Another way would be to just initialize a `MersenneTwister` stream on each
process, and seed them differently. Since the streams are process local,
there shouldn't be any communication overhead, and streams with different
seeds should be independent.
// T
On Wednesday, July 16, 2014 7:07:09
I came across a behavior that leaves me a bit puzzled, here is example:
v = mapslices(var,randn(1000, 3, 19), [1])
[v[1,1,j] for j=1:19]
I would expect the resulting Vector to be Float64, not Any. Can anybody
tell me if I am doing something wrong or why it should be expected to be
Any?
Thanx.
Are you doing this in the REPL, or inside a function? Type inference isn't
great in the global scope, at least partly because Julia can't know that
the types won't change in the future, and therefore can't make as tight
inferences as it would otherwise.
I tried wrapping your code in a
Hi!
I want to use the function interpolation suite from the GSL library. I came
across the GSL.jl package which wraps the entire GSL library (which is
awesome).
I am stuck with setting up the interpolation object and wanted to reach out
for help. my issue is described here in greater
detail:
i'm looking for ways to improve the performance of ^ as in f(x) = x^1.4
I found this thread on integer powers, and it seems there are some benefits
of calling system pow() functions
(sometimes). https://github.com/JuliaLang/julia/issues/2741
is there anything related for non-integer powers or
If a I import a function explicitly into a module and extend it with new
methods, Julia seems to become quite confused when I replace the module,
adding more methods with exactly the same signatures as the ones already
there.
I have a complete gist outlining the problem
sorry if it's a stupid question. But I cannot find one in the online doc.
Thanks
It's inv, but I was struggling to find it too:
http://docs.julialang.org/en/latest/stdlib/linalg/#Base.inv
but note that if you want to solve a linear system Ax =b use
x = A\b
On Wed, 2014-07-16 at 13:39, Alan Chan szelok.c...@gmail.com wrote:
sorry if it's a stupid question. But I cannot find
`inv` will do that for
you. http://docs.julialang.org/en/latest/stdlib/linalg/#Base.inv
It's a little unfortunate that the help text is Matrix inverse, yet
searching for that exact phrase yields no results at all. Is it possible to
make documentation search be full-text for help text as well?
But apropos does find it:
julia apropos(matrix inverse)
INFO: Loading help data...
Base.inv(M)
On Wednesday, July 16, 2014 2:59:05 PM UTC+2, Tomas Lycken wrote:
`inv` will do that for you.
http://docs.julialang.org/en/latest/stdlib/linalg/#Base.inv
It's a little unfortunate
I think I managed to gain some futher understanding here, but I'm really
uncertain if this is correct, so I'd really appreciate if someone who
actually understands what's going on would confirm or correct this:
What I *think* happens, is that each time the file is included, a *new*
type with
On Wednesday, July 16, 2014 12:07:09 AM UTC-5, James Delaney wrote:
If your goal is that each call to rand(), regardless of process,
sequentially pulls a number from the RNG stream, then perhaps
you just need to create a MersenneTwister([seed]) object.
mystream = MersenneTwister(1)
##
On Wednesday, July 16, 2014 2:09:41 AM UTC-5, Tomas Lycken wrote:
Another way would be to just initialize a `MersenneTwister` stream
on each process, and seed them differently. Since the streams are
process local, there shouldn't be any communication overhead, and
streams with different seeds
Yes, I think you've got the right idea. Really there's Qux, QuxOld, QuxOldOld,
QuxOldOldOld, etc., but it's not showing you those Old parts of the name.
Regarding variables of old types: yep, that's frequently a source of
confusion. The example at the end of the FAQ section shows an example:
Yeah, sorry - I suspected there might be something I was missing. According
to the wikipedia page about the MT
http://en.wikipedia.org/wiki/Mersenne_twister one of its disadvantages is
just that:
It can take a long time to turn a non-random initial state—particularly an
initial state with
With the latest Julia, you can do this by
sumabs2(x)
Dahua
On Wednesday, July 16, 2014 9:57:54 AM UTC-5, Neal Becker wrote:
As a first exercise, I wanted to code magnitude squared of a complex
1-d array. Here is what I did:
mag_sqr{T} (x::Array{Complex{T},1}) =
Dahua Lin wrote:
With the latest Julia, you can do this by
sumabs2(x)
Dahua
On Wednesday, July 16, 2014 9:57:54 AM UTC-5, Neal Becker wrote:
As a first exercise, I wanted to code magnitude squared of a complex
1-d array. Here is what I did:
mag_sqr{T} (x::Array{Complex{T},1}) =
To follow up on Dahua's comment:
If you're interested in using the fastest code, the code that Dahua posted is
the fastest since it uses several clever tricks to get the absolute best speed.
