia 0.4, but not with 0.3:
FloatArray = Union{Array{Float32, 1}, Array{Float64, 1}}
function sum(x::FloatArray).
Any idea?
Am Dienstag, 18. August 2015 11:07:16 UTC+2 schrieb Uwe Fechner:
Hello,
I want to write a function, that can operate on any one dimensional
array of floating point numbers.
The following works, but only for Float64:
function sum(x::Array{Float64,1})
The following does not work:
function sum(x::Array{AbstractFloat,1})
Any idea?
Uwe
Re: [julia-users] Segmentation fault during Julia compilation
Hi Jamseson,
Thanks for explaining memory allocations on PPC and providing pointers on
resolving segmentation fault, pointers are really helpful and I am working
on those. I am able to compile Julia master branch after turning ‘MEMDEBUG‘
flag on from options.h file, compilation went smooth and I am able to see
the Julia prompt. Although I will continue to work on finding root cause of
segmentation fault, occur at a time of Julia initialization.
I think when we turn on the ‘MEMDEBUG‘ flag it will reduce a performance of
Julia bit as with MEMDEBUG no memory pools are used and all allocation is
treated as big.
Apart from performance issue, I have few questions in my mind and I would
like to discuss those,
1. Apart from performance hit, is there any other functionality has
impacted due to turning on ‘MEMDEBUG’ flag OR what are side effects of
turning ‘MEMDEBUG’ flag on?
2. Should I use these settings (turning MEMDEBUG flag on) in production
environment or in release mode?
-Mahesh
On Fri, Aug 14, 2015 at 10:03 PM, Jameson Nash vtjn...@gmail.com wrote:
It's a JIT copy of a julia function named new. The last time this error
popped up, it was due to an error in the free_page function logic to
compute whether it was safe to free the current page (since PPC using large
pages). One place to check then is to ensure the invalid pointer hadn't
accidentally being deleted by an madvise(DONTNEED) for an unrelated page
free operations.
Beyond that, I would suggest trying with the `MEMDEBUG` turned on in
options.h (which will also disable the `free_page` function).
Also, when you have gdb running, there are many more useful things to
print than just the backtrace. For starters, I would suggest looking at
`disassembly` and `info registers`. Also, go `up` on the stack trace and
look at `jl_(f-linfo)`, `jl_(jl_uncompress_ast(f-linfo, f-linfo-ast))`,
and `jl_(args[0])` / `jl_(args[1])`
On Fri, Aug 14, 2015 at 9:07 AM Mahesh Waidande
mahesh.waidande1...@gmail.com wrote:
Hi All,
I am working on building/porting Julia on ppc64le architecture. I am
using Ubuntu 14.10 on top of ppc64le hardware, while compiling Julia
code(master branch) I am getting segmentation fault. I tried to debug
segmentation fault with tools like gdb/vgdb , valgrind , electric-fence
etc. but I not able to find a root cause of it. I need some
help/pointers/suggestions on how I resolve it.
Here are some details which will help you to diagnose a problem,
1. Machine details :
$ uname -a
Linux pts00433-vm1 3.16.0-30-generic #40-Ubuntu SMP Mon Jan 12 22:07:11
UTC 2015 ppc64le ppc64le ppc64le GNU/Linux
$
2. Snapshot of ‘make debug’ log
make[1]: Leaving directory '/home/test/Mahesh/julia/julia/base'
make[1]: Entering directory '/home/test/Mahesh/julia/julia'
cd base /home/test/Mahesh/julia/julia/usr/bin/julia-debug -C native
--output-ji /home/test/Mahesh/julia/julia/usr/lib/julia/inference0.ji -f
coreimg.jl
Electric Fence 2.2 Copyright (C) 1987-1999 Bruce Perens
br...@perens.com
Segmentation fault
Makefile:175: recipe for target
'/home/test/Mahesh/julia/julia/usr/lib/julia/inference0.ji' failed
make[1]: *** [/home/test/Mahesh/julia/julia/usr/lib/julia/inference0.ji]
Error 139
make[1]: Leaving directory '/home/test/Mahesh/julia/julia'
Makefile:64: recipe for target 'julia-inference' failed
make: *** [julia-inference] Error 2
3. gdb stack trace
test@pts00433-vm1:~/Mahesh/julia/julia/base$ gdb --args
/home/test/Mahesh/julia/julia/usr/bin/julia-debug -C native --output-ji
/home/test/Mahesh/julia/julia/usr/lib/julia/inference0.ji -f coreimg.jl
GNU gdb (Ubuntu 7.8-1ubuntu4) 7.8.0.20141001-cvs
Copyright (C) 2014 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later
http://gnu.org/licenses/gpl.html
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law. Type show copying
and show warranty for details.
