Amazing, just what I was looking for.
However :-/ I did exactly s your read me, installed, and using exactly
your example I get:
julia y = S*x
fatal error on 2: ERROR: ParallelSparseMatMul not defined
Worker 2 terminated.
ProcessExitedException()
is it enough to just write
using
The `using ParallelSparseMatMul` must be after any `addprocs` statements.
On Fri, Feb 14, 2014 at 2:21 PM, Jon Norberg jon.norb...@ecology.su.sewrote:
Amazing, just what I was looking for.
However :-/ I did exactly s your read me, installed, and using exactly
your example I get:
julia
It seems that a lot of the machine learning examples [1] are pulling in the
data from DataFrame format from RDatasets and then using an array
function to convert them to normal arrays.
Where is array defined? I'm having trouble figuring out how to get data
from DataFrames into a normal array and
Thank you
On Friday, February 14, 2014 10:19:22 AM UTC+1, Amit Murthy wrote:
The `using ParallelSparseMatMul` must be after any `addprocs` statements.
On Fri, Feb 14, 2014 at 2:21 PM, Jon Norberg
jon.n...@ecology.su.sejavascript:
wrote:
Amazing, just what I was looking for.
julia DataFrames.array(tab
will show where all the versions are defined.
On Fri, Feb 14, 2014 at 4:35 AM, Spencer Russell s...@mit.edu wrote:
It seems that a lot of the machine learning examples [1] are pulling in
the data from DataFrame format from RDatasets and then using an array
I'm trying to use 2 levels of parametric: one on an abstract type and
another in a function. This is to ensure that a parameter 'x' passed is
consistent with an Dict passed in as well:
abstract Test{TI:Integer}
function test{TI:Integer,T:Associative{Symbol,TI}}(a::T, x::TI)
end
# ERROR: TI not
Le vendredi 14 février 2014 à 04:58 -0800, Fil Mackay a écrit :
I'm trying to use 2 levels of parametric: one on an abstract type and
another in a function. This is to ensure that a parameter 'x' passed
is consistent with an Dict passed in as well:
abstract Test{TI:Integer}
no. that misses the usefulness of abstract types. Here's the answer:
abstract A{S}
type B{T} : A{T} end
typealias C{T:A} Ptr{T}
z = Ptr{B{Int}}
import Base.eltype
eltype{T}(::Type{A{T}}) = T
eltype{T:A}(::Type{T}) = eltype(super(T))
eltype{T}(::Type{C{T}}) = eltype(T)
julia eltype(z)
Int64
On
Treated in the last part of this section of the FAQ:
http://docs.julialang.org/en/latest/manual/faq/#how-should-i-declare-abstract-
container-type-fields
See also
https://github.com/JuliaLang/julia/issues/3766
--Tim
On Friday, February 14, 2014 04:58:34 AM Fil Mackay wrote:
I'm trying to use 2
I've been working on making a package for the Twitter API. To build
methods, I've been copying code between functions, so that every function
nearly has the same pattern:
#No default argument
function get_help_configuration(; options = Dict())
endpoint =
julia using DataFrames
julia methods(array)
ERROR: array not defined
julia methods(DataFrames.array)
ERROR: array not defined
Pkg.status() says I'm using DataFrames 0.4.2, and at startup Julia says
it's Version 0.2.0 (2013-11-16 23:44 UTC).
On Fri, Feb 14, 2014 at 7:29 AM, Isaiah Norton
On Thursday, February 13, 2014 11:06:53 PM UTC-5, Fil Mackay wrote:
I would say the deployment strategy of Julia is the same as Python.
Install a binary distribution as a prereq of your own package..?
Yes, for now, this is similar to Python: Julia programs are run through the
julia
I'm really, really frustrated to compile Julia on our server. Anyone who
can share me a binary Julia compiled on Red Hat Enterprise Linux Server
platform? Many thanks!
array is in DataArrays.
-- John
On Feb 14, 2014, at 7:40 AM, Spencer Russell s...@mit.edu wrote:
julia using DataFrames
julia methods(array)
ERROR: array not defined
julia methods(DataFrames.array)
ERROR: array not defined
Pkg.status() says I'm using DataFrames 0.4.2, and at
You can do Pkg.checkout(DataFrames) to use the master branch of the
package, but those are just warnings and shouldn't cause any problems.
On Friday, February 14, 2014 6:14:49 AM UTC-8, Wim wrote:
Dear all,
I'm having a problem with the DataFrames package, which also prevents me
from using
On Fri, Feb 14, 2014 at 7:58 AM, Fil Mackay f...@vertigotechnology.comwrote:
How can I do this? Surely parametric parameters can build on one another..?
Not in method dispatch, at least not yet. I'm not sure how you can be
shocked by this. There's a tower of dispatch power:
(a) single
Very very cool Madeleine!
On Thursday, February 13, 2014 6:49:20 PM UTC-5, Madeleine Udell wrote:
Thanks for all the advice, everyone. I've just finished a parallel sparse
matrix vector multiplication library written in straight julia for shared
memory machines, using Amit Murthy's
function get_stuff(endpoint, options)
#URI encode values for all keys in Dict
encodeURI(options)
#Build query string
query_str = Requests.format_query_str(options)
#Build oauth_header
oauth_header_val = oauthheader(GET, endpoint, options)
return
Anyone know of or is working on a computer vision package? eg, something
that can compute common image features like hogs, perform various
normalizations, etc.
Glad I could help!
On Friday, 14 February 2014 13:03:32 UTC-6, Randy Zwitch wrote:
Thanks Eric, this looks like exactly what I was hoping for. My biggest
mental block was the assignment of the k/v into the options dict, as I
didn't think of/wasn't aware of setindex!
On Friday, February
@nalimilan on Github had some RPM's he was building, I'm not sure for which
distribution exactly.
What are your errors when compiling?
On Fri, Feb 14, 2014 at 1:22 AM, xiongji...@gmail.com wrote:
I'm really, really frustrated to compile Julia on our server. Anyone who
can share me a binary
Le vendredi 14 février 2014 à 13:20 -0800, Elliot Saba a écrit :
@nalimilan on Github had some RPM's he was building, I'm not sure for
which distribution exactly.
That's for Fedora 19 and 20, you can find them here (not up to date...):
http://nalimilan.perso.neuf.fr/transfert/
But that may not
On Sat, Feb 15, 2014 at 4:13 AM, Stefan Karpinski ste...@karpinski.orgwrote:
On Fri, Feb 14, 2014 at 7:58 AM, Fil Mackay f...@vertigotechnology.comwrote:
How can I do this? Surely parametric parameters can build on one
another..?
Not in method dispatch, at least not yet. I'm not sure how
When I build an array using a comprehension as follows:
a = [i for i=1:3]
its type is 3-element Array{Int64,1}.
However, when I build a second array as:
b = [a[i] for i=1:3]
its type is 3-element Array{Any,1} instead of Array{Int64,1}.
How can I build b so that it
Is dictionary an abstract type in that example though?
test{TI:Integer}(a::TI, x::Dict{String, TI})
would work just fine.
On Fri, Feb 14, 2014 at 11:30 PM, Fil Mackay f...@vertigotechnology.comwrote:
On Sat, Feb 15, 2014 at 4:13 AM, Stefan Karpinski ste...@karpinski.orgwrote:
On Fri, Feb
Is there a BandedArray data structure in Julia, or is there plans for one?
(using SparseArray seems slow, though perhaps I'm not doing it correctly.)
Sheehan
why not use the abstract Test as the type for TI
so you have something like
function test{TI:Test,T:Associative{Symbol,Test}}(a::T, x::TI)
end
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