If you're interested in writing a faster implementation than the one you wrote,
you need to avoid
Help data is hand-written; if no one has documented a function yet, then it
just tells you how many methods there are. `methods(sumabs2)` would show
you the method signatures.
If you want to add to the help documentation, you can edit this file:
On Wednesday, July 16, 2014 11:28:12 AM UTC-4, Dahua Lin wrote:
sumabs2(x)
That's cheating! Neal, I think what you wrote will work just fine, and
sumabs2 will work even better. It's a little tough to see what all is
being done in its implementation because the base library is trying to eek
For me at least (using v0.3.0-rc1), sumabs2 is not an exported function.
Usually those do not have any associated help.
You can still call it as Base.sumabs2, but non-exported functions may not have
a stable interface.
--Tim
On Wednesday, July 16, 2014 11:36:57 AM Neal Becker wrote:
Why is
It was exported after rc1 and will be available in the next rc (hopefully
with docs!).
Cheers,
Kevin
On Wednesday, July 16, 2014, Tim Holy tim.h...@gmail.com wrote:
For me at least (using v0.3.0-rc1), sumabs2 is not an exported function.
Usually those do not have any associated help.
You
I think that one of the main reasons it is so difficult to find is that inv
should generally be avoided.
As Mauro wrote, if you can, rephrase your problem so that you can use x = A\b.
On Tuesday, July 15, 2014 3:46:49 PM UTC-4, Sam L wrote:
I haven't heard of it before, but if I understand correctly, the Jupyter
project is about splitting the language-agnostic parts of IPython out into
its own project. Looks interesting!
IPython was already split into
Hi Ben,
That character is probably defined as a unicode 6 math symbol. UTF8proc
is a library that julia uses to do Unicode normalization and it currently
does not support unicode 6 characters. Luckily it seems like Steven
Johnson is on a quest to make every possible unicode symbol
On Wednesday, July 16, 2014 3:40:31 PM UTC-4, Ben Arthur wrote:
julia const ∪ = union
∪ is already defined to union in Julia.
julia const ¬ = !
ERROR: syntax: invalid character ¬
The ¬ character (U+00AC) is in category Sm (math symbols), and Julia does
not allow all possible math symbols
On Wednesday, July 16, 2014 3:56:49 PM UTC-4, Jake Bolewski wrote:
That character is probably defined as a unicode 6 math symbol.
(No, ¬ has been in Unicode since at least Unicode 4, and probably
earlier.)
Here is a small argument: in SymPy the negation prints with that character.
So to match the output I was hoping
to utilize (U+00AC) as ! for input. Easy to work around, but it might be
nice to have.
On Wednesday, July 16, 2014 4:04:07 PM UTC-4, Steven G. Johnson wrote:
On Wednesday, July
Also, it gave them a chance to design a snazzy new website! The logo with
sun and planets a la Galileo is just brilliant.
On Wed, Jul 16, 2014 at 12:15 PM, Steven G. Johnson stevenj@gmail.com
wrote:
On Tuesday, July 15, 2014 3:46:49 PM UTC-4, Sam L wrote:
I haven't heard of it before,
On Wednesday, July 16, 2014 4:12:05 PM UTC-4, j verzani wrote:
Here is a small argument: in SymPy the negation prints with that
character. So to match the output I was hoping
to utilize (U+00AC) as ! for input. Easy to work around, but it might be
nice to have.
Another argument is
I wonder if we should extend utf8proc to understand the ConScript unicode
http://en.wikipedia.org/wiki/ConScript_Unicode_Registryregistry too.
That way we can support Klingon and Elvish identifiers in Julia. ;-)
Ismael, although ASCIIString is a subtype of String, Array{ASCIIString,1}
is not a subtype of Array{String,1}. As far as I understand Julia has been
designed this way for practical reasons. You can read about it in the
manual's section: Types.
You might want to rewrite your function as
It inspires Unicode envy too; they have a Unicode codepoint (♃) for their
project, whereas I don't think we'll ever get the Julia logo into Unicode.