This GDB was configured as powerpc64le-linux-gnu.
Type show configuration for configuration details.
For bug reporting instructions, please see:
http://www.gnu.org/software/gdb/bugs/.
Find the GDB manual and other documentation resources online at:
http://www.gnu.org/software/gdb/documentation/.
For help, type help.
Type apropos word to search for commands related to word...
Reading symbols from
/home/test/Mahesh/julia/julia/usr/bin/julia-debug...done.
(gdb) b repl.c:532
Breakpoint 1 at 0x10003a34: file repl.c, line 532.
(gdb) r
Starting program: /home/test/Mahesh/julia/julia/usr/bin/julia-debug -C
native --output-ji
/home/test/Mahesh/julia/julia/usr/lib/julia/inference0.ji -f coreimg.jl
[Thread debugging using libthread_db enabled]
Using host libthread_db library
/lib/powerpc64le-linux-gnu/libthread_db.so.1.
Electric Fence 2.2 Copyright (C) 1987-1999 Bruce Perens
br...@perens.com
Breakpoint 1, main (argc=7, argv=0x3478) at repl.c:533
533 {
(gdb) c
Continuing.
Program
Re: [julia-users] Re: How to define an array of floating point numbers?
Here, my point is the word Abstract
On Tuesday, August 18, 2015 at 3:42:16 PM UTC+2, Kristoffer Carlsson wrote:
They are the same. The vector is just a type alias for the array of size
1. Personally I like vector because to me it is clearer to read.
On Tuesday, August 18, 2015 at 12:09:23 PM UTC+2, Sisyphuss wrote:
Is `function sum{T:FloatingPoint}(x::AbstractArray{T,1}) ` or
`function sum{T:FloatingPoint}(x::AbstractVector{T}) ` better?
On Tuesday, August 18, 2015 at 11:29:15 AM UTC+2, René Donner wrote:
Hi,
I think this does what you want:
function sum{T:FloatingPoint}(x::Array{T,1})
println(hi)
end
Cheers,
Rene
Am 18.08.2015 um 11:22 schrieb Uwe Fechner uwe.fec...@gmail.com:
Ok, the following definition works on Julia 0.4, but not with 0.3:
FloatArray = Union{Array{Float32, 1}, Array{Float64, 1}}
function sum(x::FloatArray).
Any idea?
Am Dienstag, 18. August 2015 11:07:16 UTC+2 schrieb Uwe Fechner:
Hello,
I want to write a function, that can operate on any one dimensional
array of floating point numbers.
The following works, but only for Float64:
function sum(x::Array{Float64,1})
The following does not work:
function sum(x::Array{AbstractFloat,1})
Any idea?
