Actually I could do:
function f2{T}(t, a::Vector{T})
Dict{T, code_typed(t, (T,))[1].rettype}((x, t(x)) for x in a)
end
but this does not solve the problem as the compiler still is unable to
determine the exact return type of f2.
On Monday, November 14, 2016 at 4:06:20 PM UTC+1, Lutfullah
Thank you.
However, my problem is that t(x) does not have to have type T.
And this is exactly the question - how to determine type of t(x) given that
we know that x has type T.
On Monday, November 14, 2016 at 12:29:56 AM UTC+1, Ralph Smith wrote:
>
> Until the issue with generators is resolved,
y next this dictionary is populated with data?
With kind regards,
Bogumil Kaminski
This I understand - thank you.
However, as I have written in my first post eval is only an example showing
the problem.
The real use case is when we have some constant reference data, eg. list of
first names that has 1 entries, and want to store it directly in Julia
code as an array literal
I have just found that adding the following type annotation solves the
problem:
s3 = string("x = Any['0'", join([string(", ", i) for i in 1:256]), "]")
but I do not understand exactly why (it seems that the core reason is how
Julia handles map on tuples but I am not sure why adding Any
Could someone help me to understand why the following code works slowly and
how to make it run faster?
function run()
# this is fast
s1 = string("x = [0", join([string(", ", i) for i in 1:256]), "]")
p1 = parse(s1)
@time eval(p1)
@time eval(p1)
# here starts the slow
Hi,
First of all you initialize w as Float64 which you do not probably want.
For creating vector of strings you colud write:
w = [col * string(i) for i in 1:10]
or:
w = Array(ASCIIString, 10)
for i = 1:10
w [i] = col * string(i)
end
Bogumil
On Wednesday, January 1, 2014