On Wednesday, July 16, 2014 4:22:36 PM UTC-4, Stefan Karpinski wrote:
Also, it gave them a chance to design a snazzy new website! The logo with
@Leah Hanson
Note that helpdb.jl is autogenerated from the .rst files.
doc/stdlib/base.rst
https://raw.githubusercontent.com/JuliaLang/julia/master/doc/stdlib/base.rst
now
contains the documentation, so it should be included in upcoming releases.
There is a problem where some versions of
On Wednesday, July 16, 2014 3:56:49 PM UTC-4, Jake Bolewski wrote:
Luckily it seems like Steven Johnson is on a quest to make every possible
unicode symbol available in Julia so this should be fixed soon.
https://github.com/JuliaLang/julia/pull/7630
On Wed, Jul 16, 2014 at 12:39 PM, Florian Oswald florian.osw...@gmail.com
wrote:
do you think this log issue may be worth a mention in the performance tips
section of the manual? I would have never guessed that there could be a
fast/slow issue with such basic functions. Little do I know!
The
On Wednesday, July 16, 2014 4:45:00 PM UTC-4, Steven G. Johnson wrote:
On Wednesday, July 16, 2014 3:56:49 PM UTC-4, Jake Bolewski wrote:
Luckily it seems like Steven Johnson is on a quest to make every possible
unicode symbol available in Julia so this should be fixed soon.
On Wed, Jul 16, 2014 at 1:04 PM, Steven G. Johnson stevenj@gmail.com
wrote:
∪ is already defined to union in Julia.
Note that it also supports infix syntax:
julia A = Set(1,2,3)
Set{Int64}({2,3,1})
julia B = Set(2,4,5)
Set{Int64}({4,2,5})
julia A ∪ B
Set{Int64}({4,2,3,5,1})
On Wed, Jul 16, 2014 at 1:12 PM, j verzani jverz...@gmail.com wrote:
Here is a small argument: in SymPy the negation prints with that
character. So to match the output I was hoping
to utilize (U+00AC) as ! for input. Easy to work around, but it might be
nice to have.
On Wednesday, July 16,
I agree. What I meant is that since inv is avoided, it's not used nor well
referenced in search engines or on this list. :)
ஃ isn't too bad (via http://shapecatcher.com/)
peace,
s
On Wed, Jul 16, 2014 at 4:38 PM, Steven G. Johnson stevenj@gmail.com
wrote:
It inspires Unicode envy too; they have a Unicode codepoint (♃) for their
project, whereas I don't think we'll ever get the Julia logo into Unicode.
On
Hello,
I have a code for calculating some physical quantities. I had a very rough
day trying to understand, why my results differ so much from what I should
get. Up until I have tried the same code in Python and I got the correct
result. Both codes are completely identical, but why is there a
Oh, excellent. The Julia Unicode character is U+B83 – 'ஃ' – the Tamil
character visarga.
On Wed, Jul 16, 2014 at 2:08 PM, Spencer Russell s...@mit.edu wrote:
ஃ isn't too bad (via http://shapecatcher.com/)
peace,
s
On Wed, Jul 16, 2014 at 4:38 PM, Steven G. Johnson stevenj@gmail.com
Thanks for that Pablo! I figured it out, while reading the Multiple
Dispatch PDF, I also saw this other way:
function Pkg.add(pkgs::String...)
Pkg.update()
for pkg in pkgs
Pkg.add(pkg)
end
end
The is also the alchemical symbol for oil
http://www.fileformat.info/info/unicode/char/1f746/index.htm ༜
Changing the line for nu=1:nu_max to for nu=0:(nu_max-1) makes julia agree
with python.
On Wednesday, July 16, 2014 1:55:40 PM UTC-7, Andrius Popovas wrote:
Hello,
I have a code for calculating some physical quantities. I had a very rough
day trying to understand, why my results differ so
In such cases I'd recommend printing the values at each step to see where they
first differ.
--Tim
On Wednesday, July 16, 2014 01:55:40 PM Andrius Popovas wrote:
Hello,
I have a code for calculating some physical quantities. I had a very rough
day trying to understand, why my results differ
And we have also, Julia the Language Predator
https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcT1MtrE5eL7PDMoMJRwci9TPqfoeUWNXMStctBOEUzufAipdrvm8Q
https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcT1MtrE5eL7PDMoMJRwci9TPqfoeUWNXMStctBOEUzufAipdrvm8Q
Quarta-feira, 16 de Julho de
And Florian Oswald writes:
I would have never guessed that there could be a fast/slow
issue with such basic functions. Little do I know!