Uwe
[julia-users] Re: JuliaCon 2015 videos
Iain requested for it to be taken down temporarily, since it was missing slides. The Geospatial analysis video is also taken down until further word from Yee Sian. -viral On Tuesday, August 18, 2015 at 9:42:36 PM UTC+5:30, Frank Kampas wrote: The optimization video is private On Thursday, August 13, 2015 at 1:22:09 PM UTC-4, Viral Shah wrote: Folks, I am happy to announce that the videos are almost all ready, and I will start posting them in batches. I am starting with Jeff’s talk on our Youtube channel: https://www.youtube.com/user/JuliaLanguage Direct link to the video: https://www.youtube.com/watch?v=xUP3cSKb8sI -viral
[julia-users] Re: Julia nightlies
Elliot has been busy lately, and this has not been maintained for a long time. We should probably remove that link from our website. -viral On Tuesday, August 18, 2015 at 9:52:17 PM UTC+5:30, Uwe Fechner wrote: Hello, the nightly Julia builds from: https://launchpad.net/~staticfloat/+archive/ubuntu/julianightlies are currently quiet outdated. As far as I understand they should be auto-generated every night from the head branch of the Julia repository. This seams to be currently broken. Any idea, why? The outdated version is causing problems with failing checks of Travis. Best regards: Uwe
Re: [julia-users] Re: JuliaCon 2015 videos
If you have Escher installed and ready to go (See instructions on escher-jl.org) you can safely skip the first 20 mins of my Escher.jl video. A better talk on Escher is this one I gave recently at a conference in Bangalore: https://youtu.be/UeVUsOX5rAo?t=150 it definitely packs more content in lesser time. On Tue, Aug 18, 2015 at 9:42 PM, Frank Kampas fkam...@gmail.com wrote: The optimization video is private On Thursday, August 13, 2015 at 1:22:09 PM UTC-4, Viral Shah wrote: Folks, I am happy to announce that the videos are almost all ready, and I will start posting them in batches. I am starting with Jeff’s talk on our Youtube channel: https://www.youtube.com/user/JuliaLanguage Direct link to the video: https://www.youtube.com/watch?v=xUP3cSKb8sI -viral
Re: [julia-users] ANN: SimJulia 0.3.4
Thanks for the effort Ben. I'm curious... do you have any performance comparisons of SimJulia vs other (simpler) code designs? How much speed would I be giving up if I was using events in a tight inner loop? On Tue, Aug 18, 2015 at 1:25 PM, Ben Lauwens ben.lauw...@gmail.com wrote: Hi SimJulia, a (combined continuous time / ) discrete event process oriented simulation framework, has reached another milestone: - v0.3.4 synchronizes the API with SimPy v3 but using some specific Julia semantics - It is a complete rewrite allowing a more powerful and unified discrete event approach - 10 min tutorial is included to give users a taste of discrete event simulation with (Sim)Julia - A detailed topical guide is also written For the moment the continuous time part is not operational. A *quantized state system* (QSS) solver is being developed for continuous system simulation using the discrete event framework. Once this is done v0.4 will be tagged. New ideas or interesting examples are always welcome and can be submitted as an issue or a pull request on GitHub. Enjoy! Ben
Re: [julia-users] package compatibility for julia v0.3 and v0.4
On Aug 18, 2015 2:54 PM, Samuele Carcagno sam.carca...@gmail.com wrote: I understand that if I release a new version of a package foo and put `julia 0.4` in `REQUIRE`, the package won't be updated in julia v0.3 installations. If, after the updated package is released, somebody tries to install package foo from julia v0.3 what happens? Will they still be able to install the older version of the package? I'm asking because I'd like to release an updated version of a package to make it work with julia v0.4. The changes, however, are incompatible with julia v0.3. I tried using @compat to work around the issues but couldn't figure out how to fix this deprecation warning without breaking v0.3 compatibility: `uint8(x::Integer) is deprecated, use x % UInt8 instead` @compat UInt8(x) Cheers, Sam
Re: [julia-users] ANN: SimJulia 0.3.4
Thanks for the detailed response Ben. I absolutely see the value in event-style programming. I have my own version of this... it may not be as feature-full, but it's pretty fast. I use it mainly to process various financial events (exchange messages, order book updates, etc) but it's fairly flexible. I have a few random things in a repo which I just made public (but it's certainly not something that belongs in metadata)... if you're curious at all, check out the pubsub.jl and scheduler.jl files at https://github.com/tbreloff/CTechCommon.jl. When I have some time I'll try to see if it makes sense to start using SimJulia instead or in addition to my own scheduler. On Tue, Aug 18, 2015 at 2:36 PM, Ben Lauwens ben.lauw...