On Thursday, March 26, 2015 at 8:06:41 AM UTC+11, Phil Tomson wrote:
>
>
>
> On Wednesday, March 25, 2015 at 1:52:04 PM UTC-7, Tim Holy wrote:
>>
>> No, it's
>>
>> f = @anon x->abs(x)
>>
>> and then pass f to test_time. Declare the function like this:
>>
>> function test_time{F}(func::F)
>> ....
>> end
>>
>
> Ok, got that working, but when I try using it inside the function (which
> would be closer to what I really need to do):
>
> function test_time2(func::Function)
> fn = @anon x->func(x)
>
No, as Tim said, you do @anon outside test_time with the function you want
to use and pass the result as the parameter. Note also his point of how to
declare test_time as a generic.
Cheers
Lex
> sum = 1.0
> for i in 1:1000000
> sum += fn(sum)
> end
> sum
> end
>
> julia> @time test_time2(abs)
> ERROR: `func` has no method matching func(::Float64)
> in ##26503 at /home/phil/.julia/v0.3/FastAnonymous/src/FastAnonymous.jl:2
> in test_time2 at none:5
>
>
>
>
>
>> --Tim
>>
>> On Wednesday, March 25, 2015 01:30:28 PM Phil Tomson wrote:
>> > On Wednesday, March 25, 2015 at 1:08:24 PM UTC-7, Tim Holy wrote:
>> > > Don't use a macro, just use the @anon macro to create an object that
>> will
>> > > be
>> > > fast to use as a "function."
>> >
>> > I guess I'm not understanding how this is used, I would have thought
>> I'd
>> > need to do something like:
>> >
>> > julia>
>> > function test_time(func::Function)
>> > f = @anon func
>> > sum = 1.0
>> > for i in 1:1000000
>> > sum += f(sum)
>> > end
>> > sum
>> > end
>> > ERROR: `anonsplice` has no method matching anonsplice(::Symbol)
>> >
>> >
>> > ... or even trying it outside of the function:
>> > julia> f = @anon abs
>> > ERROR: `anonsplice` has no method matching anonsplice(::Symbol)
>> >
>> > > --Tim
>> > >
>> > > On Wednesday, March 25, 2015 01:00:27 PM Phil Tomson wrote:
>> > > > I have a couple of instances where a function is determined by some
>> > > > parameters (in a JSON file in this case) and I have to call it in
>> this
>> > > > manner. I'm thinking it should be possible to speed these up via a
>> > >
>> > > macro,
>> > >
>> > > > but I'm a macro newbie. I'll probably post a different question
>> related
>> > >
>> > > to
>> > >
>> > > > that, but would a macro be feasible in an instance like this?
>> > > >
>> > > > On Wednesday, March 25, 2015 at 12:35:20 PM UTC-7, Tim Holy wrote:
>> > > > > There have been many prior posts about this topic. Maybe we
>> should add
>> > >
>> > > a
>> > >
>> > > > > FAQ
>> > > > > page we can direct people to. In the mean time, your best bet is
>> to
>> > >
>> > > search
>> > >
>> > > > > (or
>> > > > > use FastAnonymous or NumericFuns).
>> > > > >
>> > > > > --Tim
>> > > > >
>> > > > > On Wednesday, March 25, 2015 11:41:10 AM Phil Tomson wrote:
>> > > > > > Maybe this is just obvious, but it's not making much sense to
>> me.
>> > > > > >
>> > > > > > If I have a reference to a function (pardon if that's not the
>> > >
>> > > correct
>> > >
>> > > > > > Julia-ish terminology - basically just a variable that holds a
>> > >
>> > > Function
>> > >
>> > > > > > type) and call it, it runs much more slowly (persumably because
>> it's
>> > > > > > allocating a lot more memory) than it would if I make the same
>> call
>> > >
>> > > with
>> > >
>> > > > > > the function directly.
>> > > > > >
>> > > > > > Maybe that's not so clear, so let me show an example using the
>> abs
>> > > > >
>> > > > > function:
>> > > > > > function test_time()
>> > > > > >
>> > > > > > sum = 1.0
>> > > > > > for i in 1:1000000
>> > > > > >
>> > > > > > sum += abs(sum)
>> > > > > >
>> > > > > > end
>> > > > > > sum
>> > > > > >
>> > > > > > end
>> > > > > >
>> > > > > > Run it a few times with @time:
>> > > > > > julia> @time test_time()
>> > > > > >
>> > > > > > elapsed time: 0.007576883 seconds (96 bytes allocated)
>> > > > > > Inf
>> > > > > >
>> > > > > > julia> @time test_time()
>> > > > > >
>> > > > > > elapsed time: 0.002058207 seconds (96 bytes allocated)
>> > > > > > Inf
>> > > > > >
>> > > > > > julia> @time test_time()
>> > > > > > elapsed time: 0.005015882 seconds (96 bytes allocated)
>> > > > > > Inf
>> > > > > >
>> > > > > > Now let's try a modified version that takes a Function on the
>> input:
>> > > > > > function test_time(func::Function)
>> > > > > >
>> > > > > > sum = 1.0
>> > > > > > for i in 1:1000000
>> > > > > >
>> > > > > > sum += func(sum)
>> > > > > >
>> > > > > > end
>> > > > > > sum
>> > > > > >
>> > > > > > end
>> > > > > >
>> > > > > > So essentially the same function, but this time the function is
>> > >
>> > > passed
>> > >
>> > > > > in.
>> > > > >
>> > > > > > Running this version a few times:
>> > > > > > julia> @time test_time(abs)
>> > > > > > elapsed time: 0.066612994 seconds (32000080 bytes
>> allocated,
>> > >
>> > > 31.05%
>> > >
>> > > > > > gc time)
>> > > > > >
>> > > > > > Inf
>> > > > > >
>> > > > > > julia> @time test_time(abs)
>> > > > > > elapsed time: 0.064705561 seconds (32000080 bytes
>> allocated,
>> > >
>> > > 31.16%
>> > >
>> > > > > gc
>> > > > >
>> > > > > > time)
>> > > > > >
>> > > > > > Inf
>> > > > > >
>> > > > > > So roughly 10X slower, probably because of the much larger
>> amount of
>> > > > >
>> > > > > memory
>> > > > >
>> > > > > > allocated (32000080 bytes vs. 96 bytes)
>> > > > > >
>> > > > > > Why does the second version allocate so much more memory? (I'm
>> > >
>> > > running
>> > >
>> > > > > > Julia 0.3.6 for this testcase)
>> > > > > >
>> > > > > > Phil
>>
>>