See also Jean-Michel Muller's text on Elementary Functions,
Algorithms and Implementation and others (including, imho,
Markstein's IA-64 and Elementary
It's a little late now, but I have updated Lint.jl to warn on extending a
deprecated function. It parses the deprecated.jl and keeps a list. If a new
function definition matches the signature of a deprecated function, it'd
give a lint error. The definition of a match is more relaxed than the
Hi Stefan,
I've got no idea about the technical implications, i.e. why julia's log is
slower (on some systems?) than libm's and what's involved in bringing
performance up to par. I mentioned the manual only because this is the
second time that someone suggests the system-log trick to me, and it
any reason of avoiding inv?
namely, different linear regression, logistical regression, gradient
descent, neural network backprop, etc?
Interesting. Can you (or did you) file an issue?
Cheers,
Kevin
On Wed, Jul 16, 2014 at 4:36 PM, Laszlo Hars laszloh...@gmail.com wrote:
I hoped that at some point Julia error messages would again appear in
STDERR. No luck. Even the Release candidate 0.3.0-rc1 fails to write, e.g.
Thanks!
Could you perhaps point me to a place where these sort of things may be
documented?
Trying to adopt the language has been less painful than Python was. Python
however, has better documentation currently for these kind of small things.
Python is also far more mature so go_figure!
I don't know, how to file an issue. Where can I get instructions? (The
STDERR behavior may be intentional. I kept asking about it in this group,
for months, but nobody was interested.)
On Wednesday, July 16, 2014 6:36:23 PM UTC-6, Kevin Squire wrote:
Interesting. Can you (or did you) file an
Yep, Python documentation is quite good, and Julia's has a little ways to
go. (Did you see Python's documentation 10 years ago, though? ;-)
There wasn't much, if any, documentation about n:n-1 empty ranges or
splicing without removal, so I just added some (
I think the change in the STDERR behavior was intentional, once we got rid
of the limitations of using libreadline. Now we can run multiple
simultaneous independent REPL loops, thanks to Keno's work. Redirecting
STDERR and having that propagate to the stderr stream used by the REPL was
a
Per Jameson's response and my own testing, it seems like the STDERR issues
have been resolved (perhaps after rc1?):
$ julia -e 'throw(DomainError())' 1 stdout.txt 2 stderr.txt
$ cat stdout.txt
$ cat stderr.txt
ERROR: DomainError
in process_options at ./client.jl:213
in _start at
Or not. See https://github.com/JuliaLang/julia/issues/7633.
On Wed, Jul 16, 2014 at 7:13 PM, Kevin Squire kevin.squ...@gmail.com
wrote:
Per Jameson's response and my own testing, it seems like the STDERR issues
have been resolved (perhaps after rc1?):
$ julia -e 'throw(DomainError())' 1
the STDERR behavior was intentional
1) It just does not seem to be logical: warning messages still get written
to STDERR. Why? And what is the use of STDERR, in general? STDERR is in the
documentation, without telling anything about it.
2) The REPL behavior ought to be documented, otherwise new
Most multimethod systems seem to have a way to call the next-most-general
method of a function, but I'm not seeing a good way to do that in Julia.
This is complicated by the fact that my sense of what's idiomatically
clean in Julia isn't very well honed yet. If Julia had CLOS's
Usually the answer to this is to factor your methods so this is unnecessary
(e.g. by having a secondary method that does the actual work). If that
doesn't work for you for some reason, there's also `invoke(bar,(Fu,),f)`
which does what you want.
On Wed, Jul 16, 2014 at 9:03 PM, Markus Roberts
If you replace call-next-method() with invoke(bar,(Fu,),f) then it does
what you want. The situation in Julia is a bit more complicated since you
don't know which argument(s) you want to push further up the type lattice.
The invoke function works by letting you specify exactly which less
specific
It's a bit of numerical computing lore that inv is bad – both for
performance and for numerical accuracy. It turns out it may not be so bad
http://arxiv.org/pdf/1201.6035v1.pdf after all, but everyone is still
kind of wary of it and there are often better ways to solve problems where
inv would be
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