@gmail.com wrote: Tom The event scheduling in SimJulia is based on the standard PriorityQueue* implementation of Julia and the overhead of SimJulia is really limited. If you throw away the process oriented way of defining a simulation model, you can do theoretically better, i.e. pure event simulation. This is certainly true for small models but for bigger ones you (I for sure) will end up developing a massive spaghetti code. I have used the previous version of SimJulia in some large simulation models and compared to SimPy or commercial simulation software (ARENA) the speedup I measured was often more than two orders of magnitude. We are right now porting two large projects (HR planning and medical battlefield management) from ARENA to SimJulia. The benefits of Julia are enormous. No more mixed code (SIMAN and Visual Basic) and being able to troubleshoot the internals of the simulation are for me the most important advantages. * The standard PriorityQueue is based on a binary heap and in my PhD I have shown that if you have no prior knowledge on the timing of the events, no event calendar (tree, heap, sorting, ...) can do better in a real implementation if you consider the extreme low overhead of its implementation (Vector based, no pointers and limited number of comparisons). I know that some other heap mechanisms (Binomial, Fibonacci, ...) have better time complexities but I have never found one that performed as well on real use cases as as binary heap. Most people are afraid of this process oriented programming. Programmers prefer the idea of sequential execution and the main philosophy of processes is asynchronous tasking. It takes my master students some weeks to get used to it but once they passed this difficulty, they are ready to tackle real world simulation problems. Often (almost always) we are doing Montecarlo simulations of 1000 or more repetitions and use the SimJulia framework in the inner loop. We are also working on simulation based optimization and the SimJulia framework is also used in the inner loop. To estimate the loss of speed compared to a more direct approach is very difficult to predict. One thing I am quite sure of is the ease of development when using process based simulations. I hope that I somehow answered your question. Please feel free to contact me if you need some assistance with your SimJulia coding. Ben On Tuesday, August 18, 2015 at 9:52:21 PM UTC+2, Tom Breloff wrote: Thanks for the effort Ben. I'm curious... do you have any performance comparisons of SimJulia vs other (simpler) code designs? How much speed would I be giving up if I was using events in a tight inner loop? On Tue, Aug 18, 2015 at 1:25 PM, Ben Lauwens ben.l...@gmail.com wrote: Hi SimJulia, a (combined continuous time / ) discrete event process oriented simulation framework, has reached another milestone: - v0.3.4 synchronizes the API with SimPy v3 but using some specific Julia semantics - It is a complete rewrite allowing a more powerful and unified discrete event approach - 10 min tutorial is included to give users a taste of discrete event simulation with (Sim)Julia - A detailed topical guide is also written For the moment the continuous time part is not operational. A *quantized state system* (QSS) solver is being developed for continuous system simulation using the discrete event framework. Once this is done v0.4 will be tagged. New ideas or interesting examples are always welcome and can be submitted as an issue or a pull request on GitHub. Enjoy! Ben
Re: [julia-users] Metaprogramming: the use of `$`
On Tue, Aug 18, 2015 at 11:24 AM, Sisyphuss zhengwend...@gmail.com wrote: On Tuesday, August 18, 2015 at 2:11:40 PM UTC+2, Yichao Yu wrote: On Tue, Aug 18, 2015 at 6:49 AM, Sisyphuss zhengw...@gmail.com wrote: I'm reading the documentation Metaprogramming chapter. To me, the `$` operator is different from the one in the string interpolation. In the string, `$` is a runtime interpolation. In the expression, `$` is a compile-time interpolation. No. Both of them happens at whenever the expression is evaluated. It happens for `@eval` at compile time because that's when the macro expansion happens. for i in 1:3 println($(rand())) end It prints different values each iteration, so it's run-time interpolation. julia for i in 1:3 println(:($(rand( end 0.0513060369910221 0.006705158934253941 0.2977791327125763 In one example provided, `ex = :(a in $:((1,2,3)) )`, I find that it's equal to `ex = :(a in (1,2,3) )`. So I do not see the intention of `$`. The examples are meant to show you the basic rules not necessarily the best/only way to do it. Really don't see the rules from these examples. The rule is to show you how to splice an expression. In this example, the tuple (1,2,3) is **interpolated as an expression into a conditional test:** In the next example, there is `:a + :b` in the expression, but the sum of `Symbols` is not defined. Is it wrong? It just construct an expression and to show you that the result is quoted symbol rather than the symbol themselves.
Re: [julia-users] Constructor or convert(), Upper case or lower case
On Tuesday, August 18, 2015 at 11:29:00 AM UTC-4, Sisyphuss wrote: My point is these inconsistent rules are very confusing. The experience gained in one type cannot be extrapolated to another. I think most people here will agree with you. The discussion on how to spell conversion and/or construction took 2.5 years and over 100 comments to reach consensus and implement the required code changes to make it happen (see issue #1470). Furthermore, the ability to do this only happened recently, so we're still settling on how to best use these new features. It may be possible to deprecate the lowercase symbol function in favor of Symbol, but that'll cause a decent amount of code churn. `float` is an interesting case as it's regularly used to generically mean: convert to a floating point number *OR* a complex number with floating point components, so that's why it's still here but `int` isn't.
[julia-users] Julia nightlies
Hello, the nightly Julia builds from: https://launchpad.net/~staticfloat/+archive/ubuntu/julianightlies are currently quiet outdated. As far as I understand they should be auto-generated every night from the head branch of the Julia repository. This seams to be currently broken. Any idea, why? The outdated version is causing problems with failing checks of Travis. Best regards: Uwe
[julia-users] Re: JuliaCon 2015 videos
The optimization video is private On Thursday, August 13, 2015 at 1:22:09 PM UTC-4, Viral Shah wrote: Folks, I am happy to announce that the videos are almost all ready, and I will start posting them in batches. I am starting with Jeff’s talk on our Youtube channel: https://www.youtube.com/user/JuliaLanguage Direct link to the video: https://www.youtube.com/watch?v=xUP3cSKb8sI -viral
Re: [julia-users] Metaprogramming: the use of `$`
On Tuesday, August 18, 2015 at 2:11:40 PM UTC+2, Yichao Yu wrote: On Tue, Aug 18, 2015 at 6:49 AM, Sisyphuss zhengw...@gmail.com javascript: wrote: I'm reading the documentation Metaprogramming chapter. To me, the `$` operator is different from the one in the string interpolation. In the string, `$` is a runtime interpolation. In the expression, `$` is a compile-time interpolation. No. Both of them happens at whenever the expression is evaluated. It happens for `@eval` at compile time because that's when the macro expansion happens. for i in 1:3 println($(rand())) end It prints different values each iteration, so it's run-time interpolation. In one example provided, `ex = :(a in $:((1,2,3)) )`, I find that it's equal to `ex = :(a in (1,2,3) )`. So I do not see the intention of `$`. The examples are meant to show you the basic rules not necessarily the best/only way to do it. Really don't see the rules from these examples. In the next example, there is `:a + :b` in the expression, but the sum of `Symbols` is not defined. Is it wrong? It just construct an expression and to show you that the result is quoted symbol rather than the symbol themselves.
Re: [julia-users] Constructor or convert(), Upper case or lower case
My point is these inconsistent rules are very confusing. The experience gained in one type cannot be extrapolated to another. Again, think about `sparse()`. What will we use when we implement the `SparseMatrixRSC`? On Tuesday, August 18, 2015 at 2:26:12 PM UTC+2, Yichao Yu wrote: On Tue, Aug 18, 2015 at 6:04 AM, Sisyphuss zhengw...@gmail.com javascript: wrote: We use `Int8(x)` to construct an `Int8`, but use `float(x)` to convert to a floating point. Float is not a type. FWIW, `AbstractFloat(1)` works. We defines `Symbol` as an object, but use `symbol()` to construct symbols. `Symbol(a)` works, just not for arbitrary types as `symbol`. However, `Int*` don't allow converting from arbitrary types either so it is consistent. From what I understand, `int*` are deprecated because you should be explicit about what you want to do. For symbols this is less of an issue. I am so confusing. Hopefully you don't literally mean this.
